157 results about "Information bottleneck method" patented technology

The invention provides an interpretable node classification prediction method based on adversarial causal graph learning. The method comprises the steps that a constructed prediction model comprises a redundancy filtering module and an adversarial causal graph learning module; a redundancy filtering module and an adversarial causal graph learning module realize a graph information bottleneck mechanism; the redundancy filtering module adopts a two-layer graph attention network GAT structure to carry out information aggregation, and node embedding is obtained; the confrontation causal graph learning module adopts a learnable sub-graph sampler based on an attention mechanism to generate a causal interpretation sub-graph for node embedding, performs gradient disturbance optimization on interpretation sub-graph embedding based on a PGD confrontation training strategy of a causal enhancement mechanism, generates confrontation embedding, and obtains final disturbance interpretation sub-graph embedding through multiple rounds of disturbance iteration; performing end-to-end prediction model training through multi-target loss joint optimization; and after training is completed, embedding of the nodes is input into a classifier, and a prediction result is output. According to the method, the structural transparency and interpretability of the model are remarkably improved.

The invention discloses a multi-modal multi-scale retrieval enhancement generation method, system and device applied to external knowledge questions and answers and a medium. The method comprises question perception, multi-modal multi-scale query fusion coding, dense recall and answer generation. Analyzing a key query phrase from the question through a fine-tuned instruction language model, and accurately positioning a region of interest corresponding to the phrase in an image by using an open set visual positioning model; multi-source information is compressed and distilled into an optimal query vector through a deep fusion network integrating multi-head self-attention and an information bottleneck theory; executing a maximum inner product search to recall related knowledge; guiding the large language model to synthesize all information to generate a final answer; the system, the equipment and the medium directly perform feature fusion in the vector space based on the method, so that challenges such as information loss and cascading errors caused by a traditional normal form can be effectively dealt with, high correlation and high accuracy of retrieval knowledge are ensured, and accurate and reliable image-text questions and answers are realized.

The invention belongs to the technical field of graph classification, discloses a graph classification method based on subgraph integration and position sensing, and relates to a graph classification method SIPA combining substructure embedding and node position sensing. The SIPA firstly extracts sub-graphs through two different strategies so as to capture multi-sample sub-structures in the graphs, the structural features of the sub-structures are coded by adopting a graph convolutional network, and information of different sub-graphs is effectively fused through an attention mechanism. Then, anchor nodes are introduced to calculate relative position information of nodes, and the information is embedded into node representation to better capture global position features. And finally, a graph information bottleneck mechanism is used for optimizing node representation and removing redundant information irrelevant to a classification task. The method not only effectively learns the local structure information, but also enhances the perception capability of the relative position information of the nodes. Experimental results show that SIPA is superior to an existing baseline model in five data sets, and the superiority of SIPA in a graph classification task is verified.

The invention discloses a source load risk management and control method for cooperative operation of multiple types of adjusting power supplies and new energy, and the method comprises the steps: depicting a prediction error tail dependency relationship through employing an extreme value theory and a Copula function, constructing a distribution robust optimization model in combination with a Wasserstein distance, and obtaining the standby capacity sharing, triggering threshold and risk index of the multiple types of adjusting power supplies; performing conformal prediction based on the prediction residual error to generate a dynamic confidence interval, and mapping the dynamic confidence interval into a pipeline radius of model prediction control; executing rolling MPC containing information bottleneck regularization under the constraint of the reserve capacity and the pipeline, and generating a robust scheduling strategy; and constructing a polyhedral feasible region model of the flexible load group according to the scheduling strategy and the risk index, and performing scaling to obtain a group control action. And the new energy consumption level and the system operation safety are obviously improved.

The invention discloses a data analysis and prediction method and system based on multi-modal data fusion, and the method comprises the steps: collecting multi-modal original data, and carrying out the preprocessing of the multi-modal original data, and generating normalized data; setting sliding window parameters, and performing sliding fragmentation on each modal data to obtain sliding window fragment data; extracting modal features, and fusing the modal features into sliding window level fusion feature representation; inputting the improved variational information bottleneck model, and performing feature compression and reconstruction; all fusion features are aggregated, a prediction result is output through modeling reasoning, and the model is optimized. According to the method, the improved sliding window algorithm and the variational information bottleneck model are fused, so that efficient analysis and accurate prediction of multi-modal data are realized.

The invention relates to the technical field of knowledge maps, in particular to a few-sample knowledge map completion method and system fusing relation perception information bottleneck. The method comprises the following steps: S1, preprocessing an input triple; s2, building a global aggregation module, and updating entity embedding; s3, establishing a relationship aggregation module, and updating relationship embedding; s4, establishing a relationship-based information bottleneck module, filtering noise irrelevant to tasks, and meanwhile, retaining relationship-specific information; s5, building an EM attention pooling module, adaptively aggregating multi-path semantic representation, and highlighting the correlation between the entity and the relationship; and S6, establishing a score calculation module, calculating a triple score and outputting the triple score. The invention provides a few-sample knowledge graph completion method and a few-sample knowledge graph completion system fusing relation perception information bottleneck, which are used for solving the problems of insufficient relation and entity representation coupling, high redundant information interference and difficulty in modeling due to high-order relation dependence in a knowledge graph completion task, and realizing efficient inference of potential relation facts.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a behavior cloning model training method, device, equipment and medium, and the method comprises the steps: obtaining multi-modal input, carrying out the feature extraction and splicing of the multi-modal input, and obtaining an input representation; utilizing a preset decoder to extract a potential representation of the input representation, and utilizing a preset strategy head to decode the potential representation into action data; calculating a potential representation mutual information loss function value by using an information bottleneck principle, and calculating a mean square error loss value of the action data and preset real action data; iteratively updating parameters of a preset decoder and a preset strategy head according to the mutual information loss function value and the mean square error loss value to obtain an updated model; detecting whether a joint loss function value in the updating model is smaller than a preset threshold value or not in real time; and when the joint loss function value is smaller than a preset threshold value, stopping parameter iteration updating to obtain an optimization model.

The invention discloses a single-classification industrial control system anomaly detection method based on double-view information bottleneck fusion, which belongs to the technical field of industrial control system anomaly detection and comprises the following steps of: in a training stage, simultaneously constructing a time sequence view and a pseudo image view; respectively extracting characterization through a time dynamic encoder and a pre-trained visual backbone; performing cross-view compression in the variational information bottleneck fusion module and establishing projection branches only depending on sequence representation; a normal subspace is learned around a center vector, an accurate hypersphere is established to describe normal data, and a center distance is used as an abnormal score and a quantile threshold is used to complete discrimination; in the detection stage, online recognition can be performed only by inputting a time sequence view. According to the method, dual-view prior and variational information bottleneck are fused, end-to-end collaborative optimization is carried out on the dual-view prior and the variational information bottleneck and deep support vector data description, an accurate decision boundary can be established under the condition of a small number of samples, and a solution for industrial control system anomaly detection under the condition that the samples are limited is provided.

A robust multimedia recommendation method based on information bottleneck, including: a user representation matrix and an item representation matrix are learned based on a deep graph neural model; mutual information between multimedia content and representation information of the multimedia content is minimized based on an information bottleneck theory to compute a first loss function; a user-item interaction matrix is reconstructed based on the user representation matrix and the item representation matrix to compute a second loss function; and the first loss function and the second loss function are combined to perform multi-task learning to update parameters of the deep graph neural model until the deep graph neural model converges. A robust multimedia recommendation device and a robust multimedia recommendation medium are further provided.

The invention provides a metallurgical furnace working condition intelligent identification method fusing multi-modal data. A double-branch multi-level residual structure feature encoder is designed, and metallurgical furnace process variable data features and image data features are fully extracted. Aggregation and separation among modal features are carried out through a comparative learning method, feature semantic alignment is realized, and feature distribution is optimized. Under the constraint of orthogonal loss, a feature decomposer is utilized to effectively separate out inter-modal correlation features and modal private features, and an information bottleneck theory is utilized to promote redundancy removal of key correlation features of process variable data and image data. A multi-level and multi-dimensional feature fusion module is constructed, single-level feature interaction fusion and inter-level feature fusion are sequentially realized under the action of bidirectional cross attention, and fusion features rich in information content are obtained. And finally, the information is sent to the classifier, so that intelligent and accurate identification of the metallurgical furnace working condition under multi-modal data input is realized.

The invention belongs to the field of data detection, and provides a false news detection method and system based on fact-emotion dual uncertainty, and the method comprises the steps: obtaining a text and an image of each piece of news in a social network; the method comprises the following steps: performing feature extraction on a text and an image to obtain text embedding and image embedding, performing Gaussian reweighting on the text embedding and the image embedding respectively, then performing calculation to generate Gaussian uncertainty representations, and screening the Gaussian uncertainty representations based on a variational information bottleneck strategy to obtain multi-modal uncertainty representations; processing the multi-modal uncertainty representation to generate a fact inconsistent representation, and processing the multi-modal uncertainty representation based on an emotion manipulation graph convolutional network to generate an emotion inconsistent representation; and fusing the fact inconsistent representation and the emotion inconsistent representation to generate fusion features, and performing classification based on the fusion features to obtain a false news detection classification result. According to the method, the limitation of cross-modal complementarity caused by the uncertainty of multi-modal data is relieved.

The invention relates to the technical field of data processing, in particular to a large model reasoning optimization method based on context incremental updating, which comprises the following steps: processing a multi-modal data stream through timestamp alignment and a filtering algorithm, extracting features by adopting a shared encoder and a private encoder, and realizing feature decoupling through a depth information bottleneck principle. A dynamic emotion map is constructed by using Gaussian process regression and a random process algorithm, and self-adaptive updating control is realized by combining meta-learning and Bayesian optimization. Incremental state management is realized by adopting a neural Turing machine, reasoning consistency is guaranteed through a generative adversarial network, model parameters are optimized in combination with a digital twin system and reinforcement learning, and a mental health service response is finally generated through a conditional generation model and hierarchical reinforcement learning. According to the method, the problem of asynchronism of multi-modal emotion feature dynamic evolution and context increment updating is effectively solved, accumulated drift of emotion state tracking is eliminated, and the continuity of reasoning logic is guaranteed.

The invention provides a point cloud completion method and system based on a high-dimensional feature field and structured tensor decomposition, and belongs to the field of artificial intelligence. Comprising the following steps: encoding geometric information carried by sparse point clouds through a space geometric encoder, and distributing the geometric information to three mutually orthogonal two-dimensional feature planes and a three-dimensional feature grid to jointly form a high-dimensional feature field in which space local information is reserved; for any query point in the space, combining the space coordinates of the query point and the local features sampled in the high-dimensional feature field as input, driving a decoder based on structured tensor decomposition, and reconstructing the geometric attribute value of the query point; a continuous geometric field function implicitly defines a three-dimensional surface after completion, and point cloud completion is completed by sampling the function and extracting a zero contour surface of the function. The technical problems that when sparse and incomplete point cloud data are processed, due to information bottleneck and lack of geometric priori, the complementation quality is poor, and macrostructures and microscopic details are difficult to consider at the same time are solved.

The invention discloses a dense monitoring Internet of Things remote estimation method based on multi-cell semantic enhancement, and belongs to the field of wireless communication. The method comprises the following steps: firstly, an edge device observes a reasoning target, encodes an observation value according to a semantic codebook, and transmits the observation value to an edge server; secondly, the edge server carries out vector quantization on the received signal and sends a quantized code word index to a cloud server through a forward link; and finally, the cloud server performs dequantization and joint decoding on the received quantization index to obtain an estimated value of the reasoning target in the multiple cells, and a remote estimation task is completed. In addition, a loss function is constructed based on an information bottleneck theory and a straight-through estimation gradient approximation method, and the system is trained to be optimal by adopting a two-stage training strategy. According to the method, semantic information extraction of the remote estimation task in a multi-cell scene is realized, the performance of the remote estimation task can be improved while code word redundancy is inhibited, and the utilization efficiency of spectrum resources is remarkably improved.

The invention relates to the technical field of social network information propagation prediction, in particular to an event propagation prediction method based on group influence. The method comprises the following steps: constructing a social network graph of target event participating users, calculating an activation probability matrix among the users by using historical propagation data, and performing social graph structure enhancement based on a threshold value to obtain a de-noised social graph; applying a graph neural network on the de-noised social graph and introducing information bottleneck constrained self-supervised contrast learning optimization node embedding, and reserving features useful for group division and propagation prediction; dynamically dividing user groups related to propagation according to the similarity and propagation context of de-noising node embedding to form a group-level propagation sequence; based on the group representation, utilizing an attention mechanism and a group relation graph to carry out modeling on propagation influence among the groups to obtain global group representation of the event; global group representation is combined with candidate user embedding, propagation probability distribution is calculated, and future participating users of corresponding events are predicted.

The invention discloses a multi-mode joint information source channel coding system for satellite-ground semantic communication. The multi-mode joint information source channel coding system comprises a joint information source channel coder and a decoder, the encoder is used for sequentially performing feature extraction, feature fusion, data cutting and phase modulation operation on input multi-modal data to generate a transmitting signal, and transmitting the transmitting signal to a ground base station through a channel; and the decoder is used for sequentially performing real and virtual part separation, data expansion, feature extraction and modal data recovery operation on the received signal so as to reconstruct multi-modal data. According to the system, public semantic information among multi-modal data can be mined, the time-varying low-signal-to-noise-ratio channel characteristics of a satellite downlink are combined, the information bottleneck theory highly related to data reconstruction quality is used as guidance to optimize a coding space, complex field transmission signals with the high compression ratio are generated in a self-adaptive mode, and the multi-modal multi-domain transmission signals are obtained. The reconstruction quality of the multi-modal data can be ensured, and the bandwidth and the energy consumption overhead of satellite communication can be remarkably reduced.

The invention discloses a concrete crack intelligent detection method based on four-cardinal-number coordinate attention enhancement, and belongs to the technical field of computer vision and civil engineering detection. According to the invention, a QuadCoord-YOLO network is constructed by improving a YOLO11 network architecture, and a CoT-CSP module and a QCE module are innovatively introduced: the CoT-CSP module deeply fuses a context converter mechanism and a CSP structure, so that the long-distance dependency relationship modeling capability is enhanced; the QCE module proposes a four-cardinal parallel channel attention mechanism for the first time, realizes three-path parallel multi-dimensional feature enhancement in combination with coordinate attention and deep convolution, and breaks through the information bottleneck of single-path compression of a traditional SE module. Experimental results show that according to the method, the bounding box detection mAP50 is improved from 72.6% to 78.1%, the segmentation mAP50 is improved from 58.9% to 66.6%, and only 3.2% of parameter quantity (82, 456 parameters) is increased. The method can be applied to an intelligent structure health monitoring system, and provides accurate technical support for concrete structure condition evaluation and maintenance decision.

The invention discloses a model optimization method and device based on hatred content classification. The method comprises the steps that text features and image features of expression package samples are obtained; based on the text features and the image features, multi-modal features of the expression package samples are obtained; using the multi-modal features of the plurality of expression package samples as training samples to train a deep learning model; optimizing a multi-modal distribution space of the deep learning model to enable the first probability distribution to be consistent with the second probability distribution; and on the basis of the optimized deep learning model, a hatred content classification model is obtained in combination with a variation information bottleneck mechanism. According to the method, emotional uncertainty caused by indirect expression is quantified by using a variance vector of a multi-modal feature in a deep learning model, and a multi-modal distribution space of the deep learning model is optimized, so that first probability distribution and second probability distribution are kept consistent; therefore, the recognition capability of the hatred content classification model on indirect expression of the hatred content is effectively improved.

The invention relates to the technical field of machining, and discloses a high-precision machining control method and system based on deep learning, and the method comprises the steps: collecting the operation data of a machine tool in real time through a multi-sensor array, and carrying out the data preprocessing; extracting a minimum feature subset most relevant to energy consumption from machine tool operation data by using an information bottleneck principle; constructing a lightweight energy consumption prediction model based on a sparse Bayesian learning method; the processing track is decomposed into two levels of macroscopic path planning and microcosmic speed planning, and linkage adjustment of the macroscopic path planning and the microcosmic speed planning is achieved through a collaborative optimization algorithm; an improved Transform deep learning architecture is adopted to process historical trajectory data, key points and modes in a trajectory are identified through a self-attention mechanism, and trajectory parameters are dynamically adjusted; energy conservation and emission reduction in the mechanical part machining process are achieved by bringing the energy consumption index into the trajectory optimization target, and energy consumption is reduced.

The invention relates to an Internet of Vehicles cooperative sensing method based on semantic communication, and belongs to the technical field of intelligent connected automobiles. The method fuses a cooperative vehicle terminal, an own vehicle terminal and a wireless communication channel, and comprises the following steps: introducing an information bottleneck theory to the cooperative vehicle terminal to establish a variational optimization target, and extracting compact multi-view image features; mapping the multi-view image features into aerial view semantic features by using a semantic encoder based on a cross-view attention mechanism; mapping the extracted semantic features into a symbol stream through joint source channel coding, dynamically adjusting a coding strategy according to a channel state, and transmitting the coding strategy to an own vehicle through a wireless channel; semantic features are recovered by using a joint source channel decoder at an own vehicle end, positioning errors among multiple vehicles are corrected through a feature space calibration module, cross-vehicle feature alignment and fusion are realized by using an attention-based feature fusion module and a feature interaction precision enhancement module, and a global collaborative semantic segmentation map is generated. According to the method, compact features can be extracted through information bottleneck differential training so as to reduce transmission redundancy, anti-interference transmission under a time-varying channel is ensured by utilizing joint information source channel coding, the problem of spatial dislocation is solved through a multi-stage feature fusion mechanism, and the cooperative sensing precision under a low signal-to-noise ratio is remarkably improved.

The invention provides an interpretable safe longitudinal federal representation learning method and device for multi-modal big data. According to the method, for the problem of target domain modal data scarcity, in the target domain modal representation learning process, an attention mechanism is used for supplementing information of a source domain modal into a target domain modal, and an algorithm framework is established under a longitudinal federated learning framework, so that on one hand, it is guaranteed that data of a source domain and data of a target domain are not locally output, and the algorithm framework is established under a longitudinal federated learning framework; therefore, the data security is improved; on the other hand, the problem of insufficient modal data of the target domain is solved, and the performance of downstream tasks is improved; besides, in the process of constructing the loss function, an information bottleneck theory is introduced, redundant information between input and intermediate representation is minimized, and related information between representation and a target task is maximized, so that efficient information compression and feature extraction are realized, and the interpretability of extracted representation for downstream tasks is improved.

The invention relates to the technical field of audio generation, and discloses a GAN-based personalized timbre feature enhancement and singing sound generation method, which comprises the following steps: acquiring audio data of a target singer, inputting a Mel spectrogram into a content encoder and a style encoder, applying an information bottleneck constraint to a style feature vector, and generating a singing sound; splicing the content feature vector and the style feature vector, inputting the spliced content feature vector and style feature vector into a generator network, inputting the generated intermediate spectrum representation and the corresponding real spectrogram into a discriminator, and calculating the Euclidean distance between the style feature distribution of the generated sample and the style feature distribution of the target sample, and inputting the final optimized intermediate spectrum representation into a neural network vocoder, reconstructing to obtain an audio waveform signal, and outputting the audio waveform signal as generated singing sound. The personalized timbre feature enhancement and singing sound generation method based on the generative adversarial network is adopted, and the technical effects of accurately extracting the timbre features through the style encoder and reconstructing the high-quality singing sound waveform through the generator are achieved.

The invention discloses a system for improving item recommendation accuracy based on space-time diffusion and information bottleneck. The system comprises the following modules: a normalization processing module, a space aggregation module, a time aggregation module, a time diffusion module, a space diffusion module, an information bottleneck module and a prediction module. The system can effectively model an error behavior which is not solved by a current next basket recommendation technology, and through an innovative spatial diffusion module, the system does not depend on strong association among commodities or historical preferences of users any more, but focuses on learning of article spatial combinatorial logic in a shopping basket, so that the system is more intelligent. Therefore, the system can identify the targeted and hidden combination strategy of the user, thereby improving the understanding of the real purchase intention of the user, and achieving the purpose of improving the article recommendation accuracy.

The invention relates to the field of network security, and discloses a network security situation awareness method and system based on deep learning, and the method comprises the following steps: obtaining multi-source network data; extracting time feature representation of nodes in the dynamic space-time diagram sequence through a multi-scale time feature extraction network; extracting spatial feature representation of the nodes through a graph neural network; fusing the time feature representation and the spatial feature representation; constructing a decoupling encoder based on the information bottleneck; and performing anomaly detection and attack path tracing based on the time hidden variable, the space hidden variable and the coupling hidden variable. By constructing a multi-scale time feature extraction network and introducing a dynamic receptive field adjustment mechanism, space-time coupling features are separated into a time hidden variable, a space hidden variable and a coupling hidden variable, and coupling information is comprehensively utilized during anomaly detection to improve detection precision. And an attack propagation path is accurately reconstructed based on pure spatial features during attack path tracing.

The application discloses a kind of high-impact weather condition-oriented wind power prediction information filtering method.It realizes the dynamic soft selection and reweighting of different input variables under different weather conditions through instance-level attention recalibration, improves the effective signal-to-noise ratio, and reduces the interference of noise channels on the learning process.At the same time, it removes irrelevant random disturbances from the information flow by variational information bottleneck compression regularization term, alleviates overfitting and noise memory problems caused by relying only on error minimization, and improves cross-condition generalization ability.Compared with the prior art, the present application can effectively filter out random disturbances irrelevant to the prediction task under the condition of strong noise and distribution fluctuation caused by high-impact weather, improve the stability, robustness and cross-scene generalization ability of the wind power prediction model, and have good engineering application value.

The application provides a trust feature completion and trust relationship prediction method and system for missing trust information, comprising: constructing a social relationship graph and a trust relationship graph to obtain social attribute features and trust attribute features respectively; compressing the social attribute features and the trust attribute features, and constructing a feature converter to convert the compressed social attribute features; constructing a user trust relationship prediction model, taking the compressed features of the user as input, and outputting a prediction label; taking the converted compressed social attribute features as input, and outputting a prediction label; taking a real label as supervision information, combining a reconstruction loss and an information bottleneck loss, training the model, and outputting a trust relationship. The application constructs a social behavior graph and a trust relationship graph, and learns the social attribute features and the trust attribute features of the user respectively. This multi-view learning method can more comprehensively capture the feature information of the user, and improve the accuracy of trust relationship prediction.

Systems and methods for particularly t-cell receptor complex optimization with reinforcement learning. Classifiers using variational information bottleneck with attention of experts (AVIB classifiers) can be fine-tuned (110) for different representations of desired t-cell receptor (TCR) sequences for a patient. Proximal policy optimization (PPO) models can be trained (120) with reinforcement learning using the AVIB classifiers as reward functions to achieve higher affinity in generating interaction sequences for the desired TCR sequences through automated decision making. The interaction sequences can be clustered (130) based on k-mer profiles to select the interaction sequences having highest binding scores in each cluster as final sequences. A biological functional potency of the final sequences can be validated (140).

The application discloses a kind of based on information bottleneck's debiasing recommendation method, comprising:1. Construct original data: user-product interaction matrix of user interaction to product, user biased attribute matrix;2. Using biased attribute encoder learns user biased representation in user-product interaction data;3. Based on deep graph neural network learns user and product representation matrix;4. Based on information bottleneck theory minimizes the mutual information between user representation and biased representation, user subgraph representation and biased representation, calculates loss function;5. Recomputes interaction matrix based on user and product representation matrix, calculates loss function;6. The loss function of joint step 4-step 5 is carried out information bottleneck learning, and model parameter is updated to model convergence.The application is based on the thought of information bottleneck, learns unbiased user representation under the demand of meeting recommendation task, so as to effectively reduce recommendation bias, guarantee the accuracy of recommendation.

The invention discloses a multi-modal sentiment analysis method based on sentiment consistency distillation and information bottleneck, and the method comprises the steps: extracting the sentiment features of an image, a text and an audio mode, and mapping the multi-modal sentiment features into a multi-modal semantic vector; then, decoupling the multi-modal semantic vector into a modal specific semantic vector and a modal common semantic vector; extracting an emotion consistency signal between the modal pairs through an attention mechanism, and guiding and fusing emotion association information into a modal public semantic vector through a knowledge distillation mechanism; noise information in the modal specific semantic vector is removed based on an information bottleneck theory; and finally, splicing the optimized modal specific semantic vector and the modal public semantic vector into a unified emotion representation, and outputting a corresponding emotion category through a classifier. According to the method, the problems of alignment deviation and information pollution caused by noise interference in multi-modal data can be effectively relieved, and the calculation efficiency and the model stability are considered while the emotion recognition precision is improved.

The application discloses a supplier portrait visualization display method and system based on a knowledge graph, relates to the technical field of data processing, and comprises the following steps: performing information entropy driven compression on a pre-constructed supplier knowledge graph through a graph information bottleneck algorithm to generate a supplier compressed subgraph; obtaining a supplier feature set by positioning a target node in the supplier compressed subgraph; screening a target portrait template from a portrait template library according to the supplier feature set; inputting the supplier compressed subgraph, the supplier feature set and the target portrait template into a double-channel collaborative embedding architecture to generate a supplier portrait embedding vector; and associating the supplier portrait embedding vector with the target node for visualization display. The application is used to solve the problems of insufficient expression of supplier features, lack of adaptability of portrait templates and insufficient interpretability of visualization results in the prior art.