467results 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 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 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 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.

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.

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 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 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.

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.

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 invention relates to a doped element distribution detection method and system based on a hyperspectral imaging technology. The doped element distribution detection method based on the hyperspectral imaging technology comprises the steps that light of different wavelengths is adopted to irradiate an ion implantation area on the back face of a wafer; the method comprises the following steps: capturing spectral characteristic data of doping elements in an ion implantation region on the back surface of a wafer through hyperspectral imaging equipment, wherein the spectral characteristic data comprises space-spectral cube data; and identifying the three-dimensional distribution of the doped elements through a spectrum matching method according to the spectral characteristic data, and matching the spectral characteristic data with a quantitative relation model of the spectral intensity and the doping concentration to obtain the doping concentration of the doped element.

The embodiment of the invention provides a defect detection method based on spectral imaging and related equipment, and the method comprises the steps: carrying out the image collection of a to-be-detected transparent medium through a line light source and a spectral camera, and obtaining a clear spectral image set; performing calculation according to the spectral image set, and determining a plurality of regions of interest on the transparent medium to be detected according to a calculation result; calculating according to the spectral feature extraction results of each region of interest under a plurality of feature analysis wavebands, and determining a test value corresponding to each region of interest; and performing judgment according to the test value corresponding to each region of interest and a preset judgment threshold value corresponding to the transparent medium to be detected, and determining a defect detection result of the transparent medium to be detected. According to the embodiment of the invention, the region of interest can be selected for analysis, and the specific feature analysis wave band is selected for spectral feature analysis, so that the surface defects and smudginess of the transparent medium can be effectively distinguished. The method can be widely applied to the technical field of defect detection.

The disclosed embodiments relate to system, method and apparatus for forming ML sessions so that an AI system can identify, track and diagnose a user through the user's bathroom sessions. As used herein, a session is a period of time during which a user is active in the toilet. The session may include certain inactivity if such inactivity lasts only for a predefined duration. In an exemplary embodiment, a system is configured to capture images of excreta and, using ML algorithms, classifies the toileting session using Digital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information to doctors and healthcare providers can revolutionize healthcare by notifying when further urine or fecal screening is necessary. The disclosed embodiments of ML and image identification through AI requires no user behavior change and provides a link between medical records and specific excreta patterns.

The invention discloses a small and micro water body quality monitoring method and system based on the Internet of Things, and relates to the field of water body quality monitoring, the system is composed of a plurality of functional modules, and the system comprises a data acquisition module which uses a laser radar and an RGB camera carried on an unmanned aerial vehicle to acquire a whole image of a water body, and uses Canny edge detection to extract a water body boundary; according to the boundary of the water body, the unmanned aerial vehicle flies in a gridding path, laser vertical irradiation is carried out on the water surface, and point cloud data are collected; during vertical irradiation, collecting a water surface image of a vertical area; the point cloud processing module is used for carrying out noise reduction processing on the point cloud data by utilizing statistical filtering; on the basis of IMU and real-time position data, auxiliary positioning is carried out, an NDT algorithm is adopted to carry out registration on the point cloud data, three-dimensional parameter calculation is carried out on the registered point cloud data, and vegetation points and non-vegetation points are distinguished through machine learning; and the image analysis module is used for carrying out RGB color space clustering on the water surface image.

The invention provides a coal-based solid waste storage yard drainage basin percolate calculation method and system and electronic equipment, and relates to the field of solid waste storage yard drainage basin scale percolate calculation. The percolate generation amount of the coal-based solid waste storage yard drainage basin can be accurately calculated in combination with an existing hydrological model, and the ability to understand and evaluate a hydrological cycle process in a complex terrain can be improved; therefore, the problem that in the prior art, a large amount of labor, financial resources and time are consumed when long-time sequence field detection is carried out due to the fact that the number of monitoring stations of a hydrometric station is small is solved.

An insect monitoring system for luring and capturing insects including a digital camera with dual UV and white LEDs and a processor configured to acquire visual insect data to recognize insect species in conjunction with previously tagged insect groups; and evaluate insect migration and / or dispersion within a habitat range. A method utilizing UV tagging mechanisms for monitoring and predicting insect migration and / or dispersion including providing insect monitoring stations for collecting data indicative of a previously tagged insect groups' presence within a habitat range, providing the data to a server, retrieving previously received data, and determining a change in the data to provide historical, real-time, and predictive information to a user.

The invention discloses a comprehensive evaluation method for a paint removal effect of a composite material aircraft skin, and relates to the technical field of paint removal of the composite material aircraft skin. In a paint layer residual information evaluation stage, regions are divided before paint removal, and an infrared spectral imager is used for scanning and imaging after paint removal to obtain spectral data of pixel points; comprise the paint layer characteristic absorption peak intensity value and the pixel point number, the paint layer residue condition can be accurately quantified, the average paint layer characteristic absorption peak intensity value is used as an important index, the relative degree of paint layer residue in each area can be accurately judged, and the accuracy of paint layer residue detection is improved. By calculating the spectral characteristic abnormal area proportion of the paint layer and determining the included angle deviation between the distribution direction of the paint layer residues and the stress direction of the fibers, the structural characteristics of the composite material can be fully considered, and the potential influence of the paint layer residues on the stress performance of the skin can be predicted in advance by analyzing the included angle deviation. And the problem of stress concentration caused by unreasonable distribution of paint layer residues is avoided.

Provided in the present application are an analysis method and device for coffee particles. The device comprises: a vibration source configured to drive the coffee particles to vibrate at least twice; a camera configured to respectively capture images of the coffee particles after the at least two vibrations, obtaining a to-be-detected image set containing images of coffee particles with different distributions; one or more processors communicatively coupled to the camera; and a memory storing instructions executable by the one or more processors, wherein the instructions, when executed by the one or more processors, cause the one or more processors to: acquire initial recognition information of the coffee particles in each to-be-detected image of the to-be-detected image set; determine final recognition information of the coffee particles based on the initial recognition information of the coffee particles in at least some frames of to-be-detected images in the to-be-detected image set.

Methods and systems for physical expansion and imaging of biological samples are described herein. In one aspect of the disclosure, a method for preparing a biological sample for the purpose of generating images of its ultrastructure with an imaging instrument includes a) physically expanding the sample by at least a factor of two in at least one dimension; and b) bulk labeling a plurality of components of the sample with at least one reagent to introduce contrast.

A method and system are provided for measuring plasma protein levels in whole blood while a plasma donor is connected to an apheresis machine. A refractometer associated with the apheresis machine is capable of receiving a portion of a disposable tubing set including an integrated cuvette and a prism. The integrated cuvette of the disposable tubing set is inserted into a receiving space of the refractometer associated with the apheresis machine such that a light source and a sensor are precisely aligned and oriented with respect to a sensing surface of the prism and the integrated cuvette. Calibration of the refractometer is performed using an anticoagulant pumped through the disposable tubing set including the integrated cuvette and prism. Based on the light intensity associated with this calibration, the whole blood is measured to determine plasma protein levels and donor eligibility.

An image spectrum technology-based geological sketching method, including: based on collected image-spectrum data information of a tunnel face, extracting mineral end members and spectrum to determine types and contents of minerals, extracting texture features and feature waveband spectrums to determine stratum lithology, extracting crack features and identifying crack fillers to obtain a crack identification result; obtaining degrees of crushing of surrounding rocks according to numbers and relative areas of cracks; analyzing weathering variation proportions of minerals according to stratum lithology and mineral analysis result for surrounding rocks, acquiring color differences between different degrees of weathering according to spectral color difference identification results, obtaining degrees of weathering of the surrounding rocks; identifying water outflow form and water outflow position of tunnel face to be detected; and obtaining a geological sketch map for the tunnel face by performing labeling and summarization by using different labeling symbols according to the results.

This invention provides a method and system for autonomous collaborative analysis of drone swarms based on virtual simulation, relating to the field of electronic digital data processing technology. By deploying monitoring points along the boundary line of the fire target area and using reconnaissance drones to collect thermal radiation response parameters of target plants in real time, a fire prediction model is established. The fire prediction model provides optimization strategies for task allocation and path planning for the drone swarm. At the same time, through the construction of a virtual simulation environment and parameter calibration, the fire prediction model is effectively verified and improved, thereby enhancing the overall efficiency of drone swarms in fire monitoring and response.