602 results about "Semantic enhancement" patented technology

The invention discloses an automatic label labeling and classifying method and system oriented to unstructured system documents, and relates to the technical field of artificial intelligence. The method comprises the steps that semantic structure pre-analysis is conducted on an original system text, and a system semantic structure tree is constructed; establishing a system semantic enhancement vector space based on the semantic units and the logic relationship thereof; performing semantic deconstruction on the preset tag and extracting a feature vector; realizing cross-space semantic matching of the document and the tag through a system semantic attention mechanism; a confidence evaluation module is introduced to screen high-confidence labels from the three dimensions of structural integrity, coverage and logic consistency; and outputting a final label and a score through semantic conflict detection and resolution. According to the method, the problems that in the prior art, unstructured system text labeling accuracy is low and large-scale labeling samples are dependent on polysemy ambiguity, high context dependency, complex semantic structure and the like are solved, and labeling accuracy and robustness are remarkably improved.

The invention discloses a semantic enhancement auxiliary inquiry system and method based on a medical knowledge graph, and the system comprises a patient information receiving module, a theoretical knowledge graph storage library, an evidence-based knowledge graph storage library, a double-track diagnosis path construction module, a medical knowledge conflict judgment module, and a semantic enhancement report generation module. The double-track diagnosis path construction module inquires the received patient information in a theoretical knowledge graph storage library and an evidence-based knowledge graph storage library which are independent from each other in parallel, and a theoretical diagnosis path and an evidence-based diagnosis path are generated respectively; the medical knowledge conflict judgment module dynamically compares the two paths in real-time interaction, and performs priority judgment according to a preset medical judgment rule; finally, the semantic enhancement report generation module presents the two paths and the conflict judgment result to the user at the same time. Transparency and interpretability of the whole interrogation process are assisted, and reliable semantic enhancement decision support is provided for doctors when the doctors face complex medical knowledge conflicts.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention discloses an electronic medical record LLM generation method based on animal injury, which realizes dialogue structuring and timestamp synchronization through multistage speech recognition and role affiliation. Using standardized medical term mapping and coding to align the free text to a standardized medical entity, and constructing a high-confidence medical entity network based on a semantic anchor point pool; according to the method, context-sensitive entity relationship extraction is realized by combining a large language model and a semantic enhancement template, a high-accuracy structured relationship chain is generated through clinical logic rule set verification, and finally, an electronic medical record template under diagnosis and treatment specifications is automatically filled and privacy desensitization processing is completed. The semantic consistency, the structural accuracy and the data security of automatic generation of the electronic medical record are improved, and standardization and intelligent circulation of medical information are effectively promoted.

The invention provides a multi-modal fusion and semantic enhancement train positioning method and system, and belongs to the technical field of rail transit, and the method comprises the steps: carrying out the time-space alignment of data, obtaining a dense point cloud, constructing a dense semantic point cloud, and dynamically estimating the confidence coefficient weight of each type of sensors; a set residual error and a Manhattan structure constraint residual error of a plane are constructed, laser radar point cloud parameters are obtained, and visual projection constraints are constructed at the same time; constructing a comprehensive degradation scoring function to carry out degradation judgment on the current environment; when the degradation result is yes, introducing a structure and motion information independent of an external environment, maintaining trajectory estimation, and constructing a compensation constraint; introducing a prior semantic constraint and a large model semantic factor constraint; and constructing a global optimization objective function, dynamically adjusting the weight of each modal factor, obtaining an optimal estimation state, and outputting a high-precision train positioning result. According to the method, high-precision and robust track estimation in an extreme scene is realized, so that the continuity, safety and intelligence of train positioning are guaranteed.

The invention belongs to the technical field of intelligent control, particularly relates to a deep learning-driven intelligent home scene dynamic adaptation method, and aims to solve the problem that an existing intelligent home system is difficult to realize high-precision personalized scene adaptation in a multi-user and multi-device environment due to dependence on a static rule. The method comprises the steps of collecting multi-source heterogeneous user behavior data and performing semantic enhancement preprocessing, constructing a hierarchical time sequence behavior coding model to extract local time sequence dependence and cross-equipment long-range association features, clustering to generate a dynamic scene prototype and mapping the dynamic scene prototype into an executable condition-action rule, after the rules are deployed, a closed-loop optimization mechanism is constructed through explicit and implicit user feedback, and online incremental updating and self-adaptive evolution of the behavior model and the scene rules are achieved. According to the technical scheme, the user complex behavior mode can be deeply understood, the scene adaptation precision is continuously optimized, the individuation level, logic consistency and system robustness of intelligent services are improved, and meanwhile privacy safety and real-time response are guaranteed through edge calculation.

The invention provides a code generation and evaluation method and system based on RAG and a multilevel decision tree, and the method comprises the steps: integrating project related design documents, and constructing a knowledge base capable of semantic retrieval through a vectorization technology; associating business demand description with related documents in the knowledge base based on an RAG technology, and performing demand semantic enhancement to generate a technology demand cue word; receiving the technical requirement cue word by adopting a large language model so as to generate a complete code conforming to business logic; constructing a four-level decision tree evaluation system, and sequentially executing code quality scanning, deployability verification, dynamic test verification and demand satisfaction verification through an evaluation assembly line to generate an evaluation result; and generating an optimization suggestion according to the evaluation result so as to trigger an iteration generation process when the code does not pass the verification, thereby solving the problems of disjunction between code generation and business requirements, low verification efficiency and insufficient iteration optimization.

The invention provides a science and technology information deep analysis method and system based on cross-modal semantic enhancement, and relates to the technical field of science and technology information analys.The method comprises the steps that firstly, a cross-modal semantic anchor point set is constructed, and the cross-modal semantic anchor point set comprises text theme anchor points extracted from science and technology information texts, visual object anchor points extracted from images and the association mapping relation of the text theme anchor points and the visual object anchor points; constructing a semantic conduction path between anchor points based on the cross-modal semantic anchor point set, realizing bidirectional information transmission, generating a cross-modal semantic enhanced representation, performing hierarchical semantic analysis on the enhanced representation to obtain a topic association rule, a technical element dependency relationship and a concept evolution sequence, and integrating the topic association rule, the technical element dependency relationship and the concept evolution sequence into an analysis conclusion; the analysis conclusion is reversely mapped to adjust the association mapping relation strength, an updated set is obtained, finally, a structured science and technology information analysis report is generated based on the updated set, logic connection of all modules is achieved, and comprehensive and accurate science and technology information analysis is provided for users.

The invention relates to the technical field of natural language processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a document interpretation and report generation method, device, equipment and medium, which comprises the following steps: receiving an original document set to generate a structured document object, executing optical character recognition on an image content set to generate a recognition text set, the recognition text set and the text content set are combined into a unified text sequence, element item extraction is executed based on the interpretation template parameter set to generate an interpretation element set, a retrieval enhancement context is retrieved and generated from the domain knowledge base, and the unified text sequence, the interpretation template parameter set and the retrieval enhancement context are input into a language model to generate an interpretation result. And generating report content based on the historical report template set. According to the method, automatic closed loop of document interpretation and report generation is realized through multi-modal unified processing and semantic enhanced reasoning, the efficiency is improved, and the manual dependence and compliance risk are reduced.

The invention belongs to the related technical field of product detection, provides a multi-view zero sample anomaly detection method and system based on cross-modal prompt reasoning, and aims at solving the problem of multi-view zero sample anomaly detection by constructing core technologies such as multi-view pose estimation and alignment, static-dynamic prompt collaboration, vision-language progressive fusion, feature space semantic enhancement and the like. And a set of end-to-end anomaly detection and reasoning system is formed. Particularly, a collaborative mechanism of a dynamic learnable prompt pool and a static attribute prompt library is designed, deep fusion of prompts is realized through cross attention, and multi-view feature compression and semantic decoding are performed by adopting a visual angle self-adaptive hybrid expert model. Zero sample anomaly detection and visual question and answer performance is further improved on multiple industrial public data sets, and the method can be widely applied to industrial precision part quality inspection, intelligent manufacturing and other complex scenes needing high-precision and multi-view perception and semantic reasoning.

The invention discloses an incomplete multi-view multi-label data classification method based on semantic enhancement and pseudo-label uncertainty perception, and the method comprises the steps: employing a dual-channel feature extraction and decoupling module to obtain the shared semantic representation and specific representation of each view in each sample for a constructed incomplete multi-view multi-label data classification network model; performing cross-view fusion on the shared semantic characterization and the specific characterization, obtaining a unified shared characterization and a unified specific characterization corresponding to each sample, performing feature fusion, obtaining a fusion characterization of each sample, inputting the fusion characterization of the sample output by the dual-channel feature extraction and decoupling module into a classifier for multi-label prediction, and performing multi-label prediction on the fusion characterization of the sample. Therefore, a multi-label classification prediction result is obtained, and model training is carried out based on a total contrast learning loss function and a joint supervision classification loss function. According to the method, the classification performance and the model robustness on incomplete multi-view multi-label data are remarkably improved through training learning under the guidance of semantic enhancement and uncertainty.

The invention relates to the technical field of intelligent decision rule base construction, and discloses an intelligent decision framework construction method and device based on a dynamic ontology. The method comprises the following steps: acquiring original data streams from a plurality of heterogeneous data sources in real time, and constructing an initial dynamic ontology structure through a dynamic ontology modeling unit; performing semantic annotation and relation extraction on the original data flow by using the structure to generate semantic enhanced data; generating a candidate decision rule set based on the semantic enhancement data, and obtaining a verified rule set through consistency verification and conflict detection; calculating the adaptability score of the verified rule set in combination with the real-time environment data, and screening out an optimal decision rule subset according to the score and a preset threshold value; the method is integrated into an intelligent decision rule base, and an optimization loop is triggered based on an update state of the base. According to the method, the data utilization efficiency and the rule quality are improved, the rule base can dynamically adapt to the environment, and decision effectiveness and reliability are enhanced.

The invention relates to a multilevel semantic enhanced educational knowledge graph construction method, belongs to the technical field of artificial intelligence and educational information processing, and aims to solve the problems of insufficient semantic understanding, inconsistent structure, high labor cost and the like in existing educational knowledge graph construction. According to the method, an education text is preprocessed, a multi-level knowledge point catalog and a dependency relationship are constructed, entity relationship extraction and attribute completion are performed in combination with a large language model, a knowledge point precedence relationship is identified by utilizing a random forest model, and the structure normalization is further improved by adopting an entity disambiguation mechanism. And the constructed knowledge graph is managed in a centralized manner through a visual interface and imported into a graph database to realize structured storage. The method has the advantages of being clear in structure, accurate in semantics, high in construction efficiency and the like, and is suitable for automatic knowledge modeling and management of multidisciplinary education content.

The invention discloses a remote sensing image semantic change detection method based on semantic enhancement Transform and three-dimensional convolution, and aims to solve the problems of insufficient semantic and change information fusion and difficult narrow and long ground feature feature extraction in the existing method. According to the method, an SET-3DC network is constructed, 'feature extraction-feature interaction-semantic enhancement-change information fusion 'is taken as a core link, multi-scale dual-time-phase features are extracted through a dual-branch feature extractor (DFE), cross-branch feature interaction is realized through a channel feature exchange module (CFE), and multi-scale dual-time-phase features are extracted through a multi-scale dual-time-phase feature extraction module. A semantic branch decoder (SEAA-SD) based on semantic enhancement and axial attention strengthens local details, global semantics and narrow and long ground feature features, a change information extraction module (CIE) based on three-dimensional convolution fuses multi-source change information and aligns time-space correlation, and a network is optimized in combination with a joint loss function. Through multi-module cooperation and innovative structural design, the precision and internal consistency of semantic segmentation and change detection are improved, and the method is suitable for a high-precision remote sensing image semantic change detection scene.

The invention relates to the technical field of electrical digital data processing, and discloses an AI semantic enhanced unstructured manufacturing document data structuring method, which comprises the following steps: acquiring a character recognition stream of a target document to extract a semantic anchor point and topological coordinates thereof, and vectorizing a to-be-structured field to generate a to-be-processed vector; querying an engineering logic mapping table to determine association intensity, and calculating a logic gravitational field intensity value of the to-be-processed vector relative to the semantic anchor point according to the association intensity; associating the to-be-processed vector to a target semantic anchor point according to the field intensity value, starting a logic polarization program when the to-be-processed vector is identified to be interfered by the homogeneous semantic anchor point in the association stage, extracting bias characteristics to generate a polarization vector, and modulating the native gravitational field intensity; according to the method, the attribution ambiguity of similar semantic entities in a dense distribution scene is solved by introducing the logic gravitation constraint, the structured conflict caused by spatial distribution and engineering logic decoupling is eliminated, and the topology consistency in the data conversion process is ensured.

The invention discloses a multi-modal rumor detection method fusing texts, images and social propagation structures, and belongs to the technical field of natural language processing, computer vision and causal reasoning. Specifically, the invention provides a unified causal inference framework, and hybrid deviation in multi-modal data is effectively stripped by integrating text anti-fact causal inference and an image dot product causal intervention mechanism. Under the framework, social propagation structure features are further fused, and a multi-head collaborative attention mechanism is adopted, so that deep alignment and semantic enhancement in cross-modal features are realized. Adversarial samples are generated through projection gradient descent for adversarial training, and model parameters are optimized in combination with anti-fact loss, so that the classification accuracy and generalization ability of the model are improved. According to the rumor detection method, a causal reasoning normal form is introduced into a rumor detection task, the effectiveness of an anti-fact and intervention mechanism in a complex information scene is verified, and a new theoretical support and method path are provided for constructing a credible multi-modal information system.

The invention discloses a target guiding navigation method which comprises the following steps: S1, coding an environment image by adopting a multi-teacher distillation pre-trained RADIO framework, extracting an initial visual feature, and splicing the initial visual feature with a target embedded vector to obtain a cross-modal fusion initial input feature; s2, generating causal alignment fusion features through global hybrid factor estimation, residual elimination and anti-fact co-occurrence matrix intervention; and S3, inputting the features into the LSTM, and generating a navigation action strategy through double-commentator conservative value estimation, Gaussian perturbation and semantic post-view playback training in combination with a cross attention fusion semantic graph relationship and time sequence features. According to the method, multi-source prior knowledge is fused to improve visual feature generalization, hybrid interference is separated through a causal de-confusion mechanism, semantic enhancement is combined to reinforce learning training, the problems of category dependence, false correlation, insufficient exploration efficiency and the like of an existing method are solved, and the stability, causal rationality and training effect of a navigation strategy are improved.

The invention provides an efficient image super-resolution method and device based on dual guidance semantic enhancement, and relates to the technical field of image super-resolution, visual content generation and the like in computer image process.The method comprises the steps that an image super-resolution model containing a pre-training diffusion model backbone structure is constructed, and a pre-training diffusion model is constructed; a trainable LoRA module is inserted into a U-Net structure and a VAE encoder of the image super-resolution model; calculating the perception image loss of the generated image and the real image by adopting a dual-guidance quality enhancement training strategy; based on a semantic alignment method of score matching, performing semantic feature constraint on the generated image through a condition vector of a pre-training diffusion model to form semantic consistency loss aligned with real image distribution; and carrying out end-to-end training on the image super-resolution model under joint optimization of perceptual image loss and semantic consistency loss, and after the training is finished, realizing high-resolution generation of a low-resolution image only through one-time U-Net reasoning.

The invention discloses a hierarchical semantic enhanced large model zero sample learning method, which is characterized by comprising the following steps: firstly, performing semantic enhancement on category names by using a large language model to generate rich text description, and constructing a dynamic and hierarchical semantic prototype by combining original semantic information, the prototype comprising global concepts, local attributes and relation representations; secondly, extracting global semantic features and local detail features of the image by adopting a vision-language large model and convolutional neural network double-branch structure, and fusing the global semantic features and the local detail features through an attention mechanism to obtain enhanced visual representation; finally, multi-level global alignment, local alignment and relation alignment are designed and jointly optimized, accurate mapping of enhanced visual features and hierarchical semantic prototypes is achieved on multiple granularities, and classification of invisible categories is finally completed. The objective of the invention is to solve the problem of limited model generalization ability caused by insufficient semantic representation and single vision-semantic granularity in the existing zero sample learning method, and to enhance semantic representation by introducing knowledge of a large language model and innovatively implement hierarchical alignment, so that the robustness of the zero sample learning method is improved. And the recognition precision and robustness of the model in traditional and generalized zero sample learning scenes are remarkably improved.

The invention provides a class incremental learning method based on prompt guidance and multi-modal fusion, and relates to the technical field of artificial intelligence and computer vision. The method comprises the following steps: firstly, performing semantic extension on a category label, and constructing semantic enhanced text representation through a text encoder; then block embedding and hierarchical feature extraction are carried out on the input image by using a pre-trained visual encoder, a cross-modal unified embedding space is constructed, a bimodal prompt gating fusion module is introduced into the unified embedding space, and adaptive weighting is carried out on text prompt and image prompt according to gating weight to generate fusion prompt; through a bimodal prompt collaborative filtering module, screening out a prompt set most relevant to the current task according to the similarity of the semantic features of the image and the text; the pre-training backbone network is frozen in the increment stage, only prompt parameters and fusion layer weights are optimized, a joint loss function is used for parameter updating, finally, image and text data are input in the reasoning stage, cross-modal similarity is calculated, and a classification prediction result is output.

The invention discloses a recommendation method based on semantic enhancement and a heterogeneous hypergraph network. The recommendation method comprises the following steps that semantic information in an explicit feedback text is coded and serves as an auxiliary signal of a recommendation task; classifying the articles into predefined categories by using LLM, constructing article-category association, and mining a potential co-occurrence relationship of the articles; constructing a heterogeneous hypergraph network; spreading and aggregating hypergraph information; performing semantic alignment and model training; and performing recommendation calculation based on the final representation of the user and the representation of the article, and outputting a recommendation result. According to the method, through technical paths of semantic coding, hypergraph modeling, information spreading and alignment supervision, comment semantics of LLM coding are aligned to the recommendation space through GAE, and the problem of degradation of LLM representation in the recommendation space is effectively solved.

A context-aware intelligent retrieval method suitable for multi-domain heterogeneous data relates to the technical field of intelligent retrieval and big data processing, combines context-aware domain semantic enhancement with a cross-domain semantic hash retrieval model, and realizes effective alignment between a source domain and a target domain in a unified feature space. A domain sharing encoder and a domain special encoder are constructed, and a residual fusion strategy is adopted to integrate domain sharing features and domain special features, so that the relation between image detail information and contexts is reserved, and cross-domain feature migration is realized. In the training process, effective modeling of domain specific information and domain shared information is ensured by utilizing a domain classification loss and distribution alignment strategy.

This invention relates to the field of text processing technology, and in particular to a method, device, and medium for processing navigational notice text based on semantic enhancement. The method includes: first, acquiring a content carrier to be processed; then, acquiring the semantic vector and glyph feature vector of the content carrier; concatenating the two types of vectors to form an enhanced text representation; extracting temporal features from the enhanced text representation to obtain temporal features containing forward and backward logical relationships within the text; acquiring the weights of words and sentences in the temporal features and performing weighting to obtain weighted word representations and weighted sentence representations; performing correction processing on the weighted representations to generate corrected text; and finally, validating the corrected text and outputting the target text. This invention can improve the accuracy and efficiency of content carrier processing.

The invention discloses an IETM intelligent retrieval method based on multi-modal fusion and a knowledge graph, and the method comprises the following steps: data layer construction: integrating multi-dimensional data, and establishing a multi-modal database; knowledge layer modeling: defining an IETM domain ontology through an ontology modeling tool, constructing a knowledge network, cleaning data and unifying entity annotations by using a rule engine, and forming an RDF triple knowledge graph which can be understood by a machine; and application layer retrieval: based on hierarchical semantic fusion of multi-modal data and a three-level progressive retrieval mechanism, carrying out deep analysis and accurate retrieval on the query intention of the user. According to the method, semantic enhancement is carried out on the text, three-level feature extraction is carried out on the image, and spatio-temporal joint coding is carried out on the video, so that deep semantic alignment and cross-modal semantic consistency improvement of the multi-modal data are realized, and the quality and effect of multi-modal data fusion are effectively improved.

The invention discloses an electric power equipment safety long text knowledge retrieval method based on sparse and dense joint enhancement, which comprises the following steps of: encoding an electric power equipment safety long text and retrieval query by utilizing a pre-training language model for performing supervision and fine tuning on an electric power equipment safety field label data set, and generating a semantic word vector; a weighted sparse vector fusing semantic information and a dense semantic vector enhanced through nonlinear transformation are generated through fine-grained semantic enhancement branches; respectively calculating sparse similarity based on a common item weight product and dense similarity adopting a double-distance weighted fusion strategy combining a cosine distance and an Euclidean distance; and carrying out weighted summation on the two similarity scores through the learnable weight to obtain a final similarity, and realizing accurate sorting retrieval of the long text knowledge. The method gets rid of dependence on global features of sentence vectors, can accurately capture local key information in a long text, and has high retrieval precision and semantic robustness.

The invention discloses a three-dimensional semantic scene completion method based on camera enhancement, a medium and equipment, and the method comprises the steps: obtaining a depth map and an optical flow graph through a left image and a right image, extracting a two-dimensional feature from the left image, and extracting a two-dimensional feature from the depth map; mapping the left image to obtain context features; performing depth optimization based on the two-dimensional features, the two-dimensional features and the depth map to obtain depth estimation distribution; obtaining and generating an expanded three-dimensional feature map through context feature operation expansion, and sequentially executing three-dimensional deformable cross attention and deformable self-attention operations on the three-dimensional feature map to output updated three-dimensional features; and geometric semantic enhancement is carried out on the updated three-dimensional features, and finally three-dimensional semantic scene completion is completed. The depth prediction precision is improved, depth estimation optimization is carried out, finally the geometric structure and detail prediction capability of the model is enhanced, and three-dimensional semantic scene completion is completed.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

The invention provides an intelligent document understanding method and system combining natural language processing and deep learning, and relates to the technical field of natural language processing and information management. Fusing the semantic pre-annotation result and the original text feature of the document, inputting the fused semantic pre-annotation result and the original text feature of the document into a cross-modal semantic enhancement model to obtain a document enhanced semantic representation, performing bidirectional semantic interaction with a dynamic business knowledge network based on the document enhanced semantic representation, generating an associated interaction result, and constructing a structured semantic asset; finally, the structured semantic assets are input into an intelligent document application engine, semantic feedback data are collected to optimize model parameters, document understanding accuracy and practicability can be improved, and diversified business requirements are met.

The invention discloses a multi-source remote sensing image classification method fusing a frequency domain attention mechanism and a cross-modal Transform, and relates to the technical field of remote sensing image processing. The method comprises the following steps: performing frequency domain enhancement on a hyperspectral image through a frequency domain attention mechanism; semantic enhancement is carried out on the laser radar image through depth separable convolution operation and a multi-head self-attention mechanism; respectively extracting deep semantic features corresponding to the two modal enhancement features through a Transform encoder; respectively weighting the deep semantic features of the two modals through a channel attention branch and a space attention branch, and performing multi-level feature fusion by using a learnable weight to obtain a fusion output feature; and aggregating and fusing the global semantic vectors of the output features in two modes of global average pooling and attention pooling, splicing the two global semantic vectors, and mapping the spliced global semantic vectors to a category space to obtain a ground feature classification result of the multi-source remote sensing image. According to the method, the classification precision under the conditions of complex city scenes and small samples is improved.

A semantic sensing system includes a processor, a memory, a plurality of wireless communication enabled devices and at least one sensing element, the memory storing a plurality of mapped endpoints wherein the processor is configured to apply semantic drift or entropy to determine non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards a first endpoint supervisor in relation with the non-affirmative determinations.