221 results about "Semantics encoding" patented technology

The invention provides a multi-round dialogue intention recognition method and system based on self-adaptive semantic understanding, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a natural language dialogue text of a current round of a user, and taking the natural language dialogue text as original input data; based on original input data, multi-level semantic features are extracted through a dynamic semantic coding algorithm, and semantic vector representation of a current round of dialogue is generated; setting three fixed anchor points in a semantic vector space based on a current round semantic vector and a historical dialogue state vector to form a triangular analysis structure; performing gridding segmentation on the triangular analysis structure, and generating a feature adjustment value according to distribution characteristics of segmented grids; and dynamically correcting the extraction process of the context-related features by using the feature adjustment value to obtain the corrected context-related features. According to the method, end-to-end optimization is realized in multiple rounds of interaction scenes such as customer service and intelligent assistants through full-process design.

The invention discloses a laboratory quality management document intelligent generation method and system based on retrieval enhancement, and relates to the technical field related to data processing.The method comprises the steps that semantic coding is conducted on a preset standard text, and a vector knowledge base is constructed; retrieving associated standard terms according to the document theme, and extracting structured data from a laboratory business system; embedding the standard terms and the business data into a Prompt template, and calling a preset large language model to generate a text; and performing paragraph splicing and hierarchical control on the generated text, automatically checking compliance by utilizing term consistency of rule model fusion and a numerical value comparison algorithm, and outputting a quality management document. The technical problems that in the prior art, standard term retrieval and matching are not accurate, laboratory business data fusion is difficult, and consequently document compiling efficiency and quality are poor are solved, and the technical effects that minute-level automatic generation of laboratory quality management documents is achieved, and document compiling efficiency, quality and compliance are improved are achieved.

The invention relates to an introspection lifelong SLAM method and system based on visual language model hidden space representation, and the method comprises the steps: extracting a semantic tag based on an RGB-D image through a semantic encoder, and generating a scene map and a semantic topological graph based on the RGB-D image and the semantic tag; generating a dynamic mask based on the scene map, obtaining a dynamic mask coverage rate, and screening key frames with high static confidence values based on the coverage rate; calculating camera pose estimation corresponding to the key frame in real time, sampling the key frame to realize layering of the key frame, and performing layering rendering by using a NeRF model to obtain a virtual view; the hidden space difference degree of the virtual view and the corresponding real image is calculated, whether error introspection needs to be carried out or not is judged based on the hidden space difference degree, and the system is used for achieving the method. Compared with the prior art, the method has the advantages that open semantic reasoning of VLM, high-precision reconstruction of NeRF and real-time positioning of SLAM are combined, and positioning and mapping accuracy is improved.

The invention discloses a security reinforcement learning-driven large language model security decision agent, and the decision agent comprises a high-level semantic planner which is used for receiving a target and constraint instruction in a text form, receiving a language or visual observation signal of an environment at the same time, and outputting text formatted security risk information and suggested action planning; the low-layer action actuator is used for receiving low-dimensional observation and semantic codes of the environment, and the semantic codes are output by the high-layer semantic planner after text embedding conversion; the strategy network of the low-layer action actuator outputs a final safety action; the training alignment module is used for optimizing the strategy network and the value network; a high-level semantic planner is fed back and prompted through reward and cost signals collected through environment interaction, and parameters of a strategy network and a value network are trained through a security reinforcement learning algorithm. According to the method, the decision cannot violate the given text security constraint while the decision of the given text target is completed.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

The invention relates to the technical field of robots, and discloses a task planning method and system for a robot, and the method comprises the steps: obtaining a user instruction; based on the large language model and the semantic coding model, retrieving in a memory bank to obtain environment perception information related to the user instruction; inputting a first cue word determined based on the user instruction and the environment perception information into the large language model, and generating an overall action sequence corresponding to the user instruction; and for each sub-action in the whole action sequence, determining a second cue word, inputting the second cue word into the large language model, and generating a detailed action plan corresponding to the current sub-action until each sub-action in the whole action sequence is traversed. According to the method, the natural language or unstructured instruction can be received, the intention of the user can be understood, and the executable overall action sequence and the detailed action plan of each sub-action in the sequence are generated, so that the task issued by the user is completed, it is ensured that the robot can execute the task, and the accuracy and efficiency of task execution are improved.

The invention relates to the technical field of artificial intelligence dialogue systems, in particular to an intention correction and optimization method and system for multi-round dialogue context memory. According to the method, joint semantic coding is carried out on user input and historical dialogues, key semantic elements are extracted to construct an intention evolution relation graph, context consistency verification is carried out on an initial intention recognition result, and an intention correction candidate set is generated when conflicts are detected; and dynamically adjusting the context coding weight of the historical dialogue based on the corrected intention recognition result. According to the method, the intention recognition accuracy and context coherence in multiple rounds of conversations are effectively improved, and the semantic migration risk is reduced.

The invention discloses a data analysis method and system based on a large model. The method comprises the steps that natural language query of a user is received; performing semantic coding and intention analysis on the natural language query based on a pre-trained large language model; based on metadata retrieval of vector similarity, obtaining metadata related to natural language query semantics from a vector database; generating corresponding structured query parameters through the large language model in combination with the obtained metadata and the natural language query; according to the structured query parameter, generating an executable structured query language statement; and executing the structured query language statement to obtain a data query result. According to the method, the problems of insufficient semantic comprehension ability, low metadata retrieval precision and poor query generation controllability in the prior art are solved.

The invention discloses a venous thrombosis risk assessment method based on a large language model, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: collecting thoracic surgery diagnosis and treatment data of a patient, carrying out the space-time alignment, generating a standard diagnosis and treatment data flow, and carrying out the homomorphic encryption of the standard diagnosis and treatment data flow, and forming an encrypted patient data package; inputting the encrypted patient data packet into a multi-task large language model, performing feature extraction and semantic coding by a feature coding layer, performing time sequence modeling and risk probability calculation by a risk quantification layer, and outputting a venous thromboembolism risk level of a patient; and performing feature decoupling and potential space mapping on the encrypted patient data packet to obtain thrombus semantic potential features. Through the multi-task large language model, the dual machine learning algorithm and the homomorphic encryption, the accuracy of venous thrombosis risk early warning is improved, and the safety of the risk assessment process is enhanced.

The invention discloses an automatic causal structure generation method based on semantic representation and logical reasoning of a large language model. The method comprises the following steps: acquiring an input text; performing semantic coding and clustering on the obtained input text by utilizing a large language model, and establishing a candidate causal variable set; causal relationship detection is carried out on the established candidate causal variable set based on anti-fact intervention and do-calculation; performing causal direction judgment, and generating a directed acyclic causal graph meeting logic consistency; and on the basis of the generated directed acyclic causal graph, natural language interpretation is generated by using a large language model, and logic consistency closed-loop verification is carried out. According to the method, automatic generation from the natural language to the causal structure is realized, the causal variable set is automatically extracted and constructed from the unstructured natural language text, the defects that variables need to be manually defined and modeling depends on field experts in the existing causal modeling process are avoided, and the labor cost and professional threshold of causal structure construction are remarkably reduced.

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.

The invention discloses a language migration detection method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the steps of obtaining a first language source code and a second language migration code, wherein the second language migration code is obtained by migrating the first language source code to a second language environment; analyzing the first language source code to obtain a first abstract syntax tree, and analyzing the second language migration code to obtain a second abstract syntax tree; respectively encoding each first node of the first abstract syntax tree and each second node of the second abstract syntax tree through a pre-trained semantic encoder to obtain a first semantic vector of each first node and a second semantic vector of each second node; and inputting the first semantic vector and the second semantic vector into a pre-trained logic detection model, and determining whether the logic of the first language source code and the logic of the second language migration code are consistent or not through the logic detection model, thereby realizing comprehensive detection of the business logic of the source code and the migration code.

The invention discloses a track user association method based on semantic perception and space-time coding, relates to the technical field of location service and user behavior analysis, and aims to solve the problems of excessive dependence of POI identifiers, limited space-time representation capability, insufficient cross-city generalization capability and the like of the existing TUL method in practical application. By introducing a pre-trained large language model to carry out POI category semantic coding, multi-frequency sine space-time coding and a double-flow transfer learning mechanism, the method can effectively improve the prediction precision and the model generalization ability.

The invention discloses a similarity comparison-based large model supply chain automatic restoration method and device, and the method comprises the steps: firstly carrying out the semantic coding of an original vulnerability code through a pre-training model, and constructing a semantic and structure parallel dual-channel representation in combination with an abstract syntax tree and other program structures; cWE type intelligent classification is performed on vulnerabilities by using a locally deployed large language model subjected to LoRA fine tuning, and meanwhile, a zero sample semantic matching mechanism is introduced, so that the recognition capability of unknown vulnerability types is improved. And searching the closest historical case from the knowledge base through similarity vector comparison, and extracting a repair abstract to construct a model to generate a prompt. Patch codes and repair instructions are generated through a large model, automatic filing and knowledge base updating are supported, and the continuously-enhanced automatic repair capacity is achieved. The method can be widely applied to automatic vulnerability repair scenes of supply chain components such as large model plug-ins, code interfaces and dependent packages, and the safety, functionality and interpretability of code repair patches are greatly improved.

The invention discloses an aspect-level sentiment analysis optimization method based on multivariate external knowledge fusion, which relates to the technical field of sentiment analysis optimization, and comprises the following steps: constructing a multivariate external knowledge source comprising a Chinese sentiment dictionary, a domain knowledge graph and a user comment prior mode library; the sentiment module is used for providing vocabulary-level sentiment polarity, entity attribute relations and high-frequency evaluation semantic modes; semantic coding is performed on the input text and the specified aspect words to generate context semantic representation, and global semantic features and local position features are extracted in combination with aspect word position information; based on a semantic coding result, converting the multivariate external knowledge sources into structured knowledge representations, and dynamically adjusting contribution weights of various types of knowledge through a gating fusion mechanism to generate fused knowledge representations; performing dependency syntactic analysis on the input text, constructing an original syntactic structure, and calculating the correlation strength of each grammatical component and aspect words in combination with context semantic representation; and pruning the original syntactic structure according to the correlation intensity.

The invention provides a semantic communication method and system, electronic equipment and a computer readable storage medium, and relates to the field of communication, in particular to the field of semantic communication and artificial intelligence. According to the specific implementation scheme, the method comprises the steps of inputting information source data into a pre-trained semantic encoder for semantic encoding, obtaining information source semantic features of the information source data, mapping the information source semantic features to a target codebook, and generating an information source index vector; transmitting the information source index vector from the transmitting end to the receiving end through the communication channel, and obtaining a channel index vector; obtaining a channel semantic feature according to the target codebook and the channel index vector, inputting the channel semantic feature into a pre-trained semantic decoder for semantic decoding, and generating reconstruction data; wherein the target codebook is determined according to the signal quality of the communication channel and an initial codebook; the initial codebook is obtained through joint training with the semantic encoder and the semantic decoder.

The invention relates to the technical field of natural language processing, and provides a multi-granularity text compression method and system for large-model multi-round dialogues, and the method comprises the steps: carrying out the semantic coding of historical question and answer pairs and a current dialogue question through a pre-training model, obtaining a semantic vector, generating a historical dialogue semantic representation through a self-attention mechanism, and carrying out the semantic representation of the historical dialogue. A semantic vector of a current dialogue problem is combined, a correlation enhancement vector is obtained through a target attention mechanism, weighted fusion is carried out on the correlation enhancement vector and historical dialogue semantic representation, a fusion vector is obtained, a feature vector is generated through a maximum pooling and average pooling combination strategy, clustering is carried out, positive and negative sample pairs are constructed, and semantic similarity loss calculation is carried out. Iteratively training the classification model; and based on the trained classification model, screening question and answer pairs related to the current dialogue question, and generating a structured abstract. Redundant content is remarkably reduced while key semantic information is reserved, and efficient context representation adaptive to large model reasoning is formed.

The application discloses a safe decision-making agent of a large language model driven by safe reinforcement learning, and the decision-making agent comprises: a high-level semantic planner, which is used for receiving target and constraint instructions in a text form, receiving language or visual observation signals of an environment, and outputting safe risk information and a suggestion action plan in a text format; a low-level action executor, which is used for receiving low-dimensional observation and semantic encoding of the environment, wherein the semantic encoding is output by the high-level semantic planner after text embedding conversion; a strategy network of the low-level action executor, which outputs a final safe action; a training alignment module, which is used for optimizing the strategy network and a value network; reward and cost signals collected through environment interaction are fed back to the high-level semantic planner, and parameters of the strategy network and the value network are trained through a safe reinforcement learning algorithm. The application is convenient for realizing the decision-making of a given text target while ensuring that the decision-making does not violate a given text safety constraint.

A method for training a speech synthesis model includes obtaining training data; obtaining an initial speech synthesis model; training a semantic encoding network and a semantic decoding network in the speech synthesis model respectively based on a style sample speech, a timbre sample speech, an input sample text, and an output sample speech in training samples of the training data, to obtain a trained speech synthesis model.

The invention discloses a Tibetan teaching evaluation method and system based on artificial intelligence. The Tibetan teaching evaluation method comprises the following steps: S1, collecting multi-modal original data formed in a Tibetan teaching process; s2, preprocessing is carried out, a corresponding Tibetan dialect classification result is obtained, and parameter configuration required by the semantic coding model is loaded; s3, generating a student semantic embedding representation and a standard answer semantic embedding representation based on the semantic coding model; s4, constructing a semantic consistency scoring model and a training sample set; s5, optimizing parameters of the semantic consistency scoring model by adopting a fractional order cuckoo search algorithm; and S6, executing an automatic scoring task of the answer text of the student. According to the method, semantic coding and the fractional order cuckoo algorithm are fused, automatic scoring of Tibetan answering is achieved, and the method has the advantages of being high in adaptability, accurate in scoring and high in fairness.

The invention relates to a Text2SQL (Structured Query Language) caching method and system based on semanteme, and belongs to the field of natural language processing and database query, and the method comprises the following steps: carrying out weighted splicing on a high-dimensional semantic vector and a structural feature vector to form fused semantic coding representation; a structured semantic cache library and a retrieval module are constructed, and first K candidate results with the highest vector similarity are obtained through retrieval; a lightweight semantic consistency verification mechanism is introduced to further screen candidate results; after screening is passed, multiplexing and complementing are carried out, and a final structured query language SQLnew is obtained; and a dynamic cache life cycle management mechanism is introduced, and regular updating and cleaning are performed according to scores of cache entries. According to the method, the query efficiency is greatly improved on the premise that the accuracy is guaranteed, frequent calling of a large model is reduced, parameterized multiplexing and incremental construction are supported, and a feasible technical path is provided for constructing a low-delay and high-availability dialogue type data analysis system.

The invention relates to a rapid reasoning semantic communication method and system based on non-autoregressive decoding. The method comprises the following steps: step 1, constructing a semantic communication model of non-autoregressive decoding based on a neural network; 2, sequentially inputting the training batches into a semantic encoder and a source-channel joint encoder to obtain a semantic encoding matrix; step 3, obtaining a disturbed semantic coding matrix; 4, sequentially inputting the obtained disturbed semantic coding matrix into a source-channel joint decoder and a non-autoregressive semantic decoder to obtain a plurality of output probability matrixes; 5, training the semantic communication model of the non-autoregression decoding by using a cross entropy loss function, and finishing the training of the semantic communication model of the non-autoregression decoding when a preset number of times of training is reached; and step 6, reasoning through the trained non-autoregressive decoded semantic communication model, carrying out semantic communication, and obtaining a semantic reconstruction sentence. According to the method, the reasoning speed of the deployed semantic communication system is greatly improved, and the communication time delay is reduced.

The invention discloses a knowledge graph enhanced retrieval generation system and method based on a big language model and subsequent representation, and relates to the technical field of enhanced retrieval generation, the system comprises: a big language model module for completing knowledge graph construction and answer generation through a preset cue word instruction or template, integrating semantic vectorization and a reordering model to optimize a retrieval result; the knowledge base module comprises a vector library and a knowledge graph; the vector library performs semantic coding on graph nodes to form a vector set, and integrates a similarity index and a reordering strategy; the knowledge graph contains entity nodes and relation edges, provides a triple extraction object and a retrieval source for the large language model, and provides a node jump state space for the subsequent representation module; and the subsequent characterization module is used for bearing node retrieval jump tracks and preference memories through a matrix, analyzing node relevance, consolidating jump plots, characterizing retrieval strategy dynamic states and providing support for retrieval optimization and question and answer generation. According to the method, the behavior flexibility of large language model enhancement generation can be effectively improved.

The invention relates to a patient follow-up visit management method and system, and solves the problems that generation of follow-up visit content by means of a fixed template is lack of personalized consideration, and actual situations such as special requirements, living changes and current disease treatment stages of patients cannot be fully combined. The method comprises the following steps: performing feature coding on a first draft of follow-up content and personalized adjustment information, identifying semantic association features of sentence components through a semantic role labeling technology, calculating statement similarity of to-be-fused content by applying a semantic matching technology based on a cosine similarity algorithm and a word vector model, and optimizing a content logic structure so as to eliminate redundant information; and finally, the follow-up visit content of this time is formed. And based on a preset follow-up plan, the follow-up content of this time is combined with the features extracted from the health data of the patient, and a voice notification strategy is generated by adopting a reinforcement learning algorithm. The follow-up visit management method has the following effect that the accuracy and efficiency of follow-up visit management of the patient are improved.

The invention discloses a big language model illusion detection method and system, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps that 1, a question and answer data set is selected, twenty internal state vectors generated when a large language model generates answers to an input question are extracted, and the internal state vectors are semantic coding vectors of a hidden layer in the model reasoning process; 2, the twenty internal state vectors are constructed into a sample matrix, and a covariance matrix of the sample matrix is calculated; 3, calculating a standardized determinant of the covariance matrix, wherein the standardized determinant is a twentieth-power root of a determinant value of the covariance matrix; 4, taking the obtained standardized determinant value as a feature F1, and taking the token number of the answer output by the large language model as a feature F2; 5, training a support vector machine dichotomy model by using the question and answer data set, wherein labels of training samples are illusion or non-illusion; and step 6, inputting F1 and F2 into the trained support vector machine dichotomy model, and outputting a hallucination or non-hallucination detection result.

The invention discloses a warehouse level code completion method based on multi-level code graph retrieval and fusion, which comprises the following steps: (1) for a code warehouse, carrying out graph structure extraction from three levels of warehouse level, module level and function level, and abstracting the code warehouse into a multi-level and multi-semantic code graph; (2) performing semantic coding and structure coding on each code snippet of the code graph; for the context code snippets of the codes to be complemented, semantic retrieval and structure retrieval based on a graph neural network are fused, and code subgraphs which are related to the context code snippets in terms of semantics and structures and are complementary with the context code snippets in information are selected through a reordering mechanism; and (3) introducing a structure fusion mechanism, merging the retrieved code sub-graph and the context code snippets of the codes to be complemented, serializing the merged code sub-graph and the context code snippets into a text which can be read by the large language model, and finally outputting target codes to be complemented by the large language model. The method can effectively solve the problem that an existing method is insufficient in utilization of code structure information.

The invention discloses a similarity calculation method based on semantic editing fusion. The method comprises the following steps: calculating global semantic similarity of a source text and a target text by utilizing a semantic coding model; obtaining a first keyword list of the source text and a second keyword list of the target text through word segmentation processing, taking each first keyword in the first keyword list as a target word, and respectively forming a plurality of to-be-compared word pairs with a corresponding word in the second keyword list and a plurality of adjacent second keywords; based on the to-be-compared word pairs, calculating the semantic similarity between each group of target words and the corresponding words by utilizing a semantic coding model, and determining the editing distance between the source text and the target text; and determining the local semantic similarity of the source text and the target text according to the editing distance, and combining the global semantic similarity to obtain the final text matching similarity. According to the method, the problem that text matching only depends on character-level surface matching and neglects semantic association between word pairs is solved, and the text matching precision is improved.

The invention provides a robust Deep JSCC semantic communication system based on mutual information maximization, and relates to the field of semantic communication and joint source channel coding, and the system carries out semantic coding on to-be-transmitted source data according to a preset compression ratio and power constraint at a transmitting end to generate a channel input sequence; at a receiving end, performing semantic decoding on a noisy receiving signal transmitted by the additive white Gaussian noise channel to obtain reconstructed data and execute a downstream semantic task, and calculating a mutual information lower bound between channel input and the receiving signal by using a mutual information estimation module; a composite loss function fusing task loss, reconstruction loss and mutual information loss is constructed, and parameters of a mutual information estimator, a semantic encoder, a semantic decoder and a task execution module are alternately optimized through a joint training mechanism, so that the semantic encoder learns robust semantic representation with a self-adaptive compression ratio. The system does not need to depend on a discrete codebook, and the anti-noise capability of semantic features is improved through explicit maximization of mutual information.

The invention discloses an associated field reasoning method based on a semantic model, and the method comprises the following steps: S1, constructing a multi-dimensional semantic enhancement data set, and fusing the data set with field basic information, context features, domain knowledge and an association relationship; s2, a semantic model is constructed, the semantic model comprises a semantic fusion module, a causal semantic coding module and a symbol and semantic fusion reasoning module, according to the associated field reasoning method based on the semantic model, associated field matching can be more accurate in medical, financial, e-commerce and other scenes, and the reasoning efficiency is improved. For example, medical accurate recommendation check items, financial accurate association risk control early warning fields and e-commerce reasonable matching association commodities, deviation caused by semantic ambiguity or association non-fitting business logic is avoided; reasoning response is rapid, and real-time business requirements can be met.

The invention relates to the technical field of network service quality monitoring, in particular to an intelligent SLA default prediction method and system oriented to government and enterprise private line services, and the method comprises the following steps: collecting government and enterprise private line network KPI time sequence data and SLA contract text data, carrying out sliding window statistical feature extraction and wavelet transform frequency domain feature extraction on the network KPI data, and carrying out SLA default prediction on the network KPI data; performing semantic coding on the contract text data by using a pre-training language model, constructing a dual-channel deep learning model, and training the model based on a weighted cross entropy loss function; the method has the advantages that the network state and the contract terms are dynamically associated through the attention mechanism, and the problem of data islands in a traditional method is solved; space-time-semantic joint modeling: the CNN-LSTM captures the dynamic change of the KPI, the Transform analyzes the legal semantics of the contract, and fine-grained risk modeling is realized; and an interpretable-driven operation and maintenance decision: quantifying a risk contribution degree by an SHAP value, and directly guiding network optimization and contract management.