175 results about "Radiation transfer" patented technology

The embodiment of the invention discloses a waste incineration SNCR precise control method based on a mechanism model and dynamic prediction, and relates to the technical field of waste incineration power plant flue gas denitration. The method comprises the steps that operation parameters of the garbage incinerator are obtained; constructing a mechanism model, namely a thermodynamic-combustion coupling model, and realizing three-dimensional flow field reconstruction and axial temperature field estimation by utilizing a virtual sensing technology and based on an energy conservation equation, a radiation transfer equation and CFD reverse solution and inversion of a flue gas temperature and a flow field velocity vector; combining the mechanism model and actually measured data, optimizing spray gun parameters by using a particle swarm optimization algorithm, and realizing multi-target balance of denitration efficiency and ammonia escape; and the flow, angle and layout of the spray gun are dynamically adjusted according to the optimized parameters. The method is high in denitration efficiency and low in reducing agent consumption.

The invention discloses a crop agronomic parameter and yield estimation method and system coupling a crop growth model and remote sensing data, and relates to the technical field of agronomic parameter and yield estimation.The method comprises the steps that crop growth data are collected, a CERES-Rice model is constructed according to the crop growth data, and rice agronomic parameters and yield data are output; a PROSAIL canopy reflection model is constructed; hyperspectral data are output through a PROSAIL canopy reflection model, and parameter inversion optimization is carried out in combination with remote sensing data; and outputting final rice agronomic parameters and yield data according to the model after inversion optimization. The remote sensing radiation transmission model and the crop growth model are adopted to monitor agricultural parameters and yield of crops, the adjustment of parameters directly influencing crop states shared between ecological physiology and the radiation transmission model is emphasized, the availability of a hyperspectral instrument is increased, narrow-band spectral reflectivity data is collected, and the measurement accuracy is improved. And the accuracy of agricultural parameter and yield monitoring is improved.

The invention discloses a data assimilation method for infrared hyperspectral data of a clear sky channel. The data assimilation method comprises the following steps: acquiring the infrared hyperspectral data of the clear sky channel; a linear or non-linear relationship to convert variables from a mode space to an observation space using a fast radiation transfer mode; satellite infrared hyperspectral atmospheric vertical detection data channel screening is carried out in combination with a principal component method; a channel which is not influenced by cloud pollution is searched by observing the difference between the brightness temperature observed by the infrared and the brightness temperature simulated by the background field; eliminating the statistical deviation between the radiation value observed by the satellite instrument and the radiation value simulated and calculated according to the background field profile; eliminating observation data with errors exceeding a preset threshold value on the mixed earth surface type; and outputting a data assimilation result of the infrared hyperspectral data of the clear sky channel. Analysis fields of all layers of the global atmosphere are improved, the numerical forecasting level is effectively improved, typhoon forecasting intensity errors are remarkably improved, and path errors before landing are superior to conventional observation data tests.

The invention discloses an underwater LED fish gathering lamp control method based on a light field scattering model, and the method comprises the following steps: obtaining water optical characteristic parameters and environmental parameters of a target water area in real time, and building an underwater light field scattering model through employing a radiation transfer equation in combination with a Monte Carlo simulation method; with improvement of target fish school gathering efficiency and reduction of energy consumption as optimization objectives, environment parameters are taken as constraint conditions, illumination effects under different LED parameter combinations are simulated through a light field scattering model, and LED control parameters with optimal comprehensive performance are determined; a control instruction is generated according to the LED control parameters, and the working state of each LED unit is adjusted; meanwhile, fish school gathering data and current change values of water temperature, salinity and turbidity are monitored in real time and fed back to the light field scattering model for parameter correction, and closed-loop control is formed. The underwater LED fish gathering lamp control method solves the technical problems of low light field distribution prediction precision, insufficient fish school visual matching performance, poor environmental adaptability and unbalanced energy consumption optimization in the existing underwater LED fish gathering lamp control method.

The invention relates to an organic matter near infrared spectrum inversion method and device for removing moisture based on radiation. The method comprises the steps that after a soil sample is pretreated, multiple moisture content gradients are regulated and controlled, the natural moisture permeation state is simulated, visible light-near infrared spectrum data and the organic matter content are collected, and a soil spectrum library is constructed; quantifying the interference weight of moisture on the spectrum by using a physical radiation transmission model to obtain a preliminary correction spectrum; moisture and organic matter features are extracted through a deep learning multi-branch architecture, a moisture characterization index is constructed, cross-domain feature alignment is realized in combination with a domain adversarial network, and an accurate correction spectrum is obtained; all the modules are integrated to construct a soil moisture elimination reconstruction spectrum model, after layered verification and iterative optimization, a to-be-detected soil spectrum is input into the model, organic matter content near infrared spectrum inversion is completed, and a result is output. According to the method, moisture interference is removed through combination of physical radiation and deep learning, the inversion precision is improved, and the method is suitable for rapid detection of organic matters in various moisture-containing soil.

The invention provides a wetland ecosystem health evaluation method based on multi-source remote sensing data, and belongs to the technical field of wetland ecosystems, and the method comprises the steps: carrying out the dense dark pixel and Kalman filtering cooperative atmospheric correction of a multi-spectral remote sensing image to obtain a surface reflectance image, and achieving the precise segmentation of a wetland landscape through a graph cut theory, a spectral unmixing model based on an improved Gaussian kernel is utilized to embed physical constraints to invert water quality parameters, a laser radar canopy height priori constraint three-dimensional radiation transmission model is combined to invert vegetation parameters, and a mixed pixel decomposition result is optimized through a Markov random field. A water quality, vegetation and landscape three-dimensional comprehensive health evaluation system is constructed, degradation dominant factors are identified, and the technical problem that it is difficult for multi-source remote sensing data to cooperatively invert wetland ecosystem multi-dimensional health state parameters is solved.

The invention discloses a winter wheat LAI inversion method based on scenarized parameter calibration and ensemble learning. The method comprises the following steps: fusing a remote sensing image with auxiliary geographic information such as terrain and soil, and performing typical planting scene division on a research area by adopting a spectral clustering method; then scene optimization is carried out on highly sensitive parameters (such as LAI, Cab, Cm and ALA) in the radiation transfer model PROSAIL based on a Markov chain Monte Carlo (MCMC) algorithm, and parameter posterior distribution more consistent with actual observation is obtained; further utilizing optimization parameters to generate a high-quality and homogenized simulation sample set, and mapping the simulation sample set to a Sentinel-2 waveband to construct a training data set; and finally, performing training by adopting various machine learning models (such as LightGBM, XGBoost, RF and KNN), and constructing a fusion model through a stacking integration strategy to realize high-precision inversion of LAI. Experimental results show that the method has higher fitting precision and stronger generalization ability in a plurality of typical planting scenes.

The invention relates to a rice canopy optical and microwave integrated radiation modeling method, which comprises the following steps of: acquiring and uniformly processing rice canopy and environmental parameters, and constructing a same physical parameter system shared by optical and microwave models; the optical part is used for calculating bidirectional reflection factors and optical reflectivity of the rice canopy under different observation conditions based on an aquatic vegetation radiation transmission model; the microwave part is based on a vector radiation transfer equation, adopts a matrix multiplication method to calculate canopy multiple scattering, and simulates underlying surface scattering in combination with an improved integral equation model to obtain a multi-polarization backscattering coefficient; the optical reflectivity and the microwave backscattering coefficient are output under a unified parameter system, and optical and microwave collaborative forward modeling is achieved. According to the invention, a rice canopy optical and microwave integrated collaborative simulation framework is established based on a radiation transfer principle, a physical consistent basis is provided for crop parameter inversion, multi-source remote sensing data application and farmland environment monitoring, and the application prospect is wide.

The invention belongs to the technical field of remote sensing, and relates to a multispectral remote sensing atmospheric correction method based on fusion of a physical model and a neural network, which comprises the following steps: S1, classifying aerosol parameter data obtained based on foundation observation, remote sensing inversion or meteorological model and satellite data fusion inversion by adopting an unsupervised clustering algorithm, extracting a typical aerosol mode with physical representativeness; s2, in combination with the typical aerosol mode, simulating the apparent reflectivity of an observation channel under a plurality of atmospheric states and observation geometric conditions by using a radiation transfer model, generating a lookup table covering a wide parameter space, and constructing a training data set; and S3, constructing a nonlinear regression model, taking the training data set as a training sample, learning a mapping relation among an observation angle, an aerosol condition and surface reflectance, performing atmospheric interference correction on an actual multispectral remote sensing image under a pollution condition, and outputting a surface reflectance result.

The invention relates to an interstellar medium radiation transfer calculation method and device based on ray tracing. The method comprises the following steps: discretizing a three-dimensional space model of an interstellar medium by using a Cartesian grid to obtain a plurality of grid units, and emitting a plurality of uniformly distributed light rays at the center of each grid unit to construct a discretized radiation transfer equation; for each of the light rays, screening out the grid units through which the light rays penetrate by adopting an octree algorithm; calculating the length of a path where each light ray intersects with the grid units when the light ray passes through the grid units by adopting a flat plate method; and solving a discretized radiation transfer equation according to the path length to obtain a parameter equation of the interstellar medium. By adopting the method, the calculation amount of the ray tracing numerical integration in the radiation transfer equation can be reduced.

The invention discloses a deep sea color inversion method fusing satellite radiation simulation under a multi-level sea color background, and the method comprises the steps: carrying out the simulation through employing a simulation algorithm based on an OSOAA radiation transmission model, obtaining simulation data including simulation wave band apparent reflectivity, and constructing a multi-level sea color data set; secondly, constructing a deep sea color inversion network with the orientation and wave band feature joint characterization capability, including a residual network and a binary attention mechanism, and performing sea color inversion based on multi-level sea color data; and finally, evaluating by using a test part of the data set, collecting real satellite remote sensing and sea color data, and testing. According to the method, the problems of low robustness and reliability of deep learning in a complex scene and the like caused by scarcity of current sea color marking data are solved, the problem of end-to-end inversion of sea color parameters such as chlorophyll concentration is solved, and the inversion efficiency is improved.

The invention discloses a background spectrum accurate acquisition method for infrared remote measurement in a complex scene, and the method comprises the steps: carrying out the division of sky and ground object backgrounds for an input background image, and selecting different radiation spectrum generation modes based on a division result; if yes, sky radiation spectrum extraction is carried out based on an MODTRAN radiation transfer model; if the background is judged to be a single ground object background, performing background spectrum extraction by adopting ELM-EMD (Extreme Learning Mode-Empirical Mode Decomposition) which is extracted in real time based on a measurement spectrum and changes slowly at low frequency; and if the background is not single, fusion of the two spectrum extraction modes is adopted. The method has the advantages that the background classification method with the visible image as the reference is adopted, the difference of infrared radiation characteristics of the sky background and the ground feature background is analyzed, different spectrum extraction modes are selected according to the difference, and therefore accurate obtaining of the target smoke plume transmittance spectrum is achieved.

The invention relates to the technical field of infrared measurement, in particular to an intelligent calibration method and system for temperature measurement of an infrared sensor, and the method comprises the steps: enabling an infrared camera to align with a blackbody radiation source to form a calibration scene, collecting the output of a plurality of groups of pixels under different radiation temperatures and different internal part temperature states, fitting a temperature signal response model containing an exponential term and a bias term according to least square to obtain amplitude, exponential, temperature offset and output bias parameters; during measurement, a thermal image containing a target object and two reference blackbody sources is obtained, and parameters such as emissivity, environment equivalent radiation temperature, transmission medium and optical transmission, camera temperature and the like are synchronously obtained; performing non-uniformity and offset correction and abnormal pixel replacement on the thermal image; the initial temperature is inverted based on radiation transfer decomposition and compensation of internal radiation of the camera; and carrying out statistical averaging on the temperatures of the two blackbody reference regions to obtain a double-point correction coefficient, and outputting correction temperature or temperature distribution. According to the method, the problems that temperature inversion model parameters are difficult to adaptively update along with working conditions and on-site temperature measurement errors are difficult to effectively suppress when an existing infrared sensor array is in a long-distance non-contact temperature measurement scene can be solved.

The invention relates to an intelligent design method of a radiation shielding material based on cross-scale error feedback lock step, which comprises the following steps: determining a material component set and acquiring an atomic-scale graph structure of the material component set, and extracting intrinsic performance parameters such as heat conductivity and attenuation coefficient through an atomic-scale prediction module; and then, constructing a design variable vector in combination with a multi-physical field condition, calculating equivalent performance parameters of the composite material, establishing a cross-scale lock step mechanism, and triggering a molecular dynamics and Monte Carlo recalculation module to carry out correction when the deviation between a predicted value and a mesoscopic recalculation result exceeds a limit. Based on the correction parameters, a candidate material scheme is generated by using a multi-objective optimization algorithm; and performing gamma-ray and neutron shielding performance simulation on the candidate schemes through a Monte Carlo radiation transfer model accelerated by a deep neural network, and comparing with an experimental result to realize self-adaptive material design driven by cross-scale errors. According to the method, the design precision and efficiency of the radiation shielding material are remarkably improved.

The invention provides a multi-sensor data fusion method and system based on an intelligent pod. According to the method, a radiation data flow of an intelligent pod multispectral sensor in an electromagnetic interference field is collected; based on the data, the quantitative attenuation coefficient of the radiation value of each wave band in the interference field is calculated by using a radiation transfer equation. And generating a directional beam of the phased-array antenna by using the coefficient, and scanning a target area to obtain echo data. And finally, performing joint sparse representation on the original radiation data stream and the target echo data to generate a fusion result. The invention discloses multi-sensor data fusion based on an intelligent pod. According to the method, the anti-interference wave beam scanning target is dynamically generated by quantifying the attenuation of electromagnetic interference on radiation of each wave band, and the sensing performance of the intelligent pod in a complex electromagnetic environment is improved by combining the joint sparse representation and fusing the multi-source data.

The invention discloses a forest leaf area index estimation method based on a three-dimensional radiation transmission model and deep learning, and belongs to the technical field of ecological remote sensing. The method comprises the steps of 1, obtaining a modeling data set based on a DART model, and preprocessing the modeling data set to obtain a modeling training set; 2, constructing a pre-trained 1D-CNN forest LAI inversion model, and carrying out the training optimization of the pre-trained 1D-CNN forest LAI inversion model based on the modeling training set, and obtaining an optimization model; 3, constructing a migration data set based on Landsat satellite image data and actually measured LAI data, and performing parameter optimization on the optimization model based on the migration data set to obtain a migration learning forest LAI inversion model; and step 4, processing the Landsat image data needing to be predicted based on the transfer learning forest LAI inversion model to obtain an estimation result. According to the invention, a forest LAI estimation result with higher precision can be provided.

The invention belongs to the technical field of water quality parameter inversion, and particularly relates to a machine learning-based suspended matter concentration remote sensing inversion method and system, and the method comprises the steps: collecting a satellite remote sensing image and synchronous actual measurement suspended matter concentration data, and obtaining a space gridding water body remote sensing reflectivity matrix through radiometric calibration, atmospheric correction and water body mask. Reconstructing and denoising through wavelet decomposition, and normalizing and standardizing the spectrum to obtain a spectrum numerical sequence; multi-scale waveband combination and differential features are constructed based on the sequence, and sensitive features are screened out by using XGBoost. A physical constraint term is constructed in combination with sensitive characteristics and a water body radiation transmission rule, and an intermediate inversion result is obtained through numerical iteration. And performing deviation compensation on an intermediate result by using a deep learning residual error, and finally performing spatial smoothing, consistency verification and high-concentration saturation optimization to obtain a high-precision and spatially continuous suspended matter concentration spatial distribution result. According to the method, efficient feature mining and physical mechanism deep fusion are realized, and the explanatory and generalization ability of the model is greatly improved.

The invention discloses a coal mine gas cloud contour infrared spectrum inversion method. The method comprises the following steps: 1, simulating an underground gas cloud formation environment; 2, acquiring infrared image data containing gas cloud by using an infrared spectrum detection device; step 3, carrying out de-noising processing on the infrared image data; 4, performing temperature drift correction and radiation calibration on the denoised infrared image, and converting a DN value into radiation brightness; 5, based on the multi-frame de-noised image, performing target detection and contour extraction on the gas cloud by using a Methane-YOLO model, performing multi-view geometric matching of feature points, performing adjustment optimization of three-dimensional coordinates in combination with a bundle method, generating a point cloud model, and reconstructing a curved surface; 6, based on the three-layer radiation transmission model and the Lambert-Beer law, inverting the gas concentration; and step 7, based on the CEEMDAN-VMD secondary decomposition and CNN-LSTM hybrid network, carrying out joint training on the space contour and time sequence data, and carrying out three-dimensional space concentration distribution prediction.

The invention provides an atmospheric aerosol particle parameter inversion method and device and electronic equipment. The inversion method comprises the following steps: performing parametric description on non-spherical aerosol particles to obtain an aerosol parameter vector; constructing and obtaining a trained physical guidance neural network model; applying the trained physical guidance neural network model to radiation transmission model forward modeling, calculating and outputting a derivative of the input aerosol parameter, and determining and outputting a Jacobian matrix relative to the aerosol parameter through automatic differentiation; and performing inversion iteration based on the Jacobian matrix and the to-be-inverted atmospheric top observation quantity, and determining a target aerosol parameter corresponding to the to-be-inverted atmospheric top observation quantity, thereby solving the problems that a traditional scattering model is non-derivable and a derivative is difficult to obtain, and improving aerosol remote sensing inversion precision and efficiency.

The invention discloses a remote sensing multi-parameter integrated inversion normal form method, system and equipment based on AI-Agent. According to the method, a deep learning neural network is dynamically driven through AI-Agent, a refining mechanism (RM)-Transformer-MoE size nested model, a physical method, a statistical method and expert knowledge are coupled, a DL-C-PSK normal form is constructed, a high-precision multi-source database is established based on the normal form, an appropriate radiation transfer equation is constructed through geophysical logical reasoning, and a high-precision multi-source database is established. And inversion of parameters such as surface temperature, surface emissivity, atmospheric water vapor content and near-surface air temperature is realized. According to a causal relationship between an input wave band and an output parameter, a direct synchronous inversion or iterative inversion mode is adopted to ensure multi-parameter high-precision synchronous inversion. Wherein the core of the deep learning neural network comprises RM logic derivation, SHAP model interpretation, Transform model architecture and a Transform-MoE size nested model, so that the interpretability, the adaptability and the precision of the model are improved. Through an AI-Agent driven RM-Transform-MoE nested model, deep coupling of physics-statistics-knowledge is realized, compared with a traditional SW method, the inversion precision is greatly improved, and verification shows that the technology is suitable for the fields of global climate observation, environment monitoring and the like.

The invention provides an offshore area atmosphere and ocean parameter collaborative inversion method and system, and relates to the technical field of satellite remote sensing, and the method comprises the steps: obtaining to-be-inverted parameters which comprise an atmosphere parameter and an ocean parameter, calling a multi-parameter physical information neural network model to carry out the ocean water-leaving radiation calculation of the ocean parameter, and calculating the ocean water-leaving radiation of the ocean parameter; an atmospheric aerosol model is called to carry out parameterization calculation on atmospheric parameters, and scattering parameters and absorption coefficients of atmospheric aerosol are obtained; inputting the ocean water-leaving radiation quantity and the scattering parameter and the absorption coefficient of the atmospheric aerosol into a forward radiation transfer model, and carrying out radiation transfer calculation to obtain entrance pupil radiation brightness of the top of the atmospheric layer; and finally, performing parameter inversion by combining a satellite measurement radiation signal to obtain an inversion parameter corresponding to the to-be-inverted parameter. According to the invention, problems existing in an atmospheric correction algorithm for ocean water color remote sensing in the prior art are solved.

The invention relates to a system deviation correction method for improving the salinity inversion precision of an interference type microwave radiometer, and relates to the technical field of satellite ocean remote sensing data processing. The method comprises the following steps of: acquiring an area, strictly purifying and screening observed brightness temperature data of a preset time window in the area to eliminate the influence of radio frequency interference, sun and moon pollution and the like, acquiring high-purity original data, calculating simulated brightness temperature by utilizing a radiation transmission model, and subtracting the simulated brightness temperature from the purified observed brightness temperature to obtain a brightness temperature deviation sample; and then a robust statistical method based on a sliding window and a median is adopted to carry out statistical calculation on the deviation sample to obtain a stable and reliable deviation correction value, and finally a two-dimensional lookup table is generated and applied to sea surface salinity inversion. According to the method, the observation brightness temperature for salinity inversion can be efficiently and accurately corrected from the source, and the contradiction between stability and adaptability in the prior art is solved.

The embodiment of the invention provides a remote sensing image atmospheric correction method and system for an ocean near-shore scene, and relates to the technical field of ocean remote sensing image atmospheric correction. According to the method, after remote sensing data is obtained, solar flare correction is executed by extracting a target mask and using a secondary simulation model of a satellite signal in a solar spectrum. And then a multi-band darkness index is calculated, and an optimal aerosol optical thickness value is inverted through a radiation transmission model. And calculating the atmospheric correction coefficient of each wave band according to the optimal aerosol optical thickness value so as to generate correction result data. According to the method, on the basis of robust flare correction of the SWIR proxy, scene-adaptive flare proxy is constructed by utilizing water body reflection and path radiation physical characteristics of the SWIR under a deepwater condition, and flare deduction is realized. Inversion is carried out through multiband combined darkness indexes, and the spectral characteristics of the deep water body reflected in the visible spectrum are utilized, so that single-band random noise is smoothed, and the accuracy of an AOT estimation result is improved.

The invention relates to the technical field of ground feature component analysis, and discloses a hyperspectrum-based mineral alteration feature extraction method, which comprises the following steps: acquiring hyperspectral remote sensing image data of a to-be-processed region, and carrying out atmospheric correction and noise reduction preprocessing; extracting an initial end member spectrum after preprocessing; constructing an objective function of nonlinear spectral unmixing, wherein the objective function comprises a data fidelity term; an abundance constraint term; punishing a physical consistency regularization term of a deviation between a spectrum generated by the physical model and a current end member spectrum based on a differentiable radiation transfer model; solving the objective function to obtain a final end member spectrum and an abundance matrix corresponding to the end member spectrum; and identifying altered minerals by utilizing the final end member spectrum, and extracting mineral alteration characteristics of the to-be-processed region in combination with an abundance matrix corresponding to the end member spectrum. According to the method, the space-spectrum composite kernel function is introduced, the nonlinear unmixing problem is solved in the high-dimensional feature space, and the complex nonlinear mixing effect existing in the surface environment can be known.

The invention relates to a tail flame polarization state simulation method and device considering solid-phase particle size distribution, a medium and equipment. The method comprises the following steps: constructing a particle size distribution function; determining a cumulative distribution function based on the particle size distribution function, discretizing the cumulative distribution function, and calculating the representative particle size of each region; optical factors of the solid-phase particles in all the areas are calculated; calculating optical parameters of the particle swarm; taking each representative particle size and number density as particle phase input of an N-S equation of fluid mechanics to obtain two-phase flow tail flame flow field data and nozzle outlet data under different particle size distribution conditions; the method comprises the following steps of: taking a plurality of grids as input of a tail flame polarization radiation transfer model, calculating each solid phase parameter of each grid, taking an extinction coefficient and a single scattering coefficient of each cell as input of a two-phase flow tail flame vector radiation transfer equation, and outputting a polarization state of the tail flame. And the tail flame polarized radiation closer to the reality is obtained through simulation.

The invention provides a cloud atmosphere high-fidelity vector radiation simulation method based on physical constraints. The method comprises the following steps: A1, generating cloud layer micro-physical parameters by a WRF model; a2, integrating simulation parameters generated in the step A1, and inputting the simulation parameters into an MYSTIC model for three-dimensional radiation transfer simulation; a3, constructing a PC-GAN (U-Net + + + differentiable radiation layer + double-branch discriminator) physical constraint generative adversarial correction network, and defining adversarial loss, physical loss and KL divergence loss; a4, simulation data are input into PC-GAN, CALIPSO, PLODER3 and other actual measurement data are compared with the simulation data to calculate loss, physical loss is fed back, and cloud micro physical parameters are iteratively optimized until RMSElt is radiated; 5%; a5, backfilling the cloud layer micro-physical parameters corrected in the step A4, carrying out MYSTIC model radiation transfer simulation again, and carrying out satellite / foundation verification; and A6, outputting high-fidelity radiation simulation data. The method can be widely applied to meteorological satellite data verification, climate model development, cloud characteristic inversion and remote sensor design, and has the technical advantages of high precision and high efficiency.

The invention relates to the technical field of limited space fire hazard flashover prediction. The invention provides a limited space fire hazard flashover prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring sensor observation data and initial fuel distribution data; inputting the space-time coordinate vector and the initial fuel distribution data into a Mamba neural network to obtain predicted values of temperature, incident radiation and fuel mass fraction; calculating a space-time derivative of the predicted value, and constructing a physical residual term containing energy conservation, radiation transfer and fuel consumption equation residual, a boundary residual term and an observation residual term; weighted combination is carried out on the three types of residual errors to construct a loss function, and network parameters and to-be-learned physical parameters are updated through an optimization algorithm; and predicting a temperature field based on the updated network, and judging the time of detonation when the temperature reaches a detonation critical value. The problems of poor reliability and prediction deviation caused by lack of physical rule constraints of an existing data-driven fire prediction model are solved.

The embodiment of the present invention discloses a method for precise control of SNCR of waste incineration based on a mechanism model and dynamic prediction, which relates to the technical field of flue gas denitrification in waste incineration power plants. The method comprises: obtaining the operating parameters of the waste incinerator; constructing a mechanism model, i.e., a thermodynamic-combustion coupling model, using virtual sensing technology, based on the energy conservation equation, the radiation transfer equation, and CFD inverse solution, inverting the flue gas temperature and flow field velocity vector, and realizing three-dimensional flow field reconstruction and axial temperature field estimation; combining the mechanism model with measured data, optimizing the spray gun parameters using a particle swarm optimization algorithm, and realizing a multi-objective balance between denitrification efficiency and ammonia slip; and dynamically adjusting the spray gun flow rate, angle, and layout according to the optimized parameters. The method of the present invention has high denitrification efficiency and low reducing agent consumption.

The invention discloses a CO2 sensitive wave band satellite hyperspectral atmospheric radiation transmission process simulation method and device. The method comprises the following steps: screening a CO2 sensitive wave band in a satellite detection wave band; an absorption coefficient lookup table is generated based on the wavenumber-pressure-temperature three-dimensional grid, local recalculation or interpolation calculation is dynamically selected through interpolation confidence evaluation, and the atmospheric stratified gas optical thickness is obtained; the reanalysis data and the aerosol characteristic parameters are fused, and an aerosol vertical profile and multi-mode particle size distribution are constructed; setting atmospheric radiation transmission boundary conditions; calculating the hyperspectral radiation intensity in the target direction by using a radiation transfer solver; a simulated radiance is generated in conjunction with the instrument response function. According to the method, a dynamic interpolation-recalculation hybrid mechanism is innovatively introduced, the balance of calculation precision and efficiency is controlled through a confidence coefficient threshold value, a multi-source aerosol parameterized model is coupled, high-fidelity simulation spectrum data can be quickly generated, and precision verification and algorithm optimization of satellite CO2 inversion are supported.

The invention belongs to the technical field of quantitative remote sensing, and relates to a mid-infrared hyperspectral emission and reflection characteristic integrated inversion method, which comprises the following steps: acquiring mid-infrared hyperspectral satellite remote sensing data, and optimizing sensing entrance pupil radiance data to obtain optimized entrance pupil radiance data; the intermediate infrared hyperspectral satellite remote sensing data comprises sensing entrance pupil radiance data; performing atmospheric correction on the optimized entrance pupil radiance data by using a radiation transfer model to obtain surface brightness temperature and atmospheric downward radiation, and calculating solar irradiance reaching the surface; constructing a mid-infrared hyperspectral earth surface bi-directional reflectivity inversion framework, and performing earth surface bi-directional reflectivity inversion based on the mid-infrared hyperspectral earth surface bi-directional reflectivity inversion framework to obtain earth surface bi-directional reflectivity and earth surface equivalent radiance; constructing a scene self-adaptive framework, and performing inversion based on the scene self-adaptive framework to obtain the surface temperature and the surface emissivity; therefore, accurate separation of surface emission radiation and surface reflection radiation is completed.