183 results about "Context based" patented technology

The invention discloses an LLM-based customer intention recognition and response system, method, device and medium, and relates to the technical field of intelligent dialogues, the system comprises a multi-modal input processing unit used for collecting and fusing multi-source interaction data of an intelligent voice outbound system to form multi-dimensional input representation; the dynamic context enhancement LLM unit is used for performing context understanding based on the multi-dimensional input representation and a preset knowledge base and generating a user feature portrait; the reinforcement learning dialogue strategy generation unit is used for dynamically generating a dialogue strategy according to the user feature portrait and generating response content based on the dialogue strategy; and the multi-modal output unit is used for converting the response content into multi-modal output information and performing adaptive adjustment on the multi-modal output information according to the real-time scene parameters. According to the invention, accurate identification and intelligent response to the intention of the user are realized, and the interaction experience and service quality of the intelligent dialogue system are remarkably improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a dynamic question and answer matching method and system based on context awareness. The method comprises the following steps: acquiring history of multiple rounds of dialogues and user background information, and generating a semantic understanding model of a current dialogue; based on the semantic understanding model and the current user input, matching the most relevant answer from the question and answer library, and dynamically adjusting the matching strategy; acquiring real-time feedback data of the user, updating a personalized recommendation algorithm based on the interest change of the user, and generating a personalized answer recommendation list; dynamically updating the content of the question and answer library according to the personalized recommendation list and the user behavior data; and inputting the user feedback and the updated question and answer library data into a personalized recommendation system to generate final personalized answer recommendation. Through context sensing and real-time dynamic adjustment, the matching precision of the question answering system is remarkably improved, the interaction experience of the user is improved, and the adaptability and flexibility of the system are enhanced.

The embodiment of the invention discloses a context-based big language model dialogue processing method and device and electronic equipment, and can solve the problem of how to provide a dynamic management method based on context similarity analysis and intelligent screening to optimize big language model interaction. The method comprises the steps of obtaining current text information input by a user; coding the current text information and a plurality of pieces of pre-stored historical dialogue information to obtain a current text vector and a plurality of historical dialogue vectors; according to the semantic similarity between each historical dialogue vector and the current text vector, screening the plurality of historical dialogue vectors to obtain screened target historical dialogue information; splicing the current text information and the screened target historical dialogue information to obtain a target input text; the target input text is input into a target language model, target output response information is obtained, and the target language model comprises a large language model.

A method for generating personalized responses in a conversation with a user includes generating real-time contexts capturing an environment of the user over time, including generating a particular real-time context based on a first data stream corresponding to a first modality in an environment of the user and a second data stream corresponding to a second modality in the environment of the user, generating historical contexts based on the real-time contexts, in response to receiving a conversational cue provided by the user, generating a current real-time context based on data corresponding to the first modality and the second modality in a current environment of the user, and generating, based on the current real-time context, a personalized response to the conversational cue, including identifying, based on the current real-time context, relevant user information, including identifying one or more relevant historical contexts, and generating the personalized response using the relevant user information.

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and / or execute database queries and / or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

The invention discloses a context learning-based database query generation method and system and a storage medium, and the method comprises the steps: extracting keywords and phrases from a natural language query, retrieving values similar to the keywords in a database, and returning values most related to the keywords and corresponding columns thereof, retrieving description information related to the keywords and the phrases in a database directory; performing mode selection aiming at the most relevant value of each keyword and the corresponding column, namely screening out the column irrelevant to the natural language query, further selecting a table necessary for generating the SQL query, and finally reducing the mode to a minimum column set necessary for generating the SQL query; and correcting logic and grammar errors of the method model through the database mode, the natural language query, the generated candidate SQL query and the execution result thereof. According to the method, the efficient SQL query statement is generated by intelligently understanding the data context and the query requirement.

Disclosed herein are methods, systems, and computer-readable media for causing a machine learning model to generate improved answer data based on an extended reality environment. In an embodiment, a method may include receiving the query, identifying at least one extended reality component associated with the extended reality environment as relating to the query, the at least one extended reality component comprising at least one of an object, a recording, or transcript information, and generating a prompt based on the query and the at least one extended reality component. The method may further include transmitting the prompt to a machine learning model, in response to the transmitted prompt, receiving answer data from the machine learning model, and based on the received answer data, generating content in the extended reality environment.

The invention provides a context understanding-based software operation prompt automatic optimization method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the multi-dimensional context feature data of user software operation, carrying out the feature coding through a multi-modal feature fusion network, modeling the time sequence dependence through a bidirectional long-short-term memory network, and carrying out the feature coding through the bidirectional long-short-term memory network. And a context semantic representation vector is generated based on a multi-head attention mechanism, and a prompt generation strategy is continuously optimized in combination with reinforcement learning. According to the method, personalized customization of software operation prompts can be realized, the accuracy and timeliness of prompt contents are improved, and the software use experience of a user is enhanced.

The invention provides an SQL statement generation method and system based on long and short term memory of a large language model, and relates to the field of man-machine interaction, the SQL statement generation method comprises the following steps: obtaining a preset large language model, and extracting mode design information of a target database of the large language model through a database administrator; analyzing and complementing the pattern design information, and generating long-term memory to introduce a large language model; extracting query elements through multiple rounds of conversations between a user and the model, and generating short-term memory to introduce a large language model; and obtaining an SQL generation task, and generating an SQL statement according to the large language model after the long-term memory and the short-term memory are introduced, so as to extract information from the target database. According to the method, the long and short term memory ability is introduced into the large language model, the flexible interaction ability of the large language model is fully utilized on the basis of a prompt word project of context learning, the user is gradually prompted and recommended to improve the query request, and the accuracy and availability of the generated SQL can be improved.

The invention relates to the technical field of natural language processing, in particular to a context-based Prompt generative model optimization system, which comprises a context analysis module, a cue word generation module, a context optimization module, a semantic check module and a structure reconstruction module. According to the method, the context path and the semantic hierarchy information of the semantic unit are introduced, the fine degree of semantic matching degree recognition is improved, semantic guide deviation caused by statement template solidification is avoided, the cue words are recombined in combination with the semantic coherence weight and the logic dependency relationship, and the recognition accuracy is improved. The consistency and expression accuracy of the prompt content in the context are enhanced, the prompt word insertion sequence and connection mode are dynamically adjusted through a semantic conflict detection and structure rechecking mechanism, coherence and stability of a semantic structure and controllable generation of the prompt content are kept, semantic conflicts and expression chaos caused by static matching are effectively avoided in the generation process, and the generation efficiency is improved. And dynamic adaptation of prompt configuration and smooth optimization of language output are integrally realized.

A computing platform is configured to: (i) train a large language model (LLM) by carrying out a first machine learning process on a first training data set that includes first construction-based data associated with one or more of a user, a plurality of reference construction projects, a construction-based application of the computing platform, or combinations thereof, (ii) receive a request to generate a construction activity summary, which includes a context-based prompt, (iii) generate the construction activity summary by inputting the request into the LLM, the construction activity summary including a contextual response, and (iv) retrain the LLM by carrying out a second machine learning process on a second training data set that includes the first training data set and one or more of the context-based prompt, the construction project data, an evaluation, the contextual response, the construction activity summary, a given timeframe, the request, input, or combinations thereof.

The invention provides an intelligent education resource sharing and authority management system based on a block chain, and belongs to the technical field of intelligent education, and the system comprises a block chain network layer, a resource right confirmation module, an intelligent contract module, a distributed storage network, a cross-chain interaction gateway and a dynamic optimization module, comprising a resource authentication chain, an authority management chain and a cross-chain interaction chain, and the chains realize data intercommunication through relay nodes; the resource right confirmation module adopts a three-level Hash nested structure to generate a non-tampering digital fingerprint, and binding and uplink the educational resource metadata and the creator DID; the intelligent contract module comprises a dynamic authority management contract and a resource transaction contract, and supports an access control strategy based on context awareness. According to the intelligent education resource sharing system, the core problems that the right of the education resources is difficult to confirm, the sharing efficiency is low, and the right management is inflexible are solved, and the safe, intelligent and extensible intelligent education resource sharing system is constructed.

The invention discloses a tool calling semantic analysis method and system for a low-resource data environment, and relates to the technical field of semantic analysis, and the method comprises the following steps: constructing a logic expression of each tool based on a context-independent grammar, and generating initial training data in combination with a cue word project and a large language model; performing iterative training on a pre-configured semantic analysis model according to the obtained training data in combination with a self-training method to obtain an optimized semantic analysis model; and performing similarity calculation on the tool candidate data and the training data through a recall model to obtain vector scores of the tool candidate data and the training data, and screening out tools which do not conform to user semantics to obtain a final semantic analysis model. According to the method, the initial analysis data set based on the context-independent grammar is constructed, and the analysis capability of the model is enhanced in combination with the self-training strategy, so that the performance of the model in a low-resource environment is improved, and the semantic analysis capability is effectively expanded.

There is a need for more effective and efficient predictive natural language summarization. This need is addressed by applying hybrid extractive and abstractive summarization techniques in a unique processing pipeline to generate a cohesive and comprehensive summary of a multi-party interaction. A method for generating the summary of a multi-party interaction includes receiving a multi-party interaction transcript data object comprising a plurality of interaction utterances from at least two participants; using an extractive summarization model to identify a key sentence of the multi-party interaction transcript data object; identifying an interaction utterance from the multi-party interaction transcript data object that corresponds to the key sentence; generating a contextual summary for the multi-party interaction transcript data object based at least in part on the interaction utterance; and generating a reported contextual summary for the multi-party interaction transcript data object based at least in part on the contextual summary.

The invention relates to a large language model dynamic routing method and device based on context learning model characterization and a readable storage medium. Query is embedded and mapped to a language model input space by using a projection model, semantic alignment is realized, a representative evaluation set covering multi-dimensional capability is automatically screened from a benchmark question bank, and the representative evaluation set is used for evaluating the multi-dimensional capability. Performance characteristics of the model on an evaluation set are efficiently obtained at a time, and high-quality context model capability representation is formed; then real-time query embedding and context model capability representation are combined, a lightweight routing language model is used for supervised learning, so that the fine-grained model distinguishing capability is achieved, an increment embedding updating mechanism is designed, and when a new model is accessed or an old model is upgraded, cold start can be rapidly completed only through a very small number of fixed questions, so that the efficiency is improved. The calculation and maintenance cost is greatly reduced, and the accuracy, real-time performance and flexible expansibility of model routing are effectively improved.

The invention discloses a remote sensing target detection large model construction method based on context feature deep learning, and the method comprises the following steps: S1, dividing an original data set according to a strategy, forming a training set, a verification set and a test set, and guaranteeing that each subset has representativeness in sample number, scene diversity and target distribution; s2, constructing a remote sensing target detection large model by adopting a deep learning method based on context features, and fusing multi-level features and introducing a query optimization mechanism; and S3, performing model testing and performance evaluation, selecting optimal model parameters, performing comprehensive verification on the test set, and evaluating the comprehensive performance of the model in the aspects of detection precision, speed and robustness. According to the method, the pre-trained DINOv2 backbone network, the context feature enhancement module and the intensive supervision strategy are introduced, so that rich context information can be captured in a multi-scale and complex scene, and the detection precision is greatly improved.

The invention discloses a multi-source data intelligent question answering system based on a large language model and an application method, and the method provides a unified registration and publishing normal form of multi-source data, and carries out the unified question completion and question classification of context-based user question semantics through the large model. According to types, knowledge graph query, service interface API calling and document content understanding are divided, calling results are summarized and extracted through a large language model, and finally automatic generation of answers is achieved. The method has the capabilities of question semantic understanding and complex task decomposition and calling, and has the advantages that the large model can accurately understand user intentions and drive various applications to obtain question answers while the large model can be efficiently accessed to an application system, and the large model can be integrated into answer styles most convenient for users to understand; and the universality and accuracy of intelligent question answering are effectively improved.

The invention relates to the technical field of intelligent questioning and answering, in particular to an intelligent questioning and answering method based on context semantics and dynamic retrieval, which comprises the following steps of: splicing data to form a complete dialogue text, extracting a dialogue core theme by using a large model in combination with a thinking chain and small sample learning, and extracting the dialogue core theme based on the dialogue text and a current question. Generating a dynamic keyword set in combination with BM25 and BERT MLM, finally splicing the current problem, the keyword set and the core topic into a multi-dimensional semantic representation, and vectorizing the multi-dimensional semantic representation by using a BGE-M3 embedding model to generate an embedded vector; by combining context topic extraction and dynamic keyword generation technologies, the system can capture core intentions and semantic changes in multiple rounds of dialogues in real time, so that knowledge base retrieval does not depend on fixed keyword matching any more, but dynamically adjusts a retrieval strategy according to a dialogue context; therefore, the matching precision of the question and the knowledge base content is remarkably improved.

The invention discloses a vertical federal learning feature selection method based on context awareness, and belongs to the technical field of artificial intelligence and data privacy protection. According to the method, firstly, an unsupervised sparse network is utilized at a client to initialize the importance of local features so as to accelerate convergence and reduce calculation complexity; and then, obtaining the embedded representation of each client in a pre-training stage, and screening the embedded representation by combining context features through a server side, thereby indirectly identifying key features. In the feature selection stage, the client side performs local feature screening according to the significant embedded index issued by the server, and the influence of irrelevant features on calculation and communication is further reduced. According to the invention, an attention mechanism is introduced to dynamically evaluate contributions of different participants, so that fair weight distribution is realized. According to the method, through staged joint optimization, the communication and calculation cost in the federation training process is effectively reduced, and meanwhile, the prediction precision and interpretability of the model are improved.

The invention provides a multi-attribute controllable text generation method based on context-aware multi-granularity prompt fusion. The method comprises the steps of constructing a single-attribute prompt variant set, constructing an intra-layer multi-granularity prompt fusion module and performing general multi-attribute task prompt. According to the method, diversified soft prompt variants are trained on a single-attribute data set, so that the flexibility of single-attribute control is improved; an in-layer multi-granularity prompt fusion mechanism based on context awareness is designed, and an attribute prompt fusion weight is dynamically generated, so that hierarchical fusion of prompts is realized; by introducing universal multi-attribute prompt and adopting two-stage optimization training, the generation accuracy and text fluency under multi-attribute combination control are effectively improved. According to the method, flexible and efficient text generation control can be realized under different attribute combination conditions, and the method is widely applicable to multi-attribute controllable generation tasks in natural language processing.

The invention belongs to the technical field of knowledge tracking, and discloses a knowledge tracking method and system supporting a dynamic context, and the method comprises the steps: constructing a knowledge tracking model supporting the length of the dynamic context based on a question set of a student; and based on the knowledge tracking model supporting the dynamic context length, obtaining a final student answering prediction result. Through a question encoder and an answer encoder, a historical answer sequence of a student is mapped into high-dimensional feature embedding, and a question level perception vector and joint question-answer interaction representation are generated. The attention score matrix is dynamically adjusted based on the directional penalty function matrix, and the adaptability of the model to the dynamic change of the context length is enhanced. A multi-head self-attention mechanism and zoom dot product attention are utilized to extract knowledge states of time sensitivity and context dependence from student interaction data.

The invention provides a multi-objective optimization method fusing a classifier and an active learning strategy, and belongs to the technical field of artificial intelligence and optimization design, and the method specifically comprises the steps: obtaining a sample data set containing a plurality of target performance function values; performing non-dominated sorting on the sample data set to extract a Pareto frontier point set, and extracting geometric features from the point set as a geometric feature vector sequence; using the geometric features and the corresponding convex marks to train a classifier model of a Transform architecture, wherein the classifier model supports a multi-head attention mechanism and a position coding structure; judging the convex confidence coefficient of the current Pareto front by using a classifier model, and obtaining a candidate scheme; and inputting the candidate scheme into a double-attention mechanism prediction model based on context reasoning, and predicting the multi-target performance of the candidate scheme. According to the method, the experiment frequency can be reduced, the target performance can be accelerated to be achieved, and the intelligence and efficiency of the multi-target optimization process are improved.

The invention relates to the technical field of computers and artificial intelligence, in particular to an industrial economy query word segmentation retrieval method, system and equipment based on context enhancement, large model fine tuning and a vector library and a medium. Firstly, a user inquiry data set is used for performing fine tuning operation on a large model after corpus training is completed, so that the large model can learn and master the context understanding and multi-modal fusion process, and a fine-tuned large model capable of obtaining key phrases from user questions is obtained; the method comprises the steps that firstly, a fine-tuned large model is obtained, then semantic analysis and context completion are conducted on a new question input by a user through the fine-tuned large model, a plurality of new keywords are obtained, finally, a database is inquired according to the new keywords, and an inquiry result is returned to the user. Therefore, an automatic, efficient and accurate database query scheme is provided, the method has the advantages of being low in cost, easy to implement and capable of achieving dynamic self-adaptive optimization, a brand new solution is provided for database query, and practical application and popularization are facilitated.

The invention discloses a language model training method based on context position coding and Fourier transform, which belongs to the technical field of computer natural language processing, and comprises the following steps: S1, preprocessing text data input by a user; s2, performing context position coding on the preprocessed text information; s3, after context position coding, Fourier transform is carried out on each relative position coding vector; s4, performing model pre-training and instruction fine tuning on the position coding vector after Fourier transform, and measuring the difference between a prediction result and a real target by using a minimization loss function; and S5, finally completing model tensor distributed parallel training. According to the method, the accuracy in the model training process is improved, the method is suitable for a long context scene, and accurate text generation can be realized.

The invention provides an urban area dynamic representation learning method based on context representation learning, and belongs to the field of urban function and spatio-temporal data mining, and the method comprises the steps: S1, constructing a continuous dynamic graph according to public transport data; s2, an urban area dynamic representation extractor under multiple time dimensions is constructed, the extractor is used for extracting structure information and semantic information in the continuous dynamic graph, nodes in the continuous dynamic graph are converted into representation vectors of fixed dimensions, and the representation vectors are dynamically updated along with time; s3, pre-training the extractor by using a plurality of prediction tasks, including designing a corresponding loss function to guide the extractor to learn more universal urban region characterization; and S4, utilizing the trained plurality of extractors to obtain the dynamic representation of the urban area, and combining the dynamic representation of the urban area with context representation learning to be applied to a plurality of downstream tasks. According to the method, comprehensive space-time semantic information can be captured, and the dynamic region representation of the city is dynamically captured from multiple time granularities.

The invention relates to the technical field of natural language processing (NLP), in particular to an intelligent self-adaptive document segmentation method oriented to an RAG system. The method comprises the steps that S1, a deep learning model is adopted to dynamically adjust the size and the step length of a window according to document content density, structure information and context semantics; s2, in combination with a window representation result, calculating the context semantic similarity of the segmentation blocks by adopting a language model, and automatically adjusting the size of an overlapping region based on the context semantic similarity; and S3, based on a context segmentation block representation result, associating the segmentation block with the context by introducing a BERT model, automatically adjusting an overlapping part and a window size, and optimizing a segmentation effect. The invention aims to provide the intelligent self-adaptive document segmentation method oriented to the RAG system so as to improve the document segmentation efficiency and quality and optimize the recall rate and the generation quality of the RAG system.

The invention discloses an AI processing method and system for long-term memory of mechanical design knowledge, and the method comprises the steps: carrying out the semantic analysis and feature extraction of heterogeneous knowledge, such as parameterization formulas, engineering charts, technological processes and design principles, in the field of mechanical design through a multi-agent collaborative learning system; and establishing a multi-dimensional modular knowledge base and context-based Prompt strategy coupling mechanism. The method solves the problems of low efficiency, insufficient analysis accuracy, limited innovation support capability and the like in the existing mechanical design knowledge processing technology, and particularly solves the technical problems of high calculation complexity, inaccuracy in reasoning, poor adaptability and the like when multiple types of design knowledge are processed. Through the implementation of the method, the processing efficiency of the design knowledge and the design automation level are remarkably improved, the technical threshold of a designer is reduced, the design period is shortened, efficient analysis, accurate matching and innovative application of the mechanical design knowledge are achieved, and powerful support is provided for intelligent development of mechanical design.

The invention provides an embedded data synthesis method and device fusing retrieval and large model distillation and a medium. The method comprises the following steps of: preprocessing an unstructured document in a vertical field, and dividing the unstructured document into multi-granularity text blocks with a hierarchical association relationship; forming a context based on the combination of the multi-granularity text blocks, injecting disturbance information corresponding to the priori knowledge in the vertical field into the context, calling a generative model to generate a retrieval query according to the context, and determining a target text block corresponding to the retrieval query as an initial positive sample; false negative sample text blocks are filtered according to the incidence relation between the text blocks, and a positive sample set and a negative sample set are formed; and constructing a comparative learning training sample, and training the semantic representation model by using the comparative learning training sample to generate an embedded vector for the retrieval task. According to the method, the retrieval task construction efficiency and authenticity can be improved, the positive sample coverage integrity is improved, and the contrast learning training stability and retrieval precision are enhanced.

A system and method for automatic generation of database queries using zero-shot, context-based machine learning may output and / or execute database queries and / or analytics insights or plots based on text prompts, and may include or involve: wrapping a text prompt to include database structure information; generating, by a large language model (LLM), a query based on the wrapped prompt, where the query may include one or more database operations; and extracting data or information items from a database based on the query. Some embodiments may include additional prompt or query processing operations such as, e.g., wrapping queries to include corresponding database operations, validating that queries do not include malicious or undesirable commands, and automatically performing appropriate computer actions based on generated queries. Some embodiments of the invention may relate to databases and text prompts describing user actions input to a computer and collected by a desktop data collection software.

The invention relates to the technical field of natural language processing, in particular to a Chinese text error correction method, device and equipment based on a context fusion thinking chain, and the method comprises the steps: carrying out the feature coding of Chinese text data to be corrected based on a pre-training language model, and executing the multi-granularity feature fusion, enhancing the context representation by applying a context awareness attention mechanism, detecting the position of an error character based on the enhanced context representation, and determining an error type; dynamically generating an error analysis process; and correcting errors in combination with a thinking chain reasoning process, and generating a corrected text and a corresponding thinking chain error correction basis. Therefore, through innovative combination of multi-granularity feature fusion and thinking chain reasoning, not only is the accuracy of Chinese text error correction improved, especially the capability of processing complex error types improved, but also the interpretability of the error correction process is realized, a clear and understandable error correction basis is provided for a user, and the practicability of an error correction system and the user experience are remarkably improved.