641results about "Method using image detector and image signal processing" patented technology

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 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 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 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.

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 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.

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.

The invention discloses a meat quality detection model compression method and system based on edge calculation, and the method comprises the following steps: S1, collecting image and spectral data, and constructing a multi-modal training data set; s2, constructing a fusion model, and training to obtain a basic model; s3, keeping the basic model as a cloud model, and copying the basic model as a to-be-compressed model; s4, performing mixing precision quantification on the to-be-compressed model; s5, hardware sensing pruning processing is executed; s6, deploying the model to an edge device, and performing model acceleration configuration; s7, the edge device conducts reasoning and monitors the confidence degree in real time; and S8, when the confidence coefficient is low, uploading the data to the cloud for re-checking. According to the invention, efficient compression and deployment of the meat quality detection model on the edge equipment are realized, and the response speed and stability of the system are improved.

This invention discloses an automatic identification method, system, device, and medium for greenhouse gas point source emissions, relating to the field of environmental monitoring technology. The method includes: acquiring a greenhouse gas emission inventory and satellite remote sensing data of the area to be monitored; performing initial screening of emission sources based on the greenhouse gas emission inventory to determine preliminary greenhouse gas sources; retrieving a greenhouse gas concentration distribution map using the satellite remote sensing data, and employing a Mask R-CNN model to perform target detection and pixel segmentation on the greenhouse gas concentration distribution map to determine greenhouse gas plumes; the greenhouse gas plumes include specific locations and shapes; performing quantitative analysis of the greenhouse gas plumes using the IME algorithm, and combining this with the preliminary screening of greenhouse gas sources to determine the final emission sources. This invention can improve the accuracy and real-time performance of greenhouse gas emission source identification.

The invention belongs to the technical field of environment monitoring, and discloses a water area ecological environment monitoring system, which comprises a data acquisition unit used for acquiring aquatic vegetation coverage data, aquatic plant type data, water quality index data and biological index data of various organisms of a to-be-monitored area; the evaluation index acquisition unit is used for acquiring pollution degree evaluation index data strongly related to the to-be-detected area according to the aquatic vegetation coverage data, the biological index data, the water quality index data and the aquatic plant type data; and the evaluation monitoring unit is used for evaluating the pollution degree of the to-be-monitored area according to the pollution degree evaluation index data to obtain the pollution degree of the ecological environment of the water area, completing the monitoring of the current period, uploading a monitoring result to the cloud platform for storage, and performing the monitoring of the next period. According to the method, the limitation of traditional single index evaluation is solved, and the comprehensiveness, accuracy and timeliness of water area ecological environment monitoring are remarkably improved.

This disclosure provides an imaging method for detecting spectrum shift and a biosensor for the imaging method. The imaging method includes receiving a first light beam by a photodiode array to simultaneously obtain a first image signal with a first image intensity corresponding to a first filter and a second image signal with a second image intensity corresponding to a double-layered filter including the first filter and a second filter; receiving a second light beam by the photodiode array to simultaneously obtain a third image signal with a third image intensity corresponding to the first filter and a fourth image signal with a fourth image intensity corresponding to the double-layered filter, and determining a spectrum shift from the first light beam to the second light beam by comparing the first and the second image intensity to the third and the fourth image intensity.

The invention discloses a photovoltaic cell panel invisible defect detection device based on multispectral imaging. The photovoltaic cell panel invisible defect detection device comprises a photovoltaic cell panel to be detected; the excitation module is configured to enable the photovoltaic cell panel to be detected to generate a short-wave infrared band under the irradiation of an excitation light source; the excitation light source comprises a near-infrared light-induced excitation light source and / or an electroluminescent driving power supply; the spectrum acquisition module comprises a multispectral imaging system composed of a variable optical filter and an area-array camera, the variable optical filter adopts a wavelength programmable narrow-band filtering assembly, and the transmission wavelength of the variable optical filter covers a 1050-1250 nm characteristic spectrum band; and the image acquisition and processing system is configured to control switching of spectrum channels of the variable optical filter, control the area-array camera to shoot multispectral images in sequence and process the images. The device overcomes the adverse effect of the sunlight environment on photoluminescence imaging or electroluminescence imaging of the photovoltaic cell panel, so that the imaging level of invisible defects of the photovoltaic cell panel is improved.

The invention discloses a device and method for detecting the water locking performance of a nursing pad, and relates to the field of material performance tests.The detection method comprises the following steps that S1, a nursing pad sample is fixed to a detection platform, a composite dynamic pressure sequence containing sine fluctuation, stepped pressurization and random disturbance is applied, constant-temperature liquid is injected into the sample synchronously, and the water locking amount is calculated; s2, scanning the three-dimensional distribution of liquid in real time, capturing the expansion state of a core body, triggering sudden stop when the edge leaks, and calculating the water locking efficiency; s3, taking an absorption core material, soaking the absorption core material in normal saline, performing centrifugal dewatering, and calculating the water retention capacity; and S4, constructing a three-level evaluation system output performance report. Composite multi-point pressure is adopted to simulate human body activity mechanics characteristics, a multi-mode sensing technology is combined to track the liquid three-dimensional permeation process in real time, the defects that a traditional method is single in pressure and insufficient in detection dimension are overcome, and dynamic process analysis support which cannot be achieved through traditional static detection is provided for product optimization.

The invention relates to the technical field of blood cell detection, and provides an intelligent blood cell detection device and a blood cell image splicing method.The intelligent blood cell detection device comprises a device shell, a base, a micro-motion positioning assembly, an imaging support, an observation assembly, a placement table and a two-way positioning assembly, the base is fixedly installed in the device shell through the damping foot pad, the micro-motion position adjusting assembly is installed on the base, the imaging support is installed on the micro-motion position adjusting assembly, the observation assembly is installed on the imaging support, the placing table is arranged on the base, and the bidirectional position adjusting assembly is installed on the base. According to the technical scheme, the problems that in the prior art, when a specimen is subtly observed, the observation position is difficult to accurately adjust in a small distance, adjacent images are difficult to obtain, and meanwhile the images are difficult to accurately splice are solved.

An imaging device includes: a light source including an emission surface from which a sub-terahertz wave is emitted to a measurement target; and a detector including an image sensor that detects the intensity of a reflected wave generated by the measurement target reflecting the sub-terahertz wave emitted from the emission surface.

The invention relates to the technical field of water quality detection, and further relates to a remote sensing water quality pollution detection method based on a super-resolution hybrid GAN, and the method comprises the steps: 1, inputting a remote sensing reflectivity image into a super-resolution hybrid generative adversarial network, and carrying out the collaborative reconstruction through a generator and a discriminator, so as to obtain a high-resolution remote sensing reflectivity image; step 2, carrying out weighted summation on the high-resolution remote sensing reflectivity image according to wave bands by utilizing the wave band pollutant absorption sensitivity coefficient calibrated by an experiment to obtain an original concentration estimation value of each pollutant; 3, obtaining the final concentration of each pollutant based on the confrontation consistency loss of the super-resolution hybrid generative adversarial network in combination with the maximum absorption peak wavelength of the pollutant; and 4, generating a comprehensive water quality pollution index. According to the method, the targets of high precision, high reliability and high adaptability of pollution detection under the driving of the remote sensing image are effectively realized.

A change in a substance in a depth direction of an analyte is easily estimated. An analysis and observation device includes: a library holding section that holds a substance library in which a substance is associated with a type of an element constituting the substance and a content of the element; and a component analysis section that estimates a type of an element constituting a substance and a content of the element based on a spectrum, and estimates the substance based on estimated characteristics and the substance library. The component analysis section estimates a type of an element constituting a substance, a content of the element, and the substance at each of a plurality of positions having different analysis depths.