424 results about "Intent recognition" patented technology

This invention discloses a method, system, storage medium, and electronic device for edge-end intent recognition and atomized task planning based on multimodal fusion, relating to the technical field of human-computer interaction. The method includes collecting multimodal data from the user, including visual modal data, speech modal data, and perceptual modal data; performing time alignment and feature-level fusion on the collected multimodal data to generate a unified multimodal feature representation; filtering based on the multimodal feature representation to remove invalid interaction signals; inputting the filtered multimodal feature representation into a multimodal large-scale model deployed on the edge for fusion inference to identify the user's operation intent; and matching the identified user operation intent with a predefined atomic instruction library to decompose complex intents into ordered atomic instruction sequences. This invention solves the problems of incomplete single-modal interaction information, lack of automatic task decomposition capability, high edge-end inference latency, and severe interference from invalid interaction signals.

The present disclosure provides a kind of intelligent question answering method, device, equipment, storage medium and program product, it is related to big language model, artificial intelligence technical field such as intelligent question answering.The method comprises: the intent recognition of the question information of user is carried out in combination with the special dictionary of target field, and the intent recognition result is obtained;Recall the candidate answer information set matched with question information in the pre-set question and answer knowledge base based on the intent recognition result;Determine the target answer information corresponding to the question information based on the similarity of candidate answer information in candidate answer information set and question information.The present embodiment discloses that, in target field, the intent recognition of user is carried out in combination with natural language understanding algorithm, by understanding user intent, finding corresponding knowledge base and recalling corresponding target answer information, it can be more accurately generated answer information for the question information of user, improve the adaptability of intelligent question answering in specific field, and can be used for timely interaction with user.

This invention discloses a brain-computer interface (BCI) smart zoom glasses and a visual adaptive adjustment system, relating to the field of smart glasses technology. It includes an EEG signal acquisition module that attaches to the user's head to collect signals, extracts feature vectors after noise reduction, and transmits them to a core control module; a visual environment perception module that collects target and environmental data, generates structured data packets, and transmits them synchronously; a core control module that integrates multi-source data, identifies visual intent, and decides on adjustment parameters; a smart zoom execution module that adjusts the lens focal length and provides closed-loop feedback to calibrate accuracy; a visual adaptive adjustment module that adjusts lens parameters based on the environment and fatigue state; a human-computer interaction module that supports manual operation, status display, and vibration feedback; and a data storage and update module that stores data and iteratively optimizes the model. This invention achieves accurate visual intent recognition through brain-computer interaction; personalized modeling adapts to different user needs, meeting the product development and application requirements of the wearable smart device manufacturing field.

The application provides a smart environmental protection large model desulfurization scene identification interaction method, system, medium and product, which comprises the following steps: firstly, the desulfurization role scene is matched through multi-modal instructions, and a graphical sand table and a global conversation state are initialized; after a diagnosis report generation instruction is received, multi-dimensional operation parameters of equipment associated with a role ID and a space-time context are obtained from a real-time database; a diagnosis result is obtained through analysis of a diagnosis large model and is mapped to a corresponding equipment graph element of the sand table; fault keywords are extracted to match a three-level differentiated solution scheme of quick solution, medium-term optimization and long-term upgrade; in response to a target scheme selection instruction, a quantitative evaluation result is generated through an efficiency estimation model combined with real-time working condition data, and finally, the result is visually presented to an interaction terminal. The whole process realizes a closed-loop intelligent interaction from intention recognition to decision support, and improves operation and maintenance efficiency and precision.

The present disclosure provides a voice instruction processing method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of voice interaction. The specific implementation scheme of the voice instruction processing method is as follows: performing multiple path recognitions on a to-be-processed voice instruction to obtain multiple recognition results, wherein the multiple path recognitions include first operation intention recognition of performing an operation on a graphical interface element of a voice interaction device and second operation intention recognition of performing an operation on the voice interaction device; obtaining a target recognition result from the multiple recognition results; and performing the to-be-processed voice instruction according to the target recognition result.

The present application relates to the technical field of artificial intelligence service, and more particularly to a connection management customer service method and system based on AI. The method comprises the following steps: receiving an initial user statement sent by a client; performing intent analysis on the initial user statement to obtain an intent recognition result; wherein the intent recognition result contains a service appeal pointed to by the initial user statement; in response to the intent recognition result, sending a business verification request to at least one business system according to the service appeal; wherein the business verification request is used to query a business precondition required for executing the service appeal; receiving a business verification result returned by the business system based on the business verification request. The present application can effectively inherit the dialogue history, avoid the dead loop of answering irrelevant questions, break through the closed loop connection between intent recognition and business execution, and eliminate the service fault that can only be recorded but cannot be executed.

The application belongs to the technical field of artificial intelligence medical treatment, and discloses a medical health service system and method based on a multi-granularity semantic analysis and knowledge reasoning engine, which comprises the following steps: through a multi-granularity semantic analysis module, coarse-grained intention recognition and fine-grained medical entity state extraction are performed on the unstructured input of a user; a dynamic probability reasoning engine starts two reasoning paths in parallel: a generative reasoning path generates a group of candidate diagnosis hypotheses by using an LLM, and a symbolic reasoning path performs multi-hop probability reasoning on a unique probability knowledge graph to calculate another group of candidate diagnosis paths and their cumulative probabilities; a reasoning fusion and verification module cross-verify the results of the two paths, and only when the hypothesis of the LLM is verified by the high-probability path of the PKG, a final health service response containing a conclusion and an interpretable path is generated. The application significantly improves the accuracy, reliability and transparency of automated medical consultation services.

Embodiments of the present application disclose an intention recognition method and device, electronic equipment and medium, relating to the technical field of artificial intelligence. A specific embodiment of the method comprises: constructing an intention knowledge graph reflecting the association relationship between historical intention labels based on at least one historical intention label in historical dialogue text; performing semantic topic division on at least one historical intention label in the intention knowledge graph to obtain at least one historical intention label set; performing semantic similarity matching on target dialogue text based on the at least one historical intention label set to obtain at least one candidate intention label associated with the target dialogue text; and performing intention recognition on the target dialogue text based on the at least one candidate intention label to determine the target intention of the target dialogue text. The intention label system is dynamically expanded, the intention semantic association is utilized to improve the interpretability of the recognition result, and the complex customer intention recognition requirement is adapted.

This application discloses a voice interaction processing method, apparatus, terminal, and server, relating to the field of Internet technology. The terminal can: convert user voice commands into text and transmit the text to a server; perform first-type intent recognition based on the text to obtain a first-type intent, and generate first interactive content based on the first-type intent; receive second interactive content sent from the cloud, the second interactive content being generated by the server based on a second-type intent, the second-type intent being obtained by the server through second-type intent recognition based on the text; and execute an interactive action based on at least one of the first and second interactive contents. This application can achieve a balance between reducing voice interaction latency, improving privacy protection, and supporting complex voice interactions during voice interaction.

The application discloses a medical inquiry intention recognition method, device, equipment and medium, by introducing a feature vector representing the complexity of the text to dynamically control the fusion weight of the local semantic feature (from the medical field BERT model) and the global semantic feature (from the large language model), the problem that the prior art cannot adaptively adjust the model focus according to the characteristics of the inquiry text is solved. When the text is complex, the system automatically gives a higher weight to the large language model which is better at processing complex semantics and long dependencies to grasp the global intention; when the text is simple, the medical field BERT model which is more efficient in calculation and accurate in local understanding is more focused. This dynamic weighting fusion mechanism effectively combines the precise understanding ability of the special-purpose model in the field and the advantages of the general large model in processing complex texts and macro context, significantly improving the accuracy of medical inquiry intention recognition in various complexity text scenarios.

This disclosure discloses a file management method, apparatus, electronic device, and storage medium based on a large model, relating to the field of computer science, and particularly to artificial intelligence technologies such as large models, deep learning, and image processing. The method includes: acquiring descriptive information for file management; performing intent recognition on the descriptive information for file management using a large model to obtain a file management intent, wherein the file management intent includes the file to be managed on the terminal and the management action corresponding to the file to be managed; generating an executable file management script based on the file management intent; and executing the file management script to manage the file to be managed.

This invention relates to a multi-task motion pattern recognition system and method for underwater exoskeleton robots. Through innovative data preprocessing and network architecture design, it utilizes a small amount of IMU data to synchronously, in real-time, and with high precision complete motion pattern classification and continuous motion phase prediction, thereby driving the exoskeleton to achieve precise and synchronous assisted control. The multi-task motion pattern recognition system for underwater exoskeleton robots includes a sensing module for acquiring raw posture data of a diver; a data processing module for receiving and calculating relative IMU posture data within the human coordinate system to construct an input tensor; an intent recognition module, including a multi-task deep neural network based on an inverted Transformer architecture, for receiving the input tensor and synchronously outputting motion pattern classification results and continuous motion phase prediction results; and a control module for generating control signals for the underwater exoskeleton based on the motion pattern classification results and continuous motion phase prediction results.

The application provides a multi-modal feature fusion-based opponent intention pattern recognition method, relates to the technical field of intention recognition, and obtains a plurality of opponent time slice execution opponent situation information feature extraction by calling historical game data from an adversarial deduction system, performing time slice segmentation, generating a plurality of sample multi-modal situation information features and a plurality of preset intention label identifiers as training data, performing parameter iterative optimization training of an opponent intention recognition model, locally observing game information in a preset time window length, inputting the opponent intention recognition model for intention reasoning, and outputting a current opponent intention probability distribution. The technical problems that the prior art relies on single modal data, leading to feature fragmentation, resulting in insufficient intention recognition accuracy and poor adaptability to local observation information are solved. The technical effects of effectively integrating multi-modal time series data, reducing the uncertainty of information in the game process, improving the accuracy and robustness of intention recognition are achieved.

This disclosure provides an interactive recommendation method, electronic device, storage medium, and computer program product. The method may include: acquiring user input information for the current round of dialogue, inputting the input information into an intent recognition model to obtain an intent recognition result for the current round of dialogue; invoking a target service matching the intent recognition result, and returning a response result corresponding to the input information of the current round of dialogue through the target service; and initiating a next round of dialogue or ending the dialogue based on the user's feedback information regarding the response result. Through the technical solution of this disclosure, not only can the real-time performance of user interaction be effectively improved, but accurate response results can also be output to the user.

The application provides an intention recognition method, device, electronic equipment, storage medium and program product, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing an input vector based on the latest round of requests and historical dialogue data input by a user in a multi-round dialogue scenario; inputting the input vector into a pre-trained request rewriting model to obtain an output to-be-recognized request; the to-be-recognized request is a first rewritten request composed of a stop generation mark and the latest round of requests or a second rewritten request composed of a non-stop generation mark and a rewritten request; identifying the target intention of the user based on the to-be-recognized request; and the request rewriting model is used for judging whether the latest round of requests depends on the historical dialogue data, and outputting the first rewritten request or the second rewritten request according to the judgment result. According to the application, the historical dialogue dependence judgment and the request rewriting task are simultaneously completed in a single inference by one request rewriting model, so that the intention recognition is more efficient, more accurate and less computationally intensive.

The application discloses a customer intention recognition method and device, an electronic device, and a storage medium. The method comprises the following steps: obtaining a data processing result of a chat record, and inputting the data processing result into a pre-configured agent group; obtaining an analysis result and a user portrait through a first agent and a second agent in the agent group, wherein the first agent is used for planning and summarizing customer emotions, and the second agent is used for extracting a user portrait; and obtaining a customer intention recognition result according to the analysis result and the user portrait. Through the application, not only basic data or simple labels can be output, but also targeted optimization suggestions can be provided for follow-up actions of customer managers based on large model agent technology, and planning schemes for subsequent customer development and customer retention are generated in combination with customer insight results, so that the follow-up direction of employees is clear, customer insight is converted into an actual business growth strategy, and customer conversion and retention efficiency is improved.

Embodiments of the present application provide a data table query method, device, equipment, medium and program product. The method comprises: receiving a natural language query request of a user, performing intent recognition to generate an intent label; determining a target retrieval range from a preset knowledge base based on the intent label, and performing multi-path recall in the target retrieval range to obtain a candidate data table set; first, the first language model is used to coarsely screen the candidate data table set to filter irrelevant data tables, and then the second language model is used to finely sort the coarsely screened results, and finally a target data table list is generated and output. The method is used to improve the matching of the final output result and the user's demand and the accuracy of the query result, and to improve the efficiency of data table query.

This application discloses a link switching method, apparatus, electronic device, and computer-readable storage medium, relating to the field of communication technology. The method includes: receiving link switching description information, which is obtained by a user through voice, text, or image input; performing intent recognition on the link switching description information to obtain link switching intent information; generating a link switching command based on the link switching intent information; and executing the link switching command to switch the current transmission link to a target transmission link. By performing link switching processing based on the user-input link switching description information, the method achieves a direct response to the user's link switching requests, increasing user participation and improving user experience. Furthermore, by recognizing the intent of the user-input link switching description information, the accuracy of link switching control is improved.

The present disclosure provides a data processing method, device, storage medium and computer equipment. The method receives resource checking system building demand information, which includes natural language demand, resource checking demand document and data table design document; performs demand intention recognition on the natural language demand based on the resource checking demand document to obtain a resource checking target, and extracts a resource associated entity corresponding to the resource checking target from the resource checking demand document; constructs a resource checking relationship graph and checking rule according to the resource checking target, the resource associated entity and the data table design document; converts the resource checking relationship graph and the checking rule into resource checking operation steps, and generates execution code corresponding to the resource checking operation steps; and deploys the resource checking system based on the execution code. The method can improve the data processing efficiency.

The application discloses an intelligent asset management method and system based on multi-modal interaction and intention understanding, and belongs to the technical field of Internet of Things and artificial intelligence interaction. The method comprises the following steps: collecting a voice instruction of a user in response to the passing of user identity authentication; performing intention recognition and slot filling on the voice instruction, and extracting structured operation information containing an operation intention, an asset identifier and an operation quantity; generating a structured business record based on the structured operation information; performing authority decision according to the structured business record, and controlling to open a storage door after the decision passes. The system comprises an identity authentication module, a voice collection module, a sensor array, an edge computing main control, an electronic lock control module and a communication module. The application automatically maps a physical access operation into a structured business record, realizes in-process compliance control and multi-modal data cross verification, has a delay record degradation path, and solves the technical problems of log hollowing and low compliance data collection rate of existing asset management equipment.

Intent recognition is described. Obtained data to be recognized is preprocessed to obtain target data. Feature extraction processing is performed on the target data based on an intent recognition model to obtain a linear feature and a non-linear feature of the target data, where the intent recognition model is obtained by optimizing and training a bidirectional encoder representations from transformers (BERT) model. Intent recognition processing is performed based on the linear feature, the non-linear feature, and the intent recognition model to obtain an intent recognition result of the obtained data to be recognized.

The present disclosure provides a text recall method, a training method, device and equipment of a text recall model, relates to the technical field of computers, and particularly relates to the technical fields of artificial intelligence, large models, natural languages, deep learning and the like. The specific implementation scheme is as follows: a target query instruction is acquired; the target query instruction is input into a trained intent recognition model to obtain a first target vector output by a first target network layer in the intent recognition model; the target query instruction is input into a main model of a text recall model to obtain a prediction result output by the main model; the prediction result is used to indicate a prediction probability of a word unit in a word table library at a predicted word unit position, the main model comprises a second target network layer, and an input vector of a next network layer of the second target network layer is determined based on a second target vector output by the second target network layer and the first target vector; and a target recall text is generated according to the prediction probability of the word unit in the word table library at the predicted word unit position.

The application discloses a text intention recognition method, an electronic device, a storage medium and a program product; the method comprises the following steps: obtaining a to-be-recognized user question; if there is no corresponding target natural language expression text of the to-be-recognized user question, determining a target structured language text corresponding to the to-be-recognized user question and a first target prompt text, and the first target prompt text is determined according to the vertical field type corresponding to the to-be-recognized user question; inputting the target structured language text and the first target prompt text into a pre-trained intention recognition large model to perform intention recognition, and determining a target natural language expression text corresponding to the to-be-recognized user question, wherein the intention recognition large model is generated by training according to prompt texts corresponding to different vertical field types, thereby solving the problems of low efficiency and low accuracy of structured language intention recognition; the intention recognition large model is used for intention recognition, the target natural language expression text is accurately determined, and the speed, the efficiency and the cost are low.

This invention discloses a power dispatching decision-making method and system based on multi-agent collaboration, belonging to the field of power dispatching technology. It includes: acquiring user dispatching query requests for power system fault scenarios; inputting the dispatching query requests into a dispatching decision model, parsing the dispatching query requests through an intent recognition module to obtain an execution plan; based on the execution plan, semantically aligning real-time status information, logical chain information, and rule subsets to obtain a fusion matrix; rendering the fusion matrix to obtain dispatching suggestion text; performing rule compliance review on the dispatching suggestion text, and iteratively optimizing the dispatching suggestion text if the review fails; and reviewing the optimized dispatching suggestion text to obtain the power dispatching decision text. This invention ensures that the recalled information is not fragmented but a coherent reasoning chain embedded with domain logic, thereby improving the accuracy of power dispatching decisions.

The application provides a task-aware adaptive expert routing control method, acquires a natural language instruction, performs intention semantic slicing processing by using a prompt word engineering management module to extract task attribute features and generate a task semantic anchor point; calls an agent management module, maps the task semantic anchor point to a preset expert ability manifold space matching degree detection, generates an expert routing contribution degree matrix by calculating a business matching weight; calls an agent unified interface service to load a target expert member set, performs semantic consistency conflict resolution processing in a multi-agent business logic processing module, generates a logic aggregation processing result and determines a final task execution result. The application solves the problems of task intention recognition ambiguity and inaccurate expert resource matching in traditional routing allocation by semantic slicing and manifold space matching, and improves the logic consistency of the task execution result.

The application discloses a kind of equipment knowledge management system and method;The system comprises: multi-modal data access module, for accessing multiple format files and extracting multi-modal elements;Knowledge processing module, for the structural treatment of extracted elements and constructs knowledge graph;Retrieval question and answer module, for executing user intent judgment based on user question input;According to the intention recognition result, when judging triggering question and answer, generate answer and its generation basis;Fault diagnosis module, for determining triggering fault diagnosis according to the intention recognition result, constructing fault tree according to knowledge graph and outputting troubleshooting scheme;Process application module, for determining triggering process application according to the intention recognition result, extracting BOM structure and process elements, generating process outline, multi-level process and SOP content, recommending associated resources based on process;Authority management module, for realizing access control based on directory and secret level.The application provides efficient and accurate equipment knowledge management platform for users.