1888 results about "Textural feature" patented technology

The invention relates to the field of semiconductor packaging material defect detection, in particular to a semiconductor packaging material defect detection method based on deep learning, which comprises the following steps: acquiring a surface image, and extracting a two-dimensional contour and a feature point set; preprocessing the image, and separating a packaging material main body area; constructing a two-dimensional defect identification model based on Transform, and outputting a two-dimensional detection result; scanning suspected and unknown defect areas to obtain three-dimensional point cloud data, and extracting geometric and texture features; fusing two-dimensional and three-dimensional data through a space-time alignment model; utilizing the multi-modal fusion model to output defect positions and types; and evaluating the defect importance based on the material node connectivity and the stress distribution, and generating a visual detection report. According to the invention, high-precision detection of semiconductor packaging material defects is realized, the defect identification rate, the positioning precision and the detection efficiency are improved through multi-modal data fusion and a deep learning model, and a visual report can be generated based on material structure quantification defect importance.

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.

The embodiment of the invention discloses an outer wall thermal insulation defect diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an infrared thermal imaging and visible light image sequence of a target building outer wall, the former comprising continuous temperature distribution data, and the latter comprising textural feature data in time-space alignment with the latter; performing dynamic temperature gradient analysis on the infrared thermal imaging image sequence to generate a three-dimensional heat conduction abnormal map, extracting surface deformation characteristics from the visible light image sequence to generate a structure deformation distribution map, and performing multi-modal characteristic fusion on the two to obtain a joint defect characteristic matrix; performing defect type classification and region positioning on the matrix based on a pre-trained deep residual neural network model, outputting a defect type identifier and a corresponding region boundary coordinate, and finally generating a diagnosis report containing a repair priority score and a material matching suggestion according to the defect type identifier and the corresponding region boundary coordinate, and sending the diagnosis report to a user terminal for visual display. And efficient and accurate external wall thermal insulation defect diagnosis is realized.

The invention provides a magnetic core intelligent cutting parameter self-adaptive optimization system based on multi-mode perception, and relates to the technical field of data processing.The method comprises the steps that a multi-mode sensor module is integrated on magnetic core cutting equipment, and the module comprises a force sensor, a visual sensor and a temperature sensor; the acquisition units are respectively used for acquiring cutting force dynamic signals, cutting track image sequences and cutter temperature time sequence data in real time; magnetic core surface texture features and three-dimensional contour data are captured through a visual sensor, and an initial cutting parameter set is generated in combination with a magnetic core material type recognition result, associated parameters in a historical process database and preset process constraint conditions; and first workpiece trial cutting is executed based on the initial cutting parameter set, multi-modal data fusion collection is synchronously started, cutting force frequency domain feature vectors, a tool temperature change rate curve and cutting surface defect image features are obtained, and multi-modal data are obtained. According to the invention, multi-objective collaborative optimization of processing efficiency and energy consumption is realized.

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

The invention discloses a keyboard key defect detection method and system based on machine vision, and the method comprises the following steps: collecting the multi-modal data of keyboard keys, and carrying out the fusion preprocessing of reflection component separation, dynamic gamma correction and multi-modal constraint alignment; constructing a double-flow deep neural network for defect detection, wherein the first branch network adopts an improved U-Net architecture embedded with a CBAM attention module to extract texture features; the second branch network adopts a PointNet + + architecture to process three-dimensional geometrical characteristics; realizing cross-modal feature association through a feature fusion layer, wherein a fusion weight is adaptively adjusted according to a material type; and outputting a detection result based on the cascade classifier, wherein the method comprises the following steps: positioning a suspected defect area by a first-stage YOLOv5 network; the second-stage ResNet50 network is used for completing defect classification; and the dynamic threshold segmentation algorithm adjusts the judgment boundary according to the material type. According to the invention, high-precision and high-efficiency detection of keyboard key defects is realized.

The invention relates to a fabric flaw detection and traceability system based on edge calculation and computing power scheduling, which is suitable for intelligent quality control in a textile production process. The system comprises an acquisition unit, a modeling unit and the like. The acquisition unit acquires fabric images and environmental data through a multispectral imaging device and a process parameter sensor, and constructs time-aligned multi-modal feature tensors. The modeling unit extracts texture features by using unsupervised comparative learning in combination with fabric material characteristics, and generates potential texture fingerprint vectors. And the detection unit adopts a target detection network of a channel attention mechanism to identify fabric flaws and output positions, types and severity. The traceability unit analyzes correlation between defects and process parameters through time sequence causal reasoning, and constructs a causal atlas. And the optimization unit generates a process optimization vector according to the causal atlas and the risk score, and realizes visual display and edge control feedback, thereby constructing a real-time defect control and explainable traceability-oriented closed-loop quality management system.

The invention relates to the technical field of cultural heritage digital protection, in particular to a super-volume historic building three-dimensional simulation modeling method based on multi-source heterogeneous data, and the method comprises the steps: firstly collecting node multi-source heterogeneous data such as laser point cloud, images, structural mechanical parameters and historical repair records, and then carrying out node feature enhancement through a node feature enhancement module; using an improved generative adversarial network to strengthen node edge features, adopting an adaptive threshold segmentation algorithm to extract surface texture features, converting mechanics and size data into a three-dimensional constraint condition parameter matrix, then using a topological relation verification algorithm, using a graph neural network to traverse and verify a component connection relation, and obtaining a three-dimensional confrontation model; and a re-calibration mechanism is triggered when the deviation exceeds the limit, the weight is adjusted based on a Bayesian optimization algorithm, fusion verification is carried out again, finally, hierarchical grid division is adopted to construct high-precision sub-models, and the sub-models are spliced into an integral three-dimensional model, so that the model precision and reliability are improved, and reliable digital support is provided for ancient building protection.

The invention relates to a remote sensing image ground feature recognition method based on deep learning, and the method comprises the steps: carrying out the data collection and preprocessing of a ground surface target region, eliminating the position deviation through geometric correction, processing the illumination difference through combination with radiation equalization, and generating a ground feature registration image; performing multi-dimensional feature fusion processing on the image, performing tensor fusion on vegetation spectral features, earth surface texture features, point cloud data features and linear ground feature features, and constructing a ground feature fusion matrix; a bilateral convolutional neural network is adopted to extract spectral response characteristics and spatial correlation characteristics, and characteristic interaction is realized through an attention mechanism to generate a ground feature probability distribution diagram; and finally, carrying out noise filtering, boundary refining and vectorization conversion processing on a classification result, and outputting a ground feature classification vector diagram. According to the method, three technical bottlenecks of insufficient cooperative utilization of multi-source heterogeneous data, insufficient spectrum-space feature fusion and poor GIS compatibility are solved, and the operation efficiency of territorial investigation, disaster monitoring and other scenes can be remarkably improved.

The invention provides a remote sensing AI intelligent interpretation-based cultivated land change pattern spot intelligent identification method and system, and the method comprises the steps: firstly obtaining a multi-temporal remote sensing image data set of a target region, comprehensively recording the cultivated land conditions of the target region in different time periods, carrying out the preprocessing operation of the obtained multi-temporal remote sensing image data set, and carrying out the recognition of the cultivated land change pattern spots. Then feature extraction operation is carried out on the preprocessed multi-temporal remote sensing image data set, a spectral feature sequence and a texture feature sequence of a target area are generated respectively, farmland features are described in detail from different angles, and finally, a time sequence analysis method is combined to analyze the farmland features. And performing change detection processing on the generated spectral feature sequence and texture feature sequence to generate a cultivated land change pattern spot data set of the target area, thereby effectively improving accuracy and efficiency of cultivated land change pattern spot identification.

The invention discloses an unmanned aerial vehicle-mechanical arm system cooperative control method for precise spraying. According to the method, through a depth camera and a point cloud reconstruction algorithm, three-dimensional modeling of a target surface and semantic recognition of a spraying area are achieved, and a spraying path and spraying posture data are generated. Based on spraying task requirements, a spraying film thickness experience estimation model is established, and path point-level spraying parameters are obtained. Load mass change, mechanical arm mass center change and spraying reaction force of each path point are calculated, and a coupling dynamic model is constructed in combination with the system state. And dynamic compensation control input is generated by using an adaptive sliding mode control algorithm. After spraying is completed, the actual spraying effect is collected through image and point cloud detection, color, film thickness and texture features are extracted and compared with task requirements, error feedback is generated, and control parameters are adjusted online. According to the method, the problems of dynamic change and error compensation in the spraying process are effectively solved, and the method is suitable for a high-precision spraying task in a complex environment.

The invention discloses a wheat single grain appearance anomaly detection method based on a deep learning model and hyperspectral imaging. The method comprises the following steps: step 1, collecting different types of wheat grain samples; step 2, acquiring hyperspectral image data of the wheat grains by using a visible light-near infrared and short wave near infrared hyperspectral imaging system, and extracting spectral information and image information; step 3, preprocessing the spectral data, and verifying a preprocessing effect; 4, screening spectral characteristic wave bands, extracting texture characteristics and morphological characteristics in combination with a gray-level co-occurrence matrix, and constructing a middle-level data fusion model; step 5, constructing an atlas feature fusion deep learning model to realize high-level fusion of spectrum and image features; and step 6, pixel-level classification is carried out on the hyperspectral image, and a spatial distribution visualization result of the appearance abnormity of the wheat grains is generated. The method has high precision, nondestructive testing and strong generalization ability, and is suitable for wheat quality grading, processing and sorting and storage safety management.

The invention discloses a furniture processing control system based on artificial intelligence, and relates to the technical field of intelligent manufacturing and industrial automation. According to the system, cutter vibration frequency spectrum, plate texture features and environment temperature and humidity data are collected in real time through a distributed sensor array, multi-source data are fused through a space-time attention mechanism, and a joint feature matrix is generated. And based on the joint feature matrix, utilizing a depth map neural network to deconstruct a topology constraint relation of non-standard customization requirements, and generating an initial processing parameter set. And driving the digital twin model to perform virtual processing according to the initial processing parameter set, and predicting a processing node deformation error and generating a compensation vector in combination with the three-dimensional laser point cloud and infrared thermal imaging data. The compensation vector and real-time working condition data are received, a path cost function is evaluated through Monte Carlo tree search and a time sequence convolutional network, the cutter feeding speed and the cutting depth are corrected, and dynamic regulation and control of cutter path parameters are achieved.

The invention discloses a magnetic material defect detection system based on image recognition, which adopts a first acquisition module, a first determination module, a second determination module, a second acquisition module and a third acquisition module, and is characterized in that the first acquisition module is used for performing multi-angle illumination control on the surface of a magnetic material by adopting a polarized light imaging device; mirror reflection generated by the strong light reflection characteristic is inhibited by adjusting the angle of a polaroid and the incident angle of a light source, original image data with uniform illumination distribution are obtained, and if it is detected that an overexposure area exists in an image, exposure parameters and the polarization angle are automatically adjusted to obtain a high-quality image suitable for follow-up processing; and the first determination module is used for performing multi-scale wavelet transform decomposition according to the acquired high-quality image, identifying a surface defect area by analyzing texture features and edge information in different frequency components, and judging that the surface defect area is a potential defect area if a wavelet coefficient in the high-frequency component exceeds a preset threshold value. The detection performance is obviously improved.

The embodiment of the invention relates to the technical field of image processing, in particular to a TP laminating process production process defect diagnosis method and system based on image analysis, and the method comprises the steps: firstly collecting a real-time image sequence corresponding to a to-be-detected laminating assembly continuously transmitted on a TP laminating process production line; the sequence comprises component surface images and edge region images at different fitting stages; secondly, performing defect sensitive feature enhancement processing on the real-time image sequence to obtain a defect sensitive feature set containing surface texture features, edge contour features and regional gray features; then performing feature correlation analysis processing on the defect sensitive feature set through a trained TP fitting defect diagnosis model to generate a defect preliminary diagnosis result; and finally, determining defect types and position distribution information according to the preliminary diagnosis result, and further generating a production process defect diagnosis report containing defect diagnosis contents, thereby realizing accurate diagnosis of the TP laminating process production process defects.

The invention discloses a small sample industrial defect detection system based on a multi-stage diffusion model. The small sample industrial defect detection system comprises a multi-stage diffusion generation module and a defect detection module based on multi-scale attention and physical constraint. The multi-stage diffusion generation module divides the diffusion generation process into three stages of global structure reconstruction, local detail refinement and texture feature synthesis through a stage control mechanism, and gradually guides feature evolution and improves the generation effect aiming at the quality and diversity problems of defect image generation under the small sample condition; the defect detection module based on multi-scale attention and physical constraint adopts a coding structure fusing local window attention and global attention, through multi-scale feature extraction and fusion, significant features of a defect area are effectively captured, gradient smoothing loss and edge energy consistency loss are introduced at a decoder end, and the defect detection accuracy is improved. Therefore, high-quality reconstruction of a normal area and effective suppression of an abnormal area are realized.

The invention relates to the technical field of crack detection, in particular to a building crack intelligent detection and prediction method based on multi-modal data fusion, which comprises the following steps: constructing a multi-source heterogeneous data set including an image data set, a thermal imaging data set and a vibration data set; simulating complex environment interference through a GAN (Generative Adversarial Network), and dynamically enhancing image training data; designing a double-branch multi-mode fusion network, and extracting the edge and texture features of the crack; designing a multi-modal fusion branch, and carrying out feature alignment and weighted fusion on vision, thermal imaging and vibration data; and based on a long short-term memory network LSTM and a structural mechanics model, a crack trend prediction module is developed, and early warning is provided for building structure safety. According to the method, intelligent monitoring and early warning of building structure safety are realized by constructing a multi-source heterogeneous data set, enhancing image training data through a GAN, extracting features by using a double-branch multi-mode fusion network, predicting a crack trend based on an LSTM and a structural mechanical model, and finally integrating system deployment.

The invention relates to the technical field of industrial product defect detection, and discloses a defect detection method combining vision and X-ray detection technologies, which comprises the following steps of: synchronously acquiring vision image data and X-ray transmission data in a surface coverage area of a detection object, performing combined preprocessing to complete noise suppression, artifact elimination and time alignment, and then acquiring the X-ray transmission data; according to the method, surface texture features and internal structure features are extracted respectively, cross-dimensional information mapping is performed through a multi-modal association module to generate a fusion feature set, surface damage, internal holes and boundary discontinuous defects are identified, and finally, a hierarchical detection report is generated according to defect space distribution and severity and is updated continuously. According to the method, the advantages of vision and X-ray detection are integrated, cooperative detection of surface and internal defects is achieved, the comprehensiveness, accuracy and real-time performance of defect detection are improved through multi-modal data synchronous collection, feature fusion and dynamic report generation, and the method is suitable for part quality detection in the industrial manufacturing field.

The invention relates to a medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement. The method comprises the following steps: acquiring and preprocessing a medical image; inputting the image into a segmentation model based on an encoder-decoder architecture; the encoder synchronously extracts local texture features and models long-range spatial dependence through residual error convolution blocks and residual error Mama blocks which are alternately connected; fusing and enhancing the jump connection features between the encoder and the decoder through a boundary enhancement module to optimize boundary characterization; integrating a multi-scale gating attention module in a decoding path, and adaptively selecting and fusing multi-scale context features; and finally outputting the high-precision segmentation mask. The method effectively solves the problems that in the prior art, long-range dependence and local details are difficult to consider, the multi-scale feature fusion capability is insufficient, boundary segmentation is fuzzy and the like, and the segmentation accuracy, the boundary continuity and the clinical practicability are remarkably improved.

The invention relates to the technical field of wetland damage identification, in particular to a coastal wetland damage dynamic identification method and system based on a remote sensing technology, and provides the following scheme: calling multispectral remote sensing data through a cloud computing platform, performing cloud masking, atmospheric correction and resolution resampling, and generating a high-quality remote sensing image data set; collecting ground feature samples, and constructing a random forest classification model based on spectrums, vegetation indexes, water body indexes and texture features; applying the classification model to a remote sensing image, generating a coastal wetland classification chart, and distinguishing a natural wetland from a damaged area; through time sequence analysis, ground feature changes of the same spatial position are identified, and dynamic evolution characteristics, including change amplitude, rate and conversion relation, of the damaged area of the wetland are extracted; and evaluating the precision of the classification model by using the confusion matrix, and optimizing the model according to a verification result. According to the invention, automation and precision of dynamic monitoring of wetland damage are improved, and scientific support is provided for wetland protection and management.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention relates to the technical field of image detection, in particular to a visible light and infrared image combined photovoltaic defect detection method based on an unmanned aerial vehicle, which comprises the following steps of: determining a photovoltaic power station detection area, carrying out synchronous aerial photography by using a visible light camera carried by the unmanned aerial vehicle and an infrared thermal imager, and carrying out local division to extract texture and temperature characteristics; the screening area performs fitting affine parameter generation on an extraction center point, corrects an infrared image to extract contour lines and texture change features, and screens a defect area to extract a positioning coordinate set; according to the method, through synchronously screening generated visible light and infrared image pairs, the resolution of an abnormal region is enhanced, a complex background and an abnormal target are accurately separated, fine-grained adaptive registration is realized through central point extraction and affine parameter derivation, and defect region characteristics are verified bidirectionally through a temperature contour closing proportion and a texture density variable quantity; thermal features and texture features are fused in the defect screening process, the recognition capability of weak anomalies is enhanced, and the defect positioning accuracy is improved.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention provides a computer visual defect detection system and method on an electric control board production line, and relates to the technical field of data processing.The method comprises the steps that according to collected multi-angle images, the overall area of an electric control board is partitioned in combination with illumination conditions and visual angle information; performing image partitioning processing to generate a plurality of image sub-regions, and extracting texture features, brightness distribution features and geometric edge features in each image sub-region; rare defect feature enhancement processing is executed, multi-scale repeated superposition is carried out on low-frequency abnormal textures, and directional extension is carried out on edge fractures; performing difference comparison with the corresponding normal area combination features, and performing normalization correction in combination with the illumination condition and the visual angle information; multi-angle reproducibility analysis is executed, when the same suspected defect is detected at different angles, a reliable defect area is formed, otherwise, an interference area is removed, and an electric control board defect detection result is generated; according to the invention, the accuracy of defect detection is improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an HTCC ceramic defect automatic identification method based on industrial model assistance, which comprises the following steps: step 1, collecting sintering temperature gradient, lamination pressure and green body water content process parameters in the HTCC production process in real time; step 2, performing non-contact dielectric property scanning on the sintered HTCC substrate on a production line conveyor belt; 3, based on the coordinates of the dielectric abnormal region, controlling an annular polarization light source to inhibit the incident angle reflected by the metallization layer from irradiating the target region; 4, inputting the dielectric anomaly frequency band feature vector and the defect image texture feature in the step 3 into a cross-modal attention network to generate a fusion feature map; and 5, establishing an association mapping library of the defect type and the process deviation type. Through accurate real-time monitoring, intelligent defect identification and classification, automatic process adjustment and a continuously optimized feedback mechanism, the efficiency, quality and intelligent level of the ceramic production process are greatly improved.

The invention provides a surface quality monitoring method and system based on image-signal multi-modal data, and relates to the technical field of machining process detection, and the method comprises the steps: obtaining a workpiece surface image and a main shaft vibration acceleration signal in a milling process, inputting the workpiece surface image and the spindle vibration acceleration signal into a surface roughness classification model to obtain a machining state recognition result; wherein in the surface roughness classification model, shallow texture features of a workpiece surface image are extracted through an image processing module, and frequency domain features of a spindle vibration acceleration signal are extracted through wavelet transform and a frequency attention mechanism; and splicing projection features of the shallow texture features and the frequency domain features into a combined feature vector, carrying out dynamic weight distribution and fusion on the combined feature vector based on a self-adaptive fusion strategy of a gated attention mechanism to obtain weighted fusion features, inputting the weighted fusion features into a grade classifier, and outputting the grade of the surface roughness.

The invention relates to the technical field of data processing, and discloses a seawall feature information extraction method and system based on multi-source remote sensing data. The method comprises the following steps: carrying out wave band combination enhancement, geometric correction and radiation correction on a multi-source remote sensing image to obtain standardized data; extracting a seawall target by using multi-scale segmentation; recognizing structural features through profile analysis and spectrum matching; calculating vegetation indexes and texture features to obtain ecological information; establishing a precision evaluation system; and finally constructing a feature information database. Through the technical means of multi-source data fusion processing, multi-scale feature extraction, ecological feature evaluation and the like, the precision and efficiency of seawall feature information extraction are improved, and the problems existing in the aspects of data acquisition, processing and evaluation in a traditional method are solved.

The invention provides a seed packaging bag AI intelligent anti-fake traceability management system and method, and relates to the field of household kitchens. The AI intelligent anti-fake traceability management system for the seed packaging bag comprises an intelligent anti-fake packaging bag which is provided with a natural random physical texture layer, a dynamic optical structure layer covering the natural random physical texture layer and an encrypted identifier storing a digital identity (DID), and the microstructure of the natural random physical texture layer forms a uniqueness feature which cannot be copied. The dynamic optical structure layer generates a variable optical effect during physical interaction. Unique biological fingerprints are formed through microcosmic random textures of a packaging base material, the optical variable effect of a microlens array is overlaid, each packaging bag has the unclonability of the physical level, and in combination with dynamic comparison of a multi-mode AI engine on real-time texture features and block chain anchoring initial values and optical living body frequency analysis, the non-clonability of the packaging bag is improved. And a three-dimensional verification closed loop which is difficult to break by a counterfeiter is realized.

According to the face data privacy protection method and device provided by the embodiment of the invention, a credible authority management mechanism is constructed through a distributed network and a smart contract, and a multi-dimensional feature extraction scheme fusing geometric features and texture features is innovatively designed. The system adopts data fragmentation and homomorphic encryption technologies to carry out security processing on feature vectors, and dispersedly stores encrypted data in a block chain network, so that decentralized management of the feature data is realized. According to the method, feature comparison and identity verification are carried out in a ciphertext domain, the whole process is traceable in combination with a timestamp and an operation record, the problems of data security and privacy protection in a traditional face recognition system are effectively solved, and a safe and credible technical solution is provided for the field of biological feature recognition.