23061results about "Material analysis by optical means" patented technology

The invention discloses a PCBA circuit board welding spot detection method based on multi-modal data fusion, and relates to the technical field of electronic manufacturing quality detection.The PCBA circuit board welding spot detection method comprises the steps that a distributed sensing network is constructed, multi-modal data are collected, welding spot information is obtained in an omnibearing mode, and time-space alignment of the multi-modal data is carried out; performing feature extraction on the multi-modal data, dynamically weighting each modal feature through an attention mechanism, and highlighting key defect characterization; a welding spot spatial topological graph is constructed by using a graph neural network, and a spatial relationship between welding spots is modeled. By integrating optical, X-Ray, thermal, mechanics, electricity and other multi-dimensional data, the information limitation of single-mode detection is broken through, the complementation of different mode data is utilized, the attention mechanism is combined to dynamically weight each mode feature, the complex defect is accurately identified, the graph neural network is utilized to model the welding spot space topological relation, the associated defect is further captured, and the defect detection accuracy is improved. And the defect classification accuracy is improved.

The invention relates to the technical field of PCB detection, and discloses a PCB defect detection method and system, and the method comprises the steps: collecting multispectral imaging data through an image collection module, and generating an original image data set; the defect analysis server receives the synchronous imaging data to construct a three-dimensional surface topology matrix; in combination with the original image data set and the real-time imaging data, performing multi-scale decomposition on the three-dimensional surface topological matrix, extracting texture features, positioning a defect region, outputting defect type space distribution features through a layered recognition model, and updating the original image data set; and dynamically calibrating the detection parameters according to the feature categories. The system comprises an image acquisition module group, a data transmission module, a three-dimensional modeling module, a defect identification module and a parameter calibration module. According to the scheme, the accuracy, comprehensiveness and efficiency of defect detection are improved, and the detection requirements of modern PCB production are met.

Disclosed are methods and apparatus for qualifying a photolithographic reticle. A reticle inspection tool is used to acquire images at different imaging configurations from each of a plurality of pattern areas of a test reticle. A reticle near field for each of the pattern areas of the test reticle is recovered based on the acquired images from each pattern area of the test reticle. A lithography model is applied to the reticle near field for the test reticle to simulate a plurality of test wafer images, and the simulated test wafer images are analyzed to determine whether the test reticle will likely result in an unstable or defective wafer.

The invention discloses a ppb-grade methane gas online detection method, equipment, a medium and a product, and relates to the field of data processing. The method comprises the following steps: acquiring an infrared absorption spectrum signal, and obtaining original spectrum data; performing multi-scale noise separation and baseline correction on the original spectral data to obtain de-noised effective spectral features, and extracting methane specific absorption peaks from the de-noised effective spectral features; comparing the methane specific absorption peak with the interference gas spectral line through a multi-spectral line weight matching algorithm to obtain methane characteristic intensity; carrying out self-adaptive calibration on the methane characteristic intensity according to the drift compensation model to obtain calibrated characteristic intensity; obtaining a ppb-level methane concentration value through a concentration inversion model; and inputting the sorted concentration time sequence data into an anomaly detection model to detect a methane leakage event. By designing signal processing and algorithm analysis, the dependence on equipment hardware performance is reduced, and the use cost of ppb-level methane gas detection is reduced.

The invention relates to the technical field of wire disaster prevention analysis, and discloses an overhead transmission line icing monitoring and disaster prevention method and system. The method comprises the following steps: acquiring meteorological parameters, stress data and icing images through distributed sensing nodes; performing time-space synchronization processing on the data; calculating icing state information; evaluating an icing risk level; selectively starting the deicing device; and storing the data and extracting a law to optimize a disaster prevention plan. According to the invention, accurate monitoring of the icing state, scientific assessment of risks, intelligent selection of deicing measures and continuous optimization of disaster prevention strategies are realized, so that the ability of the power grid to deal with icing disasters is significantly improved, and economic losses and potential safety hazards are reduced.

The invention relates to the technical field of defect detection, and discloses an integrated circuit mask plate defect multispectral cooperative detection method, which comprises the following steps: carrying out preliminary scanning on a mask plate by using a multispectral camera, selecting an optimal wave band combination, switching to a target wave band through a liquid crystal adjustable optical filter, and obtaining a high-confidence multispectral image; the optical path offset is calculated through the linear relation, the deflection angle of the micro-mirror is dynamically adjusted, and images are collected for the second time after imaging offset is compensated; image filtering and registration are completed in the FPGA, and a high-contrast fusion image is generated; if the confidence coefficient is insufficient, triggering a wave band reselection mechanism to recheck; dynamically optimizing a defect type-spectrum mapping table based on real-time detection data; when the environment fluctuation exceeds the limit, an anti-interference mode is automatically switched, and micro-mirror calibration is triggered; and monitoring performance degradation of the optical assembly in real time, switching redundant optical paths, cleaning damaged parts, triggering manual reinspection when conflicts occur, and finally generating a visual report. According to the invention, the detection accuracy of the integrated circuit mask plate can be improved.

A sensor and method for page-based optical data storage. The sensor includes a partially-reflective mirror, a reflective mirror disposed substantially collinear with and spaced apart from the partially-reflective mirror, and an active layer disposed substantially collinear with and between the partially-reflective mirror and the reflective mirror. The partially-reflective mirror has a first and second side and transmits a portion of light incident on the first side. The reflective mirror reflects light incident on the reflective mirror toward the partially-reflective mirror. The active layer absorbs at least a portion of the light oscillating at the resonant frequency.

The invention relates to the technical field of electric digital data processing, in particular to an agricultural planting optimization system and method based on big data analysis. The method comprises the following steps: acquiring agricultural planting environment parameter data, and performing preprocessing and standardization to obtain a standardized environment feature vector; acquiring image, sound and smell data of crops; performing deep feature extraction on the image, sound and smell data to generate a multi-modal feature representation matrix; and performing multi-modal data fusion based on the standardized environment feature vector and the multi-modal feature representation matrix to obtain a comprehensive feature space. According to the invention, through multi-modal feature fusion and digital twinborn modeling, the precision and real-time performance of crop growth risk and pest and disease prediction are improved, and optimal regulation and control and efficient resource utilization of precision agriculture are realized.

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention discloses a traditional Chinese medicine identification and analysis system based on clustering analysis, and relates to the technical field of medicine analysis and identification, and the system comprises the following modules: a data acquisition module, which is used for obtaining traditional Chinese medicine multi-modal data and processing asynchronous stream data, the multi-modal data comprises microscopic images, spectral data, metabonomics data and electronic nose / tongue sensing data, and the step of processing the asynchronous stream data comprises timestamp marking, multi-device data calibration and feature caching. According to the traditional Chinese medicine identification and analysis system based on clustering analysis, through collaborative design of a multi-modal hypergraph space-time encoder and a drug property causal intervention mechanism, multi-dimensional and multi-level leap-type improvement of a traditional Chinese medicine identification technology is realized, and the core contradiction of disjunction of a traditional clustering algorithm and domain knowledge is solved; and a universal framework of cross-modal dynamic learning is provided for the field of medical informatics.

The invention discloses a forest fire monitoring system based on satellite remote sensing, particularly relates to the field of forest fire monitoring, and is used for solving the problems of incomplete data coverage, inaccurate boundary recognition, difficulty in sheltered area restoration and the like in a forest fire monitoring process, and accurately capturing comprehensive coverage of a fire area through fusion of multi-source remote sensing data. And full space-time monitoring under complex weather and topographic conditions is realized. Dynamic changes of spectrums and vegetation before and after a fire disaster are continuously tracked, and continuous basic data are provided for evaluating the influence of the fire disaster. And in combination with the analysis of spectrum range and thermal signal propagation, the fine identification capability of a fire high-temperature area and an edge area is enhanced, and particularly, the boundary identification of a low-disturbance area is effectively optimized. According to the method, the space-time reconstruction and interpolation algorithm is adopted, data missing caused by smog and cloud shielding is comprehensively restored, the integrity and continuity of the image are ensured, and the accuracy of disaster assessment and decision support is improved.

The invention relates to the technical field of nondestructive testing, solves the problems of insufficient flaw detection precision and adaptability of a metal component caused by restriction in the prior art, and particularly discloses a flaw detection system and method for the metal component. The flaw detection system comprises a multi-modal sensor group, a cross-modal feature alignment module, a self-adaptive weight distribution module, a defect decision module and a closed-loop control unit, the flaw detection method comprises the following steps: S100, synchronously acquiring data through the multi-mode sensor group; s200, performing space-time registration on the multi-source data of the metal component by using the dynamic calibration matrix; s300, evaluating the confidence coefficient of the sensor in real time, and generating a defect feature map; s400, inputting the defect feature map into a defect classification network, matching a defect map database, and outputting a defect type and a quantization parameter; and S500, automatically adjusting the detection motion track and the sensor according to the defect parameters. According to the multi-method combined detection for the metal component, the detection precision is improved, and a detection result with higher accuracy can be obtained through comprehensive evaluation according to multi-source data.

Described herein are systems and methods for analyzing biological samples. Including a method for processing an analyte, comprising providing a fluidic device comprising the analyte and one or more polymer precursors; selecting a discrete area within said fluidic device; providing an energy source in optical communication with fluidic device; and selectively supplying a unit of energy generated from the energy source to the fluidic device to generate a polymer matrix within the fluidic device, wherein the polymer matrix is within the discrete area or adjacent to the discrete area.

The invention discloses an AR intelligent photovoltaic inspection method and system, and the method comprises the following steps: S1, obtaining a panoramic image and heat distribution data of a photovoltaic module, and generating a data set; s2, multi-modal data preprocessing is carried out, key fault features are extracted, and fault areas are marked; s3, focusing and analyzing the marked area, and identifying a thermal abnormal point; s4, constructing an electrical topological graph, and generating a fault point priority; s5, performing ant colony optimization to adjust the inspection path, and marking the position of a fault point; s6, displaying the path, the fault point and the risk heat map in the AR equipment; and S7, generating an inspection report and performing feedback optimization. According to the invention, through multi-modal data fusion, dynamic path optimization and augmented reality technologies, efficient detection, accurate positioning and visual display of photovoltaic module faults are realized, and the inspection efficiency and the intelligent level are improved.

The invention discloses an electronic nose system for monitoring indoor air quality. The electronic nose system comprises a vacuum pump assembly, a flowmeter assembly, a two-position three-way valve assembly, a two-position two-way valve assembly, a reaction chamber, an air inlet and an air outlet. The system can be switched into different channels by adjusting the vacuum pump assembly, the flow meter assembly, the two-position three-way valve assembly and the two-position two-way valve assembly, then the flow direction and the flow speed of standard gas and sampling gas are controlled, a reaction chamber with a sensing chamber array is connected into the channels, and calibration of the system and detection of the gas are achieved.

The invention discloses a multi-temporal-spatial-scale cyanobacterial bloom early warning method for middle and large lake and reservoir water areas, and belongs to the field of cyanobacterial bloom prediction and risk monitoring. The method comprises the following steps: generating a pixel-level FAI index based on target water area remote sensing data, resampling meteorological data into a pixel level, then constructing a spatial-temporal distribution data set, training an Autoformer-ST-GNN time sequence model to realize FAI index prediction, dividing cyanobacterial bloom levels according to the FAI index prediction, and obtaining a global change trend; water quality monitoring points are arranged in key areas to collect data, historical water quality and meteorological data are utilized to train a DMC-PatchTST model fused with a blue-green algae migration period, and multi-time-scale prediction of the density of blue-green algae at the monitoring points is achieved; and finally, combining the global trend with a monitoring point prediction result to construct a space-time multi-scale cyanobacterial bloom comprehensive early warning system. According to the method, multi-source data and multiple models are fused, so that cyanobacterial bloom time-space multi-scale comprehensive early warning is realized, and the method is accurate and comprehensive.

Disclosed in the present invention is a neural network-based defect detection method for gluing quality on aircraft skin. The method includes: data acquisition: taking photos of aircraft skin by using a camera to acquire image data; preprocessing the acquired image data; annotating the data by using annotation software to acquire a data set for network training; establishing a defect detection network model based on feature erasure and boundary refinement, where the defect detection network model includes a feature extraction network, a semantic-guided feature erasure module, a multi-scale feature fusion network, and a defect prediction network based on boundary refinement, which are sequentially connected, the data set is used for training the network model, and trained model parameters are saved; and detecting a directly collected skin gluing image by using the trained network model and outputting detection results.

The invention discloses an intelligent multi-gas detection module data processing system and method based on NDIR, and relates to the technical field of gas detection.The method comprises the steps that a multi-wavelength NDIR sensor is used for obtaining light intensity changes of a gas sample under different wavelengths, and original spectral signals are generated; performing denoising processing on the spectral signal by adopting wavelet transform, and performing zero calibration and dynamic baseline deduction; based on the Beer-Lambert law in combination with environmental factors, establishing a relation model between gas absorption and spectral signals; updating model parameters by using a recursive least square algorithm, and continuously optimizing gas concentration prediction; separating gas signals by adopting a non-negative matrix factorization algorithm, and predicting the concentration of each gas; and the edge end operates the lightweight model in real time, and regularly uploads the model to the cloud end for global optimization and federated learning. The method can effectively improve the condition that the precision is insufficient when the model is used for a long time in the prior art.

The invention discloses an intelligent measurement and calculation method and system for forest carbon reserves, and relates to the technical field of carbon reserve measurement and calculation, and the system obtains a forest electronic distribution diagram through a GIS technology and surveying and mapping data, collects various carbon reserve related data, and then carries out the correction and denoising of the collected data through a FLAASH algorithm and other preprocessing technologies; the AI sampling optimization model extracts forest features based on a convolutional neural network CNN, and dynamically adjusts a sampling area; secondary acquisition is carried out, data preprocessing is carried out, the total carbon reserve of the forest is estimated by calculating the carbon reserve of each grid, and the forest succession stage, the carbon sink change and the ecological carbon cycle are further analyzed; and finally, in combination with the dynamic influence coefficient of the carbon reserves and a preset threshold value Q, judging the change trend of the carbon reserves, triggering early warning and giving a coping strategy.

Described herein are systems and methods for analyzing biological samples. Including a method for processing an analyte, comprising providing a fluidic device comprising the analyte and one or more polymer precursors; selecting a discrete area within said fluidic device; providing an energy source in optical communication with fluidic device; and selectively supplying a unit of energy generated from the energy source to the fluidic device to generate a polymer matrix within the fluidic device, wherein the polymer matrix is within the discrete area or adjacent to the discrete area.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention discloses a transformer equipment oil leakage monitoring method and system based on image recognition, relates to the technical field of image processing, and effectively eliminates static background interference by constructing a standardized image sequence S (t), performing gray gradient analysis and pixel feature extraction, recognizing candidate oil spot areas and constructing a disturbance active score Sx. The edge structure and the morphological stability coefficient of the dynamic oil stain suspected area are further extracted, and the credible level quantitative judgment of the dynamic oil stain suspected area is realized by integrating the characteristic indexes and the morphological stability coefficient, so that the dynamic identification capability and the anti-interference capability are realized; the oil leakage identification accuracy and the system automation risk response capability in a complex environment are improved, false alarm and missing alarm are effectively avoided, and the operation safety of equipment is guaranteed.

The invention provides a power chip packaging detection method and device, and the method comprises the steps: obtaining a structure image, an infrared thermal imaging frame sequence and a working electrical parameter data set of a target power chip after packaging, and carrying out the layering and regional peak analysis, so as to construct a morphology feature parameter set and a thermal anomaly feature set; inputting the morphology feature parameter set, the thermal anomaly feature set and the working electrical parameter data set into a preset defect identification model to obtain a chip packaging anomaly identification map; and performing hierarchical division and node aggregation on the chip packaging abnormity identification map to obtain a packaging defect type result. By constructing a multi-modal packaging detection system taking a structural image, an infrared thermal imaging frame sequence and a working electrical parameter data set as input, accurate identification of the power chip is realized, the accuracy of packaging defect detection is effectively improved, and the problems of low identification precision and information isolation of an existing detection method are improved.

The invention discloses an SF6 gas leakage detection method and system based on a photoacoustic spectrum analyzer, and the method comprises the steps: arranging a sampling end to collect an SF6 gas sample, and obtaining stable gas input through constant-current sampling and steady-state pretreatment; steady-state gas is guided into the photoacoustic spectrum analyzer, and resonance frequency stabilization and signal amplification output are achieved through the self-tuning unit; executing double-microphone differential detection and digital filtering processing, and outputting a stable SF6 detection signal with a high signal-to-noise ratio; performing time calibration, abnormity elimination and consistency processing on the detection signal to generate standardized detection data; inputting the standardized data to a Transform model, and performing inversion to generate an SF6 leakage source position and a diffusion path; and displaying an inversion result on a monitoring interface, triggering a sound-light alarm when the inversion result exceeds a limit, and uploading the inversion result to a cloud monitoring platform. According to the invention, the intelligent photoacoustic spectrum system combining photoacoustic-fluid steady-state control and self-tuning detection is constructed, so that high-sensitivity detection and accurate positioning of SF6 gas leakage are realized.

The invention provides an industrial park soil and groundwater pollution early warning grade evaluation method and system.The method comprises the steps that production raw material components of pollution source enterprises, waste discharge data and soil heavy metal concentration evolution data in historical pollution events are integrated, optical sensing data of earth surface and underground monitoring points are combined, and the early warning grade of soil and groundwater pollution is evaluated; constructing a dynamic characteristic factor library reflecting pollution source characteristics; carrying out spatial concentration gradient calculation on the optical sensing data based on the characteristic factor library, and generating a dynamic heat map containing heavy metal distribution and migration trends at the same time; performing cross-modal analysis on the pollution source characteristics and the dynamic heat map, and establishing a spatio-temporal evolution trajectory of a pollution diffusion path; according to the diffusion rate and direction in the track, differentiated pollution risk early warning intervals are divided, and a targeted grading prevention and control strategy is generated. According to the invention, the timeliness of pollution early warning and the targeting of prevention and control measures are improved.

The invention relates to the technical field of performance detection, and discloses a blade structure performance detection device and method, and the method comprises the steps: carrying out the laser ultrasonic scanning of a titanium alloy engine blade, and obtaining multi-mode spectrum feature data; performing parameter inversion and iterative optimization calculation according to the multi-modal spectrum characteristic data to obtain material parameter distribution data of a local area of the blade; performing annular scanning analysis on the titanium alloy engine blade based on the material parameter distribution data to obtain local structure feature data; performing modal decomposition on the local structure feature data to obtain fatigue damage accumulation data; and inputting the accumulated fatigue damage data into a dual decoupling diagnosis model for comparative learning and antagonistic feature separation to obtain a target diagnosis result, the method overcomes the limitation of a traditional linear superposition method, accurately predicts the fatigue damage evolution law under a complex load, effectively solves the problem of working condition interference, and improves the diagnosis accuracy. And the robustness and generalization of fault diagnosis are enhanced.

Video inspection systems are disclosed. In one embodiment a video camera for use in a pipe inspection system includes a camera head including an outer housing, a camera module assembly including video and non-video sensors, a processing circuit, and a data link receiver.

There is provided a system and method of defect examination on a semiconductor specimen. The method comprises obtaining an input image indicative of difference between an inspection image of the specimen and a corresponding reference image; and processing the input image using a trained machine learning (ML) system, to generate an output image representative of a defect map indicative of distribution of defect of interest (DOI) candidates in the input image. The ML system comprises a plurality of ML models operatively connected therebetween and previously trained together to perform defect detection on the input image based on Fourier transform. The output image is usable for further defect examination.

The invention discloses a soil salinity inversion method, system and device based on multi-mode remote sensing data fusion and a storage medium, and is applied to the technical field of soil quality monitoring, and the method comprises the steps: obtaining radar and optical remote sensing data of a to-be-inverted region of soil salinity, and carrying out the conductivity measurement, and taking the obtained data as an inversion index of the soil salinity; obtaining multi-modal remote sensing features based on radar and optical remote sensing data fusion, optimizing a remote sensing feature combination by combining the radar and optical remote sensing data and based on a random forest algorithm and utilizing a recursive feature elimination method, and performing model performance evaluation on the remote sensing feature combination by adopting cross validation to obtain an optimal remote sensing feature combination; and based on the optimal remote sensing feature combination, constructing and training a soil conductivity prediction model based on a random forest algorithm, and inputting to-be-measured data to the conductivity prediction model after hyper-parameter adjustment to complete soil salinity inversion. According to the method, more accurate inversion of the soil salinity under the interaction influence of a complex environment and human factors is realized.

The invention provides an intelligent water and soil conservation monitoring and management system, and belongs to the technical field of hydraulic engineering and environment monitoring. The data acquisition layer acquires data in real time by using an acoustic sensor network, machine vision monitoring equipment, Internet of Things monitoring equipment, satellite remote sensing monitoring, unmanned aerial vehicle inspection and other technologies. And the data transmission layer transmits the acquired data to the data processing and analyzing layer by adopting a wireless communication network. The data processing and analysis layer analyzes multi-source data through data fusion and preprocessing, GIS technology application and big data analysis and machine learning. And the decision support and application layer comprises a prevention and control measure generation and optimization unit, a real-time early warning and emergency response unit and a user interaction and decision support unit, provides a water and soil conservation and dike reinforcement strategy for a user, and has a feedback optimization mechanism. By comprehensively applying various monitoring technologies and data analysis methods, the accuracy and timeliness of water and soil loss monitoring are improved, and powerful guarantee is provided for dike safety.