549 results about "Semantic relation" patented technology

A system and method for latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses spatiotemporal media into navigable latent representations while preserving geometric and semantic relationships through tensor structure maintenance. A latent hyperspace manager organizes compressed representations as geodesic trajectories within a geometric manifold structure based on differential geometry principles. A geodesic trajectory mapper computes optimal navigation paths through the high-dimensional space, while symbolic anchors positioned at semantically significant locations serve as persistent reference points. Spatiotemporal routing protocols manage navigation decisions across multiple temporal scales. A strategy caching system preserves successful navigation patterns for reuse, enabling continuous learning. The system generates synthetic content during navigation to support infinite zoom capability, allowing exploration beyond original media boundaries. Cross-modal fusion combines diverse input modalities into unified representations, applicable to immersive media exploration, scientific visualization, and surveillance analysis.

The invention discloses a semantic segmentation method for a low-resolution road scene, and aims to solve the problems of difficulty in small target recognition, fuzzy details, texture information loss and the like existing in a low-resolution image in the conventional semantic segmentation technology. The method comprises the following steps: (1) collecting a low-resolution road scene image and a corresponding semantic tag; (2) constructing a semantic segmentation model consisting of an edge guidance module (BGM), a double-domain feature decomposer (DDFD), a domain alignment attention fusion module (DAAFM) and a double-layer attention context aggregation module (HACAM); (3) designing a joint loss function to carry out multi-scale supervision on semantic regions, edges and middle features; (4) carrying out model training by utilizing the road scene image; and (5) outputting a semantic segmentation result map and an edge prediction map. The boundary perception capability is enhanced by introducing learnable pixel difference convolution, the extraction precision of a small target and a global structure is improved by combining frequency domain and spatial domain feature alignment, and context semantic relationship expression is optimized by fusing a channel and a spatial attention mechanism. The method effectively improves the semantic segmentation precision and boundary restoration capability of the model in a low-resolution complex road environment, and is suitable for intelligent analysis tasks of road images in scenes of automatic driving, intelligent traffic, severe weather and the like.

The invention relates to the field of agricultural information processing and artificial intelligence, in particular to a farmland yield prediction method and system based on a heterogeneous graph neural network, and the method comprises the steps: obtaining multi-source farmland data, and extracting an initial feature vector of a farmland plot node; on the basis of the initial feature vector, constructing a heterogeneous graph structure containing multiple semantic relationships; performing node feature updating on the heterogeneous graph structure by using a heterogeneous graph neural network, and dynamically aggregating information of multiple types of neighbor nodes through relation-aware message passing and an edge propagation gating mechanism; performing enhancement processing on the node features by using a space-time dependency enhancement mechanism and a knowledge-guided reasoning mechanism; and outputting a regression prediction result of the farmland yield through a prediction module based on the enhanced node features. The invention aims to realize modeling and intelligent yield prediction based on multi-source heterogeneous data in an agricultural system, and improve the environmental adaptability, prediction generalization ability and interpretability of farmland yield prediction.

The invention discloses a multi-modal metadata alignment fusion method and device, equipment and a storage medium, and the method comprises the steps: carrying out the metadata extraction of structured data, unstructured text data and image data, and generating multi-modal metadata with semantic annotations; establishing a shared semantic embedding space, and mapping the multi-modal metadata to the shared semantic embedding space for coding to obtain a unified spatial vector; determining an alignment candidate pair from the unified spatial vector through similarity calculation, and identifying a semantic relationship of the alignment candidate pair; and performing conflict detection on the aligned candidate pairs and the corresponding semantic relationships, and resolving conflicts based on weight weighting fusion to obtain a unified metadata system. According to the method, improvement and optimization are carried out from multiple aspects of multi-modal data processing, semantic understanding, alignment accuracy, conflict resolution and the like, the defects in the prior art are overcome, and a more accurate and comprehensive multi-modal metadata alignment fusion result can be provided.

The invention discloses a method for converting a natural language into a low code based on multi-modal reinforcement learning, which comprises the following steps of: performing word segmentation, embedding and multi-layer feature extraction on a natural language instruction input by a user, combining a self-attention mechanism and a graph attention mechanism, extracting and optimizing an original semantic feature vector, and automatically identifying a business field; semantic relationship triples in the domain knowledge graph are fused, and a multi-modal semantic alignment and context enhancement strategy is adopted, so that the accuracy and stability of semantic representation are remarkably improved; when semantic drift or ambiguity is detected, a multi-candidate correction mechanism is triggered to obtain a better analysis result, and the robustness of semantic understanding is enhanced; and finally, mapping an analysis result into an instruction which can be identified by a low-code platform, automatically generating a code structure, continuously optimizing a semantic model and a knowledge graph based on user feedback, realizing adaptive learning, and improving the conversion efficiency and quality from a natural language to codes.

The invention discloses a policy recommendation method and system based on a heterogeneous knowledge graph, and relates to the field of policy recommendation, and the method comprises the steps: obtaining an original policy text from a plurality of heterogeneous policy data sources, and extracting policy-related information; mapping the policy-related information into policy entity nodes; mapping the enterprise portrait data into enterprise entity nodes; defining and generating multiple types of edges between the policy entity nodes and the enterprise entity nodes based on a preset semantic relationship so as to construct a heterogeneous knowledge graph, constructing a graph neural network model, and executing embedding calculation on various nodes in the graph so as to obtain node representation vectors; and obtaining a matching degree, and outputting a recommended policy entry set according to the matching degree as policy recommendation information of the target enterprise. The method not only breaks through the problems of format dependence, rule stiffness and semantic shallow hierarchy of a traditional policy recommendation system, but also has relatively high universality, interpretability and actual deployment value.

The invention discloses a pavement throwing detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, the model adopts an LMSADet detection head, a multi-scale feature extraction and space attention mechanism is introduced into a task branch, and multi-scale modeling is decoupled from a backbone network and a neck network and integrated to the detection head so as to fit a detection task to directly optimize local details and scale differences of a throwing target. In order to suppress background interference and improve the recognition effect of fuzzy boundaries, the neck network is added into an MSHA module so as to efficiently capture the semantic relation between the thrown object and the background and enhance the regional understanding ability. A C3ESP module is introduced into the backbone network, deep features are extracted through stacking depth separable convolution, and information loss is avoided in combination with residual optimization fusion; meanwhile, a PEMA attention mechanism is introduced, the importance of different receptive field features is dynamically adjusted, the model focuses on key features, data information is captured more comprehensively, and therefore the detection performance is remarkably improved.

The invention relates to an automatic assembly system and method for multi-scale digital twin components, and the system comprises a semantic relationship construction module which is used for carrying out explicit definition on the structural features, functional attributes, spatial layout requirements and logic dependency relationships of the multi-scale components, and constructing semantic relationships among the three types of components; the semantic reasoning and constraint engine module is used for carrying out logical reasoning through the semantic relationship constructed by the semantic relationship construction module and judging whether the component combination meets the assembly constraint or not; the assembly generation and configuration module is used for generating an assembly topological structure and a connection sequence of a system shelf according to the component candidate set output by the semantic reasoning and constraint engine module; and the man-machine interaction and visualization module is used for supporting a feedback closed loop between the engineer and the system and providing a visual display interface. The problems that an assembly method depends on artificial experience and is difficult to support high-frequency and multi-scene production line reconstruction are solved, and the method has higher semantic interpretation capacity, automatic combination capacity and context adaptive capacity.

The present application provides a large language model-based query statement generation method, apparatus, and device, and a medium. The large language model comprises a plurality of encoders. The method comprises: acquiring a query text; performing vectorization processing on the query text to obtain a target vector representation corresponding to the query text; inputting the target vector representation into the plurality of encoders, to perform encoding processing on the target vector representation by means of weight matrices of the encoders, so as to obtain context vectors corresponding to the encoders, wherein different encoders have different weight matrices; and generating a target query statement on the basis of the context vectors corresponding to the encoders. A model is allowed to focus on different information features in different attention heads, independently capture different aspects of a query text, and process information at multiple abstraction levels, thereby better capturing complex and abstract semantic relationships, effectively distinguishing the importance of information when processing complex and fuzzy questions, improving the accuracy of query statement generation.

The invention relates to a cross-modal document information extraction method based on space-semantic alignment, and belongs to the field of artificial intelligence, computer vision and natural language processing. According to the method, the spatial feature and semantic information bidirectional alignment model is designed, by constructing the spatial feature and semantic feature bidirectional alignment model, the document layout information can dynamically adjust attention distribution of text semantic features, meanwhile, semantic information reversely optimizes the spatial features, collaborative modeling of spatial layout and semantic information is achieved, and the document layout efficiency is improved. Therefore, the accuracy and robustness of complex document information extraction are improved. According to the method, a hierarchical cross-modal information extraction model is designed, through the hierarchical cross-modal information extraction model, the overall structure of a document is recognized on the global level, local key content is focused on the regional level, fine modeling is conducted on fine-grained texts and visual elements on the entity level, and accurate recognition of a cross-modal entity and the semantic relation of the cross-modal entity is achieved; and the generalization ability and applicability of information extraction are enhanced.

The invention relates to a man-machine interaction dialogue method, system and device based on a natural language and a medium. The method comprises the steps that firstly, multi-modal interaction data is acquired and preprocessed, and segmented words and syntax are analyzed through a natural language processing technology to construct intention feature vectors; combining the intention feature vector with a historical dialogue record, and using a pre-trained language model to generate context semantic elements containing a semantic relationship; if the context semantic elements are matched with the preset scene feature library information, predicting a user intention change trend by adopting a reinforcement learning model to obtain an intention prediction result; and finally, evaluating user intention change based on an intention prediction result, extracting associated domain knowledge by utilizing a knowledge graph if significant change occurs, and inputting the associated domain knowledge into a dialogue generation model to obtain a natural language reply sequence. According to the method, the understanding precision of the user intention is improved, the dynamic prediction of the intention change is realized, the relativity and coherence of reply are guaranteed, and a more efficient processing path is provided for natural language man-machine interaction.

The invention discloses an entity digitization and link framework algorithm based on a heterogeneous graph attention network, and the algorithm comprises the following steps: S1, heterogeneous information network construction: carrying out the unified modeling of all multi-source heterogeneous data into a heterogeneous information network containing various types of nodes and various types of edges, s2, meta-path definition and guidance: defining "meta-paths" connecting different types of nodes to capture a complex deep semantic relationship, S3, heterogeneous graph attention network embedding: adopting an attention mechanism to enable a model to automatically learn importance of different neighbor nodes and different meta-paths, generating a final embedding vector of each entity, and establishing a heterogeneous graph attention network model; according to the method, the information fidelity is higher, modeling is directly conducted on different types of nodes and relations on a heterogeneous graph, more abundant and heterogeneous semantic information in data can be reserved compared with a multi-view method, and the end-to-end learning ability is higher; and the complexity of manually designing a fusion strategy is reduced.

The invention discloses a retrieval enhanced warehouse-level code completion method based on semantic topology and disambiguation redundancy pruning. Firstly, a key value code library for accurate mapping of source code fragments and induction sub-graphs is constructed through a specific slicing algorithm; in the retrieval stage, four-level layered optimization is adopted, structural similarity is evaluated by extracting a deep semantic relationship, trimming accurate duplicate items and using a new graph-based measurement (tradeoff is performed on editing according to topological importance), results are reordered to maximize correlation and diversity, candidate items are systematically refined, and the retrieval efficiency is improved. The problems of retrieval redundancy solidification and surface similarity misleading are solved, meanwhile, cross-module dependence is analyzed through an external perception identifier disambiguator, and the problem of cross-file symbol ambiguity is solved; and finally, fusing an optimization result to generate a prompt to drive the LLM to generate higher-quality output. According to the invention, through coordination of semantic and structural signals, strong performance can be obtained even in a large-scale and resource-limited code library. Meanwhile, the design allows it to be orthogonally complementary to other cross-file methods, providing collaborative improvements when used in combination.

The invention discloses a special reasoning model-oriented data set construction method, and particularly relates to the technical field of data set construction. According to the method, domain task text information, rule constraint information and causal dependency information are analyzed, and a dependency structure chart of a target reasoning task is constructed; on the basis of the dependency structure diagram, topology path expansion and anti-fact topology path backtracking analysis are carried out according to the semantic relation direction and the constraint reverse relation, forward path topology feature data and reverse topology verification data are formed, and therefore a forward and reverse dependency consistency matrix is established for topology conflict recognition, and a consistency correction result is output; and finally, according to a consistency correction result, carrying out topology label labeling, topology equivalence judgment and multi-path admission screening to obtain a topology fidelity sample set, and carrying out dependent signature labeling and topology label coding to generate a special reasoning model training data set of the target reasoning task. And the reasoning path reliability and the data set structuring level of the special reasoning model are improved.

The invention provides a ship design drawing structured vector analysis method and an intelligent question and answer method. The method comprises the following steps: constructing a drawing primitive initial data set; generating a semantic annotation entity set; mapping the semantic annotation entity set to a two-dimensional coordinate system to generate a relation annotation entity pair set; repeated entities with the same semantic content and the coordinate position error lower than a set threshold value are removed, and an effective entity relation set is constructed; converting the effective entity relationship set into a structured semantic relationship set; inputting the drawing primitive initial data set into a neural network target detection model to generate an image recognition entity set; and fusing the structured semantic relationship set and the image recognition entity set to construct a drawing semantic map, finely adjusting the large language model, and outputting a text question and answer result. According to the method, ship drawing automatic analysis and semantic question and answer efficient fusion are realized, and the structure understanding precision and the interaction response capability are improved.

The invention discloses a medical clinical data quality analysis method and system based on a knowledge graph, and relates to the technical field of medical data analysis. The method comprises the following steps: constructing a dynamic medical field knowledge graph fusing medical ontology and semantic relationship, accessing multi-source heterogeneous clinical data into the graph through entity linking and standardized mapping, and generating a personal data sub-graph containing time sequence information for each patient; through combination of logical reasoning based on a semantic rule base and anomaly detection based on a graph neural network, deep discovery and accurate positioning of known contradictions and unknown mode quality problems are realized, and a targeted repair scheme is generated by utilizing the traceability of a graph; quality problems are quantitatively graded and fed back to the atlas, and closed-loop management is formed; according to the method, the limitations of isolated examination and lack of semantic understanding of a traditional method are overcome, the transformation from passive verification to active prevention is realized, and the control precision and efficiency of the medical data quality are remarkably improved.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

The invention relates to a knowledge graph enhanced multi-modal file retrieval method, and belongs to the field of artificial intelligence and multi-modal information retrieval. The invention aims to solve the problems of multi-modal information semantic segmentation, weak semantic reasoning ability and low semantic matching precision in the existing multi-modal archive resource retrieval process. Through four stages of archive multi-modal data preprocessing and feature extraction, knowledge graph construction and enhancement, semantic retrieval request analysis and intention modeling, and multi-modal semantic matching and sorting, a semantic relationship is enhanced by utilizing a knowledge graph, so that the semantic relevancy and context consistency of a retrieval result are remarkably improved; a user is allowed to input and inquire in various forms such as texts, images and voices, and semantic extension retrieval is supported. According to the method, more efficient and accurate archive resource retrieval can be realized, and the user retrieval experience and archive knowledge utilization are improved.

A system and method for generation-augmented latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses media into latent representations while preserving geometric, temporal, and semantic relationships. A latent hyperspace manager organizes compressed data as geodesic trajectories, and a geodesic trajectory mapper computes navigation paths. Symbolic anchors provide persistent reference points, while spatiotemporal routing coordinates decisions across multiple scales. A strategy caching system preserves successful navigation patterns for reuse as procedural memory. A synthetic content generator including latent diffusion models, neural radiance fields, and context-aware refinement produces augmentation for continuous zoom, bidirectional traversal, and rotational reorientation. A user input interface and zoom controller enable interactive exploration and reconstruction, supporting applications in immersive media, visualization, and surveillance.

The invention discloses a dialogue scene data visualization analysis method and system based on a knowledge graph, and relates to the technical field of data visualization analysis, and the method comprises the steps: obtaining dialogue scene data and context attributes, extracting related entity nodes and relation edges from a pre-constructed knowledge graph to construct a knowledge sub-graph, the dialogue data and the knowledge subgraph are fused to obtain dialogue semantic structure data, node importance analysis, semantic path analysis and intention clustering analysis are carried out based on the data to generate an analysis result set, and a dialogue semantic relation graph, a dialogue process path graph and a dialogue hotspot distribution graph are constructed respectively; a comprehensive visual analysis result is generated through unified coordinate mapping and weighted fusion, and meanwhile, a local semantic structure and a visual result are dynamically adjusted for newly added dialogue data in an incremental updating mode, so that real-time monitoring, mode recognition, semantic evolution accurate tracking and intelligent visual display of a dialogue scene are achieved.

The invention provides a metering system authentication management system and method. The method comprises the following steps: constructing a knowledge graph for metering system authentication and auditing through to-be-audited authentication data and an associated standard document; determining constraint conditions for sampling and auditing the to-be-audited authentication data according to semantic relationship edges in the knowledge graph; determining auditing risks of different data categories in the initial auditing sample authenticated by the target enterprise metering system; determining the risk weight of each data category in the to-be-audited and authenticated data in the knowledge graph according to the association relationship between the data auditing elements in the knowledge graph and the association standard document; determining a sample utility value of each data category in the initial auditing sample during sampling auditing based on all the risk weights and all the auditing risks; and dynamically updating the initial auditing sample according to all the sample utility values. By adopting the scheme, the sampling auditing direction can be guided based on the semantic relationship and the risk difference between the authentication data so as to improve the coverage precision when sampling auditing is carried out on the metering system authentication data.

The invention discloses an electric power field credible question-answering system based on a knowledge graph and multiple agents, and belongs to the technical field of intelligent power grids. The electric power field credible question-answering system comprises the steps that an existing knowledge base is stored through a vector knowledge base, the knowledge graph and a relational database, and then a multi-agent system is constructed based on a large language model; a complex problem is decomposed into a plurality of sub-problems by adopting a logic form language in real-time dialogue, solving is carried out based on operators such as planning, reasoning and retrieval, symbolization and structuring of a problem solving process are realized, knowledge is aligned and integrated by using a semantic reasoning technology, a semantic relationship between the knowledge is identified and established, and a real-time dialogue result is obtained. And realizing credible questions and answers in a manner of recalling related knowledge. According to the method, the deep fusion of the unstructured text, the structured data and the semantic relationship is realized by constructing the multi-modal knowledge storage system, the knowledge coverage rate and the semantic consistency are remarkably improved, and the understanding and response capabilities of the system to compound problems are improved.

The invention provides a medical insurance knowledge base automatic construction method based on multi-agent collaboration, and relates to the technical field of medical insurance knowledge base construction.The method comprises the steps that firstly, multiple agents with differentiated medical insurance knowledge backgrounds and functions are defined and configured, and standard behaviors are configured for all the agents; driving multiple rounds of dialogues of the agent by the initial query, collecting and fusing dialogue data to extract a core site and a semantic relation graph, constructing a collaborative decision model to reach a consensus, and compiling the collaborative decision model into structured medical insurance knowledge information; a third-party verification agent is introduced, a knowledge pedigree diagram is constructed based on knowledge entries and traceability data, credibility is calculated through an evidence theory fusion decision algorithm, conflicts are resolved, and suggested adoption entries are output; a knowledge base is stored and generated, a retrieval enhancement generation question and answer system is built, user feedback is recorded, regular new topic discussion and artificial expert intervention are combined, continuous learning and dynamic iteration of the knowledge base are achieved, and a structured medical insurance knowledge base can be automatically built and dynamically updated to support intelligent question and answer.

The invention provides an artificial intelligence semantic processing system and method oriented to digital media creation, and relates to the technical field of artificial intelligence semantic process.The artificial intelligence semantic processing method comprises the steps that predicate argument relation pairs of language texts are extracted, object space relation pairs of sketch images are extracted at the same time, and a basic semantic unit set is constructed; the integrity and accuracy of cross-modal semantic understanding are ensured, further, semantic units are clustered by using a dynamic routing algorithm, a semantic concept cluster with a clear importance weight is generated, deep mining and structured representation of creation intentions are realized, and the creation intentions are quickly and accurately understood. An initial semantic relation graph is constructed, a graph attention network is used for dynamic reweighting, finally, an enhanced dynamic semantic graph is generated, complex association and a hierarchical structure between semantic concepts are effectively captured, finally, hierarchical analysis is carried out on the semantic graph, and a structured semantic blueprint is output, so that the dynamic semantic graph is obtained. And a reliable semantic processing technology is provided for creation of high-quality digital media contents.

The invention discloses a functional programming intelligent recommendation method and system based on a semantic knowledge graph, relates to the technical field of data processing, and constructs a high-dimensional semantic embedding space by performing semantic feature vector extraction on a multi-source functional programming corpus. The method comprises the following steps: firstly, embedding a plurality of candidate entity clusters into a semantic embedding space, preliminarily clustering the embedded vectors by utilizing a semantic aggregation pre-screening mechanism to obtain each candidate entity cluster and performing dynamic processing, then constructing a semantic relationship among the candidate entity clusters according to neighborhood distribution of the semantic embedding space, performing self-adaptive correction on inter-cluster distribution, and constructing a semantic knowledge graph generation recommendation method. A closed loop is formed from the corpus to the knowledge graph to the recommendation strategy, so that the intelligent recommendation system can accurately recognize the function semantic relationship, reduce noise interference and improve the recommendation hit rate and the personalized matching effect, and the accuracy, stability and user satisfaction of functional programming intelligent recommendation based on the semantic knowledge graph are remarkably enhanced.

The invention provides a multi-document key phrase extraction method based on graph structure node influence, and relates to the technical field of natural language processing and text mining. Firstly, a candidate phrase set is generated through noun phrase extraction and standardization; secondly, a semantic relation between phrases is captured through local subgraph construction and a sliding window mechanism, and the semantic relation is integrated into a global phrase co-occurrence graph; thirdly, dynamically dividing theme communities based on two-dimensional structure entropy minimization and a potential game model, and identifying phrase groups with high semantic aggregation; then, cross-topic nodes are processed through a structure entropy heuristic function, and flexibility of topic division is enhanced; and finally, in combination with node influence sorting, extracting key phrases with theme representativeness and propagation capability. The method does not need to label data, is suitable for multiple fields of academic literatures, news texts and the like, has high efficiency, accuracy and universality, and provides an innovative solution for multi-document key phrase extraction.

The invention belongs to the field of automatic driving algorithms, and particularly relates to a vehicle track prediction method based on implicit map feature expression. Comprising the following steps: step 1, a track-map semantic relation alignment preprocessing mechanism based on a map topological structure; 2, a negative sample generation method based on map clustering preprocessing; 3, constructing a track-map semantic feature comparison learning network; and 4, establishing a universal fusion mechanism for the semantic features of the implicit map. Compared with an existing trajectory prediction method depending on a high-precision map, the method has the advantages that high-precision trajectory prediction can be realized without high-cost map information, and the robustness and generalization of the model in a map missing or distorted scene are improved. Meanwhile, the method can be seamlessly combined with various map-free prediction models, and has universality and expansibility.

The invention provides a semi-supervised spine segmentation method based on a global-local semantic constraint visual language model. Comprising the following steps: respectively extracting spine MR slice image embedding and structure description text embedding by using an image encoder and a text encoder; learning the semantic relationship between the image blocks and the text in the local level to measure the uncertainty of the local view; the Wasserstein 2 distance between the cross-modal distribution representations is measured in a global level so as to standardize the global semantic similarity; generating a prompt guide mask based on a pre-trained GLsc model in combination with a text and an unmarked slice, and reinforcing semantic constraint through feature supervision and region alignment loss; predicting results of student and teacher models are fused to generate a high-quality pseudo label, and a segmentation network is optimized by means of supervised and consistent loss. According to the method, the prompt guide mask is introduced into spine segmentation semi-supervised learning, the VLM cross-modal uncertainty perception capability is enhanced through the two-dimensional semantic constraint, the pseudo tag quality and the segmentation precision are effectively improved, and an innovative technical normal form is provided for medical image semi-supervised segmentation.

The invention discloses a government affair complaint dispatching system and method based on a dynamic knowledge graph and a decision engine, and relates to the technical field of government affair automation. The government affair complaint dispatching system and method based on the dynamic knowledge graph and the decision engine comprises the following steps: S1, collecting complaint work order monitoring data, and preprocessing the complaint work order monitoring data; s2, constructing an initial graph structure, calculating weight data of each semantic relation edge according to a period, updating the initial graph structure, and constructing a dynamic knowledge graph; s3, evaluating the risk emergency degree of each work order, and generating an emergency work order identifier based on an evaluation result; and S4, extracting all the emergency work orders, executing scheduling evaluation calculation on the current undertaking department, and judging whether the execution department distributes the work orders again or not based on an evaluation result so as to realize order distribution execution and dynamic scheduling closed loop. The problem that high-risk complaints cannot be recognized and scheduled in time due to the fact that an order dispatching strategy cannot be adjusted in real time according to work order complexity and department bearing capacity in the prior art is solved.

The invention relates to the technical field of semantic understanding, in particular to a three-dimensional scene semantic understanding method and system based on multi-modal deep learning, and the method comprises the following steps: collecting a point cloud image and a depth map in an automatic driving scene, carrying out the normalization standardization and deletion filling, extracting texture geometric space features, and carrying out the fusion through an attention mechanism; a multi-time-step state vector is introduced to calculate change features, a spatial relation between road participation objects is modeled, a dynamic instance graph structure is constructed, semantic tags are reasoned, and fusion features are compared to generate a three-dimensional scene semantic understanding result. According to the method, the fusion quality is guaranteed through multi-source data normalization standardization, the semantic complementarity is enhanced through collaborative extraction of image texture and point cloud geometric features, the dynamic scene perception ability is improved through state vector modeling, the object interaction semantic relation is described through a spatial relation graph, and the recognition accuracy and consistency are improved through a semantic label reasoning mechanism. And the integrity and robustness of three-dimensional semantic understanding are integrally enhanced.