827 results about "Texture feature" patented technology

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

The invention relates to the technical field of digital oil painting optimization, in particular to a multi-level visual texture feature optimization method and system for a digital oil painting. The method comprises the following steps: acquiring an original digital oil painting image, performing regional image semantic deep mining, performing illusion perception noise abstract injection, and constructing an illusion perception texture feature layer; performing local continuity texture visual detection on the illusion perception texture feature map layer, performing stroke tension transmission analysis, and constructing a stroke tension perception texture map; performing multi-scale semantic region division and inter-region cross shielding mining on the stroke tension sensing texture map, and constructing a multi-layer texture detail expression map; and carrying out layer-by-layer texture time sequence drawing speculation on the multi-layer texture detail expression map, and carrying out time sequence texture visual rendering optimization to generate a time sequence layered texture rendering effect. According to the method, through multi-layer texture reconstruction, oil painting textures of different time sequence levels are improved, and the stereoscopic visual effect is embodied.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention provides an aluminum profile defect analysis method and system based on texture features, and relates to the technical field of edge detection, and the method comprises the steps: obtaining an aluminum profile surface image, and determining an extrusion direction; dividing the image into a plurality of local blocks, carrying out gradient direction and amplitude calculation on each block, and judging whether the block belongs to a high-confidence-coefficient texture region or not by combining a direction difference value and confidence coefficient; a first suppression coefficient is executed on the high-confidence-coefficient texture region for primary reduction to form a first processing image, then the updated image is judged again, a second suppression coefficient with higher strength is applied to the region still having obvious texture features, and a second processing image is generated; and finally, defect identification is carried out on the weakened image through edge detection, and residual texture false edges are removed in combination with direction consistency or connectivity analysis. The method has the advantages of light weight, low computing power consumption and high accuracy, and can be applied to online detection and quality control of the surface flaws of the aluminum profile in industrial production.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention relates to the technical field of data visualization, in particular to a multi-modal fusion anorectal data visualization analysis method and system, and the method comprises the following steps: synchronously collecting an anorectal image and a pressure signal, aligning a timestamp, carrying out the preprocessing, outputting a standardized image and a coded physiological signal, and extracting edge and texture features. After LSTM coding, splicing with image features, and fusing into a joint feature map; generating a multi-scale heat map by the feature pyramid network, and coarsely positioning a lesion; after boundary optimization, U-Net recovers details, and a pixel-level lesion probability graph is output; superposing the Jet color gradation to the original image in a semitransparent manner, and marking a pressure abnormal time period; and performing weighted scoring to generate a fourth-level clinical suggestion. According to the anorectal disease diagnosis method, through multi-modal space-time alignment, multi-scale feature fusion, boundary optimization and a quantitative scoring system, the problems of data splitting, insufficient precision and low efficiency of a traditional method are solved, a high-precision and high-robustness intelligent diagnosis tool is provided for anorectal diseases, and the scientificity and efficiency of clinical decision making are remarkably improved.

The invention belongs to the technical field of artificial intelligence image generation, and particularly relates to an image generation method and system based on style feature injection, and the method comprises the steps: carrying out the multi-dimensional feature analysis of a reference image specified by a user side through a pre-trained multi-level style extraction model, extracting a style feature vector, and carrying out the feature extraction of the style feature vector; the style feature vector comprises a color feature vector, a texture feature vector, a composition feature vector and an illumination feature vector; obtaining a text description input by a user side, and outputting triple information including a scene entity, an entity attribute and a spatial relationship; performing weighted fusion on the triple information and the style feature vector to obtain a style enhanced semantic embedding vector; and inputting the style-enhanced semantic embedding vector into a generative adversarial network for splicing, and outputting an image. The method has the effect of remarkably improving the similarity between the generated image and the reference style.

The invention relates to the technical field of image enhancement, in particular to a field-level crop image enhancement system based on multi-source remote sensing data, and the system comprises an artifact joint detection module which analyzes the mutation or periodicity of a statistical mean value and a variance value in the row and column directions of an input field-level remote sensing image, and recognizes an artifact stripe region. According to the invention, the method achieves the high-precision removal of thin clouds and stripe artifacts through carrying out the mutability analysis of the local mean and variance of the input remote sensing image in the row and column directions, positioning a stripe artifact region, and building an attenuation numerical relationship in combination with the spectral information of an adjacent clear sky reference region, and reduces the ground feature information loss in the image. Multi-scale coefficient decomposition is further performed on the image after artifact removal, texture features are extracted from homogeneous crop canopies in adjacent undamaged areas, and high-frequency coefficients of damaged areas are dynamically replaced and reconstructed, so that the texture structures of the damaged areas are highly consistent with those of the adjacent areas, and the regional texture continuity and authenticity are improved.

The invention belongs to the technical field of hydraulic engineering detection, and particularly provides a hydraulic metal structure surface coating defect detection method and a hydraulic metal structure surface coating defect detection device based on image processing. In a wet and dry alternating environment of a hydraulic metal structure, a multispectral imaging device is used for acquiring an image of a metal structure surface coating, and the acquired multispectral image is preprocessed; a complete and clear image to be detected is obtained; extracting features related to coating defects from the preprocessed to-be-detected image, wherein the features comprise spectral features, texture features and color features; and inputting the extracted features related to the coating defects into a pre-trained classification model, and classifying the coating defects through the classification model. According to the method and the device, the state of the coating can be comprehensively described, and the type and the degree of the coating defect can be accurately identified.

An image processing method, performed by a computer device, comprising: extracting sketch texture features at multiple scales from a sketch image; extracting image noise features at multiple scales from preset noise; determining color guide information corresponding to the sketch image; encoding, for each scale, a noise feature based on a sketch texture feature and the color guide information to obtain multi-scale image features; and performing multi-scale decoding on these image features to obtain a colored image comprising a sketch texture corresponding to the sketch image and a color based on the color guide information. A related training method and apparatus are also provided to develop models for this image processing technique.

The invention relates to the technical field of image segmentation, in particular to an automatic segmentation method and system for an echocardiogram of the department of cardiology, and the method comprises the following steps: based on image data of the echocardiogram, extracting gray level distribution, edge feature and texture feature information, analyzing gray level change amplitude, screening gray level change abnormal regions, and recognizing connectivity features. According to the method, the segmentation accuracy is effectively improved by extracting image gray, edge and texture features and identifying abnormal regions, noise and artifact interference are reduced by optimizing low connectivity regions, and the segmentation accuracy is improved. A problem area is analyzed and positioned in combination with multi-frame gray level change, a segmentation result is adjusted, the processing stability and consistency are enhanced, meanwhile, an optimized alarm node is output based on a frequency trend, more accurate and stable heart image analysis is supported, and the clinical application practicability is improved.

The invention discloses a lightweight rice leaf disease identification method based on YOLO target detection. The method comprises the following steps: S1, inputting a rice leaf disease image; s2, carrying out super-resolution reconstruction on the image by using an improved Real-ESRGAN algorithm; s3, inputting the picture into an improved YOLOv8n target detection algorithm; s4, obtaining disease spot category and position information; and S5, visualizing the information on the image. The invention relates to the technical field of target detection, and has the beneficial effects that image super-resolution reconstruction is carried out aiming at the problems that a rice leaf scab target is relatively small and an image acquired in real time is relatively fuzzy, so that the resolution of the small target is improved, and the definition and texture features are improved. On the basis of a super-division model Real-ESRGAN, a group of residual dense modules containing five layers of cavity convolution layers are designed to help the network to acquire receptive fields and information of different scales.

The invention provides an appearance defect detection method and device and application thereof, and belongs to the technical field of intelligent detection, and the method comprises the steps: carrying out the preliminary detection of a to-be-detected product based on the texture features of an initial detection image; performing background and foreground separation processing on the initial detection image to obtain an effective detection image; determining a defect doubt area in the effective detection image; and based on the gray values of all the pixel points in the defect doubt area, judging whether a gray abnormal defect exists or not so as to realize final detection of appearance defects of the to-be-detected product. According to the method, a three-section composite detection process of texture abnormity pre-detection, background separation and defect post-processing verification is carried out on the detection image, so that the problem of inaccurate detection caused by the structure and material characteristics of a product and environmental interference is effectively solved, and the accuracy and robustness of defect detection are remarkably improved.

The invention belongs to the technical field of image processing, and discloses a nonferrous metal target detection method and system for a scraped car. According to the method, through deep coupling of a GAN bimodal fusion network and a double-backbone network, a scraped car nonferrous metal target detection model is constructed, so that nonferrous metals in scraped cars are efficiently and accurately sorted. A bimodal fusion network is introduced into a model input layer, a fusion image with infrared thermal saliency and visible light texture features is generated, and the problem of cross-modal information splitting is solved; according to the dual-backbone network, standard convolution is replaced by a three-branch structure of an MGHCM module, large target contours, middle target semantics and small target details are synchronously captured, and self-adaptive shunting and efficient fusion of multi-scale features are achieved in cooperation with dynamic feature routing of a DHFBlock module; and the detection head network is optimized into a rotating frame prediction and cross-modal cross entropy classification mechanism so as to adapt to the placement scene of the metal fragments at any angle.

The invention discloses a quality detection system based on three-dimensional point cloud scanning and RGB image fusion, relates to the technical field of industrial automatic detection, and aims to solve the problem of high quality detection error rate caused by the fact that a detection result is easily interfered by visual angle change and illumination conditions in an existing detection method. According to the method, the color point cloud model under the unified coordinate system is constructed through space calibration and point pixel level registration, and the structural information and texture features of the workpiece are effectively reserved. Therefore, the problem that the quality detection error rate is high due to the fact that the detected object has curvature dramatic change, a strong reflection area or frequent shielding is solved. According to the method, the detection accuracy is improved, meanwhile, the robustness and the operation efficiency are high, the industrial workpiece detection requirements with complex curved surface structures and high surface quality requirements can be met, and the actual application requirements of an industrial site for an intelligent surface detection system are met.

The invention discloses a defogging method based on an endoscope image and an endoscope system.The method is applied to a processor of the endoscope system.The method comprises the steps that the endoscope image is obtained; extracting a texture feature, at least one color feature and a global contrast feature of the endoscope image; determining that fog exists in the endoscope image according to the texture feature, the at least one color feature and the global contrast feature; determining the fog concentration of the endoscope image; and according to the fog concentration, carrying out defogging processing on the endoscope image so as to obtain a defogged image. The accuracy of fog detection and fog removal can be improved, automatic fog monitoring and removal are achieved, and the automation degree of the endoscope is improved.

The invention relates to the technical field of image recognition, in particular to a burn scar hyperplasia risk assessment method combining semantic segmentation and texture feature analysis, which comprises the following steps: acquiring an image gray gradient, color jump and texture variance, calculating pixel mutation, extracting a mutation boundary, constructing a periodic direction field, and extracting a continuous offset region. According to the method, by extracting the pixel gray gradient, the color jump and the texture variance, calculating the boundary sudden change intensity and generating the layer, the structure change characteristics can be refined, the image anomaly perception precision can be enhanced, the semantic boundary can be identified based on the sudden change sequence, and the physical continuity is prevented from interfering the segmentation accuracy. A periodic evolution record is constructed through direction gradient, the dynamic trend of the structure is disclosed, an expansion area is locked in combination with direction continuous offset and change stability, a classification label is constructed through point location density, direction consistency and a gradient module value, and the interpretability and accuracy of risk identification are improved.

The invention relates to the technical field of image enhancement, in particular to a surface process defect detection method and system for copper foil production, and the method comprises the steps: collecting a copper foil image in a production process; obtaining each image block in the copper foil image; performing edge detection on the copper foil image to obtain each edge line in the copper foil image; determining a texture feature coefficient of each image block, constructing an illumination contrast coefficient of each image block, correcting a standard deviation parameter in a Gaussian filter, and performing image enhancement on each image block by using the Gaussian filter after parameter correction and a local Retinex algorithm; and carrying out surface defect detection on the copper foil through the enhanced copper foil image. Therefore, the precision of copper foil surface process defect detection is improved.

The invention relates to the technical field of image processing, in particular to a mobile phone screen quality inspection method and system based on a screen picture inspection partition, and the method comprises the following steps: detecting partitions divided based on the surface of a mobile phone screen, recognizing a sudden change interval, analyzing the consistency of a gray gradient direction and an edge fitting vector, screening abnormal partitions, and extracting color and texture features. And evaluating deviation from a screen flatness curve, screening the subarea of which the response coverage area needs to be adjusted, and outputting a repairing and adjusting instruction set. According to the method, the image detection area is divided, and the abnormality is screened in combination with the gray slope and the edge deviation abrupt change, so that the recognition accuracy and the response speed are improved, the gradient direction and edge vector consistency are fused to enhance junction defect judgment, and the complex abnormality linkage screening is realized in combination with the color and texture features. The risk level is quantified based on the deviation flatness trend, and the functional diagram is linked to realize repair response scheduling, so that the detection reliability and decision pertinence are improved.

The invention discloses a multi-degraded image recovery method based on frequency domain decomposition, and aims to solve the problems that a single model is difficult to deal with various image degradation and recovery processes of different frequency domains are mutually coupled in the prior art. According to the method, a degraded image is decomposed into a high-frequency space and a low-frequency space through fast Fourier transform, and a double-branch network architecture is adopted for targeted processing: for the high-frequency part, a high-frequency feature adaptive processing module HFPM is designed, and detail texture features are effectively extracted and interference is suppressed through feature enhancement and cross-layer fusion technologies; and for the low-frequency part, constructing a low-frequency feature conversion enhancement module LTEM, and capturing global context information by using cyclic convolution to improve the integrity of the structure contour. According to the method, decoupling processing of frequency domain features is realized, and the image restoration performance of the model in various degradation scenes such as rain removal, noise removal and defogging is remarkably improved through the synergistic effect of high-frequency detail enhancement and low-frequency structure optimization. Experimental results show that the method has excellent recovery effect and robustness when a plurality of image degradation tasks are processed at the same time, and can be effectively applied to visual tasks such as traffic accidents with high image quality requirements.

The invention mainly relates to the technical field of tunnel engineering, and provides a tunnel surrounding rock grading dynamic correction method based on image texture features in order to improve the accuracy and real-time performance of surrounding rock grading under complex geological conditions. An improved deep residual network model integrated with an attention mechanism is input and introduced, the model improves the precision of single-point surrounding rock grade identification based on key textures, an initial prediction value of the surrounding rock grade is obtained, and meanwhile a time sequence prediction model is introduced; the whole excavation process is regarded as a continuous geological sequence based on the multi-source surrounding rock feature vectors of the current excavation cycle and at least one previous historical cycle, a time sequence trend value of the surrounding rock grade is obtained, a self-adaptive weight is set, the surrounding rock grade initial prediction value and the time sequence trend value of the surrounding rock grade are smoothed and corrected, and the surrounding rock grade is obtained. Therefore, the accuracy and the stability of the surrounding rock grading result are improved.

The invention relates to the technical field of video water flow velocity detection, and discloses a river video velocity measurement method and device based on a multi-feature fusion PSA-ResNet network, and a medium. The method comprises the following steps: constructing an STIA data set; extracting low-level textural features of the space-time image by using a CLBP multi-feature extraction algorithm, performing channel-level fusion on a feature map and an original RGB image to form a six-channel CLBP-STIA feature map, and constructing a CLBP-STIA data set according to the six-channel CLBP-STIA feature map; fusing a pyramid segmentation attention module PSA in a residual block of the residual network ResNet to construct an angle classification model, and training the model by using a CLBP-STIA data set; generating enhanced feature representation of the to-be-detected space-time image by referring to the above mode, then inputting the enhanced feature representation to the trained classification model, and outputting a corresponding texture principal direction angle; and acquiring the actual length of the velocity measurement line in the video image, and calculating the actual flow velocity of the surface flow feature on the velocity measurement line in combination with the texture main direction angle. The texture main direction is accurately estimated, so that the speed measurement precision and efficiency are improved.

The invention provides a real-time monitoring method and system for milk powder stirring processing, and the method comprises the steps: collecting a current frame image of a milk powder material in a stirring container, and obtaining a reference frame image of the current frame image before a preset time interval; calculating a space texture feature set, a time sequence color texture feature set and a dynamic flow field feature set of the current frame image; cascading the space texture feature set, the time sequence color texture feature set and the dynamic flow field feature set to obtain a high-dimensional state vector; calculating a mahalanobis distance between the high-dimensional state vector and a target uniform state cluster core in a pre-constructed state space; when the mahalanobis distance is smaller than a first threshold value, it is judged that the current stirring state is uniform mixing; when the mahalanobis distance is greater than a second threshold value, determining an abnormal state; when the Mahalanobis distance is between the first threshold and the second threshold, it is determined that mixing is being performed.

The invention discloses a method for estimating plant biomass based on map multi-modal feature extraction and fusion. The method comprises the following steps: acquiring a plant RGB image and a multi-spectral image; inputting the preprocessed RGB image and NIR wave band image into a double-model cooperation segmentation framework to realize image segmentation; binary image features, color features, texture features, reflectivity and the like are calculated, feature splicing is carried out, and high-dimensional features are constructed; carrying out dimension reduction on the high-dimensional features; and training a deep neural network through the effective features and the biomass to realize biomass estimation. According to the method, a zero sample learning-based double-model cooperation segmentation framework is utilized to realize accurate segmentation of a single plant on the premise that a large number of training sets are not needed; multi-modal feature information is extracted based on the segmented single plant image, an improved SHAP model is introduced to reduce the feature space dimension, and the inversion precision and the operation efficiency are improved while the information effectiveness is ensured; through a high-precision deep neural network model, rapid, lossless and accurate biomass acquisition is realized.

The invention discloses a cloth surface flaw detection method and device based on texture perception and anomaly detection. The method comprises the following steps: acquiring a surface image of detected cloth; inputting the surface image of the detected cloth into a deep learning network model; a multi-scale feature map is extracted through the backbone network; processing the feature map through the texture perception feature extraction module so as to fuse cross-channel and cross-space texture information; integrating anomaly detection branches through the check network, and outputting feature maps of different scales; performing frequency domain enhancement and spatial domain feature extraction operation and fusion on the features through the adaptive frequency domain convolution module; and outputting a detection result through the YoloHead detection head so as to judge whether the cloth has flaws or not. According to the method, the texture feature information in the cloth image can be effectively utilized, and the detection accuracy and robustness of the cloth surface flaws and the recognition capability of unknown flaws are improved.

The invention provides an image real-time filter processing method based on edge calculation, and the method comprises the steps: obtaining the local texture feature distribution of an input image, calculating the gradient variation of each pixel point in the horizontal and vertical directions, and calculating a quantitative index value reflecting the image texture complexity based on the statistical distribution feature of the gradient variation; executing a parallelized filter effect rendering operation based on the optimized processing task allocation scheme, and applying corresponding filter parameters to each image block through a multi-thread cooperative processing mechanism to form filter rendering configuration information suitable for the performance of the current mobile device; and carrying out fusion reconstruction on brightness, saturation and hue component data after filter processing, generating a final filter processing image through a reverse conversion process from HSV to RGB, and recording parameter configuration information in the processing process at the same time for rapid processing of subsequent similar images.

The invention relates to the technical field of image recognition, in particular to a shearing behavior dynamic correction method and system based on wear state recognition. Acquiring a digital image sequence of the cutting edge area; constructing an image feature separation network, and performing parallel feature extraction on the preprocessed digital image sequence; identifying a pixel-level high-frequency texture discontinuous region and an edge gradient direction field by utilizing continuity characteristics of a cutting edge surface periodic texture mode to obtain a target defect probability graph; identifying a projection shadow area and a low-frequency illumination halation of the edge of the bulge by using a backlighting imaging model to obtain an interference artifact probability graph; establishing spatial mutual exclusion constraints of the target defect probability graph and the interference artifact probability graph in a pixel space, and generating a defect binary mask; performing multi-dimensional texture feature calculation on an area corresponding to the defect binary mask, and constructing a surface state feature vector; according to the invention, based on the surface state feature vector, the defect mode category is discriminated, and the corresponding shearing correction parameter is generated.

The invention belongs to the technical field of image processing, and particularly relates to a real-time image monitoring method and system for a yucca extract separation process, and the method comprises the steps: obtaining a separation process diagram, and obtaining illumination unevenness according to the brightness of pixel points; determining a self-adaptive filtering size according to the illumination unevenness, and performing enhancement processing on the image to obtain an enhanced image; obtaining a separation tendency index according to the local difference and the texture feature of the enhanced image; segmenting the image according to the separation tendency index to obtain a connected region, and obtaining a solid-phase aggregation degree according to the morphological characteristics of the connected region; and finally, judging a separation end point according to the change trend of the solid-phase aggregation degree and the regional area variance in the time window. According to the method, the problem of non-uniform illumination of an industrial site is solved through the adaptive enhancement algorithm, the separation end point is accurately judged in combination with morphological characteristics, and the monitoring accuracy and robustness are improved.

The invention relates to the technical field of image processing, in particular to an underwater crack segmentation-oriented color correction and texture sharpening double-branch enhancement system, which is characterized in that a data set is used for training, reasoning and analysis, and underwater crack images are stored in the data set; the system comprises an input unit used for receiving an underwater crack image; the color correction network is used for carrying out color correction on the underwater crack image and eliminating the problems of color deviation and low contrast of an underwater environment; the texture enhancement network is used for performing texture enhancement on the image after color correction; and the output unit is used for outputting the image enhanced by the texture enhancement network, and aims to recover the color and texture features of the underwater crack image through the color correction network and the texture enhancement network so as to enhance the perceptibility of the semantic segmentation model to the underwater crack.

The invention relates to the technical field of health assessment, in particular to a traditional Chinese medicine health assessment method and system based on intelligent analysis, and the method comprises the steps: carrying out the multispectral image collection of the face and tongue picture of a user, carrying out the color gradation quantification processing and texture feature extraction of the obtained image data, and obtaining a face color gamut feature matrix and a tongue picture texture feature vector; and based on the tongue picture-complexion associated features and a syndrome description text input by the user, performing intelligent classification pre-judgment on traditional Chinese medicine syndrome types to obtain an initial syndrome label set, and based on user physique data, correcting the initial syndrome label set to obtain a syndrome feature fusion vector. Through the health fluctuation index and the health trend early warning signal, the change of the health condition of the user can be dynamically evaluated, and a personalized health intervention scheme is generated on the basis, so that the fluctuation of the health condition can be reflected in real time, intervention measures can be taken in time, and potential health risks can be prevented.