14314results about "Material analysis by optical means" patented technology

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.

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.

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 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 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 relates to the technical field of packaging, in particular to an intelligent metering method for remnant base paper rolls of a corrugated paper board assembly line. The intelligent metering method for the remnant base paper rolls of the corrugated paper board assembly line comprises steps as follows: a plurality of base paper feeding positions are arranged in the conveying direction of the assembly line at intervals, a base paper feeding frame is arranged in each base paper feeding position, and two base paper feeding rolls located on two sides of each base paper feeding frame are arranged on the base paper feeding frame; each base paper feeding roll is provided with lap recording sensors and incremental encoders, the lap recording sensors are used for monitoring the number of paper feeding laps of base paper clamped on the corresponding base paper feeding roll, the incremental encoders are used for monitoring the length of each lap after the base paper on the base paper feeding roll passes by one lap, and the lap recording sensors and the incremental encoders are connected with a controller. Intelligent metering can be realized, the operation cost of an enterprise is reduced, labor is saved, and the utilization rate of enterprise resources is increased.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention discloses a high-power microscope intelligent scanning method for a bone marrow smear, which comprises the following steps: S1, automatically dividing a scanning area: acquiring a bone marrow smear image by adopting a front camera, and selecting an optimal scanning area through a multi-dimensional quality evaluation model; s2, intelligent partitioning and focusing point distribution: dividing an optimal scanning area into N * M sub-areas, determining P focusing sampling points by adopting a layered sampling strategy, and establishing a three-dimensional coordinate mapping table; s3, multi-point focusing data acquisition: according to a preset coordinate mapping table, driving an objective table and an objective lens assembly to move to each sampling point in sequence, and constructing an N * M * P-dimensional focusing data matrix; s4, performing focal plane fitting by a least square method: performing three-dimensional plane fitting on the acquired data; and S5, intelligent scanning imaging: calculating the theoretical optimal focal length of each position in the scanning path according to the fitting function, and driving the objective lens assembly to adjust the focal length in real time for scanning imaging. According to the invention, the scanning area identification accuracy and the image definition uniformity are automatically improved in the whole process.

The invention relates to the technical field of aging detection, and discloses a xenon lamp simulation environment aging detection method and system for a water-based waterproof material. The method comprises the following steps: testing a water-based waterproof material sample in an aging detection platform and collecting first aging response data; calculating a characteristic spectrum drift parameter combination in combination with the first aging response data, and solving a multi-factor aging dynamics equation set to obtain three-dimensional environment field data; inputting the three-dimensional environment field data into a back propagation neural network to carry out multi-factor environment parameter collaborative optimization to obtain an intelligent control parameter combination; performing test parameter adjustment and detection on the aging detection platform according to the intelligent control parameter combination to obtain second aging response data; and performing spectral attenuation index calculation and aging degree grading based on the second aging response data to obtain an aging state evaluation result of the water-based waterproof material sample. According to the method, multi-dimensional quantitative characterization of the aging state of the water-based waterproof material and scientific prediction of the service life of the water-based waterproof material in an acid pollution environment are realized.

The invention discloses a ginkgo leaf extracting solution state real-time monitoring method based on image processing, and relates to the technical field of ginkgo leaf extracting solutions. The method comprises the following steps: constructing a multi-light-source imaging environment to shoot a ginkgo leaf extracting solution image; obtaining an extracting solution mask through a U-net segmentation model, and obtaining an extracting solution foreground image in combination with the ginkgo leaf extracting solution image; segmenting the extracting solution foreground image through an adaptive threshold method to obtain an extracting solution block graph, and optimizing through a watershed boundary optimization method to obtain an optimized extracting solution block graph; a block boundary probability value is calculated through a boundary probability model with double distance changes, a block gradient magnitude is calculated through a Sobel operator, block boundary confidence is obtained by combining the block boundary probability value and the block gradient magnitude, and block boundary pixels are determined; and collecting a boundary gradient feature vector sequence of the block boundary pixels, inputting the boundary gradient feature vector sequence into the extracting solution evolution trend model, outputting to obtain an oxidation risk index, and performing early warning if the oxidation risk index is greater than a preset threshold value.

A device, and method for its use, which may be worn by a firefighter for detecting the source of a fire in a smoke-filled or noisy environment, the device having directional infrared sensors for detecting the direction of maximum or minimum intensity of infrared radiation in two or more directions relative to the wearer and reporting at least the direction of maximum or minimum intensity to the wearer by a tactile indicator contacting the skin of the wearer.

The invention relates to the technical field of water level and water surface relation analysis, in particular to a lake water level and water surface relation analysis method based on a DEM and a remote sensing image, which comprises the following steps of: based on DEM and remote sensing image data, analyzing a space relation between measured data and the DEM, correcting abnormal elevation and adjusting original data, screening boundary points with small elevation difference, and calculating the water level and water surface relation of a lake; and performing statistics on water surface areas under different water levels to obtain a water level area change sequence. According to the method, through cooperative adjustment and automatic fusion based on multi-source spatial data, through elevation correction, spatial anomaly screening and time sequence change tracking, the consistency of spatial data is improved, elevation anomaly information is dynamically corrected, the discrimination capability of a water body spatial distribution boundary is enhanced, and dense water surface change information extraction is realized based on spatial overlapping data; the space node and area change trend under the water level grading condition can be automatically concluded, and the dynamic response characteristics of the lake space structure can be systematically reflected.

The invention relates to an optical fiber hydrogen sensor based on a silver film-nanowire hole array structure and a preparation method thereof in the field of photochemical sensors. The optical fiber hydrogen sensor comprises a multimode optical fiber, and a sensing area is formed on the multimode optical fiber; the sensing area is formed by stripping a coating layer with a preset length from the multimode optical fiber, thinning the optical fiber in the area, plating a silver film on the surface of the thinned optical fiber, and adsorbing a nanowire hole array structure which is sequentially and vertically arranged on the surface of the silver film in a liquid level self-assembly manner; the nanowire hole array is of a metal-medium-metal (MIM) structure, a gold-palladium nanowire array is arranged at the bottom, an intermediate medium layer is made of aluminum oxide, and a silver film with a hole array in any shape is arranged at the top. When light is transmitted to a sensing area from a fiber core of the multimode optical fiber, the light and a metal silver film can excite a surface plasmon resonance (SPR) effect, meanwhile, an MIM structure formed by a gold-palladium nanowire, an aluminum oxide dielectric layer and a porous silver film serves as an optical coupling antenna through a local surface plasmon resonance (LSPR) effect at the edge of a top hole, and the optical coupling antenna is used for transmitting the light to the sensing area. Light energy is efficiently converged and excited in an extremely bound gap plasmon (Gap Plasmon) in an aluminum oxide dielectric layer, so that far-unconventional electromagnetic field enhancement is generated in a sensing core area, and the sensitivity of the sensor is greatly improved.

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention discloses a ship carbon emission on-line monitoring method, device and equipment, and the method comprises the steps: building a comprehensive emission feature library through collecting infrared absorption spectrum, gas flow and other multi-source emission signals in a ship operation process; eliminating gas cross interference by adopting a spectrum separation analysis technology, and establishing a concentration inversion mechanism to generate an independent monitoring domain; high and low emission working condition modes are identified through working condition relevance evaluation, and intelligent allocation of monitoring resources is achieved; marine environment compensation nodes are established, layered temperature and humidity correction control is implemented, and environment-adaptive ship environment parameters are generated; the multi-stage early warning trigger time is determined by adopting an early warning threshold backward deduction analysis technology, a ship emission monitoring time sequence table and a graded monitoring instruction set are constructed, full-working-condition, all-weather and high-precision intelligent monitoring of ship emission is realized, and reliable technical guarantee is provided for ship environmental protection compliance and marine environmental protection.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

A lidar system and an inversion method for simultaneously detecting carbon sequestration efficiency of the oceanic biological pump and ocean environmental dynamic parameters. The information of Mie scattering and Brillouin scattering intensity and the information of frequency shift and linewidth of Brillouin scattering spectra are combined, and for the detection requirements of the current ocean carbon cycle and dynamic environmental parameters, the vertical profile distribution of the carbon cycle mechanism and environmental dynamic parameters in the euphotic layer is mainly detected; at the same time, the present disclosure constructs an inversion model of the carbon sequestration efficiency of the oceanic biological pump and the temperature and salinity of the environmental dynamic parameters and realizes the synchronous detection of the carbon sequestration efficiency of the oceanic biological pump and the vertical profile distribution of the ocean environmental dynamic parameters in the euphotic layer.

The invention relates to the technical field of image analysis, in particular to a cushion foaming forming quality detection method and system based on machine vision, and the method comprises the steps: collecting image data, carrying out the iterative screening of the image data, and selecting a detection sample; the method comprises the following steps: collecting a three-dimensional point cloud of a detection sample, calculating the flatness of a seat cushion by using the three-dimensional point cloud, carrying out modeling by using polarized light reflection to obtain surface cell uniformity, and carrying out feature extraction on the three-dimensional point cloud through a three-dimensional mapping model to generate a three-dimensional semantic model; extracting mechanical characteristics by using the time sequence pressure map, and generating a cushion digital model; variational self-coding is carried out on the cushion digital model, and an abnormal feature map is generated; performing information extraction and pixel-by-pixel multiplication on the abnormal feature map by using a learnable convolutional layer to generate an abnormal enhanced map; and inputting the abnormal enhancement graph and the seat cushion digital model into a multi-channel network model for final evaluation. According to the method, the image data is analyzed, and the performance of the cushion is subjected to index quantification, so that the deep detection of the quality of the cushion is realized.

The invention discloses a contour detection method and system for aluminum bar quality inspection based on an image recognition technology, and belongs to the technical field of computer vision detection.According to the method, a high-speed optical scanner is used for scanning the surface of an aluminum bar at multiple incident angles, and reflection change curves at different incident angles are obtained; a two-dimensional grey-scale map is obtained, the two-dimensional grey-scale map is converted into a depth parameter matrix, meanwhile, the incident angle position of a light beam, the scanning speed and a scanning time sequence are synchronously recorded in the scanning process, gradient second-order differential transformation is conducted on the obtained depth parameter matrix in the contour direction, and a curvature amplitude spectrum is generated; a high-speed optical scanner is used for scanning the surface of the aluminum bar at multiple incident angles, a reflection change curve and a depth parameter matrix are synchronously obtained, key parameters such as the light beam incident angle and the scanning speed are recorded, the problem that traditional single-angle scanning contour information is incomplete is solved, the three-dimensional shape of the surface of the aluminum bar can be represented more comprehensively, and the accuracy is high. And initial feature extraction deviation caused by information loss is reduced.

Provided herein are methods of performing multiplex detection of ligand binding kinetics. In some embodiments, the methods include contacting ligands with an array of nucleic acid barcoded oscillators disposed on a first surface of a substrate that comprises an electrically conductive coating, applying an AC electric field to the substrate sufficient to induce the nucleic acid barcoded oscillators to oscillate proximal to the first surface of the substrate, and detecting changes in oscillation amplitudes of the nucleic acid barcoded oscillators over a duration to produce sets of ligand binding data. In some embodiments, the methods also include contacting barcode decoding nucleic acids with the array of nucleic acid barcoded oscillators applying an AC electric field to the substrate sufficient to induce the nucleic acid barcoded oscillators to oscillate proximal to the first surface of the substrate, and detecting changes in oscillation amplitudes of the nucleic acid barcoded oscillators over a duration to produce sets of barcode decoding data.

A label (100, 200, 500, 702, 802, 900, 901, 1000, 1200, 1300, 1600) for analysing a biological sample is provided. The label (100, 200, 500, 702, 802, 900, 901, 1000, 1200, 1300, 1600) comprises a nucleic acid backbone (102, 502, 1002, 1102, 1202, 1301) and a first plurality of labelling moieties (104). The nucleic acid backbone (102, 502, 1002, 1102, 1202, 1301) has a duplex structure comprising at least 50% of nucleotides of the nucleic acid backbone (102, 502, 1002, 1102, 1202, 1301). In further aspects a marker (700, 800) comprising the label and a method for analysing biological samples is provided.

The invention discloses an automobile tire defect automatic detection device and system, and belongs to the field of automobile tire processing, through highly integrated modular design, the automobile tire defect automatic detection device comprises a multifunctional image recognition module, a depth measurement and 3D imaging module, an internal structure and air tightness detection module and an analysis and decision module, comprehensive detection of the surface, the geometric structure, the internal structure and the air tightness of the tire is realized, and efficient, accurate and intelligent tire defect detection is realized through cooperative work of each module and equipment.

The invention discloses a glass fiber particle assembly line-based magnetic suction quick-release vibration disc system and a detection method, and relates to the technical field of production and processing, the system comprises a data acquisition module for acquiring data of a vibration light-emitting chassis, a magnetic suction column, an assembly line and particles by means of a distributed sensor network, and transmitting the data to an intelligent self-adaptive control module after signal processing; the intelligent self-adaptive control module realizes intelligent dynamic regulation and control of the parameters of the magnetic suction quick-release vibration disc, optimizes the production process of the glass fiber particles, and automatically generates an accurate control instruction based on a data analysis result by integrating multi-source information and performing deep fusion and analysis, so that the production efficiency of the glass fiber particles is improved. Parameters such as the vibration frequency and amplitude of the vibration light-emitting chassis and the magnetic attraction strength of the magnetic attraction column are adjusted to meet the requirements in different production states, so that the system can quickly respond to changes in the production process, defective particles are reduced, and the production efficiency is improved.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.

The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal, and belongs to the field of gas detection. The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal. The infrared CO2 high-precision detection system comprises a sensing module, a signal conditioning and collecting module and a self-adaptive filtering processing module. According to the method, the problems of contradiction between response speed and stability, discrete signal tracking lag and sensitivity to environmental interference in the prior art are solved, the weight of a filtering channel is adaptively and dynamically adjusted, and the advantages of a fast filtering channel and a slow filtering channel are combined, so that balance between dynamic response and steady-state precision is realized; the problem of tracking lag of discrete signals is solved by analyzing the change rate of the signals in real time and dynamically adjusting a filtering strategy; and through a hybrid filter and an environment compensation algorithm, the influence of temperature and humidity changes on a detection result is reduced, so that the long-term stability of the sensor is improved.

The embodiment of the invention relates to the technical field of electrolytic copper foil testing, and particularly discloses a continuous testing method and system for warping of an electrolytic copper foil. The method comprises the following steps: periodically cutting an electrolytic copper foil sample; adjusting a test environment, and carrying out leveling positioning and reference calibration on the electrolytic copper foil sample; performing three-dimensional panoramic scanning and multi-point scanning supplement on the electrolytic copper foil sample; reconstructing a three-dimensional surface model, carrying out reference comparison, and calculating a plurality of warping key indexes; and performing qualification judgment and trend analysis on the plurality of warping key indexes, generating a warping test report, and performing feedback control of a continuous test. According to the method, the defect evolution rule is visually presented by automatically generating the three-dimensional surface diagram and the distribution curve visual report; the electroplating parameters are dynamically adjusted by triggering the process feedback control module, a test-analysis-regulation closed loop is formed, the warping degradation trend is inhibited from the source, and the product yield and the production continuity are improved.