595 results about "Image evaluation" patented technology

The invention discloses a mild traumatic brain injury image evaluation system based on deep learning, and relates to the technical field of medical imaging, and the system comprises a data source module which is used for obtaining head magnetic resonance imaging data as an input data source; the preprocessing module is used for carrying out de-noising processing and image registration on the skull magnetic resonance imaging data to realize standardized preprocessing; and the feature extraction module is used for extracting a local feature map from the multi-dimensional image data by constructing a multilayer convolutional neural network, and identifying a subcortical structure change and damage suspicious region. According to the mild traumatic brain injury image evaluation system based on deep learning provided by the invention, a standardized evaluation report automatically generated by the system not only integrates clinical information of a patient, but also provides detailed injury types, severity and follow-up visit suggestions, so that the efficiency and reliability of clinical decision making are greatly improved; and powerful support is provided for formulating an individualized diagnosis and treatment scheme.

The invention relates to the technical field of image detection, in particular to an inorganic mineral casting detection method based on image processing, and the method comprises the following steps: obtaining an image sequence, carrying out the gray scanning, extracting a connected region, screening a closed image block, generating a candidate, building an edge path, analyzing a position change, correcting a contour, and generating a track image; evaluating the direction distribution labeling consistency to generate a mask layer, extracting a feature judgment category to generate a labeling image, and analyzing an angle difference to identify an interruption calibration position to generate a labeling layer. According to the method, the integrity of target area screening is enhanced through gray abrupt change and connected path extraction in the image sequence, spatial offset interference is eliminated through boundary trajectory stability analysis, the area judgment consistency is improved, directional discrete areas are accurately recognized through standard deviation evaluation of the pixel gradient direction, and the attribution judgment accuracy is improved; according to boundary disturbance analysis, abnormal edge expression is refined, and the image sequence information coherence utilization and abnormal feature distinguishing capability is enhanced.

The subject invention pertains to an artificial intelligence-aided classification system for Alzheimer's disease (AD) screening from retinal photographs, which includes a three-in-one deep learning (DL)-based pre-diagnosis module for image assessment and a DL-based AD classification module with additional heatmaps for visualization. For the AD classification module, three kinds of DL models predict the AD-dementia / non-demented probabilities from three directions with both eyes' four images (Direction-1), both eyes' four images combing demographical information (Direction-2), and a single eye's two images (Direction-3), respectively. For the pre-diagnosis image assessment, embodiments provide one pre-processing model, with three additional models for each of the three classification tasks, including image-quality (e.g., gradable or ungradable), field-of-view (e.g., macula-centered or optic nerve head-centered), and laterality-of-the-eye (e.g., right or left eye). Also included is a cloud-based web application that can output the pre-diagnosis image assessment and a simple binary AD-dementia / non-demented classification based on retinal photographs.

The invention relates to the technical field of medical image processing, in particular to a multimodal-based digestive tract tumor lesion image segmentation method and segmentation system. The method comprises the following steps: acquiring an alimentary canal tumor lesion image, and extracting an alimentary canal tumor ultrasonic image; evaluating the tumor invasion depth based on the digestive tract tumor ultrasonic image; performing focus three-dimensional visual modeling according to the tumor invasion depth to obtain a digestive tract tumor focus model; extracting a tumor tissue pathological image according to the digestive tract tumor lesion image; performing tumor region segmentation based on the tumor tissue pathological image to obtain digestive tract tumor region data; performing tissue arrangement anomaly detection based on the digestive tract tumor region data to obtain tissue arrangement anomaly data; and calculating a tissue arrangement disorder index according to the tissue arrangement abnormal data and the digestive tract tumor area data. According to the invention, the tumor identification accuracy and the malignant region segmentation precision are improved based on the medical image processing technology.

The invention belongs to the technical field of image processing, and discloses a grape leaf AI grading processing method and system based on computer vision. Comprising the following steps: acquiring image data of grape leaves, and performing variety label marking and image optimization to obtain a clear leaf image data set; sequentially performing feature extraction and feature fusion on the clear leaf image data set to obtain fused disease condition features and fused growth condition features; constructing a leaf evaluation framework capable of being adjusted in real time based on the fused disease condition features and the fused growth condition features; performing image evaluation on the clear leaf image data set by using a leaf evaluation framework to obtain a disease degree evaluation index and a growth condition evaluation index, and performing disease degree grading and growth condition grading on the clear leaf image data set based on the disease degree evaluation index and the growth condition evaluation index; obtaining a grape leaf grading result; accurate grading of the grape leaves is achieved, and the industrial production efficiency is improved.

The invention relates to an image processing method for an underwater robot heterogeneous imager array, and the method comprises the steps: S1, collecting an original underwater image, and carrying out the brightness adjustment, and obtaining a first image with balanced brightness; s2, analyzing pixel distribution of the first image, and judging and executing de-scattering processing to obtain a second image; s3, performing multi-scale detail enhancement on the second image to obtain a third image with richer details; s4, evaluating color distribution of the third image, and performing color correction to obtain a fourth image; s5, performing adaptive denoising processing on the noise features of the fourth image to obtain a fifth image; s6, according to the contrast characteristic of the fifth image, contrast adjustment is carried out to ensure that a preset threshold value is met, and a sixth image is obtained; s7, edge enhancement processing is carried out on the sixth image, and a seventh image with the clear edge is obtained.By means of illumination compensation, scattering removal and multi-scale detail enhancement, the influence of complex optical conditions of the underwater environment on the image quality is effectively overcome.

The invention discloses a method and a device for generating a photographing point location of an inspection unmanned aerial vehicle. The method comprises the following steps: acquiring a real-time acquired image and real-time pose data of the unmanned aerial vehicle; based on a pre-constructed three-dimensional target detection model, performing three-dimensional target detection on the real-time acquired image of the unmanned aerial vehicle to obtain three-dimensional information of target inspection equipment in the real-time acquired image; based on the three-dimensional information of the target inspection equipment in the real-time acquired image, evaluating the real-time acquired image of the unmanned aerial vehicle, and giving an image evaluation result; and performing fusion analysis on the real-time pose data and the three-dimensional information in combination with an image evaluation result and a pre-constructed optimal point generation model to obtain an optimal inspection unmanned aerial vehicle photographing point. The three-dimensional information, the image evaluation result and the real-time pose data obtained by three-dimensional target detection of the real-time acquired image are fused, and image presentation and image information are considered at the same time, so that the image acquisition quality of the optimal inspection unmanned aerial vehicle photographing point location is improved.

Techniques are generally described for cropped image evaluation. In various examples, first image data representing a first image and second image data representing a cropped version of the first image may be received. An image captioning model may be used to generate first text data describing the first image data and second text data describing the second image data. A first encoder may be used to generate first data representing the first text data and second data representing the second text data. In various examples, a third data representing a degree of similarity between the first data and the second data may be generated. In some cases, first computer-executable instructions configured to cause the second image data to be displayed on a display may be generated based at least in part on the third data.

The invention provides an insulator pollution grade evaluation method based on multispectral analysis, and relates to the technical field of data analysis, and the insulator pollution grade evaluation process comprises the steps of precise alignment of a camera carried by an unmanned aerial vehicle body, multispectral camera image collection, image processing and image evaluation. The unmanned aerial vehicle body master control comprises a flight path control system, a flight attitude control system and an automatic navigation positioning calculation system, the unmanned aerial vehicle is used for carrying a multispectral camera, the multispectral camera is installed in the center of the unmanned aerial vehicle, and meanwhile a temperature detection probe and a time degree detection probe are installed on the two sides of the unmanned aerial vehicle respectively. The unmanned aerial vehicle can acquire the height image of the insulator in advance, then the unmanned aerial vehicle calculates the vertical horizontal difference from the unmanned aerial vehicle to the insulator through the automatic navigation positioning calculation system, and the unmanned aerial vehicle flies to the periphery of the insulator after accurately acquiring the position information of the insulator, so that manual information acquisition is replaced.

The invention provides a Ru porcelain image generation method fusing a Ru porcelain knowledge graph and fine tuning control, and the method comprises the steps: constructing the Ru porcelain knowledge graph, and generating prompt words based on the Ru porcelain knowledge graph through employing a large language model; constructing an improved Ru porcelain image generation model, and training the improved Ru porcelain image generation model by using the Ru porcelain image data set; and the trained improved Ru porcelain image generation model generates a Ru porcelain image according to the cue word, and a multi-mode large model Janus is adopted to evaluate the Ru porcelain image generated by the improved model. According to the method, a lightweight model suitable for Ru porcelain image generation is constructed in combination with LoRA and ControlNet; the LoRA module is used for adjusting parameters of a cross attention layer and finely reproducing texture and glazing color characteristics of Ru porcelain; the ControlNet module is used for controlling the structure of the device type and the posture to ensure that the image form is accurate; and introducing a multi-modal large model to carry out image evaluation on the generated image, and optimizing the cue word according to an evaluation result.

The invention discloses a general image evaluation method based on a hybrid expert model, which can be applied to various image evaluation scenes to automatically evaluate the quality or aesthetics of images. According to the method, a visual encoder and a text encoder are used for extracting visual and text features of an image respectively, and a quality or aesthetic score is calculated through an adapter. The visual encoder and the text encoder are integrated with a hybrid evaluation expert module, the hybrid evaluation expert module combines a shared evaluation expert and a self-adaptive evaluation expert, the shared evaluation expert is responsible for learning general evaluation knowledge, the self-adaptive evaluation expert dynamically learns specific data sets, expert contribution is adjusted through a router, and labeling deviation in multi-data-set training is effectively managed. Furthermore, a scene-based difference prompt strategy is implemented, the model adaptability is enhanced through a scene specific prompt word, the annotation deviation between data sets is reduced, and the evaluation accuracy is improved. The method not only improves generalization and adaptability of the model, but also optimizes parameter efficiency and training efficiency, and has a wide application prospect.

The invention relates to the technical field of multi-modal fusion, in particular to a sperm quality assessment method and system based on multi-modal fusion, and the method comprises the following steps: obtaining image and parameter data to generate a space-time alignment set, extracting trajectory calculation linearity, analyzing parameter fluctuation to mark an abnormal time window, extracting an image assessment deformity rate to generate a feature matrix, and grading to generate a multi-modal sperm quality evaluation result. According to the method, linear calculation is introduced in the trajectory path extraction process, spatial distribution thermodynamic mapping is combined, the dynamic characteristics of local sperm populations are dominated, an abnormal time window is constructed based on the parameter value change rate and a set threshold value, and motion consistency indexes are combined, so that the dynamic characteristics of the local sperm populations are optimized. The explanation depth of sperm head malformation data in quality judgment is enhanced, the dimension space of sperm quality description is effectively expanded, the consistency ratio between vitality and morphology is introduced, track continuous characteristics are combined, a hierarchical grading system is established, and the accuracy of sperm quality evaluation results is ensured.

Disclosed herein are system, apparatus, device, method and / or computer program product embodiments for determining, in a remote deposit system, whether a deposit attempt is illegitimate (e.g. fraudulent). Whether the deposit attempt is illegitimate may be assessed based on one or more of the following processes: comparing location data to a location parameter determined from past deposits, comparing an image capture location with a deposit location, and analyzing image-of-image characteristics obtained through image processing to identify whether an image associated with the deposit attempt is an image of an image. In some embodiments, a remote deposit status related to acceptance of the deposit attempt may be provided in real-time

The invention provides a cloud-edge collaborative intelligent tool magazine tool life prediction method and system, and relates to the field of fault prediction and health management.The method comprises the steps that in a cloud data center, a current tool health index is obtained according to workpiece machining quality information and tool surface image evaluation; according to the current cutter health index, a cutter damage monitoring and early warning mechanism is configured and sent to an edge processing unit; according to the adaptive monitoring frequency, a sensor is controlled to conduct data monitoring, multi-source monitoring data are judged according to the adaptive early warning indexes, if not triggered, dynamic updating of a tool damage monitoring and early warning mechanism is conducted in an iteration mode, and if triggered, tool damage early warning is conducted. The invention aims to solve the technical problem that the accuracy and the reliability of tool damage early warning are insufficient due to the fact that a traditional early warning mechanism is difficult to adapt to the dynamic change of a tool machining state, and can improve the adaptation degree of a monitoring early warning mechanism and the tool machining state and remarkably improve the accuracy and the reliability of early warning by introducing a dynamic monitoring early warning mechanism.

The invention discloses a visual obstetrical image examination processing method, which belongs to the technical field of obstetrical image examination processing, and comprises an image acquisition module, an image processing module, a cyclic influence evaluation module and an output and diagnosis module, the image acquisition module is utilized to acquire and preliminarily process a current obstetrical image, and the current obstetrical image is transmitted to the image processing module; an image processing module is utilized to sequentially calculate and output an image quality evaluation index V, a processing effect enhancement index CZ and a cyclic influence evaluation index CF, and based on the result of the cyclic influence evaluation index CF, the cyclic influence evaluation module is utilized to trigger adjustment of an image processing strategy according to the result of the processing effect enhancement index CZ. According to the method, the image quality and the diagnosis accuracy are improved by comprehensively and quantitatively evaluating the image quality, adjusting the processing method in a personalized manner and introducing a cyclic influence mechanism, so that richer diagnosis information and more accurate decision support are provided.

The invention discloses a laser powder bed melting additive manufacturing buckling deformation intelligent monitoring and autonomous control system and method, the system comprises a powder platform, a powder laying mechanism, a first high-definition camera, a second high-definition camera and a user side remote monitoring module, the first high-definition camera is used for shooting block surface images; the second high-definition camera is used for shooting an image of the current powder layer; the user side remote monitoring module judges whether the surface warping degree of each formed block exceeds a preset range or not according to the received block surface image, and controls the printing working condition of the corresponding formed block according to the judgment result; and the user side remote monitoring module evaluates the powder laying quality of the current powder layer according to the received image of the current powder layer and determines the powder supply amount of the powder cylinder according to the evaluation result. Therefore, the influence of the warped block on the powder laying quality of the surrounding block can be automatically monitored, then the powder supply amount is adjusted in a self-adaptive mode, and the influence of the warped block on the forming quality of the surrounding block is reduced.

The invention provides a self-adaptive accurate rust removal method and system based on unmanned aerial vehicle visual feedback, and relates to the technical field of large metal structure surface maintenance and anticorrosion treatment.The rust removal method comprises the steps that a global path is planned, and a global flight waypoint sequence is generated; a local image of the current to-be-derusted area is collected; the corrosion characteristics of the current to-be-derusted area are analyzed, and initial derusting parameters and initial derusting time are set; after each derusting operation is finished, a frame of intermediate result image is collected; based on the difference between the intermediate result image and the historical image, performing quantitative evaluation on the effect of the previous rust removal operation, and performing feature analysis on the residual rust region; self-adaptively adjusting and generating rust removal parameters of the next stage; and the steps of derusting operation, image collection, evaluation and parameter adjustment are repeatedly executed, and after the termination condition is met, the unmanned aerial vehicle is switched to the next to-be-derusted area.

The invention provides an image processing method, a training method of an image evaluation model and electronic equipment, and relates to the field of computer technology and artificial intelligence. The method comprises the steps that image data and inquiry text data are acquired, the image data are generated by an image generation model, and the inquiry text data are used for describing the requirement for quality evaluation of the image data by adopting a natural language; respectively inputting the image data and the inquiry text data into a plurality of image evaluation models, and respectively carrying out quality evaluation on the image data from corresponding evaluation dimensions by utilizing the plurality of image evaluation models to obtain a plurality of evaluation results; and integrating the plurality of evaluation results to obtain a quality evaluation result of the image data. According to the method and the device, the technical problems of relatively low accuracy and meticulousness of image quality evaluation in related technologies are solved.

The invention relates to the technical field of image data processing, in particular to a license plate recognition method and system in a strong backlight environment, and the method comprises the steps: carrying out the graying of a collected image, and obtaining a gray backlight image; extracting a grey level histogram of each image, determining a maximum value point, evaluating a peak value attribute through a grey level difference and a pixel quantity of the maximum value point, and further determining a grey level separation point; performing threshold segmentation on the image, extracting foreground and background regions, calculating a reference value index, and screening out high-quality foreground and background images through threshold judgment; sorting the high-quality images to obtain a retention index, and performing threshold judgment again to determine foreground and background images which are preferentially retained; and the license plates are arranged and combined to form a spliced image, the backlight degree and the edge features of the spliced image are evaluated, finally, the adaptation degree is obtained through sorting, license plate character codes are extracted, a complete license plate number is formed, and license plate recognition under strong backlight is achieved. The invention aims to improve the accuracy of license plate recognition in a backlight environment.

The invention discloses a degraded image adversarial learning-based simulation embroidery design image reality evaluation method, and belongs to the technical field of digital printing. The method comprises the following steps: constructing an imitation embroidery design image adversarial learning evaluation model, wherein the model comprises a multi-scale degradation algorithm module, an embroidery design image generation sub-model and an embroidery design image evaluation sub-model; through image degradation, image reconstruction and region-by-region evaluation processes, design reality evaluation of different regions of the embroidery-imitating design image is realized in combination with an adversarial technology.

The embodiment of the invention provides a scanning electron microscope resolving power evaluation method, computer equipment and a storage medium. The method comprises the following steps: acquiring a scanning image of a to-be-evaluated scanning electron microscope on a sample; performing image enhancement processing on the scanning image by using an image enhancement model to obtain an enhanced scanning image; and evaluating the resolving power of the scanning electron microscope to be evaluated through the enhanced scanning image. According to the method, on one hand, a sample is adopted as a scanning object of the scanning electron microscope to be evaluated, and the accuracy of an evaluation result is improved by utilizing the material property of a mesoporous molecular sieve, and on the other hand, the resolution of the scanning electron microscope to be evaluated is evaluated through an enhanced scanning image. Compared with the scanning image before enhancement, the enhanced scanning image has higher image quality, so that the accuracy of the evaluation result can be obviously improved by evaluating the resolving power of the scanning electron microscope to be evaluated through the enhanced scanning image.

The invention provides an unmanned aerial vehicle inspection image evaluation method based on multiple environments, and relates to the technical field of electric power circuit unmanned aerial vehicle inspection, and the method comprises the steps: collecting electric power circuit inspection images in different environments and weathers through an unmanned aerial vehicle, constructing a multi-dimensional standard database and a lightweight dual-classification model of the inspection images, and carrying out the multi-dimensional standard database and the lightweight dual-classification model; then training and adapting the lightweight dual-classification model through the collected electric power line inspection image, performing efficient and rapid quality evaluation on the input inspection image according to an adaptive image evaluation mechanism, and performing fine adjustment and iterative optimization on the lightweight dual-classification model by using a closed-loop optimization mechanism to realize dynamic updating and evolution of a threshold value, so as to improve the detection accuracy of the inspection image. The inspection images acquired by the unmanned aerial vehicle can be quickly, intelligently and automatically screened, high-quality image data is provided for subsequent defect detection and fault diagnosis of a power line, and the problems of poor environmental adaptability, high manual dependence and insufficient robustness of complex scenes in quality evaluation of the inspection images of the unmanned aerial vehicle are solved.

The invention discloses a photovoltaic panel surface pollution degree image evaluation system. The system comprises an image acquisition module, an image processing module, a multi-task evaluation module and a pollution degree determination module. Multi-spectral image data of the surface of a photovoltaic panel are collected, after radiation calibration and atmospheric correction processing, spectral reflectivity features and spatial texture features are extracted and fused, a multi-task deep learning model is input, a pollution type classification result and a power generation efficiency loss weight of each pixel region are output, and a pollution type classification result of each pixel region is obtained. And finally, generating a pollution type spatial distribution diagram, calculating the overall power generation efficiency loss percentage, and determining the pollution degree grade. According to the method, the problem that the mixed pollution type cannot be distinguished and the differentiation influence cannot be evaluated in the prior art is solved, accurate quantitative evaluation of the pollution degree is realized, and a reliable basis is provided for fine operation and maintenance of a photovoltaic power station.

A method for improved surroundings detection utilizing an optical sensor of a vehicle. The method includes detecting the surroundings of the vehicle and compiling a first surroundings model based on sensor data of the surroundings detecting sensor. The method also includes determining regions having low quality in the surroundings model by evaluating optical sensor data with an image evaluation device. The detectability of the regions having low quality are improved by taking a selected measure. The method also includes detecting the surroundings of the vehicle again with the optical sensor and comparing the sensor data of the optical sensor after taking the measure with the sensor data of the optical sensor prior to taking the measure. The sensor data following the measure is fused with the already existing sensor data in order to compile a second surroundings model.

The invention relates to the technical field of medical images, in particular to an intelligent medical image evaluation system, which comprises a data acquisition module used for acquiring a body position image sequence of a patient, imaging equipment operation data and equipment operation environment data, and acquiring body thickness data of the patient at the same time; the image registration module is used for carrying out image registration on the patient body position image sequence; the body position shaking evaluation module is used for evaluating the body position shaking degree of the patient; the scanning range evaluation module is used for evaluating the matching degree of the scanning range; the equipment operation evaluation module is used for evaluating the operation stability of the imaging equipment; and the imaging quality pre-estimation module is used for analyzing the body position shaking degree, the scanning range matching degree and the imaging equipment operation stability and pre-estimating the medical image imaging quality. According to the method, the accuracy and reliability of medical image quality evaluation are improved by quantitatively analyzing the body position shaking condition of the patient and the operation state of the imaging equipment.

The invention relates to the technical field of image recognition, in particular to a medical image intelligent evaluation system based on image recognition. The system comprises an image registration module, an image segmentation module, a preliminary fusion module, an image evaluation module, an optimization feedback module and an image output module. According to the method, the CT image and the MRI image are subjected to image registration, spatial alignment is ensured, then the region of interest is segmented and fused, namely, the skeleton contour in the CT image is superposed on the MRI image, and due to the fact that motion artifacts generated by movement of a patient in the scanning process possibly exist in the original CT image, the skeleton contour in the CT image is fused with the motion artifacts in the MRI image. If the skeleton contour does not exist in the MRI image, the overlapping degree and the blank degree of the skeleton contour and the anatomical structure edge of the MRI image are analyzed, and an optimized registration parameter or segmentation parameter is fed back, so that when the segmentation network is trained, the segmentation precision under the conditions of artifacts and low contrast is improved, spectrum and texture information of the two images is reserved to the maximum extent, and the fusion effect is guaranteed.

The invention discloses an ultrasonic image evaluation processing method and device for meibomian gland dysfunction, and relates to the technical field of data processing. A meibomian gland dysfunction disease classification model is constructed based on a lightweight first-order index integral method decoder module and a second-order explicit Adams method decoder module; and the identification capability of the meibomian gland classification model on a high-noise region and a small target region in the ultrasonic image is improved, so that the meibomian gland in the ultrasonic image can be subjected to high-precision classification evaluation processing by the meibomian gland dysfunction disease classification model. Due to the adoption of the lightweight decoder module, the model parameters of the meibomian gland dysfunction classification model are reduced, and the scale is lighter, so that the occupation of computing resources during the operation of the meibomian gland dysfunction classification model is reduced. The encoder-decoder model is trained after the unlabeled data set is pre-trained based on the fine tuning data set, so that self-supervised training of the meibomian gland dysfunction disease classification model is realized, and the training cost and time are reduced.

The invention relates to the technical field of image processing, discloses an image meaning analysis scene consistency evaluation system based on a visual model, and aims to solve the problem of insufficient recognition stability in complex environments such as illumination variation, angle deviation and local shielding in the prior art. The system comprises an image input module used for receiving and preprocessing a plurality of images; the visual large model analysis module is used for carrying out multi-dimensional semantic feature extraction on the preprocessed image; the structured description generation module is used for converting the semantic features into structured text description in a unified format; the scene consistency evaluation module is used for performing logic consistency analysis on the structured text descriptions of the plurality of images; and the result output module is used for generating and outputting a final consistency evaluation report. According to the technical scheme, the method can effectively improve the recognition stability of the system in a complex environment, achieves the multi-dimensional semantic understanding of the image content, and remarkably reduces the misjudgment rate of multi-view image evaluation.

The invention discloses a simulation medical visualization system, method and equipment based on mixed reality, and belongs to the technical field of medical testing. The method comprises the following steps: establishing an internal organ structure of a virtual patient according to size information of a simulated person, positioning and overlapping internal organs of the virtual patient and internal organs of the simulated person, and establishing data intercommunication between the virtual patient and the simulated person; transmitting the cardiopulmonary compression data to a computer host, visually displaying the cardiopulmonary compression data, and synchronously transmitting the cardiopulmonary compression data to a control end and a display end; processing the modeling image data to obtain a modeling image evaluation value, processing the modeling performance data to obtain a modeling performance index, performing comprehensive analysis to obtain a modeling state evaluation value, and performing matching according to the modeling state evaluation value to obtain an allowable deviation cardiopulmonary compression quality evaluation value; analyzing according to the cardiopulmonary compression data to obtain a cardiopulmonary compression quality evaluation value, and appointing training content according to the cardiopulmonary compression quality evaluation value.

A method for evaluating plaque load based on OCT images is characterized in that fitting is carried out through the far end and the near end of a blood vessel lumen of the OCT images, a reference lumen at a plaque is fitted out, and then a middle membrane area and a plaque area can be calculated, so that the plaque load condition is evaluated; the imaging length of the intravascular OCT is usually 50-80 mm, the intravascular OCT generally comprises a far end, a lesion part and a near end, and the image of the near end is close to a coronary artery opening and has a larger diameter; the distal end is far away from the coronary opening, the diameter is relatively small, and the diameter of the healthy blood vessel from the proximal end to the distal end generally decreases linearly; when a blood vessel has plaque, the space-occupying effect causes the diameter of the lumen of the blood vessel at the plaque to become smaller. The invention can acquire the real plaque load through the conventional OCT image, provides basis for diagnosis and is beneficial to expanding the scope and quality of OCT diagnosis.