1014 results about "Semantic space" patented technology

The invention relates to the technical field of knowledge graph construction, and discloses a knowledge graph construction method and system based on a large language model technology. The method comprises the following steps: receiving a multi-source heterogeneous data stream, and completing semantic space mapping and cross-modal feature fusion to generate a unified semantic representation vector set; constructing an initial knowledge graph skeleton; performing incremental optimization on the skeleton, and performing entity relationship disambiguation and conflict detection; and iteratively updating the knowledge representation, and outputting a target knowledge graph meeting semantic consistency. The system comprises a data receiving module, a semantic fusion module, a skeleton construction module, an optimization module and a knowledge updating module. According to the method, multi-source heterogeneous data is effectively processed, the accuracy, the dynamic updating capability and the semantic consistency of the knowledge graph are improved, and the method has wide application prospects in the fields of intelligent question answering, information retrieval and the like.

The invention provides a cross-modal retrieval method for semantic and vector fusion in a data space, which belongs to the field of cross-modal information retrieval, and comprises the following steps: firstly, collecting and preprocessing multi-modal data; generating modal embedding and storing by utilizing the pre-training model; a shared semantic space is constructed, cross-modal vector alignment is optimized through comparative learning, and a modal mapping network is designed to enhance the embedding projection effect; storing the aligned embedding by using a Milvus database, and constructing an HNSW index; user text or image query is processed, text query analyzes limiting conditions to generate enhanced embedding, and image query extracts characters through OCR and fuses the characters with image features to generate embedding; in a database, through condition screening and semantic similarity calculation, a Top-K candidate item is retrieved; performing multi-modal correlation sorting on the candidate results and returning the results; according to the method, the shared semantic space is constructed, the alignment effect of different modal embedding is optimized, efficient storage and index management of multi-modal embedding are carried out, and real-time retrieval of large-scale cross-modal data is achieved.

The invention discloses an archive knowledge base construction and retrieval method and system based on multi-modal data fusion. The method comprises the steps that heterogeneous archive data are cleaned, image features are extracted through CNN, text features are extracted through Transform, audio is converted into text and then subjected to similarity, a unified feature vector is generated, and metadata is constructed according to archive code association; creating a graph database instance, defining nodes and relationship types, importing entities and relationships, and storing feature vectors and metadata; the features are mapped to a high-dimensional shared semantic space, positive and negative sample pairs are constructed to update embedded layer parameters, self-attention is used in modalities, a shared attention mechanism is used between modalities, weights are adjusted according to archive features, and unified knowledge representation is generated; segmenting the steering quantity of the multi-modal data, storing the steering quantity into a database, and adopting hierarchical indexing and optimizing as required; related document fragments are retrieved through RAG technology vectors, answers are generated with the help of a large language model, and session feedback is provided. The file retrieval efficiency and accuracy are improved.

The invention discloses a multi-modal knowledge graph rule reasoning method and device based on a large model, and the method comprises the steps: carrying out the feature extraction and cross-modal alignment of input text data and image data, and generating a multi-modal feature vector of a unified semantic space; performing knowledge graph storage on the emotion entities and the relationships by adopting an attribute graph model to complete construction of an emotion knowledge graph; generating an interpretable inference rule from the emotion knowledge graph by using a large language model, and eliminating a conflict rule in combination with logic verification; calculating the confidence coefficient of a reasoning path based on an attention mechanism, and carrying out quantitative evaluation on a rule reasoning result; the knowledge graph and the rule base are updated online according to user feedback, and the real-time performance and accuracy of the inference system are optimized through weight adjustment and a forgetting mechanism. According to the method, through innovative technologies such as multi-modal data integration, dynamic knowledge evolution and interpretability reasoning, the limitation of a traditional sentiment analysis method in the aspects of evidence dimension, adaptive capacity, interpretability and the like is broken through.

The invention relates to a multi-modal AI data fusion processing method, device and equipment and a medium, and the method comprises the steps: firstly extracting visual, auditory and text modal features through a pre-training encoder, executing dimension alignment, and generating a standard data feature set with unified dimensions; a cross-modal semantic graph is constructed based on a cosine similarity algorithm, and the problem of semantic mismatch of heterogeneous data is solved; residual enhancement is carried out on the map nodes, and noise interference is eliminated; fusing the optimized features and the semantic topology in combination with a graph convolutional network to generate aggregation graph representation; the fusion features are mapped to a low-dimensional semantic space through a variational auto-encoder, and cross-modal correlation essence is captured; the key dimension contribution degree is quantified, a visual report is generated, and semantic association rules among modals are disclosed, so that the dimension isomerism limitation of a traditional fusion technology is broken through, quantifiable cross-modal semantic mapping is established, the whole process traceability from feature fusion to decision interpretation is realized, and the method is suitable for popularization and application. And the multi-modal decision black box problem in the fields of medical diagnosis, automatic driving and the like is effectively solved.

The invention relates to the technical field of medical artificial intelligence and knowledge graph construction, in particular to a rare disease knowledge graph construction method based on modal injection and multi-modal fusion. Comprising the following steps: S1, collecting multi-modal medical information including texts, images and genes; s2, standardization processing is carried out, and a three-layer metadata structure is constructed; s3, complementing missing modal data, and performing feature extraction and unified dimension conversion on the modal data to realize representation alignment in a shared semantic space; s4, performing multi-level semantic fusion to obtain a unified fusion semantic vector; and S5, constructing a double-layer structure system rare disease knowledge graph comprising an ontology layer and an instance layer. According to the method, multi-modal medical information of texts, images and genes is selected to construct the knowledge graph of the rare disease, the application range, coverage and accuracy of the knowledge graph are improved, correspondence adaptation of rare cases during clinical diagnosis and treatment of the rare disease can be achieved, and the method has high recognition capacity.

The invention provides a fragment data cross-modal label generation system and method based on deep transfer learning. The method comprises the following steps: extracting first high-dimensional feature vectors in different modes; mapping the first high-dimensional feature vectors of different modals into the same semantic space through a cross-modal comparison loss function to realize multi-modal alignment and fusion to obtain second high-dimensional feature vectors; labeling semantic tags corresponding to the second high-dimensional feature vectors based on the fragmented data components by adopting a small sample transfer learning algorithm; a multi-channel Hash encoder is adopted, a self-adaptive encoding strategy is called according to different modal data combinations, and the second high-dimensional feature vector is encoded into a multi-channel binary Hash code; in combination with an incremental graph neural network, the binary hash codes and the corresponding semantic tags are dynamically expanded into the historical knowledge graph; matched fine-grained tags are established for semantic differentiation features of different entity combinations in the target knowledge graph, and a cross-modal tag tree is obtained by combining three-matrix hierarchical construction.

The invention discloses a defect detection method and system based on joint distribution optimization and structural knowledge guidance. The method comprises the following steps: analyzing an equipment structured document to construct an equipment physical topological graph containing a physical connection relationship; processing the topological graph by adopting a graph convolutional network to generate a structured feature vector, and extracting a sensing feature vector of the multi-modal sensing data; constructing a knowledge-enhanced attention model, and guiding a perception feature vector to perform cross-modal fusion by using a structured feature vector as a key and a value; and training the model through a combined optimization target containing joint distribution divergence loss and Lipschitz stability constraint so as to ensure the consistency and physical authenticity of the multi-modal features in the shared semantic space. According to the method, the defect detection accuracy can be remarkably improved, and the root cause diagnosis report with physical interpretability is generated.

The invention discloses a judicial scene-oriented multi-modal data fusion method and system. The method comprises the following steps: 1) collecting multi-modal data in a judicial application scene and converting the multi-modal data into data in a uniform format; 2) mapping the data to a unified semantic space to realize cross-modal alignment; then associating the multi-modal data to obtain the text description of the same case and the corresponding image evidence as the multi-modal features of the corresponding case; 3) constructing a knowledge graph of a judicial application scene based on the multi-modal features; driving multi-modal data fusion based on the knowledge graph and constructing a multi-modal evidence chain of each entity; 4) quantifying the integrity of the knowledge graph and the reliability of the multi-modal evidence chain according to a preset index, marking abnormal nodes in the knowledge graph according to a quantification result, and adjusting the abnormal nodes; 5) generating a structured knowledge graph according to the knowledge graph and creating a dynamic desensitization report; edges in the structured knowledge graph represent relationships between legal entities, and each relationship is bound with a multi-modal evidence chain.

The invention discloses a multi-modal entity and relation extraction method and system based on cross-modal alignment and fusion, and the method comprises the steps: carrying out the processing and coding of an input text and an image, and obtaining a plurality of types of image and text features; performing feature alignment on the fine-grained and coarse-grained text features and the pixel-level image representation by taking the semantic representation of the image as an anchor point, and mapping the image and the text features to the same semantic space; performing multi-granularity feature fusion through text-guided dynamic gating aggregation, visual prefix cross-modal fusion and cross-modal image-text matching, modeling association between noun phrases and image objects in a text while increasing feature complementarity, and obtaining multi-granularity multi-modal feature representation; fusing multi-granularity multi-modal features through entity guidance attention gating, and gathering visual information related to a text entity to obtain final multi-modal fusion representation; according to the multi-modal fusion representation, task prediction of multi-modal named entity recognition and multi-modal relation extraction is carried out.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention provides a cross-domain book recommendation method and system based on a neural network, and relates to the technical field of intelligent book recommendation, and the method comprises the steps: obtaining first information containing user reading history, retrieval behaviors, research directions and paper information, and converting the first information into an initial interest topological structure and an evolution trend of a user; then, a cross-domain interest migration graph of the user is obtained through Markov clustering; and inputting the interest migration graph and the structured book text information into an extended probability latent variable semantic model to obtain a semantic embedding expression fusing user interests. Potential association scores between the user and the books are calculated through association analysis of the expression and the interest migration graph, a cross-domain recommendation list is generated according to sorting, and personalized and structured book recommendation is achieved. According to the method, deep alignment of the interest migration structure and the semantic space is realized, and the accuracy and practicability of cross-domain recommendation are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a decision-making method and device guided by a multi-modal semantic map, equipment and a medium. Extracting a visual feature vector, a language feature vector and an action feature vector, splicing to generate a multi-modal initial feature, mapping the multi-modal initial feature to a shared semantic space, constructing a multi-modal semantic map, and inputting a map-guided attention mechanism to generate a cross-modal alignment feature; the cross-modal alignment features and task targets are input into a meta-learner to generate task adaptability features, the task adaptability features are input into a parallel reasoning network to execute subtasks in parallel, and a gating fusion network integrates output results to generate a global decision. According to the method, cross-modal semantic association and task adaptability are enhanced through the combination of shared semantic space mapping, map guiding attention and a meta learning device, and the accuracy and efficiency of multi-modal decision making are improved through the combination of parallel reasoning and gating fusion.

According to the multi-modal automatic knowledge graph construction method based on the large language model, a multi-modal data stream is preprocessed, features are extracted, and the multi-modal data stream is mapped to a unified semantic space through a cross-modal alignment network after being processed through the large language model, a visual converter and a time sequence neural network. In the space, entities and categories are recognized based on a large language model, a triple is generated by combining a multi-modal feature judgment entity relationship, mapping fusion is performed through an ontology alignment algorithm driven by a graph neural network and a predefined domain ontology, finally knowledge is stored in a graph database, and dynamic updating is performed by means of incremental learning and online reasoning. Standardized APIs and visualization components are provided. According to the method, the construction efficiency and the automation degree of the knowledge graph are remarkably improved, the cross-modal information fusion and knowledge maintenance capability is enhanced, and the application requirements of intelligent retrieval, recommendation, decision support and the like are met.

The invention relates to the technical field of artificial intelligence, and discloses an AI intelligent auxiliary question answering system for student practice. According to the system, various interactive data such as texts, voices and handwritten formula images of students are collected through a multi-modal data acquisition module, semantic feature vectors of all modals are generated through a multi-dimensional feature analysis module, and then the semantic feature vectors are mapped to a unified semantic space through a cross-modal fusion module. The double-layer graph construction module constructs a domain knowledge graph and a learning behavior graph, and the dynamic reasoning and recommendation module generates a personalized problem solving path recommendation and knowledge point completion strategy based on fusion semantic representation and a graph library. In addition, the system also has the functions of interactive behavior log anomaly detection, path backtracking, knowledge forgetting curve prediction and the like. The system can comprehensively understand questions of students, provides personalized question answering service, and effectively improves the learning efficiency and knowledge mastering degree of the students.

The invention relates to the field of medical image processing, in particular to a lung cancer PET-CT fusion segmentation method and system based on multi-modal feature comparative learning, and the method comprises the steps: firstly extracting PET and CT image features, projecting the features to a shared semantic space through a semantic guide type symmetric comparative learning architecture, obtaining key region features through a focus adaptive attention sampling mechanism, and carrying out the segmentation of a target region; optimizing feature representation through a cross-modal feature difference self-calibration mechanism, constructing a multi-scale feature pyramid, fusing features of different scales by using a multi-scale hierarchical contrast learning mechanism, and performing self-supervised learning by combining an anatomical guidance self-supervised contrast learning enhancement module and using a CT anatomical structure, so as to further reinforce the features; a high-precision lung cancer lesion segmentation result is generated through a decoder network, the Dice coefficient is increased from 0.78 to 0.91, and the detection rate of lesions below 10 mm is increased from 65% to 87%. A novel efficient and accurate image processing method is provided for lung cancer diagnosis.

The invention discloses an interactive drawing generation method, system and device based on artificial intelligence, relates to the technical field of artificial intelligence, and realizes the generation of a high-quality image through multi-modal data fusion, semantic alignment, conflict detection and iterative correction. The scheme comprises the following core steps: acquiring multi-modal data such as texts, voices, sketches and the like, and processing the multi-modal data into a formatted feature data set; performing alignment and weighted fusion on the multi-modal features to generate unified semantic space features; detecting semantic inconsistency of the image and text description by using a vision-text attention mechanism, generating a conflict index matrix and triggering an alarm mark; and iteratively correcting the generated image based on the unified semantic space features, the conflict index matrix and the alarm marks. The system is composed of a data acquisition and preprocessing module, a multi-modal feature alignment and fusion module, a conflict detection and alarm module, an image generation and iterative correction module, a system control and scheduling module and the like.

The invention discloses a medical image generation method and device based on bimodal fusion, equipment and a medium, and the method comprises the steps: respectively extracting the visual features of a medical image and the semantic features of text description through an image encoder and a text encoder; mapping the two types of features to a shared semantic space by adopting comparative learning to realize cross-modal alignment; a first path captures hierarchical semantic information through a convolutional neural network, a second path retains local significant features through maximum pooling, and two paths of outputs are fused layer by layer to construct spatial context features; projecting the cross-modal features into a spatial feature map through a multi-layer perceptron, splicing the spatial feature map with coding features, and inputting the spliced spatial feature map into a decoder for up-sampling reconstruction; and performing joint optimization on the comparison loss and the structural similarity loss to realize end-to-end training. And finally, the SSIM index of the generated medical image is improved.

The invention discloses an intelligent planning method and system for a weak current system in a smart park, and belongs to the technical field of weak current intelligent design. The method comprises the steps of performing feature extraction on the weak current multi-source data of the smart park to form a weak current feature set; a multi-dimensional semantic space is constructed, semantic association features are obtained, and node features, topological relations and constraint rules of the weak current system are determined; generating a weak current knowledge graph based on the information, and performing semantic alignment on the basic information of the park to obtain a final scene demand representation; performing graph reasoning and constraint calculation according to the representation to obtain a feasible region and constraint satisfaction condition, and generating a candidate construction scheme; and screening out an optimal construction scheme from the candidate schemes according to a preset comprehensive optimization strategy and sending the optimal construction scheme to a control center. According to the scheme, the weak current scheme is promoted from demand understanding to scheme optimization, and a coherent and verifiable automatic process is formed; therefore, the manual intervention is less, the design judgment is more accurate, and the finally output construction scheme has higher engineering reliability.

The invention discloses a VLM model intelligent decision-making-based driving method and device and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: extracting key visual features from continuous multi-frame driving scene images based on a preset visual feature extraction algorithm; inputting a user instruction and a time sequence visual Token corresponding to the key visual features into a preset VLM model for multi-modal alignment, and generating a target planning Token; inputting the target planning Token and the time sequence vision Token into a preset trajectory generation model to obtain a predicted trajectory; and converting the predicted trajectory into a control signal, and controlling the mobile device to complete a moving action based on the control signal. The problem of modal difference between a semantic space and an action space is solved.

The invention discloses a multi-modal network state detection method based on large model alignment, which is used for processing multi-modal data of a communication network. The method comprises the steps that multi-modal data such as a time sequence and a text are converted into a unified semantic space through blocking and embedding, multi-modal features are aligned through cross-modal reprogramming by utilizing a text prototype library and an attention mechanism, and an injection task instruction and domain knowledge are enhanced by setting a dynamic prompt prefix so as to activate the LLM reasoning ability; then, LLM trunk model parameters are frozen, only an input and output adaptation layer is trained, and a lightweight optimal adaptation model is constructed; and generating a network state prediction result based on the adaptation layer. According to the method, dynamic alignment of multi-modal network data is achieved, the generated joint semantic representation can be further used for LLM reasoning, the purpose of more accurately sensing the network state is achieved, network state reasoning under few-sample and zero-sample scenes is supported, and the method is suitable for a heterogeneous network environment.

The invention provides a cross-modal semantic alignment method based on multi-source heterogeneous data, and relates to the technical field of data processing.The method comprises the steps that a multi-source heterogeneous data set in a preset collection task scene is input, and modal feature extraction is executed; analyzing the multi-source heterogeneous feature data set, and outputting a plurality of information bearing capacity scores; obtaining a first heterogeneous feature data source, and generating a reference template for semantic alignment; and obtaining residual heterogeneous feature data sources, establishing an alignment mapping relationship between the reference template and the residual heterogeneous feature data sources, and outputting an aligned shared semantic space. By means of the method and device, the technical problem that in the prior art, due to the fact that data of different modalities are excessively different in structure and format, semantic alignment precision is uneven, semantic omission is likely to be caused, and semantic alignment accuracy and integrity are affected is solved, multi-source heterogeneous data are analyzed, and the semantic alignment accuracy and integrity are improved. And the data source with the highest information integrity is selected as the alignment template, so that the accuracy and integrity of semantic alignment are improved.

The invention relates to a knowledge graph agent construction method and system based on multi-modal fusion, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring multi-modal data, and mapping different modal data to a unified semantic space through a cross-modal embedding technology to obtain multi-modal reference data; extracting features of the multi-modal reference data through an attention mechanism to obtain multi-modal joint features; constructing and updating a dynamic knowledge graph based on the multi-modal joint features to obtain a time sequence dynamic dependency knowledge graph; and according to the time sequence dynamic dependency knowledge graph, context-aware reasoning is carried out through a cross-modal reasoning engine to obtain a user input mode, and an interaction strategy is dynamically adjusted based on the user input mode to generate a multi-modal response. And the knowledge graph agent construction based on multi-modal fusion is realized.

The invention relates to a multi-modal understanding optimization method based on fine-grained feature extraction and global information integration. The method comprises the following steps: segmenting an input image into a plurality of local image blocks, extracting local fine-grained visual features and global visual features, and interacting the local fine-grained visual features and the global visual features based on an attention mechanism to obtain local context features; secondly, performing feature fusion on the local fine-grained visual features to generate fused visual features; mapping the fused visual features to a semantic space which is the same as the text features of the large language model to obtain projected visual features, and dynamically screening through attention weight based on the text features to obtain key visual features; and fusing the key visual features with the text features to generate joint feature representation, and inputting the joint feature representation into a large language model to generate a semantic analysis result. According to the method, global information and dynamic feature selection are introduced, so that the ability of the model to understand multi-modal content in a complex scene can be improved, and the model calculation overhead is reduced.

The invention discloses an intelligent digital human training method and system based on multi-modal interaction, and belongs to the technical field of semantic indexing.The method specifically comprises the steps that voice, vision and text data are analyzed and converted into high-dimensional feature vectors through a modal exclusive encoder, the high-dimensional feature vectors are projected to a unified semantic space through a cross-modal semantic mapping model, and the high-dimensional feature vectors are obtained; generating a semantic primitive containing a modal identifier, a core semantic tag and a feature weight; semantic primitives are used as nodes, directed edges and edge weight table association strength are established based on semantic similarity, typical scene node connection weights are strengthened, and a mesh map containing intra-modal hierarchy and inter-modal cross association is formed; constructing a double-layer index on the basis of the mesh map; semantic primitives are extracted from newly added data, the position of a new node in an association graph is determined through a graph matching algorithm, an association edge with an existing node is automatically established, and a lower-layer modal exclusive index is synchronously updated.

The invention relates to the field of radiology reports, and discloses a radiology report generation method fusing disease perception comparison and cross-modal alignment, and the method comprises the following steps: respectively extracting visual features of a medical image and text features of a radiology report through a visual encoder and a text encoder; utilizing a disease perception contrast learning module DACL to map the visual features and the text features to a unified semantic space; self-adaptive feature matching is carried out by using a dynamically updated memory matrix through a cross-modal memory alignment module CMA; a three-stage training strategy optimization model is adopted, and pre-training, reinforcement learning fine adjustment and knowledge distillation are included. Incremental knowledge is dynamically updated through text features, cross-modal feature alignment tasks are focused, coupling with feature representation is avoided, alignment efficiency is improved, a three-stage training strategy is adopted, if student model performance is better, weights are migrated, and clinical accuracy and generalization ability of the model are further enhanced.

The invention discloses a fine-grained zero-sample medical image classification method based on cross-modal feature alignment, and the method comprises the steps: 1, carrying out the partitioning of a full-section pathological image, and extracting the features of a local image block; 2, a cross-modal alignment module is used for designing a local window attention mechanism to enhance space interaction between image blocks; the semantic enhancement module is used for constructing a pathology prompt template based on a large language model to generate fine-grained category description, and expanding the distance between categories in a semantic space; and 4, performing weighted fusion on the image block features through coordinate sensing, and generating final slice-level classification prediction. According to the invention, through multi-scale space interaction of the cross-modal image block alignment module and semantic enhancement of the semantic refinement module based on the visual language model, the classification precision of the fine-grained medical image is significantly improved, and the limitation of the existing method on feature alignment and semantic differentiation is effectively solved; and an efficient solution is provided for zero-sample medical image classification.

The invention belongs to the technical field of network security, and provides an abnormal traffic detection and attack recognition method and system based on deep learning, and the method comprises the steps: data preprocessing and feature extraction, cross-modal semantic alignment and knowledge graph construction, causal enhancement association reasoning, intelligent engine optimization, cloud edge collaborative resource scheduling, and result output. According to the method, statistical features and signature features are mapped to a unified semantic space through a cross-modal semantic alignment and knowledge graph construction module, a semantic barrier between heterogeneous features is broken through, time sequence causal discovery and transfer entropy calculation are introduced, a simple correlation and a reliable causal can be distinguished, and the method has a good application prospect. According to the method, the accuracy and credibility of attack chain reasoning are improved, the false alarm rate is reduced, online self-evolution of a detection model and dynamic optimal allocation of system resources are realized through intelligent engine optimization and cloud edge collaborative resource scheduling modules, and the overall adaptability, robustness and practicability of the system are enhanced.

The invention discloses a multi-language knowledge graph construction method, system and device and a medium. The method comprises the steps of obtaining text data, voice data and image data; performing standardization processing on the text data, the voice data and the image data to obtain multi-source heterogeneous data after format unification; mapping the multi-source heterogeneous data with the unified format into a unified semantic representation space, and performing semantic annotation to generate a corresponding semantic annotation result; the embedding representations of different languages are aligned based on a comparative learning technology, the embedding of multiple languages is mapped to a unified semantic space, and a semantic annotation result is updated; and constructing a knowledge graph based on the updated semantic annotation result, and updating the knowledge graph in real time based on an incremental updating algorithm. According to the technical scheme, precise analysis of complex semantics and efficient annotation of unseen knowledge can be achieved, dynamic real-time updating of the knowledge graph is achieved, and the response speed and knowledge updating efficiency of a system are remarkably improved.

The invention discloses a listed company operation risk early warning method based on multi-source auditing and text semantic fusion, and relates to the technical field of auditing, and the method comprises the steps: S1, crawling and converging multi-source heterogeneous data of listed company financial newspapers, auditing suggestions, supervision announcements, inquiry letters, news public opinions and market transactions; according to the method, unstructured texts are subjected to cleaning, blocking and semantic vectorization processing, each text segment is embedded into a high-dimensional semantic space, a vector index is established, a bottom-layer knowledge base of an RAG framework is formed, in the stage, it is ensured that the data structure is uniform, the source is traceable, standardized input is provided for subsequent semantic retrieval and modeling, and the reliability of the system is improved. S2, a query expression is constructed based on a target company, a time window and a risk topic, dense semantic retrieval and sparse BM25 retrieval methods are comprehensively used, a time decay and source credibility weighting mechanism is introduced, and the problems that a traditional method is single in data dimension and information is split are solved.