955results about "Method using image detector and image signal processing" patented technology

Disclosed is an intelligent monitoring method for soil moisture status based on remote sensing technology, including the following steps: dividing a monitoring region into a plurality of sub-regions by a processing end, acquiring image data of various sub-regions on the basis of remote sensing satellites, acquiring multi-source data related to soil moisture status of the monitoring region on the basis of sensor devices, and pre-processing the image data and the multi-source data; comprehensively analyzing the image data and the multi-source data, and assessing soil moisture status of the various sub-regions; and comprehensively analyzing overall soil moisture status of the monitoring region according to the soil moisture status of the various sub-regions. The soil moisture status of the monitoring region is assessed after the multi-source data is comprehensively analyzed by the monitoring method, bringing about a more comprehensive and accurate analysis.

The present invention provides an integrated hyperspectral water quality analysis method, which belongs to the field of hyperspectral water quality analysis. First, data preprocessing is conducted by water quality data collection and water quality image collection in early stage; second, three dimensionality reduction methods are adopted to conduct dimensionality reduction processing, and fused dimensionality reduction is conducted by parameter trade-off selection; third, machine learning algorithms are adopted to train and test hyperspectral water quality inversion models on spectral data after dimensionality reduction; finally, the hyperspectral water quality inversion models are selected and optimized. The present invention adopts an innovative fusion strategy in the aspect of data dimensionality reduction processing, which can achieve a better data dimensionality reduction effect, effectively remove noise and redundant information, and provide a more accurate and reliable data basis.

The invention relates to the technical field of optical line protection, and discloses a river water quality monitoring method based on an unmanned aerial vehicle hyperspectral remote sensing image and machine learning, which combines the unmanned aerial vehicle hyperspectral remote sensing image with a machine learning algorithm to construct a river water quality parameter inversion model. Comprising the following steps: collecting a water sample, obtaining an unmanned aerial vehicle hyperspectral image and preprocessing; extracting an image of the river part and selecting the reflectivity of each wave band of the image; performing spectrum transformation on the original spectrum of the image, establishing an inversion data set through feature screening, and constructing a water quality parameter inversion model by using a machine learning algorithm; and simulating the water quality parameters of the river through the water quality parameter inversion model, and visualizing the simulation result. According to the method, urban inland small river water quality monitoring can be rapidly achieved at low cost, the dual problem that the time resolution and the space resolution are limited in urban small river monitoring based on satellite remote sensing images is solved, and a scientific basis is provided for urban river intelligent inversion.

The invention relates to the technical field of bridge monitoring, in particular to a bridge crack real-time monitoring method based on edge calculation, which comprises the following steps of: acquiring bridge crack data by using a hyperspectral imaging camera, a thermal imaging device and a laser radar, adaptively adjusting edge calculation nodes by using an edge learning technology, and calculating the edge calculation nodes according to the edge learning technology. A deep learning model is used for crack analysis, an anomaly monitoring model is established, an augmented reality technology is introduced to assist field investigation, and through comprehensive application of technologies such as multi-source data acquisition, edge calculation, deep learning and augmented reality, the problems that a traditional bridge crack monitoring method is low in efficiency, low in precision, incapable of real-time monitoring and the like are solved. The system has the advantages of comprehensive data acquisition, real-time and efficient processing, strong adaptive ability, comprehensive abnormity monitoring, high system reliability and visual and efficient field investigation.

The present invention relates to the technical field of soil monitoring, and disclosed is an intelligent soil moisture content monitoring method based on remote sensing technology. The method comprises: a processing terminal dividing a monitoring area into multiple sub-areas, wherein the division of the sub-areas may be by means of equal parts by area, acquiring image data of each sub-area on the basis of a remote sensing satellite, acquiring, on the basis of a sensor device, multi-source data related to the monitoring area and soil moisture content, and performing pre-processing on the image data and multi-source data; comprehensively analyzing the image data and the multi-source data, and evaluating the soil moisture content conditions of each sub-area; and, by means of the soil moisture content conditions of each sub-area, comprehensively analyzing the overall soil moisture content conditions of the monitoring area. The present monitoring method comprehensively analyzes the multi-source data to then evaluate the soil moisture of the monitoring area, such that analysis is more comprehensive, and accuracy is higher.

The invention discloses an integrated hyperspectral water quality analysis method and belongs to the field of hyperspectral water quality analysis. The method comprises the following steps: firstly, acquiring early-stage water quality data and water quality images, and preprocessing the data; secondly, three dimension reduction methods are adopted to carry out dimension reduction processing, and fusion dimension reduction is carried out through parameter tradeoff selection; thirdly, performing hyperspectral water quality inversion model training and testing on the dimension-reduced spectral data by adopting a machine learning algorithm; and finally, selecting and optimizing a hyperspectral water quality inversion model. According to the method, an innovative fusion strategy is adopted in the aspect of data dimension reduction processing, a better data dimension reduction effect can be achieved, noise and redundant information are effectively removed, and a more accurate and reliable data basis is provided; in the aspect of model training and testing, multiple machine learning algorithms are used to train and test the dimension-reduced spectral data, and the most suitable method is selected according to various indexes to train and predict the model, so that the advantages of different algorithms can be fully utilized, and the accuracy and reliability of water quality analysis are improved.

Provided herein are methods of detecting single molecules that include binding single molecules in a sample solution to a first surface of an optically transparent substrate include the optically transparent substrate is free of a metallic coating. In some embodiments, the methods include irradiating the first surface of the substrate with an incident light having an incident angle selected to achieve total internal reflection of the incident light, thereby scattering light from the first surface and from the single molecules bound to the surface in which a wavelength of the incident light is between 10 nm and 350 μm and the optically transparent substrate has a refractive index at the wavelength of the incident light exceeding that of the sample solution, and collecting an image that captures interference between evanescent light scattered from the single molecules and the first surface. Systems and additional methods are also provided.

The invention discloses a remote sensing dynamic monitoring method for water and soil loss in water conservancy, and relates to the technical field of combination of water conservancy projects and remote sensing technologies. A data collection module is used for collecting and preprocessing monitoring data of remote sensing images, weather and soil, extracting historical data stored in a storage module, and combining the current monitoring data to obtain a remote sensing result; the method comprises the steps that a soil erosion intensity index TQ, a water and soil loss rate change rate LS and a water and soil conservation measure adjustment factor BCT are substituted into a comprehensive treatment evaluation module, the adjustment direction and strength of a water and soil conservation measure are formulated based on the water and soil conservation measure adjustment factor BCT, and monitoring data and measures for adjusting the direction and strength are stored in a storage module. According to the method, by introducing scientific quantitative indexes and constructing a cyclic influence mechanism, comprehensive monitoring and quantitative analysis of dynamic changes of water and soil loss are realized, and the method not only solves the problems and defects in the prior art, but also provides scientific basis and decision support for formulation and adjustment of water and soil conservation measures.

The invention discloses an inland water body water quality multi-index remote sensing monitoring method based on a knowledge graph. The method comprises the following steps: collecting multi-source remote sensing image data, water body appearance and inherent optical characteristic data and historical water quality monitoring data of a target water body area; analyzing a response relationship between the optical characteristics of the water body and water quality components by using the obtained remote sensing reflectivity-optical characteristic-water quality parameter combined data set, and determining key information elements of remote sensing inversion of each water quality parameter of the water body; and constructing an inland water quality multi-index remote sensing inversion knowledge graph, and realizing time-space continuous monitoring of multiple water quality indexes of the water body by utilizing remote sensing images. According to the method, remote sensing monitoring results are complemented by means of a map reasoning technology, so that the completeness of monitoring data is remarkably improved; the defects of the prior art in the aspects of multi-index comprehensive monitoring and knowledge association expression are overcome, and novel technical means and decision support are provided for lake and river ecological environment management.

The invention provides an automatic online detection method and system for the surface smoothness of a precise injection mold, and relates to the field of precise manufacturing, the method comprises the following steps: S1, synchronously collecting visible light and near-infrared reflection images of the surface of the mold through multispectral coaxial imaging; s2, calculating a dynamic texture parameter based on a multi-directional gradient field weighted variance, and generating a composite reflection parameter in combination with wavelet domain fusion; s3, fusing the features by adopting a dual-path neural network, and comparing the standard library through a modified Stereer ratio model to output the degree of finish; and S4, adjusting the period in real time according to the detected dynamic standard deviation and linking closed-loop control. According to the method, the restriction of single-band characterization is broken through through multispectral data fusion, and the processing speed is increased by utilizing gradient field parallel computing hardware; in combination with a self-adaptive threshold value and a process parameter dynamic correction mechanism, the problems of poor adaptability of a fixed period, high hardware delay and single feature dimension of a traditional method are solved, and the online detection precision and the process control efficiency are remarkably optimized.

The invention relates to the technical field of metal surface coating detection, and discloses an intelligent detection method for metal surface coating defects. The method comprises the following steps: firstly, acquiring multispectral imaging data to carry out three-dimensional topology reconstruction to generate an initial three-dimensional feature model; dividing a detection area, and marking a defect core area, a transition area and a normal area; a high-resolution industrial camera is used for collecting dynamic morphology updating model parameters, identifying a surface abnormal area and calculating a theoretical contact distance with a probe, and then a scanning track is planned. In the detection process, the distance is monitored in real time, detection points are dynamically adjusted, scanning parameters are corrected according to contact stress, detection sub-domain scanning is divided, secondary imaging analysis is conducted on a scanning area, and scanning is supplemented if missing. According to the invention, the detection precision, intelligence and adaptive ability are improved, comprehensive and fine detection is realized, and the method has important application value in the field of metal surface coating detection.

The invention discloses a tacrolimus blood concentration detection device and method based on hyperspectral imaging, and relates to the technical field of drug concentration detection.The device comprises a light-proof shell, and a touch display screen is arranged on the shell; a central processing unit, an automatic lifting mechanism composed of a plurality of lifting supporting sliding rods, an object carrying platform which is arranged on the automatic lifting mechanism and can perform XY two-dimensional displacement, and a hyperspectral camera, a laser transmitter and a laser receiver which are arranged at the top of the shell are arranged in the shell; wherein the central processing unit is used for roughly adjusting the positions of the automatic lifting mechanism and the loading platform according to a touch instruction; according to the laser transmit-receive data and the obtained hyperspectral image, detecting the balance of the loading platform, and automatically and finely adjusting the positions of the automatic lifting mechanism and the loading platform; and performing automatic focusing and imaging on the hyperspectral camera according to the acquired hyperspectral image. The device provided by the invention is high in automation degree, and can quickly acquire clear and effective hyperspectral images.

The invention discloses a greenhouse gas point source emission automatic identification method, system and device and a medium, and relates to the technical field of environment monitoring. The method comprises the following steps: acquiring a greenhouse gas emission list and satellite remote sensing data of a to-be-detected area; carrying out emission source preliminary screening according to the greenhouse gas emission list, and determining a preliminarily screened greenhouse gas source; inverting a greenhouse gas concentration distribution diagram by using satellite remote sensing data, and performing target detection and pixel segmentation on the greenhouse gas concentration distribution diagram by using a Mask R-CNN model to determine a greenhouse gas plume; the greenhouse gas plume comprises a specific position and a specific form; and performing quantitative analysis on the greenhouse gas plume by using an IME algorithm, and determining a final emission source in combination with the preliminarily screened greenhouse gas source. According to the invention, the recognition accuracy and real-time performance of the greenhouse gas emission source can be improved.

The invention relates to the technical field of air pollution tracing, and particularly discloses an air pollution source accurate tracing method and system, and the system comprises a multi-source data monitoring module, a multi-source data analysis module, a pollution tracing comprehensive analysis module, a pollution diffusion model verification optimization module, and a pollution tracing analysis module. By comprehensively considering various factors, setting a proportionality coefficient to calculate a pollution traceability comprehensive index and comparing the pollution traceability comprehensive index with a threshold value, a pollution traceability area and a potential area can be effectively marked, simulation concentration is calculated according to actual parameters in combination with the verification and optimization process of a pollution diffusion model, a root-mean-square error is reduced by adjusting a source intensity value, and a pollution source diffusion coefficient is increased. According to the method, the model is continuously optimized, so that the model better fits the actual situation, finally, the real pollution source is determined according to the acceptable threshold value, scientific and accurate basis and method are provided for air pollution control, and improvement of the control efficiency and effect and improvement of the atmospheric environment quality are facilitated.

The invention provides a gas detection method based on galvanometer scanning, and the method comprises the following steps: obtaining a visible light image of a to-be-detected region collected by a visible light imaging module, processing the visible light image based on a pre-trained semantic segmentation model, and obtaining a background category matrix; acquiring gas concentration information of each scanning point obtained by scanning the to-be-detected area by the galvanometer laser telemetering assembly, wherein the galvanometer laser telemetering assembly is registered with the visible light imaging module in advance; and acquiring a background category corresponding to each scanning point according to the background category matrix, and correcting a gas concentration signal of the point based on a background correction coefficient associated with the background category. According to the gas detection method, the problems of false detection and the like caused by factors such as unknown background reflectors and unknown detection object types in the existing galvanometer scanning gas detection are effectively solved, and the accuracy of gas concentration detection is improved.

The invention discloses a hyperspectral anomaly detection method based on a waveband screening joint feature encoder. The hyperspectral anomaly detection method comprises the following steps: determining a spectral domain difference weight of a target pixel point in a pixel point position sequence in a k waveband local range; determining a target pixel reconstruction gray value according to the spectral domain difference weight of the target pixel point in the local range of the k wave band; determining a spatial domain distance weight of a target pixel point in the pixel point position sequence in a k-waveband local range; obtaining a joint feature weight in a target pixel reconstruction process according to the spectral domain difference weight and the spatial domain distance weight; obtaining the reconstructed gray scale of the target pixel according to the combined feature weight and the reconstructed gray scale value of the target pixel; determining the cosine similarity of each wave band according to a spectrum cosine similarity algorithm and the gray level of target pixel reconstruction; and determining whether the image of each wave band is redundant or effective according to the cosine similarity of each wave band.

A measurement system may direct an illumination beam including at least one of a first frequency comb or a second frequency comb to a sample, and generate a sequence of images of the sample based on the first frequency comb and the second frequency comb. The system may include one or more coding optical elements to encode data associated with one or more transfer matrix elements into the sequence of images of the sample. The system may further generate a transfer matrix dataset including measurements of at least one of the one or more transfer matrix elements associated with the sample based on at least one of spectral, spatial, or temporal analysis of the sequence of images, and generate one or more measurements of the sample based on the transfer matrix dataset.

One variation of a method includes: advancing an electrode through a coating zone; at a spray nozzle facing the coating zone, depositing a separator material onto the electrode according to a set of spray parameters to form a separator coating on the electrode; accessing an inspection signal representing a characteristic of the separator coating applied to the electrode; based on the inspection signal, interpreting a value of the characteristic of the separator coating applied to the electrode; modifying a second set of spray parameters to compensate for the value of the characteristic of the separator coating applied to the electrode; advancing the electrode through a second coating zone downstream of the coating zone; and at a second spray nozzle facing the second coating zone, depositing the separator material onto the separator coating on the electrode according to the second set of spray parameters to form a second separator coating.

Provided herein are methods of detecting target molecules. The methods include contacting a sample comprising the target molecule with a substrate that comprises a plurality of capture antibodies, or antigen binding portions thereof, that specifically bind to the target molecule to form captured target molecules, and contacting the captured target molecules with a plurality of detection antibodies, or antigen binding portions thereof, that bind to the captured target molecules to form target molecule complexes. The methods also include taking images of the target molecule complexes to produce imaged target molecule complexes, and quantifying an amount of target molecules in the sample using the imaged target molecule complexes. Additional methods as well as related devices and systems are also provided.

Devices, methods and systems are disclosed for monitoring and analysis of bodily emissions emitted from a user. The device comprises at least an image capturing apparatus, at least one light emission source and, at least one sensor for capturing and monitoring excreta and urine from the user. An artificial intelligence based analysis system is utilized to process data collected from the device and determined at least one parameter based on the analysis. The determined parameter determines health-related information, which is communicated to the user via a user interface.

The present invention relates to a system and method for analyzing foreign substances in a positive electrode material, the system includes a pretreatment part configured to prepare a monolayer sample of a positive electrode material powder, a measurement part configured to obtain an optical image of the monolayer sample using an optical microscope, and an analysis part configured to analyze the optical image of the monolayer sample, thereby obtaining information on foreign substances in the positive electrode material.

In one aspect, the present disclosure relates to a material analysis method which may comprise the steps of: (a) preparing a specimen for structure analysis; (b) training an artificial intelligence model with a training image labelled with a structure aspect of an arbitrary specimen; (c) analyzing a plurality of analysis images not labelled with a structure aspect of a specimen using the trained artificial intelligence model, and removing analysis images classified into preset noise structure aspects; and (d) analyzing, by using the trained artificial intelligence model, the analysis images from which the analysis images classified into the noise structure aspects have been removed, and classifying the analysis images into at least one target structure aspect.

The invention discloses a coarse cereal aflatoxin detection method combining hyperspectral imaging and deep learning, and belongs to the field of agricultural product quality safety detection. The method comprises the following steps: firstly, acquiring hyperspectral imaging data of coarse cereal grains, extracting space and spectral information, forming basic data, and performing preprocessing, space-time registration and feature alignment; building a hyperspectral space-spectrum double-branch feature fusion convolutional neural network deep learning model, and importing the processed data to complete iterative training; and finally, performing same-standard data acquisition and processing on to-be-detected coarse cereals, importing the trained model, and realizing aflatoxin detection and pollution area positioning through pixel-level analysis. According to the method, deep fusion of space and spectral features is realized, the detection accuracy and generalization ability are effectively improved, the detection result is stable, reliable and traceable, and the fine detection requirement of aflatoxin in coarse cereals is met.

Systems and methods for measuring water concentration in a liquid medium include a flow cell configured to fluidly couple to a pipe; a light source coupled to a first side of the flow cell; an infrared camera coupled to a second side of the flow cell opposite the first side; and at least one processor and a memory storing instructions that when executed by the at least one processor cause the at least one processor to perform operations. The operations include acquiring one or more images from the infrared camera; detecting water droplets in the one or more images using a trained machine learning model; and determining a concentration of water in the liquid medium based on the detected water droplets.

The invention relates to a method for predicting the corrosion rate of a natural gas pipeline, in particular to the field of natural gas pipeline corrosion prediction.The high-precision dynamic early warning is achieved through the four-step cooperative technology, firstly, visible light and radar data are fused to generate a three-dimensional corrosion base film fully covering the pipeline, and blind areas caused by the complex environment are effectively eliminated; secondly, speculating corrosion probability distribution of a shielding area based on a physical constraint generative adversarial network, and solving a hidden area prediction problem; then, in combination with the stress field and historical data, deducing a space-time diffusion path of corrosion through a graph neural network, and outputting a future corrosion thermodynamic diagram; and finally, dynamically adjusting model parameters according to actually measured data to form a corrosion risk decision matrix of closed-loop optimization, thereby realizing the transformation of pipeline corrosion from passive protection to intelligent prevention in a breakthrough manner.

The invention discloses a soil profile occurrence layer division method based on an indoor imaging spectrum technology. The method comprises the following steps: S1, acquiring a complete soil profile hyperspectral image by adopting a method based on pixel embedding; s2, carrying out geometric correction on the image, removing a wooden frame, and reserving a soil profile original image with a fixed width; s3, using a random forest algorithm to remove invalid pixels such as cracks and shadows; s4, carrying out smooth denoising processing on the reserved spectral data; s5, extracting a row average spectrum curve, and obtaining a fixed-depth row average spectrum curve as spectrum data of the layered strip soil sample; s6, generating a spectrum curved surface; and S7, dividing a generation layer based on the semantic segmentation network model. According to the method, accurate division of various soil profile generation layers can be realized, and powerful technical support is provided for large-scale soil classification and sustainable management of soil resources.

The invention relates to the technical field of constructional engineering environment monitoring, in particular to a constructional engineering environment monitoring method and system. The method comprises the following steps: performing feature extraction on a particulate matter real-time spectrum to obtain a particulate matter real-time feature spectrum; performing spectrum color conversion effect simulation on the particulate matter real-time characteristic spectrum, and performing scattering glare view angle analysis to obtain scattering glare view angle data; carrying out medium influence weight judgment according to the scattering glare view angle data to obtain particulate matter glare influence weight data; performing convolution calculation on the particulate matter glare influence weight data, and performing logic learning to obtain particulate matter glare view angle logic data; and carrying out automatic identification firmware design based on the particulate matter glare view angle logic data to obtain particulate matter glare view angle identification firmware. According to the invention, the construction engineering environment monitoring technology is optimized, so that the construction engineering environment monitoring technology is more perfect.

The invention relates to the technical field of geological environment monitoring, in particular to a stability monitoring method for ecological slope protection of an expressway. The method comprises the following steps: acquiring remote sensing data of an expressway monitoring area; performing water and soil loss area detection according to the remote sensing data of the highway monitoring area to obtain water and soil loss area data; performing erosion intensity analysis according to the water and soil loss area data to obtain erosion intensity data; extracting vegetation coverage data of the water and soil loss area data; performing drainage clogging analysis according to the vegetation coverage data and the erosion intensity data to obtain drainage clogging data; performing soil stability analysis based on the drainage clogging data to obtain soil stability data; and detecting the soil compaction degree according to the soil stability data. Based on the geological environment monitoring technology, the detection and prediction precision of water and soil loss, soil stability and ecological slope protection stability of the highway monitoring area is improved, and the landslide prevention and ecological protection efficiency is improved.

System for performing chemical spectroscopy on samples from the scale of nanometers to millimeters or more with a multifunctional platform combining analytical and imaging techniques including dual beam photo-thermal spectroscopy with confocal microscopy, Raman spectroscopy, fluorescence detection, various vacuum analytical techniques and / or mass spectrometry. In embodiments described herein, the light beams of a dual-beam system are used for heating and sensing.

The invention discloses a method for detecting softening resistance, chlorine salt erosion resistance and frost heaving resistance of aggregate, and relates to the field of performance detection of aggregate for pavement engineering, and the method comprises the following steps: obtaining image information of the preprocessed aggregate, carrying out partition processing on the image information by using an image partition processing technology, and obtaining a classification result of the aggregate according to a processing result; carrying out interval sampling on the aggregate based on the classification result, and carrying out identification analysis on the aggregate in combination with a directional tracking algorithm to obtain a grading result of the aggregate; carrying out softening resistance, chlorine salt erosion resistance and frost heaving performance tests on the aggregate, and summarizing the relationship between the aggregate color and the mass loss to obtain the mass loss result of the aggregate; and performing product performance fitting operation on the aggregate to evaluate the aggregate use performance satisfaction degree under the condition of a corresponding quality loss result so as to optimize the aggregate pretreatment process. According to the invention, softening resistance, chlorine salt erosion resistance and frost heaving performance tests are carried out on the aggregate to obtain the mass loss result of the aggregate, and the quality problem can be identified in advance.