11519 results about "Imaging Feature" patented technology

A Convolutional Neural network (CNNs) based system and method based on the same for image analysis including for computer aided detection / diagnosis is provided, enabling an efficient and accurate delineation of diseased volumes in images / medical images. More particularly, the system of the present invention is adapted to incorporate deformable convolution (DC) module based processor means, along the down- and up-sampling pathways of the underlying U-Net architectural framework of said system for effective understanding and attending to deformable geometry of unknown transformations, considering deformable convolution (DC), unlike the basic convolution is advantageously not constrained by predefined geometrical structures of the kernels. Said system of the present invention advantageously also permits weighted combination of image feature components along the encoder and decoder arms of the deformable convolution (DC) processor module based system architectural network due to introduction of Weight Generation (WG) modules in said system, such that dynamic assignment of importance to relevant spatial locations of the corresponding image feature maps could be given so as to also boost overall accuracy while ensembling to enable reduction in module analytic error by simultaneously maintaining the generalization in performance in respect of robustness to noise to attain accurate and faster demarcation of the ROI (region of interest). Added to the aforesaid, preferred integration of Focal Asymmetric Similarity (F AS) loss function analytic based processor module / means in said system, allowed effective handling of class imbalance for further improved performance.

Disclosed in the embodiments of the present application are a defect detection method and apparatus, an electronic device, a storage medium and a program product. The method comprises: acquiring an image under test of a target workpiece and a template image of the target workpiece, and comparing the image under test with the template image so as to obtain a differential image; inputting the image under test and the differential image into a defect detection model, and executing the following processing: on the basis of the image under test and the differential image, extracting a target image feature and, on the basis of the target image feature, performing defect detection on the image under test to obtain defect detection box information of the image under test; positioning a defect area in the differential image and, according to the defect area, determining a first detection result of the target workpiece; and, according to the defect detection box information and the first detection result, determining a second detection result of the target workpiece.

The invention discloses a multi-modal medical image data intelligent processing system, relates to the field of medical image analysis, and is applied to multi-modal medical image whole-process analysis of CT, MRI, PET, ultrasound and the like. According to the system, different modal image features are extracted and fused through a cross-modal manifold fusion network; a semantic guidance dynamic registration engine optimizes registration parameters to ensure that the registration error is less than or equal to 1.5 mm; the multi-task collaborative diagnosis network realizes multiple tasks such as disease classification; the clinical knowledge embedding and interpretable module generates a structured report and is in butt joint with an HIS system. Meanwhile, the model is optimized through a federated learning architecture, the adaptability of newly added data is improved by more than or equal to 20%, and intelligent processing and analysis of multi-modal medical images are realized.

The present disclosure relates to the technical field of artificial intelligence, and provides a multimodal large language model training method, a correlation calculation method, and a label generation method. The multimodal large language model training method comprises: on the basis of a sample text feature vector, a sample image feature vector, and a sample first multimodal feature vector which are obtained by processing image information and text description information of a sample commodity by a pre-trained multimodal large language model, training the pre-trained multimodal large language model to obtain a multimodal large language model; and processing a sample search word, the image information and the text description information on the basis of the multimodal large language model to obtain a sample search word feature vector, a first multimodal feature vector and a sample second multimodal feature vector, and training the multimodal large language model to obtain a trained multimodal large language model. The trained multimodal large language model of the present disclosure can simultaneously learn image features related to the search word and the text description information, and thus the generated second multimodal feature vector is more accurate.

The invention relates to the technical field of surface quality detection, in particular to a PVC floor surface coating defect detection method and system, and the method comprises the steps: carrying out region division on a to-be-detected coating surface, obtaining a region scanning image, and carrying out image optical compensation on the region scanning image; analyzing high-dimensional image features of each area scanning image, performing difference analysis on the high-dimensional image features and reference features, and selecting a surface defect area; a high-dimensional defect feature analysis model is established, defect positioning information and defect geometric parameters are analyzed and generated, and the defect type of the surface defect area is judged according to the defect positioning information and the defect geometric parameters; and according to the defect positioning information and the defect geometric parameters, performing quantitative evaluation on the sensitivity weight of the surface performance of the to-be-detected coating in combination with defects, and outputting an overall surface performance influence analysis result. According to the invention, the problem that the capability of identifying small-size and complex-shape defects is insufficient is effectively solved.

The invention provides an access control system based on face recognition and a recognition method, and the system comprises a multi-modal image collection subsystem which integrates a visible light camera, a depth camera and an infrared thermal imaging camera and is used for synchronously collecting a visible light image, a depth image and an infrared thermal imaging image of a face; the image preprocessing unit is used for carrying out corresponding preprocessing on the image; the feature extraction network module is used for extracting image features; the multi-modal feature fusion and alignment module is used for calculating a mutual information matrix by adopting a feature fusion strategy based on mutual information maximization and adaptive weighting, determining an adaptive weight and performing weighted summation, and performing feature alignment by applying a method based on canonical correlation analysis; and the face recognition classification and verification module verifies the image by adopting a multi-level fusion strategy. According to the method, the false recognition rate and the rejection rate are effectively reduced, the strong anti-cheating capability is achieved, and cheating attacks of various fake face means can be accurately recognized and resisted.

The invention relates to a multi-modal digital employee reception system and method based on edge calculation, and relates to the technical field of artificial intelligence, and the system comprises a multi-modal data collection module, an edge calculation module, a data fusion and decision module, a trigger activation module, an emotion recognition module, an interaction response module and a behavior prediction module. The method comprises the following steps: acquiring voice, image and action information of a user through a high-resolution camera, a microphone array and an infrared sensor; locally processing data by using an edge calculation module, and extracting voice, vision and thermal imaging features; a Transformer model is dynamically optimized through Test Time Training (TTT), multi-modal data are dynamically fused in combination with a multi-head attention mechanism, a comprehensive decision vector is generated, the system can accurately judge the emotional state of a user, user behaviors are predicted based on a fused Token sequence and historical records, personalized services are actively provided, and by dynamically adjusting a trigger threshold value and a weight distribution mechanism, the emotion state of the user can be accurately judged. The false triggering rate and the missed triggering rate are reduced, and the stability and the robustness of the system are enhanced.

The invention provides a robust multi-modal image segmentation method and system based on instance perception query, and the method comprises the steps: obtaining original image data and text description data, and generating training data through semantic analysis and causal graph construction; extracting image features and text features by adopting a multi-layer coding network, and establishing a feature dependency relationship through causal reasoning; generating instance clustering features based on attention calculation, and obtaining fusion features in combination with a multi-scale fusion strategy; and calculating query confidence and generating candidate segmentation masks, and outputting a final segmentation result through post-processing optimization. According to the method, by introducing causal reasoning and instance awareness query mechanisms, the cross-modal feature alignment precision and the robustness of the segmentation result are improved, and the method has better segmentation performance and generalization ability in a complex scene.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention relates to an optical film detection method, device and equipment based on image processing. The method comprises the following steps: collecting multiple frames of images of a multilayer optical film under different illumination conditions, and carrying out preprocessing and characteristic value calculation on the multiple frames of images to obtain a characteristic value response matrix; calculating an optimal weight coefficient of each frame of image according to the feature value response matrix, and performing multi-frame image feature fusion to obtain integrated feature representation; performing multi-scale defect feature extraction on the integrated feature representation to obtain a defect candidate region; spectral response characteristics are extracted based on the defect candidate area, matching degree calculation is carried out on the spectral response characteristics and a preset wavelength-film thickness mapping relation matrix, and a multilayer film defect distribution diagram is obtained; and performing autoregression prediction and processing control parameter optimization on the multilayer film defect distribution diagram to obtain a closed-loop control scheme. According to the invention, accurate identification and classification of different film layer defects are realized, and closed-loop control of defect detection results and manufacturing parameter optimization is further realized.

The invention relates to the technical field of intelligent vending, solves the problem that in the prior art, commodity identification cannot be accurately carried out in a multi-target scene, and provides a multi-target commodity identification method, device and system based on multi-modal data processing. The method comprises the following steps: acquiring multiple frames of real-time images in a commodity transaction scene; performing preprocessing and label information extraction on the real-time image, and determining character information corresponding to the target image and the commodity label; performing instance segmentation on the target image, and determining commodity position information; performing feature extraction on the target image, and determining commodity image feature information; according to pre-collected multi-source privatized data in an intelligent vending scene, performing fine adjustment and optimization processing on the open-source multi-modal visual language model to obtain a multi-modal large model; and inputting the commodity image feature information and the text information into the multi-modal large model for information fusion, and determining a commodity target identification result. According to the invention, commodity identification can be accurately carried out in a multi-target scene.

The invention relates to a multi-modal named entity recognition method based on cross-modal guide interactive fusion, which comprises the following steps: constructing a data set, designing a cross-modal contrast aggregation mechanism, respectively extracting image features and text features, and constructing a contrast learning mechanism to screen out image features with high association degree with text semantics for dynamic aggregation; a DINO model is introduced to extract image features, a dynamic similarity matching method is constructed, a dynamic similarity matching weight is generated based on text features and an image feature correlation matrix, a dynamic gating mechanism is utilized to adaptively select image features related to text feature context, and a cross-modal fusion and guide interaction strategy is constructed. Outputting an enhanced semantic representation vector, and mapping the semantic representation vector after multi-modal fusion into a final entity tag sequence by adopting a conditional random field decoder to complete entity recognition; the method has the advantage of remarkably improving the robustness and accuracy of multi-modal entity recognition.

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 discloses a self-prompting semantic segmentation method and device based on SAM and a storage medium, and belongs to the technical field of image segmentation. The method comprises the following steps: acquiring a target image containing a target object; inputting the target image into a semantic segmentation model based on SAM to obtain a segmentation result of the target image; the segmentation result comprises the position and the type of the defect of the target object; wherein the semantic segmentation model comprises an image encoder, a target detector, a prompt encoder and a mask decoder; the target detector is used for positioning the position area of the defect of the target object on the target image according to the image features output by the image encoder, and outputting a defect detection frame; the prompt encoder is used for encoding the defect detection frame and generating position prompt characteristics; and the mask decoder is used for fusing the image features and the position prompt features and generating a segmentation result of the target image. According to the invention, end-to-end industrial defect detection can be realized, and the efficiency of industrial defect detection is improved.

The invention relates to the technical field of data processing, and discloses a preoperative risk comprehensive assessment method and system for breast cancer and a storage medium. The method comprises the following steps: collecting clinical information and carrying out digital processing; segmenting the multi-modal image by using deep learning; analyzing a pathological section based on the image features; carrying out multi-level fusion modeling to obtain a risk heat map; performing digital twinborn simulation in combination with a knowledge graph; and dynamically processing the follow-up data to form a risk adjustment mechanism. According to the method, accurate quantification and individualized treatment decision making of the preoperative risk of the breast cancer patient are realized, and the prediction accuracy is improved.

The invention relates to the technical field of image analysis, in particular to a heat exchanger welding quality visual inspection method and system, and the method comprises the following steps: collecting original image data from a welding image, carrying out the preprocessing of the original image data, including noise removal and brightness adjustment, and obtaining a preprocessed image; according to the method, in the whole process from welding image data collection to defect recognition result generation, preprocessing operation based on high-frequency noise pixel filtering and gray value frequency adjustment is carried out on original image data, interference information in an image is effectively reduced, and the reliability of the image data is improved; meanwhile, continuous iteration deconvolution operation and pixel detail optimization are carried out on the basis of preprocessing, the distinguishing precision and texture detail discernibility of the welding image are improved, the discernibility of the welding defect under different scales is enhanced through multi-scale-level image feature decomposition, and the precision of image feature data is comprehensively improved.

A system includes a plurality of satellites orbiting around a celestial body in a plurality of orbital planes. Each satellite includes an imaging device having a field of view (FOV) to capture an image of a sky that includes celestial image features of resident space object (RSO), stars, and / or planets. The satellite processor is configured to input the image data for each image into a pre-processing software pipeline to generate for each image a contrast-enhanced image data replica of each image, and an enhanced image data replica of each image, to receive from an output of a known-unknown RSO split data processing pipeline, a determination that a candidate RSO is a known RSO stored in an RSO catalog, or an unknown RSO, and to assign to the candidate RSO based on the determination, an RSO ID of the known RSO listed in the RSO catalog, or a new RSO ID.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention discloses a circuit board surface defect detection method and system, and the method comprises the following steps: collecting a circuit board surface image, carrying out the preprocessing of the image, generating a defect candidate region, combining a deep learning model, and employing a target detection network and a channel attention mechanism to extract image features, potential defect areas are positioned and classified, a multi-scale feature fusion technology is adopted, optimization is carried out by combining image features of different scales, a defect evolution trend is predicted by utilizing time sequence dynamic analysis, and a defect detection strategy is dynamically adjusted according to a prediction result. The system provided by the invention adopts a multi-module cooperation mode, has relatively high flexibility and self-adaptive capability, and can realize efficient and accurate detection of tiny defects on the surface of the circuit board.

The invention discloses a super-resolution image reconstruction method, system and device based on dynamic frequency domain adaptive coding and contrast constraint optimization, and a medium. The method comprises the following steps: creating an original training data set; image data features and application scenes are considered, and feature extraction is performed on image data by adopting a modular design method; in combination with discrete cosine transform and inverse transform thereof, adaptive transform coding is carried out on different frequency components in the image features through a dynamic weight adjustment mechanism; the U-Net and Transform architectures are combined, isotropic design is optimized, a diffusion model and a feature fusion and reconstruction technology are applied, and diffusion process reconstruction is achieved; by introducing a contrast loss function, contrast learning is carried out between images with different resolutions, and the feature learning ability is enhanced. The image reconstruction method is more intelligent and efficient, and can effectively deal with frequency feature recovery and detail reconstruction problems in a complex image scene.

The invention relates to the technical field of computer vision detection, in particular to an unmanned aerial vehicle target detection method based on frequency-space joint attention and dynamic fusion, and the method comprises the steps: obtaining an unmanned aerial vehicle image data set, carrying out the preprocessing, and dividing a training set and a test set; constructing a target detection model, inputting the training set into the target detection model to extract image features, sequentially performing frequency domain detail enhancement, spatial domain salient region extraction and multi-scale feature adaptive fusion based on the image features, and establishing a feature sequence; screening the feature sequence to obtain an initial target query, and finishing target classification and positioning on the initial target query through a decoder; training a target detection model by using the training set, and inputting the test set into the trained target detection model to generate a detection result; on the premise that the real-time reasoning advantage of RT-DETR is kept as much as possible, the problems that in an unmanned aerial vehicle scene, a target is prone to missing detection, the scale change is large, the background is complex, and the target is fuzzy are effectively solved, and the detection precision is improved.

The invention discloses a two-stage low-illumination image enhancement method based on wavelet transform. According to the method, a two-stage U-shaped low-illumination image enhancement network is utilized, and deep extraction of image features and integration of multi-scale information are carried out on a low-illumination image; inputting the image features into a wavelet domain feature fusion and enhancement module, capturing image features of different scales on a wavelet domain by using discrete wavelets, and enhancing important features by using dynamic gating space attention and lightweight fusion curve attention; a fusion perception function and a lightweight edge loss function are constructed, the fusion perception function considers the pixel-level error and the overall perception quality of the image, and the model is guided to generate a more natural and highly realistic image by combining the pixel loss and the characteristics of the perception network; the lightweight edge loss function focuses on a high-frequency texture structure, and the reconstruction capability of the model on details is further optimized.

The invention relates to the technical field of mode recognition, in particular to a transformer substation equipment temperature measurement method and system based on visible light guidance. Firstly, a visible light image, an infrared thermogram and vibration spectrum data are synchronously collected, a dynamic compensation coefficient matrix is constructed through illumination intensity analysis, the dynamic compensation coefficient matrix is applied to the infrared thermogram, and illumination interference compensation is achieved. And then, inputting the corrected infrared thermogram and visible light image into a pre-trained heterogeneous atlas matching model, and identifying the category and three-dimensional coordinates of the target equipment. And based on the equipment category information, calling a standard temperature field template in the digital twin model library, establishing a heat conduction path model in combination with the vibration spectrum data, and generating a temperature field prediction reference map of the equipment. And finally, through pixel-by-pixel deviation analysis, inputting the temperature deviation value, the image features and the vibration data into the probability graphic model, and outputting equipment fault information. The method can realize high-precision temperature measurement and fault diagnosis.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention discloses an unmanned aerial vehicle multi-modal feature fusion target tracking method and system based on natural language description, belongs to the technical field of computer vision and image processing, and solves the problem that in the prior art, when the quality of an image collected by an unmanned aerial vehicle is poor or image features are not obvious, the target tracking capability and the long-time tracking capability are poor. Natural language description is carried out on a traffic accident scene in an image of an unmanned aerial vehicle visual angle, and a language prompt is obtained; constructing a scene-context feature pyramid network to perform context information enhancement processing on the image of the view angle of the unmanned aerial vehicle to obtain a feature-enhanced image; respectively carrying out visual coding and language coding on the enhanced image and language prompt to obtain visual features and language feature vectors, and carrying out visual-language bimodal feature local alignment; and fully fusing the obtained aligned new language features with the visual features to obtain multi-modal features for target tracking. The method is used for multi-modal feature fusion target tracking of the unmanned aerial vehicle.

The invention provides a multi-mode large model-based precision metal part defect detection method and system. The method comprises the following steps of: acquiring laser scattering image data of a precision metal part in an electromagnetic shielding environment; performing multi-scale spatial analysis on the laser scattering image data to extract a multi-modal image feature set containing a crystal boundary topological structure of the surface of the precision metal part and scattering spot distribution gradient features of the subsurface; converting process parameters, corresponding to the precision metal part, in an existing metal heat treatment process database into heat historical feature vectors, wherein the heat historical feature vectors comprise temperature gradient distribution features and time sequence phase change features; performing fusion processing on the thermal historical feature vector and the multi-modal image feature set through a multi-modal large model to obtain a fusion processing result so as to generate defect detection data; according to the invention, the detection rate and the detection efficiency of micron-sized defects in a complex electromagnetic environment are improved.

The invention relates to the technical field of image feature extraction, in particular to a motion data analysis method and system based on image processing, and the method comprises the following steps: collecting skeleton and inertial data, marking an action boundary, extracting a direction sequence, detecting a turning frame, analyzing a structural similarity labeling posture, screening a sudden change frame, and extracting an edge direction. And fusing direction optimization attitude features. According to the method, the image skeleton coordinates and the three-axis inertial data are synchronously obtained, the inter-frame displacement and the acceleration amplitude are paired, the action boundary marking time sequence precision is improved, the bidirectional change sequence is constructed according to the joint relative position difference and the angular velocity integral, the action trend recognition continuity is enhanced, and the recognition precision is improved. A main axis direction and a joint arrangement vector are extracted by sliding a frame window, a trend sequence is constructed in combination with structural similarity, attitude state dynamic labeling is realized, an edge direction is introduced to analyze and identify a sudden change frame, and the local sensitivity of skeleton offset detection and the abnormal frame detection precision are improved.

The invention belongs to the field of cross-modal pedestrian re-identification, and discloses a cross-modal pedestrian re-identification method based on a coal mine scene, and the method comprises the steps: 1, obtaining an identity representing a pedestrian image and an identity tag corresponding to the pedestrian image, and carrying out the preprocessing; step 2, constructing a cross-modal pedestrian re-identification backbone network; step 3, constructing a multi-scale feature enhancement module and adding the multi-scale feature enhancement module into the cross-modal pedestrian re-identification backbone network; 4, determining a loss function, and completing the final training of the cross-modal pedestrian re-identification backbone network; and 5, taking one modal pedestrian image as a query set, taking the other modal pedestrian image as a to-be-retrieved set, and matching the pedestrian image in the query set with the pedestrian image in the to-be-retrieved set to obtain a cross-modal pedestrian re-identification result. According to the method, cross-modal image features can be effectively fused, feature information of pedestrian images can be effectively mined, and the prediction ability and generalization ability of the model in a cross-modal pedestrian image retrieval scene are effectively improved.

The invention discloses a wood water-based paint surface defect detection method based on computer vision, and belongs to the technical field of wood paint surface detection, and the method comprises the following steps: S1, constructing a wood paint surface defect data set including four typical defects of bubbles, cracks, holes and scratches; s2, introducing a wavelet convolution module to improve the Backbone network, enhancing the image feature capture capability, and avoiding the excessive parameterization problem of a traditional method; s2.1, a wavelet convolution module (WTConv) is added into a Basic Block of the ResNet network, and a feature extraction unit is improved, so that the capability of the network for capturing paint surface defect features is enhanced, and meanwhile, the calculation complexity is reduced; and S2.2, the wavelet convolution module enhances the multi-scale feature representation of the image through wavelet transform. And S3, designing an AIFI module, and optimizing a feature fusion effect through a multi-scale capture mechanism based on K and V feature information. According to the method, the multi-scale feature capturing capability of the model is enhanced, and the detection precision of defects of different types and sizes is improved.

The invention discloses a multi-modal sentiment analysis and interactive adjustment method and system based on a large model, and relates to the technical field of artificial intelligence and natural language processing. Comprising the steps of 1, collecting dialogue video data and preprocessing the video data, 2, respectively carrying out image feature extraction, voice feature extraction and text feature extraction according to video frames, audios and texts, 3, carrying out interactive fusion on every two modal features through a cross attention mechanism, and step 4, generating a corresponding dialogue strategy according to the emotion label in the recognition result and the requirement of appointed emotion adjustment, and carrying out interaction according to the dialogue strategy, so as to obtain the emotion recognition result of the multi-layer perceptron after the feature after the interaction fusion is subjected to average pooling processing and then is input into the multi-layer perceptron to carry out emotion classification, and obtaining a classification corresponding to the highest probability as the emotion label recognition result. Dynamically adjusting a dialogue strategy according to the change of the emotion label; the method is suitable for enhancing the emotion understanding and response ability of the intelligent dialogue system and improving the user experience.