39241results about "Natural language data processing" patented technology

The invention discloses a power transmission and distribution production task cooperation system and method based on an intelligent agent, and relates to the technical field of power distribution production task scheduling, the system comprises six modules: a natural language input interaction module processes a user instruction and multi-modal information, and generates structured data; the electric power field knowledge enhancement analysis module establishes mapping from a natural language to business data; the dynamic interaction context memory module stores historical interaction data and generates a context feature vector through a bidirectional LSTM and an attention mechanism; the intelligent task scheduling and conflict resolution module is used for disassembling instructions into sub-tasks, dynamically evaluating priorities in combination with three-dimensional indexes and resolving resource conflicts; the agent task execution and cooperation module drives agents to execute tasks according to priorities and synchronize states in real time; the system closed-loop feedback optimization module analyzes the execution log and automatically updates model parameters; according to the system, the problems of term analysis deviation, strategy staticization and insufficient self-optimization capability of a traditional scheduling system are solved.

The invention relates to the technical field of intelligent operation and maintenance, and discloses an intelligent operation and maintenance method fusing multi-modal data and active learning, and the method comprises the steps: deploying a hierarchical Internet of Things equipment network in a target operation and maintenance region, collecting a heterogeneous operation and maintenance data set, and generating an operation and maintenance feature data set through employing a unified building operation and maintenance service framework cooperating with a plurality of MCPs; inputting the operation and maintenance feature data set and the user feedback information into a deep learning intention recognition model for intention recognition and demand analysis to obtain structured user demand data and an operation and maintenance task priority sequence; according to the operation and maintenance feature data set and the structured user demand data, performing real-time evaluation on the equipment operation state to obtain fault risk early warning data; autonomous decision analysis is carried out through an agent type artificial intelligence engine, an equipment maintenance scheme and a resource scheduling scheme are generated, and then the problems that in traditional operation and maintenance, fault prediction is single, the model adaptability is poor, and prediction results are difficult to convert into effective decisions are solved.

The invention belongs to the technical field of multi-modal data processing, and particularly relates to a multi-modal document retrieval enhancement generation method based on a large model, which comprises the following steps: receiving query content input by a user for a multi-modal document; processing the query content by adopting an embedded model, generating a query vector representing user query semantic information, and storing the query vector in a vector database; analyzing the multi-modal document to obtain long text information, segmenting the long text information into data blocks by adopting a recursive partitioning strategy, and numbering and marking the data blocks; carrying out vectorization processing on the data blocks by adopting an embedded model to generate document vectors, storing the document vectors into a vector database, and constructing a hierarchical index structure; retrieving in a vector database based on the query vector, and returning a retrieval result; and processing a retrieval result by utilizing a large language model to generate response content conforming to the query intention of the user. According to the method, the multi-modal document can be effectively analyzed and processed, and the accuracy and comprehensiveness of analysis are improved.

The invention discloses a traffic engineering large model intelligent question answering system and method based on a knowledge graph, and the method comprises the steps: extracting a structured degree feature, a semantic ambiguity feature and a context association feature through receiving and analyzing a natural language query statement inputted by a user, generating a retrieval intention vector, and carrying out the retrieval of the retrieval intention vector; and dynamically selecting a retrieval path according to the intention classification model. And according to the retrieval path, constructing a structured query statement or a semantic vector, and respectively retrieving in the knowledge graph and the vector database to obtain a first retrieval result and a second retrieval result. Further performing bidirectional verification through entity consistency, semantic similarity and relation connectivity indexes, screening a candidate result set, and constructing a reasoning chain; if the inference chain is broken, a large model inference gap complementation mechanism is adopted to generate relay nodes, a complete inference chain is formed, and inference type answer output is generated based on the complete chain. According to the method, the retrieval accuracy and reasoning continuity of the question-answering system are improved.

The invention discloses an interactive question intelligent agent system based on a large language model, and relates to the technical field of dialogue interaction systems. Aiming at the technical defects of the traditional BI tool, the technical scheme is as follows: a heterogeneous data management engine realizes unified access and secure access of cross-source data; the NLP semantic recognition engine converts a natural language into structured semantics through multiple steps, drives the hybrid SQL generation engine and provides visual parameters; the generation engine realizes precise generation from semantics to SQL through a two-stage architecture based on metadata and authority rules; and the visual management console is combined with multi-party configuration and parameters to visually build a number-asking agent. The method is used for realizing intelligent conversion from a natural language to a structured query language (SQL) and a data service interface, and automatically generating an interactive data visualization result.

A system and method for machine learning-based data field validation is proposed that utilizes a specific trained machine learning model data architecture that is adapted to be more resilient against training set class imbalance, using a Siamese triplet LSTM network architecture that uses three LSTMs that are trained together and operate in concert. An example non-limiting practical use includes using the Siamese triplet LSTM network architecture to validate whether free-text data fields include a single jurisdiction in an address or multiple jurisdictions in the address.

The invention relates to the technical field of database construction, in particular to a knowledge base question-answering system construction method and device, equipment and a storage medium. The method comprises the following steps: acquiring a user question instruction, and performing part-of-speech tagging and keyword vectorization on a target text to generate a retrieval text keyword vector; performing semantic linear embedding and spatial projection processing on the keyword vectors to obtain keyword semantic vectors; logic mining is conducted on the keywords, a logic relation set among the keywords is obtained, logic recognition is conducted on the target text based on the relations, and retrieval text logic is generated; performing matching retrieval with semantic vectors in a knowledge base to obtain a candidate semantic query data set; and performing coherent logic retrieval on the candidate data set according to the retrieval text logic and the keyword vector to generate a final retrieval target answer. According to the method and the system, more accurate and efficient knowledge base question-answering system construction is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a content generation and optimization method based on a knowledge graph, which comprises the following steps: constructing a multi-source knowledge database, extracting core concepts and knowledge contents, and constructing the knowledge graph. Performing semantic analysis to generate semantic vector representation and a keyword list; retrieving the associated text fragment based on the semantic vector and the keyword list, and inputting the associated text fragment into a generation model to generate initial answer content; and utilizing the knowledge graph to match the domain entity and the knowledge graph node, generating a logical reasoning path, optimizing the initial answer content, and generating the final answer content. According to the method, content generation of accurate retrieval, deep knowledge association and logical reasoning enhancement is realized by fusing a multi-source knowledge database, knowledge graph reasoning and generation optimization; semantic vector matching and keyword retrieval are combined, so that the accuracy of knowledge acquisition is improved; and through knowledge graph reasoning path construction, the answer logic is coherent.

A knowledge fusion multi-modal interaction method and apparatus based on an improved alignment method. The method comprises: acquiring multi-modal data (S101); constructing a query transformation model (S102); on the basis of a multi-stream feature extraction method, performing improved processing on a vision transformer to obtain a multi-scale vision transformer (S103); on the basis of a joint text method, training the multi-scale vision transformer to obtain a target vision transformer (S104); freezing the target vision transformer, and on the basis of the target vision transformer, performing vision-text alignment training processing on the query transformation model to obtain a target query transformation model (S105); connecting the target query transformation model to a language model to obtain a knowledge fusion multi-modal interaction model (S106); and inputting the multi-modal data into the knowledge fusion multi-modal interaction model to obtain an interaction result (S107). In the embodiments of the present invention, the knowledge fusion multi-modal interaction model aligns visual representation with text representation, so that the mutual information between the visual representation and the text representation can be maximized, and the present invention can be widely applied to the technical field of artificial intelligence.

A computer-implemented method is disclosed. The method includes: obtaining web content of a webpage; determining whether the web content complies with defined first requirements for user accessibility; in response to determining that the web content does not comply with the first requirements: identifying non-compliant web content elements of the webpage; and presenting, via a user interface of a source code editor: indications of first portions of source code for the webpage corresponding to the non-compliant web content elements; and a user interface element representing an action for altering one or more of the non-compliant web content elements.

The systems and methods disclosed herein receive an output generation request from that includes input for generating an output using a language model. The input includes a set of alphanumeric characters associated with operative standards for a first set of actions. The system divides the set of alphanumeric characters into text subsets. For each text subset, a vector representation is determined. Prompts are created for each vector representation including the set of alphanumeric characters, query contexts, keywords, and / or the text subset. Each vector representation's prompt is input into the language model, which generates a second set of actions of related actions, where subsequently generated actions are based on prior generated actions. The system aggregates the second set of actions into a third set of actions and displays a graphical layout. The graphical layout displays a representation of the set of alphanumeric characters and the corresponding actions.

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 invention discloses a personalized learning recommendation method based on a personalized knowledge graph. The method comprises the steps that the knowledge graph is constructed, and a learner fills in learning style self-description and learning target expected content; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of the students are analyzed to determine the personal basic conditions of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt the personalized learning features to the learning knowledge element sequence, and a personalized learning push path recommendation mode is constructed to assist the students in improving the learning effect.

A computer-implemented method is disclosed. The method includes: identifying, in a graphical user interface, a first user interface element and a second user interface element, the first and second user interface elements being of different types and associated with a first group of user interface elements; receiving, via a keyboard, input of a command to trigger a first interaction with both the first and second user interface elements; determining that the first interaction is not defined for one of the first and second user interface elements; and performing a related action associated with the first interaction to the one of the first and second user interface elements.

The invention provides a standard document automatic generation and multi-dimensional auditing method and system based on a large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: 1, building a distributed database of a multi-source document, and analyzing a heterogeneous text through natural language processing to obtain a standardized knowledge network; step 2, extracting index elements based on the standardized knowledge network, and forming a structured parameter library through verification and verification; and step 3, based on the structured parameter library, constructing a template library, analyzing user demands in combination with semantic matching, and automatically generating a standard document outline. The document generation efficiency and quality are improved, the manual auditing cost is reduced, and the auditing comprehensiveness and accuracy are enhanced.

An example operation may include one or more of training a generative artificial intelligence (GenAI) model to generate images based on user data using a dataset of images, executing the GenAI model based on input data from a user interface of a software application to generate an image corresponding to the input data, and displaying the image via a user interface of a software application, receiving feedback about the image via the user interface; andretraining the GenAI model based on the generated image and the received feedback about the image via the user interface.

A UI is provided for presentation on each client device participating in a virtual meeting. The UI includes visual items each corresponding to one or more participants. One of the visual items includes an avatar representing a virtual assistant that has a role of one of the participants. A communication of a first participant is provided via the UI during the virtual meeting. The communication indicates a request to perform an operation. A response of the virtual assistant to the indicated request is provided via the UI during the virtual meeting. The response of the virtual assistant is at least one of a textual response or an audio response.

According to an aspect of the present invention, there is provided a selective visual display system, comprising: a processor configured to execute coded instructions for retrieval, processing, and presentation of content to a user; a display screen configured to exhibit said visual information connected with the device to provide a medium for user interaction with a content element presented by the system; wherein the system is arranged to perform operations comprising: receiving a content element from a user; receiving a content attribute associated with the content element from a user; generating an evaluation of said content element in relation to said content attribute; wherein said evaluation is generated by a machine learning model trained on a set of content to assess the degree or manner in which the content element demonstrates said content attribute.

The invention discloses an intelligent multi-modal virtual digital human interaction system based on an AI language large model. The system comprises a high-authenticity face generation module; the high-authenticity face generation module uses an AdaAN network, based on adaptive feature fusion and voice driving and time sequence modeling of voice features, feature information related to voice is extracted, the extracted voice features are processed through a deep neural network, it is ensured that the voice and facial expressions are highly aligned in time and space, and the face recognition accuracy is improved. Collecting a bio-electricity signal, mapping the signal to facial muscle movement, generating a final facial expression, and interacting with a user; the system further comprises an intelligent interaction module, a training optimization and efficient generation module, an efficient integration module, a multi-modal data acquisition module, an AI large model core processing module, a digital human image generation and driving module, an interaction scene adaptation module and a feedback optimization module. The invention further discloses a multi-mode digital human interaction method which has wide application value.

The invention discloses an equipment fault information sharing system and method based on the Internet of Things, and relates to the technical field of equipment fault sharing. Causal relationships among parts, faults and operation parameters are identified, real-time operation data, historical fault data and maintenance records are integrated, association relationships among various types of data are combined, a fault knowledge graph is constructed, and fault information sharing is performed. Reasoning is executed through a rule engine in the knowledge graph, possible fault causes are deduced, and cross-modal fusion is performed on fault phenomena, parameter changes and historical records to obtain a more accurate fault cause reasoning result. According to the sharing system, after the parts, the operation parameters and the fault types of the electric water heater are associated, the comprehensive fault knowledge graph is constructed, so that when the electric water heater breaks down, the parts which possibly have abnormity can be displayed to maintenance personnel after reasoning, the maintenance efficiency is improved, and the maintenance cost is reduced. And the use safety of the electric water heater after maintenance is guaranteed.

Techniques for determining one or more responses associated with one or more components that are responsive to a user input are described. The system receives a user input and causes one or more components to generate one or more responses associated with the user input. The system determines one or more of the responses are responsive to the user input, causes one or more actions associated with the responses to be performed, and outputs a natural language summary of the one or more responses. If the system determines that none of the responses are responsive to the user input and / or an ambiguity exists with respect to the user input, the system can generate a request for additional information usable to resolve the ambiguity, which may be sent to another component of the system and / or output to the user that provided the user input.

The invention discloses a hybrid enhanced enterprise smart law platform system based on a generative language large model. Four modules including a hybrid enhanced legal knowledge engine, a multi-modal legal document analysis module, a risk quantitative evaluation module and a compliance verification workflow work cooperatively. The hybrid enhanced legal knowledge engine integrates multi-source data, realizes real-time updating and semantic reasoning, and comprises map construction, a rule base and an incremental learning mechanism; the multi-modal legal document analysis module performs structured analysis on the heterogeneous document to generate a feature vector; the risk quantitative evaluation module is combined with Monte Carlo simulation and an analytic hierarchy process, quantifies the risk according to a compliance reference and analysis characteristics, and outputs a thermodynamic diagram and a report; a compliance verification workflow is driven by a finite-state machine, a verification module and a conflict detection module are integrated, a generative language large model is called to generate an improved scheme, and audit records are solidified and fed back for optimization. And the system runs according to the processes of analysis, supply rule, bias calculation and verification correction, so that the intelligence and accuracy of legal affair processing are improved.

The invention relates to the technical field of intelligent operation and maintenance and artificial intelligence crossing of a power system, in particular to an intelligent power work order processing method of an RPA fused multi-modal large model, which analyzes multi-modal work order data such as texts, voices, images and the like through a domain adaptation large language model, and realizes fault key information extraction and conflict resolution in combination with a dynamic knowledge graph; performing work order priority scoring and resource allocation by using space-time constraint reinforcement learning; an analysis result is converted into an automatic execution script through an RPA engine, and a whole-process closed loop of order sending, processing and feedback is achieved; meanwhile, a feedback optimization and conflict resolution cooperation mechanism is constructed, and the knowledge graph and the model precision are continuously iterated. The method improves work order processing efficiency and analysis precision, enhances decision scientificity, and is suitable for an intelligent operation and maintenance scene of a power system.

Computer-implemented systems and methods including language models for explaining and resolving code errors. A computer-implemented method may include: receiving or accessing a log comprising an error message, the error message indicating an error in code; determining the error message from the log; determining a context associated with the error; generating a prompt for a large language model (“LLM”), the prompt comprising at least: the error message, and the context associated with the error; transmitting the prompt to the LLM; and receiving an output from the LLM in response to the prompt, the output comprising at least: an explanation of the error message, and a suggested fix for the error.

Approaches presented herein provide for the use of language models to generate tokenized descriptions of physical environments. In at least one embodiment, sensor and / or observational data can be obtained for an environment and used to generate a set of perception data. The perception data can be analyzed, along with approximate positional data within the environment, to identify a set of aligned map data. The aligned map data and perception data can be provided as input to a trained language model, which can be trained to correlate and / or fuse the information to generate a single, consistent representation of the environment. The language model can output a tokenized description of the environment, which can be in a domain-specific language, that is a compact but robust textual description of the environment.

The invention discloses an accurate advertisement putting method and system based on artificial intelligence, and relates to the technical field of information, and the method comprises the steps: collecting user behavior data and real-time situation data, and carrying out the preprocessing of the user behavior data; analyzing the preprocessed user behavior data to generate a user interest label; combining the real-time situation data with the user interest label, constructing a situation interest model by adopting a deep learning technology, and predicting a user interest change mode; using an interest-driven recommendation method to obtain an initialized advertisement putting strategy; generating feedback information based on the analysis of the initialized advertisement putting strategy; and optimizing the feedback information through a reinforcement learning algorithm to generate an optimized advertisement display strategy. By vectorizing the user interest and combining the advertisement strategy characteristics, the generator model can meet the user preference and follow the advertisement materials of the optimal display strategy, so that the visual experience and the emotion resonance of the user are improved, and finally the click rate and the conversion rate of the advertisement are improved.

The invention discloses a urinary surgery diagnosis and treatment data intelligent processing system based on artificial intelligence, and the system comprises the following modules: an original diagnosis and treatment data collection and preprocessing module which is used for collecting the data of a urinary surgery patient; the stage annotation text construction module is used for constructing a stage annotation text set; the image description structuring module is used for constructing a structured image description sequence; the cross-modal semantic fusion module is used for extracting a cross-modal joint semantic representation vector sequence; the named entity recognition module is used for outputting a structured medical entity set; the entity semantic graph construction module is used for constructing an entity semantic graph structure; the diagnosis and treatment relationship extraction module is used for outputting a diagnosis and treatment relationship set of a triple structure; the structured packaging module is used for outputting a structured diagnosis and treatment data unit; and the intelligent diagnosis and treatment decision module is used for performing illness state recognition, treatment path generation and reasoning suggestion output. According to the invention, multi-modal semantic modeling and graph neural reasoning are fused, and an intelligent diagnosis and treatment relation recognition system is constructed.

The present invention provides a deep learning-based natural language understanding method and an intelligent teaching assistant system. First, it involves constructing a knowledge database and a question database, where learning material documents are saved into the knowledge database and preprocessed natural language information is saved into the question database. The method then involves learning and understanding the natural language information in the question database, searching for related knowledge points in the knowledge database based on the understood content, selecting the best-matched learning materials corresponding to these knowledge points as samples to respond to the natural language information, and generating a record that includes the question, response, and evaluation, which is saved into the knowledge database. Finally, it generates multiple forms of responses and outputs them according to the corresponding requirements.

The invention discloses an intelligent question answering system construction method and system based on an LLM large language model, and the method comprises the steps: carrying out the corpus aggregation from a plurality of heterogeneous data sources, and dynamically capturing the text data related to a target domain; preliminarily screening the text data, and based on semantic similarity and a text quality scoring rule, removing redundant and low-quality text data or text data irrelevant to a target field; performing diversity analysis on the text data, and generating diversified text data with high similarity with real data through an adversarial generation technology; performing multi-level semantic annotation on the text data, and generating a training sample by analyzing context information in the annotated corpus; and converting the training sample into a standard format sample, and training an LLM large language model by using the standard format sample to serve as an intelligent question-answering system of a target domain. By utilizing the embodiment of the invention, the intelligent level of the intelligent question-answering system in various application scenes can be effectively improved, and further development of the intelligent question-answering technology is promoted.

The invention discloses a document information extraction method, device and system and a storage medium, and relates to the field of deep learning, and the method comprises the steps: inputting an original document for preprocessing, and obtaining a document image; the method comprises the following steps: carrying out OCR (Optical Character Recognition) on a document image by adopting a Vision Transform (ViT) to obtain text information and textbox coordinate information corresponding to the text information; determining the category and coordinate information of each layout element frame based on a deep learning yolov8-seg instance segmentation algorithm, and then performing layout region matching on the coordinate information of the layout element frames and the coordinate information of the textbox to obtain text information corresponding to each layout element frame; and taking the category of each layout element box and the corresponding text information as a layout area matching result, forming a Prompt template by combining to-be-extracted document information, taking the Prompt template as the input of a large model and performing fine tuning training, and after the fine tuning training is completed, correctly extracting the document information by the model according to the input. According to the method, character recognition of OCR, layout analysis of layout analysis and language understanding ability of a large model are combined, and key information can be accurately extracted from complex and diversified documents.