5549results about "Scattering properties measurements" patented technology

A method and device for inverting ocean-atmosphere optical parameters and storage medium. The method includes: obtaining a vector apparent reflectance ρl, ρQ, ρU of water-leaving radiance at top of atmosphere and observation geometry of a multi-angle polarized satellite, and inputting the vector apparent reflectance ρl, ρQ, ρU and the observation geometry into a trained ocean-atmosphere optical parameter model, to output a group of ocean-atmosphere optical parameters, the output ocean-atmosphere optical parameters comprise: PBOAw, FAOD / CAOD, FRm / CRm, W, CHL and SPM, the ocean-atmosphere optical parameter model is an XGBoost machine training model. In this way, the ocean-atmosphere optical parameters can be quickly and stably inverted.

Methods of creating and using digital twins of vegetation / crops in relation to their growing environments. Some embodiments include creating a digital twin of the growing environment using sensor data from a subsurface sensor suite, creating a digital twin of the vegetation crop using data from an overhead sensor suite, and using at least one mathematical model to relate the digital twin of the crop to the digital twin of the growing environment. In some embodiments, the mathematical model(s) implement a root cause analysis that uses a limiting factor approach. In some embodiments, the subsurface sensor suite is on a multi-sensor soil probe. In some embodiments, the multi-sensor soil probe comprises a video microscope, a tip stress sensor, a sleeve stress sensor, and a volumetric- water-content sensor. Other related systems, devices, methods, and software are also disclosed.

The invention belongs to the technical field of water quality monitoring, and particularly relates to a construction method and device of a turbidity testing system based on a photoelectric detector. Optical signal data and multi-wavelength optical signal data are obtained; performing intelligent switching and spectrum matching according to the signal data, determining a target wavelength, and obtaining characteristic parameters related to turbidity by combining a scattering and transmission combined measurement principle; measuring different standard solutions according to the characteristic parameters to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set, and establishing a characteristic ratio turbidity calibration model according to the data set; measuring and calculating the turbidity according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and meanwhile, performing temperature compensation; and transmitting the parameter information to a cloud database for storage, performing hierarchical processing, performing regular intelligent self-inspection at the same time, and feeding back abnormal data when an abnormality occurs. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, susceptibility to temperature drift and the like in the prior art are solved.

The embodiment of the invention provides a soil nutrient spectrum detection and regulation method and system, and belongs to the technical field of soil nutrient spectrum detection and regulation. The method comprises the following steps: collecting soil multispectral original data and environmental data, generating a calibration matrix and a nonlinear correction parameter, executing optical reflectivity conversion, and synchronously fusing a time domain reflectometry dielectric constant and a spectral moisture index to obtain fused moisture content; selecting a nutrient diagnosis characteristic wave band pair to obtain a moisture inhibition type spectral index; and predicting the soil nutrient content by using a pre-trained transfer learning model to obtain a soil nutrient content prediction value, and performing fertilization regulation and control based on the fertilization decision risk coefficient. According to the invention, extreme soil moisture measurement deviation is solved through a soil type adaptive moisture fusion technology; through multi-source risk quantitative decision and PID self-adaptive regulation and control, the fertilization amount can be dynamically optimized, execution safety is guaranteed, and a detection-decision-execution precision agriculture closed loop is formed.

The invention discloses an amphibious unmanned aerial vehicle riverway sediment concentration real-time monitoring method based on underwater light field imaging and cross-modal fusion, and relates to the technical field of riverway sediment monitoring. Comprising the following steps: acquiring aerial multispectral data and preprocessing the aerial multispectral data; an integrated polarized light field camera is carried on an amphibious unmanned aerial vehicle to capture underwater light field data, and underwater three-dimensional light field information is reconstructed and sediment scattering characteristics are extracted through an optical model and a lightweight deep learning network; the air multispectral data and sediment scattering characteristics are fused through space-time alignment and an attention mechanism, a sediment concentration prediction model is generated, and then a spatial distribution diagram of the river sediment concentration is output; and carrying out real-time monitoring and early warning on the river sediment concentration according to the spatial distribution diagram of the river sediment concentration through edge calculation and cloud collaboration architecture. The method is combined with an advanced deep learning algorithm, high-resolution silt concentration inversion can be realized, and the method has real-time performance and adapts to a complex water body environment.

The invention relates to the technical field of water pollution detection, in particular to a water quality pollution multi-dimensional analysis system which comprises the following steps: synchronously measuring an ultraviolet-visible absorption spectrum of a water sample to be detected and scattered light intensity in an orthogonal direction, and deducting spectrum baseline interference caused by suspended particulate matters in the water sample through a correction algorithm; the interferents in the water sample are selectively converted and degraded by applying a programmed potential to the inflowing water sample to be detected; the reverse pulse is periodically applied, so that the online regeneration function of the electrode is realized; analyzing the spectral data by adopting a multivariate correction algorithm, and calculating the concentration of the target pollutant; the calculation residual error of the multivariate correction algorithm is analyzed, new interference and system performance drift of current measurement are diagnosed, and a corresponding system state code is generated; and responding to the system state code, automatically starting and carrying out state checking and multi-point calibration on the instrument, and using a calibration result to update the analysis model. Through multi-dimensional perception and closed-loop operation, accuracy and reliability of water quality monitoring are guaranteed.

The invention relates to the technical field of image processing, and discloses a precast concrete T-beam bubble detection method and system. The method comprises the following steps: carrying out multiband reflection data acquisition and calibration on the surface of a T-beam, and obtaining optimized data through light intensity compensation and band screening; performing spatial registration and noise reduction processing on the data, and fusing the data into an image; edge detection and image segmentation are carried out, and pore morphological features are extracted; calculating depth data and performing spatial reconstruction to obtain pore distribution data; performing statistical analysis on the distribution data to obtain a defect evaluation result; and finally generating a detection report. According to the method, accurate positioning and quantitative evaluation of bubble defects are realized through multi-source data fusion and deep analysis, a complete early warning and evaluation mechanism is established, and the reliability and practicability of a detection result are improved.

The invention relates to the technical field of machine learning, in particular to a grain classification and recognition method and system based on image analysis, and the method comprises the following steps: obtaining grain particle image data, calculating a gray histogram and a gradient change rate, calling a gray co-occurrence matrix to extract grain particle texture density, and scanning a sliding window to obtain local feature parameters of grain particles. According to the method, through combined analysis of a gray level histogram and a gradient change rate, dynamic extraction of particle textures through a sliding window, enhancement of complex surface feature capture, normalization of contrast and direction consistency parameters, construction of weighted feature vectors, dynamic correction of variance contribution degree and suppression of environmental interference, and fusion of near-infrared and short-wave infrared reflectivity changes, the near-infrared and short-wave infrared reflectance change is improved. And quantifying the mean value and peak-to-valley ratio of spectrum difference values, matching visible light characteristics to verify multi-dimensional constraints, constructing a spectrum-texture matrix by local reflectivity rate and gradient change rate, and synchronously evaluating global similarity and spatial distribution difference by adopting Euclidean distance and local offset double-layer matching.

The invention relates to the technical field of image processing, in particular to an optical lens surface defect detection method and system based on image processing. The method comprises the following steps: acquiring an optical lens to be measured; performing surface reflectivity detection on the optical lens to be detected to obtain surface reflectivity data; performing surface curvature identification on the optical lens to be detected to obtain surface curvature data; and determining initial conditions of the illumination light source according to the surface reflectivity data, and performing optical filtering processing on the initial conditions of the illumination light source based on the surface curvature data to generate a filtering processing light source. Through a data processing technology, an image processing technology and a mode identification technology, reflection characteristic attributes and lens surface curvature identification are carried out on the surface of the optical lens, and imaging illumination light source conditions are determined to realize multi-mode imaging of the surface of the lens, so that a lens panorama enhanced image is obtained, and surface defect detection is carried out on the surface of the optical lens. And the detection accuracy of lens surface defects is improved.

The invention relates to the technical field of optical lens defect detection, and particularly discloses an optical lens laser-induced damage on-line detection method and system, a linear polarizer and a narrow-band filter are connected in series in a detection laser light path, non-target polarized light is filtered through polarization direction matching, meanwhile, interference of the environment and scattered light is inhibited through the narrow-band filter, and the laser-induced damage on-line detection system is obtained. According to the method, external source noise such as ambient light and non-target polarized light is reduced through polarization matching and narrow-band filtering, randomness of speckle noise is offset through multi-angle collection and signal fusion in a visual detection mode, exposure is dynamically adjusted, an image is fused to improve the signal-to-noise ratio, noise and real signals are decoupled through self-supervised learning, and the real-time performance of the system is improved. Dust interference is removed in combination with morphological operation; a deep learning reasoning model enhances the damage identification capability in a noise environment, defocusing blur caused by mechanical vibration is eliminated through phase conjugate correction, and the influence of photoelectric conversion noise, dust interference and the like on the detection efficiency and accuracy is remarkably reduced through multi-link linkage.

The invention relates to the technical field of precision detection, in particular to a device and a method for nondestructive testing of a wafer surface. The device provided by the invention comprises a detection light path, a motion control module, a narrow channel detection end, a wide channel detection end and a data acquisition and processing module, the motion control module is used for bearing a to-be-tested wafer and adjusting the position; the detection light path comprises an incident light path, a wide-channel light path and a narrow-channel light path; the incident light path is used for projecting a light beam and vertically projecting the light beam to a wafer to be measured; the narrow channel light path is used for collecting scattered light with the diffusion angle of [5 degrees, 24 degrees] generated on the surface of the wafer to be detected and gathering the scattered light to the narrow channel detection end, and the wide channel light path is used for collecting scattered light with the diffusion angle of [25 degrees, 70 degrees] generated on the surface of the wafer to be detected and gathering the scattered light to the wide channel detection end. According to the device for nondestructive testing of the surface of the wafer, the detection light is vertically incident, the detection light can be reflected out along the original path when the wafer is qualified, and only the defective wafer can generate scattered light, so that the wafer defect can be quickly and accurately identified. According to the wafer defect detection method, the defects of high cost, low precision and high operation difficulty of a wafer defect detection method in the prior art are overcome.

The invention discloses a multi-mode end point detection system and method for a semiconductor dry process, and relates to the field of gas concentration detection. A gas absorption cell; the reference light path adjusting unit comprises an adjustable attenuator used for adjusting signals; the first photoelectric detector is used for detecting a transmission signal; the second photoelectric detector is used for detecting a reference signal; the lateral scattered light detector is used for detecting a scattered signal of the particulate matters in the gas absorption tank; and the signal processing unit is in signal connection with the first photoelectric detector, the second photoelectric detector and the lateral scattering light detector and is used for performing compensation calculation on the transmission signal based on the reference signal and the scattering signal so as to obtain a corrected gas absorption signal. By adopting the scheme, measurement signal distortion caused by optical mirror surface pollution and particulate matter scattering interference in a semiconductor tail gas optical detection technology is avoided.

The invention relates to the technical field of water and soil conservation monitoring, and discloses a remote sensing image-based water and soil conservation monitoring method, which comprises the following steps of: 1, integrating optical, infrared and microwave remote sensing data, and constructing a soil information atlas; step 2, capturing soil humidity change in each season by using long-time-sequence data according to the soil information, and determining the influence of rainfall, snow melting and evaporation on the reflectivity; step 3, training data of each wave band through a deep neural network according to the influence information of the reflectivity; through an adaptive learning algorithm and a model parameter regular updating mechanism, the problem that in the prior art, characteristic spectrums of wet soil, vegetation and bare soil are overlapped due to seasonal changes is effectively solved, after the seasonal modulation factors are introduced, the system can accurately capture influences of natural factors such as rainfall, snow melting and evaporation on the soil reflectivity, and the soil reflectivity is improved. And model parameters are adaptively adjusted, so that the interference of reflectivity fluctuation on a monitoring result in wet and drought states is eliminated, and the stability of long-term monitoring is ensured.

The invention discloses a wafer detection scattered light collection objective lens, and relates to the technical field of optical imaging. The scattered light collecting lens is applied to wafer defect detection, the scattered light collecting lens sequentially comprises a first lens group, a second lens group, a third lens group and a fourth lens group from an object plane to an image plane in the optical axis direction of the lens, twelve lenses and an aperture diaphragm, and the twelve lenses and the aperture diaphragm are coaxially arranged; the first lens group is a catadioptric lens group, has positive focal power, and is used for imaging an object plane to an intermediate image plane through two times of reflection. The numerical aperture of the acquisition objective lens provided by the invention is equal to 0.9, the field of view is equal to 2.6 mm, the acquisition objective lens has the characteristics of large field of view, large numerical aperture and small center shielding, the capability of detecting scattered light of defects can be greatly improved, chromatic aberration is corrected by adopting a refraction and reflection mode, and a wider laser bandwidth can be realized compared with a general refraction mode of combining CaF2 and fused quartz.

The invention relates to the technical field of surveying and mapping information processing, and discloses a surveying and mapping information intelligent analysis and visualization system based on the Internet of Things. According to the system, a building space data acquisition terminal acquires geometric feature point cloud and environmental parameters of the surface of a building structure in real time through distributed Internet of Things nodes; the multi-source heterogeneous data fusion engine performs space-time alignment processing on the geometric feature point cloud to generate a building total-factor topological skeleton with an absolute coordinate system; the three-dimensional topological relation reconstruction module is used for extracting a spatial constraint relation of building components according to the topological skeleton and establishing a hierarchical building information model capable of being edited in a parameterized manner; and the dynamic visual rendering platform is used for generating a multi-scale spatial index based on the hierarchical building information model, and driving real-time light and shadow effects and interactive sectioning analysis. According to the system, full-process automatic processing and dynamic visualization of building surveying and mapping data are realized.

The invention relates to the technical field of quality detection, in particular to a medical refrigerator body manufacturing detection system and method, and the system comprises a coating reflection detection module, a sound wave response measurement module, an optical signal fluctuation analysis module, a surface defect recognition module and a structural feature state evaluation module. According to the method, the reflection amplitude gradient change of the adjacent scanning areas is compared with the spectral curve continuity, so that the sensing precision of the surface reflection distribution continuity is improved, the recognition capability of weak anomalies is enhanced, and the internal potential defects are accurately captured in combination with the sound wave echo time and structural stability analysis of a specific part; a light intensity gradient model is constructed according to a sound wave analysis result, a splicing seam and a light intensity jump area of a sunken area are identified, abnormal forms such as scratches and bubbles are further extracted, coordinates are marked, the whole process depends on dynamic feedback and multi-signal linkage calibration, the identification precision and the coverage rate are improved, and medical box body quality global efficient detection is achieved.

The invention relates to the technical field of ecological environment monitoring, in particular to a multi-dimensional data analysis-based water body pollution monitoring and processing system, which comprises a dynamic data cube construction module, a pollution event slice extraction module, a multi-dimensional feature fingerprint coupling module, a pollution source reverse traceability module and a self-adaptive early warning trigger module. The method comprises the following steps: receiving water quality parameters of an internet of things sensor, meteorological data of a meteorological hydrological station and satellite remote sensing spectral data, and constructing a dynamic data cube of unified space-time coordinates and feature axes; identifying pollution event slices by using entropy change analysis and a self-adaptive threshold method; carrying out characteristic coupling analysis on the pollution event, and fusing a spectrum fluctuation mode, a physicochemical gradient and toxicity response to generate a pollution characteristic fingerprint; pollution source positioning and contribution weight estimation are realized by adopting a dynamic time warping algorithm, and corresponding disposal equipment is driven to start based on an early warning level, so that intelligent closed-loop control of pollution identification, source tracing and treatment is realized.

The invention provides a high-speed defect identification method and system based on optical diffraction imaging, and relates to the technical field of optics, and the method comprises the following steps: constructing a composite optical diffraction unit comprising a programmable optical metasurface and a quantum phase modulator, optimizing and determining modulation characteristics through a multi-target constraint function, and collecting a quantum coding diffraction image. The quantum state phase reconstruction network is used for processing the image and extracting characteristic parameters, and a result is obtained through analysis of a defect identification model. According to the invention, rapid defect identification with high spatial resolution is realized, and the detection efficiency and accuracy are improved.

The invention relates to a rail surface defect detection method and system, and the method comprises the steps: collecting a reflection response sequence of a rail surface under various illumination conditions through the controllable dynamic disturbance of incident angles and illumination intensity of different positions of the rail surface, and representing the microscopic optical response behavior of a rail surface material; for each tiny area on the surface of the rail, dynamic reflection stability is calculated, and areas with abnormal stability are marked as defect candidate areas; analyzing the difference between the defect candidate area and a normal wheel-rail contact trace, identifying whether abnormal residues or abnormal abrasion boundaries generated due to defect evolution exist or not by combining optical heterogeneity boundary characteristics, and verifying the authenticity of the defect; for the judged defect candidate region, judging whether the optical response of the adjacent region has a general change trend or not along the contact track direction and the normal micro-region fluctuation direction of the rail surface, and determining the space connectivity and the diffusion trend of the defect and a growth defect region; the system executes the steps of the method through a processor.

The present invention relates to the technical field of ocean color remote sensing. Disclosed is an oceanic-waters subsurface chlorophyll-a maxima depth retrieval method based on remote sensing reflectance. The method comprises the following step: S1: selecting wave bands, acquiring a remote sensing reflectance product among MODIS remote sensing products, selecting remote sensing reflectance data at three wave bands, i.e., 412 nm, 555 nm, and 678 nm, and performing preprocessing. The present invention can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentration, sea surface temperature data and sea surface height data, reducing the consumption of computational resources for running machine learning, but can also effectively improve the retrieval accuracy, and can also reduce the consumption of computational resources for the retrieval of subsurface chlorophyll-a maxima and reduce the retrieval difficulty.

The invention discloses a pork quality grade lossless evaluation method based on hyperspectral imaging, and relates to the technical field of pork detection.The pork quality grade lossless evaluation method comprises the following steps that pork samples of different qualities are prepared, and standardization processing is conducted on the pork samples; performing chromatic aberration analysis on the pork samples with different qualities by using a colorimeter, and performing quality grade division on the pork samples with different qualities based on the analysis results of the muscle color, the fat color and the gloss of the pork. According to the pork quality grade lossless evaluation method based on hyperspectral imaging, reflection or transmission information of a pork sample in a plurality of continuous spectral wavebands is captured by using a hyperspectral imaging technology, accurate evaluation of the pork quality grade is realized by analyzing spectral data, higher-dimension data is provided, and the evaluation accuracy of the pork quality grade is improved. Meanwhile, rapid scanning can be achieved, the detection time is greatly shortened, the overall detection efficiency is improved, and pork quality monitoring is more efficient and timely.

The invention relates to the technical field of information, in particular to a method for detecting and monitoring the cleaning effect of a semiconductor wafer in real time in the technical field of semiconductor wafer manufacturing, which comprises the following steps: introducing a rapid chemical analysis technology, combining an electrochemical sensor and spectral analysis, monitoring metal ion residues on the surface of the wafer in real time, and shortening the detection period; instant feedback on a production line is realized; a real-time feedback control system is constructed, according to the output of the comprehensive evaluation model, the working parameters of the cleaning equipment are automatically adjusted, the cleaning process is optimized, and the usage amount of cleaning liquid and the environmental influence are reduced; an edge computing technology is integrated, data processing and analysis tasks are sunk to a cleaning equipment end, data transmission delay is reduced, and the response speed of real-time monitoring and feedback control is increased; a visual monitoring platform is developed, key parameters, detection results and prediction trends in the cleaning process are displayed in a graphical mode, and operators can conveniently master the cleaning state in real time and quickly respond to abnormal conditions.

The invention relates to the technical field of ecological environment monitoring and forest remote sensing, in particular to a forest plant stoichiometric characteristic spatial-temporal change intelligent analysis method, which comprises the following steps: S1, acquiring spectral reflectivity and element concentration values of plant leaves in a target area; s2, performing space-time registration processing; s3, constructing a three-dimensional space-time matrix, and generating a space-time cube with a dynamic weight identifier; s4, calculating the space-time gradient variation of the carbon-nitrogen-phosphorus ratio in each space unit; s5, performing feature decoupling on the space-time gradient variation; and S6, generating a stoichiometric characteristic three-dimensional atlas with altitude-time double coordinate axes. According to the method, by constructing the multi-factor driven three-dimensional space-time analysis model, decoupling analysis of long-term trend and short-term fluctuation of forest plant stoichiometric characteristics is achieved, the element flux mutation threshold value at the terrain turning position is accurately recognized, and the dynamic monitoring capacity of the ecological process is effectively improved.

The invention provides a desert photovoltaic sand damage dynamic monitoring method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the flying dust data of a photovoltaic power station region, generating an initial data set, and enabling the flying dust data to be collected through a multi-mode sensor carried by the unmanned aerial vehicle; performing multi-modal data fusion processing on the initial data set to obtain a dust feature matrix, the dust feature matrix being used for representing dust reflectivity, terrain elevation and wind speed correlation features; calculating migration probability distribution of the flying dust particles based on the sand and dust characteristic matrix, wherein the migration probability distribution is used for representing spatio-temporal evolution laws of the particles with different particle sizes; a three-dimensional dynamic sand and dust concentration field model is generated according to the migration probability distribution, a sand damage dynamic monitoring result is output based on the three-dimensional dynamic sand and dust concentration field model, and the sand damage dynamic monitoring result comprises deposition density distribution and an erosion risk area. By adopting the method, the photovoltaic sand damage of the desert can be accurately and dynamically monitored with high timeliness.

The invention discloses an intelligent detection method for water pollutants, which comprises the following steps of: transmitting a signal of a specific frequency band to a water body through an electromagnetic wave transmitting module, acquiring reflected and refracted signals by using a multi-channel receiver, extracting the characteristics of dielectric constant, refractive index and the like of the water body, and analyzing the types and the concentration range of the pollutants by adopting a pattern recognition algorithm; the method comprises the following steps: selecting a micro-fluidic chip and an electrochemical unit, selecting a corresponding biosensor for targeted detection, processing detection data through the micro-fluidic chip and the electrochemical unit, predicting the concentration, spatial distribution and trend of pollutants through a data analysis module, setting a dynamic self-adaptive threshold by the system, realizing pollution early warning, and uploading data to a cloud platform through a remote communication module. And remote monitoring is supported. The method can be widely applied to the fields of water quality monitoring, environmental protection and pollution control, and the detection precision, the real-time performance and the intelligent level are improved.

A water sensor buoy for use in a water environment including a main buoy assembly and a sensor package. The assembly includes a buoy hull, a data processing and control unit, a data transmission unit, and a power source. The sensor package is in removable engagement with the assembly. The sensor package includes a sensor housing in removable engagement with the buoy hull, and a water sensor in removable electronic communication with the data processing and control unit and the power source, and configured to sense an attribute of the water environment. The buoy has a watertight seal between the buoy hull and sensor housing, and an electrical conduit. The conduit is engageable with the data processing and control unit and the power source with the sensor housing being engaged with the buoy hull, and disengageable upon the sensor housing being disengaged from the buoy hull.

The invention relates to the technical field of assembly production lines, and particularly discloses an assembly production line workpiece defect detection method and system based on machine vision. The method aims at accurately detecting the workpiece defects of the assembly production line, firstly, position information of a lens, a workpiece and a light source of a machine vision device is obtained, a basis is provided for subsequent calculation, the detection accuracy is ensured, and then the incident angle of the mirror surface is determined according to the characteristic information of the lens, influences reflected light and imaging and is a key parameter. Then obtaining imaging influence factors according to workpiece characteristics, measuring interference of workpiece surface characteristics on imaging, providing a basis for optimizing reflectivity, calculating specular reflectance through light source position information, reflecting light reflection conditions, combining the specular reflectance and the reflection conditions to obtain collaborative optimization specular reflectance, more accurately evaluating the influence of reflection on imaging, and finally, determining the influence of reflection on imaging. If the reflectivity is greater than a preset threshold value, correction is carried out, a correction angle is obtained to adjust a lens detection angle, misjudgment caused by mirror reflection is reduced, and the detection precision is improved.

The invention relates to the technical field of information, in particular to an intelligent agricultural information big data intelligent acquisition management system and method. The method comprises the following steps: acquiring farm sensing data, and performing crop growth analysis according to the farm sensing data so as to obtain crop growth data; performing crop health state feature extraction and crop physiological feature extraction according to the crop growth data so as to obtain crop health state data and crop physiological data; performing disease and pest detection according to the crop health state data and the crop physiological data to obtain crop disease and pest data; the method comprises the following steps: acquiring farm remote sensing data, performing crop multispectral analysis according to the farm remote sensing data so as to obtain crop multispectral data, and performing crop moisture state evaluation according to the crop multispectral data so as to obtain crop moisture state data. The pesticide spraying strategy is optimized based on the information technology, and the agricultural production management efficiency is improved.

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention relates to the technical field of hydraulic ring surveying and mapping, in particular to a hydraulic ring surveying and mapping data acquisition method and system based on geographic space information, and the method comprises the following steps: S1, data acquisition and preliminary data set generation: generating an initial geographic space data set; s2, space-time alignment and coupling feature extraction: constructing a preliminary geographic space model; s3, dynamic verification and cross verification: generating a dynamic verification parameter set; s4, three-dimensional geologic modeling and parameter fusion: generating a comprehensive three-dimensional geologic model; s5, environment interference correction and data optimization: analyzing the offset of an environment interference factor to sensor data through an adaptive learning algorithm, and outputting corrected hydraulic ring surveying and mapping data; and S6, safety threshold comparison and early warning map generation: generating a visual early warning map. According to the method, deep support is provided for underground structure analysis, and the comprehensive capability of hydraulic ring surveying and mapping is improved.