3790 results about "Interaction interface" patented technology

The invention discloses an intelligent building design generation system based on multi-source heterogeneous data and an AI large model, and the system comprises a multi-source heterogeneous data collection and processing module which is responsible for automatically collecting the multi-source heterogeneous data related to a project, and carrying out the data processing; the knowledge graph module is used for carrying out structured storage and association on multi-source heterogeneous data by constructing a knowledge network covering upper planning, historical culture, architectural design standards and law and regulation contents; the AI large model fine tuning module is used for performing fine tuning on the AI large model by using data in the special architectural design field; the scheme design module is responsible for generating various design schemes, providing choices of different design styles and function layouts and optimizing the schemes; and the visual interaction and feedback module is used for providing an interaction interface with a user. The method is remarkably superior to the prior art in the aspects of design substantial content processing, multi-source heterogeneous data integration, design generation and optimization, user interaction experience and the like, and a more comprehensive, intelligent and efficient solution is provided for building design.

A system is provided for managing specialized tasks and information retrieval processes. Agents are configured to perform tasks and / or retrieve information in a specialized domain. The system receives, via an interaction interface, a message from a user for the trained generative model to generate an output, generates a context of the message, generates a request including the context and the message, executes an orchestrator configured to: receive the request, determine, using semantic decision making, one or more agents to handle the request, input the request into one or more agents to perform a task and / or retrieve information in specialized domains, generate a prompt based on the retrieved information and / or the performed task and the message from the user, provide the prompt to the trained generative model, receive, in response to the prompt, a response from the trained generative model, and output the response to the user.

The invention discloses an intelligent inspection method for fusion of Internet of Things and virtual reality based on an AI large model, particularly relates to the technical field of industrial intelligent inspection, and is used for solving the problem of end-to-end response lag caused by multi-modal data fusion delay and resource competition under an existing layered architecture. The method comprises the following steps: synchronously acquiring heterogeneous data of target equipment through a multi-source sensor and visual equipment, and generating time-space synchronous data through cross-modal feature extraction and time-space alignment; computing resources are dynamically allocated to an AI model or a rendering pipeline in combination with cross-modal correlation analysis and environmental interference assessment, and key tasks are preferentially guaranteed to be executed; performing deep correlation reasoning on the multi-modal data by using an AI large model, and generating equipment state features and an abnormal region mask; and finally, superposing the abnormal features and the three-dimensional scene through a virtual-real fusion rendering technology to form a visual interaction interface. Efficient fusion and real-time interaction of multi-modal data are realized, and the accuracy of anomaly detection and the decision response efficiency of an operator are remarkably improved.

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

The invention discloses an artificial intelligence-based bid invitation file automatic auditing method and system, and the method comprises the steps: setting an online database and a local database in the system, carrying out the format auditing, compliance auditing and other abnormal auditing of a bid invitation file through a bid invitation file auditing module, generating auditing suggestions, and carrying out the automatic auditing of the bid invitation file. After the auditing opinions are manually confirmed by a system using unit, a final bid invitation file manuscript is generated, and the system comprises the following modules: a database module, a file uploading module, a bid invitation file auditing module, a suggestion modification module, a bid invitation file generation module, a user interaction interface and a learning module. The method is convenient to use, intelligent auditing of the bid invitation file is achieved through the artificial intelligence technology, and the overall quality and efficiency of the bid invitation file are improved.

The invention relates to the technical field of artificial intelligence rescue, and discloses an unmanned aerial vehicle and robot dog rescue emergency system and method, and the system comprises an unmanned aerial vehicle cluster module which is used for air data collection, communication guarantee and emergency material distribution; the robot dog cluster module is used for ground environment detection, sample collection and special task execution; the communication cooperation module is used for ensuring information intercommunication and task cooperation between air-ground equipment; the central control module provides task instructions for the unmanned aerial vehicle cluster and the robot dog cluster; the human-computer interaction interface module is used for realizing effective communication between rescue workers and the system; the modules work cooperatively, multi-source data acquisition, three-dimensional scene reconstruction, semantic segmentation, risk assessment, fire spreading prediction and decision support information generation of a disaster site are realized, and a reliable basis is provided for rescue command decision.

Provided in the present invention are an interaction and generation method for obtaining a three-dimensional model by means of photographing, a method for obtaining a picture by means of a three-dimensional model, and a part machining and manufacturing method. The interaction and generation method comprises: a user using a terminal to capture scene information, wherein the scene information can be an outdoor or indoor scene, the terminal can be a smart phone, an AR / VR device or other smart terminals having a photographic function, and the captured scene information is RGB and / or RGBD (including depth) information; and sending an RGB image and / or RGBD image I to a monocular three-dimensional reconstruction algorithm, so as to obtain a corresponding three-dimensional model, which can be displayed on an interaction interface and superimposed with a real scene, or can be displayed on a single page.

The invention provides a natural language control method for a humanoid robot. By constructing the simple skill library and the complex skill library of the robot, the dependence on a large amount of operation data in a specific field is effectively reduced. According to the method, a large language model is used as a base of a task planner, and the capabilities of scene learning, semantic understanding and common sense reasoning of the task planner are brought into full play. By introducing a visual model in the field of computer vision, high-level semantic information and low-level spatial information of the environment can be obtained, and an accurate scene graph is constructed to serve as environment state representation. According to the voice interaction interface constructed by the invention, the naturalness of man-machine interaction is improved, so that a user can more conveniently communicate with the robot and input instructions, the user experience is effectively improved, and popularization of the humanoid robot in an actual application scene is facilitated.

The invention discloses a text classification method, device and system based on a large language model, a storage medium and a product, and relates to the technical field of text classification, the method comprises the following steps: receiving a to-be-classified text and a classification label input or selected by a user through a user interaction interface of the text classification system; and according to the to-be-classified text and the classification label, constructing prompt words, and calling a pre-trained large language model to perform different-level classification on the to-be-classified text to generate a classification result. According to the technical scheme, the low-cost and low-use-threshold text classification method is provided based on the large language model, classification of different levels can be achieved, in addition, due to the fact that the text classification result is in a light-weight and convenient-to-analyze JSON format, the system can visually display the classification result to a user and can also provide an interface for a third-party application to call, and the user experience is improved. And the application scene of text classification is expanded.

The invention discloses a medical and educational informatization comprehensive supervision and diagnosis platform system based on multi-modal data fusion. A data acquisition module acquires multi-modal data from a medical and educational information system and preprocesses the multi-modal data; the data storage module constructs a hybrid architecture to classify and store data and establishes an index. The multi-modal data fusion module deeply fuses medical records, images and physiological monitoring data of medical treatment and teaching resources, learning behaviors and teaching evaluation data features of education by means of a feature extraction technology and a fusion algorithm. The supervision and diagnosis module constructs models of medical service quality evaluation, disease diagnosis assistance, teaching quality monitoring and the like based on fusion features to realize accurate analysis. The user interaction module provides interaction interfaces for different roles and supports operations such as data query and uploading. The platform system breaks the data barrier, fully mines the data value, provides powerful support for informatization management, decision making and service optimization in the medical and education fields, and promotes the innovative development of the two fields.

The invention provides a bank financial management product recommendation method and system, and the method comprises the steps: firstly collecting a multi-modal dialogue data flow which is generated by interaction of a user terminal and an intelligent customer service system and contains voice input signals and text input contents, and then carrying out the semantic recognition of the voice input signals, and generating a structured demand description set; performing entity extraction and intention analysis on text input content to generate a multi-dimensional semantic tag set, performing time-space alignment and fusion on the text input content and the multi-dimensional semantic tag set to form a user portrait enhanced feature matrix, and performing multi-level association analysis on the user portrait enhanced feature matrix and a financial product knowledge graph based on a deep matching model to generate an initial recommendation result queue; and finally, according to the real-time environment perception data, queue context perception optimization is carried out, a dynamic adaptive recommendation list is generated, and visual rendering output is carried out through a multi-channel interaction interface, so that accurate and dynamic financial management product recommendation is realized by utilizing multi-modal data, deep analysis and combination with a real-time environment.

A method for facilitating conversational interaction with users to help the users includes transmitting a conversational interaction interface for conversationally interacting with a user to a user device, receiving a request of the user through the conversational interaction interface from the user device, identifying an information based on the request, generating an input comprising the request and the information for a machine learning model based on the request and the information, processing the input using the machine learning model, generating a response for the request based on the processing of the input, transmitting the response through the conversational interaction interface for conversationally interacting with the user to the user device, and storing the machine learning model, the request, and the response.

The invention provides a network security and data security comprehensive analysis method and system based on a large model, and the method comprises the steps: collecting multi-source heterogeneous security data of a network communication link, a data storage node and an application interaction interface, carrying out the risk behavior atomization association processing, and constructing a dynamic risk association hypergraph; based on a preset security domain knowledge graph, calling the large model to execute multiple rounds of risk attribution reasoning, performing attack chain fragment matching and evidence chain completion on a behavior hyperedge set in the dynamic risk association hypergraph, and generating a risk attribution reasoning chain; and constructing a risk evolution probability model according to the risk attribution reasoning chain and the time sequence constraint set of the dynamic risk association hypergraph, and calculating a short-term diffusion probability and a long-term evolution trend vector of each association risk path based on the risk evolution probability model to obtain a risk evolution path prediction result. The pertinence and the dynamic adaptability of protection measures can be improved, and the problem that a static protection strategy is difficult to deal with the hysteresis quality of dynamic risk changes is solved.

The invention discloses a 500kV standardized switching operation method based on a VR virtual reality technology, and aims to solve the problems of unreal environment, incomplete evaluation and low efficiency in the switching operation training of an existing 500kV transformer substation. The method comprises the following steps: constructing a full-immersive virtual reality scene, collecting actual environment data of a transformer substation, and constructing and optimizing a high-precision model; designing multi-modal interaction of the standardized switching operation process, wherein the multi-modal interaction comprises visual guidance, a multi-modal interaction interface and an intelligent sensing function; dynamic action scoring and intelligent behavior evaluation are realized, actions are captured in real time by using high-precision action capture equipment, an instant score is generated by comparing with a standard, and dynamic feedback is provided; and real-time optimization and learning migration of the virtual character model are realized, user operation habits are captured, virtual character actions are optimized, and multi-scene migration and adaptation are realized. The method is suitable for VR-based 500kV standardized switching operation work, and the training authenticity and efficiency are improved.

The invention provides an intelligent pleasure boat navigation system based on interactive pointing control and a method thereof. The system comprises a monitoring display module; a single-point navigation instruction receiving module; a route planning module; a multi-point navigation indication processing module; the real-time coordination and broadcast module is a user interaction interface module; the invention further discloses an intelligent pleasure boat navigation method. By adopting an interactive pointing control technology, a dynamic neighborhood potential field path planning algorithm and a multi-target optimization algorithm, the problems of the traditional navigation system in the aspects of safety, efficiency, user experience and multi-ship management are solved, the safety, efficiency and user experience of navigation are remarkably enhanced, the capability of multi-ship collaborative operation is realized, and the system has a wide application prospect. The method is an important progress of intelligent water traffic management in the future.

The invention discloses an interactive simulation method for calculating aging parameters of a superconducting cable and a superconducting cable system.The system comprises the steps that a cable body electromagnetic transient model, a cooling system transient thermal model and a three-dimensional multi-field finite element model are built, and cable material characteristics, environment parameters and operation condition data are input; a model interaction interface is constructed through a simulation platform, data interaction and coupling among multiple models are realized, simulation parameters are dynamically adjusted, accurate matching of model step length and dynamic characteristics of the system is ensured, and aging parameters of the superconducting cable system under complex working conditions are calculated. According to the method, the operation state of the superconducting cable is evaluated by analyzing the mixed data, aging analysis of the superconducting cable system under multiple working conditions is carried out, and decision support is provided for system maintenance.

The invention discloses a smart home control system and method based on a graph attention network, and belongs to the technical field of smart home. The method comprises the steps that S10, multi-source data from a smart home system are collected, and real-time data are collected through a plurality of sensors and a user interaction interface; s20, constructing a graph structure model by using the multi-source data; s30, calculating a correlation weight between the nodes by using an attention mechanism, and generating an optimized equipment control strategy; s40, generating an equipment control instruction according to an output result of the graph attention network; s50, based on user operation feedback, continuously adjusting and optimizing the graph attention network model through a self-supervised learning mechanism; and S60, continuously monitoring the environment parameters and the equipment state, and updating the equipment control strategy in real time. The method has the beneficial effects that efficient fusion of multi-source data, intelligent coordination of user preferences, function coordination of equipment and self-adaptive adjustment of users and environments can be realized.

Disclosed herein are methods and systems to evaluate cost values for different radiotherapy treatment plans using external AI models. A method comprises presenting a user interface providing an interaction interface between a plurality of medical professionals communicating regarding a radiation therapy treatment of a patient during a tumor board meeting or a radiotherapy treatment planning process; receiving, from the interaction interface, a first input comprising a first patient attribute of the patient and a second input corresponding to the radiation therapy treatment of the patient; executing a machine learning language processing model using the first input and the second input to predict a task associated with generating a treatment plan for the patient; receiving, from the interaction interface, a third input; and when the third input does not correspond to the predicted task, presenting an indication of the predicted task.

The invention provides a virtual expert creation method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method specifically comprises the steps: receiving an expert knowledge base uploaded by an artificial expert, and carrying out the preprocessing of the expert knowledge base; performing text splitting and extraction on the preprocessed expert knowledge base on the basis of a large language model, and constructing an initial knowledge graph on the basis of extracted knowledge points and semantic relationships; receiving a modification operation instruction of the artificial expert on the initial knowledge graph through a man-machine interaction interface of a large language model; and in response to the operation instruction, dynamically adjusting and optimizing the initial knowledge graph, and generating a virtual expert with a professional domain knowledge graph. The method is used for replacing a traditional rule-based virtual expert system, and is more intelligent and efficient.

The invention discloses an intelligent interactive Agent system based on Langchain and an implementation method, and relates to the technical field of natural language processing and artificial intelligence. Comprising the steps of 1, establishing an intelligent interactive Agent system by using an interface and an integrated tool provided by a Langchain framework, 2, managing calling of various tool modules and services through a central scheduling module, and dynamically registering and unbinding the tool modules by using the interface provided by the Langchain framework, and 3, processing user input by using a large language model through a process management module, and establishing an intelligent interactive Agent system. 4, a series of tool modules are integrated through the integrated tool management module, the tool modules comprise a text search tool, a weather query tool and a video information acquisition tool, each tool module works independently or is in linkage with other tool modules, and the tool modules are connected with the integrated tool management module. And 5, managing a user interaction interface through a front-end management module, supporting a user to query and control in a natural language, displaying a processing progress and a processing result in real time, and providing feedback and help information.

The invention relates to an environment monitoring method and system based on a digital twin technology, and relates to the technical field of environment monitoring, and the system comprises a sensor array which is used for obtaining environment data of a monitoring area; the sensor array comprises a temperature and humidity sensor, an oxygen concentration sensor, a combustible gas concentration sensor, a vibration sensor, a noise sensor and a smoke sensor; the environment monitoring host is used for acquiring and processing the environment data sent by each sensor in the sensor array to obtain an environment prediction result; and the rear-end monitoring terminal is used for displaying the environment prediction result, displaying the virtual environment model and providing a user interaction interface. According to the technical scheme, the digital twinborn technology is applied to environment monitoring, so that the environment monitoring data can be better displayed.

The invention discloses a fault diagnosis method and system for a coil cutting all-in-one machine based on a large language model, and relates to the technical field of equipment fault diagnosis, and the system comprises a table data intelligent processing module, a knowledge base construction module, a diagnosis reasoning module and a user interaction module. The table data intelligent processing module is used for carrying out automatic fault mode labeling and description standardization on a cutting and rolling all-in-one machine fault diagnosis case library provided by a user; the domain knowledge graph construction module is used for integrating the standardized table database and automatically constructing a domain knowledge graph; the diagnosis reasoning module is used for performing fault diagnosis reasoning based on a large language model and a knowledge graph; and the user interaction module provides a multi-modal user interaction interface and supports visual display of fault diagnosis. Therefore, by adopting the fault diagnosis method and system for the cutting and winding all-in-one machine based on the large language model, the intelligent level of fault diagnosis can be improved, the knowledge base construction and expansion cost can be reduced, and the usability and maintainability of the system can be enhanced.

The invention discloses a bridge management and maintenance decision-making system and method based on multi-agent collaborative optimization, and the system comprises a monitoring agent which is deployed in a cloud server and is used for obtaining abnormal data in a bridge structure and environment data; the diagnosis agent is used for evaluating the health state of the bridge by utilizing a built-in knowledge base, a built-in machine learning model and a built-in physical model based on the abnormal data; the decision-making agent is used for generating a plurality of candidate maintenance schemes based on an evaluation result, and screening out an optimal scheme by integrating the comprehensive utility of each scheme and the resource matching degree score of the resource scheduling agent on each scheme; the resource scheduling agent generates a construction plan according to the optimal scheme; the coordination / communication agent is used for ensuring efficient cooperation among the agents through a communication protocol and a negotiation mechanism among the agents; the diagnosis report, the optimal scheme and the construction plan are integrated and presented to a bridge manager through a human-computer interaction interface; and collaborative optimization of bridge management and maintenance decisions is realized through continuous feedback and self-learning.

The invention relates to the technical field of DCS health management, and provides a DCS health management data warehouse system, which comprises a data access and storage layer, a business logic layer and a display layer, and is characterized in that the business logic layer is used for processing DCS monitoring data, process parameter data and DCS business basic data and providing a data service interface; and a data interaction interface, a DCS field thematic base and an expert knowledge base are provided, so that the problems of data islands and unmined and utilized mass data in the DCS field are solved, unified acquisition, convergence and analysis of DCS process data and monitoring data are realized, DCS service basic data is governed, data quality is effectively improved, DCS data management efficiency and level are comprehensively improved, DCS knowledge fusion retrieval efficiency is improved, and the DCS field data management system is suitable for popularization and application. And the open sharing capability of the DCS data is improved.

Certain aspects of the present disclosure provide techniques for parallel interaction with machine learning models. A method includes receiving first data in a first machine learning (ML) model interface window of a parallel interaction user interface, the first window is associated with a first identifier; receiving, within a prompt entry field in a second ML model interface window of the parallel interaction user interface, second data, wherein the second data includes the first identifier; responsive to a presence of the first identifier, generating a first ML model prompt based on the first data and the second data; providing the first ML model prompt to an ML model; receiving, from the ML model, a first model response; and displaying the first model response in the second ML model interface window.

The invention discloses an intelligent medical pre-inquiry method and system based on a multi-modal large model and a medium, and the method comprises the steps: obtaining user input data to recognize a target intention, and when a knowledge question and answer intention is recognized, obtaining a knowledge question and answer module; based on the medical knowledge graph, combining with knowledge keywords in the user input data and utilizing a multi-modal large model to obtain medical answers; when the pre-inquiry intention is recognized, inquiry keywords in the user input data are extracted for slot filling to obtain slot information, and a slot filling strategy is dynamically adjusted based on the slot information and user feedback data; and performing visual display on the medical answers and the health management suggestions based on the user interaction interface, and obtaining user interaction data for human-computer interaction. According to the invention, a natural language processing and multi-modal fusion mode is adopted, so that the information input process of a patient is more convenient; and meanwhile, by introducing dynamic medical history information acquisition and interaction, real-time interaction with the patient is realized, and the user experience of pre-inquiry is improved.

The invention provides a design method and system of a touch interaction interface. Wherein user behavior habits are collected, key elements are extracted, a user participation degree evaluation system is constructed, and a behavior habit analysis report is generated; carrying out situation modeling processing by using a situation awareness layout optimization algorithm, carrying out adaptability analysis by adopting an environment adaptation technology, and generating environment adaptation interface configuration; a long-term preference tracking algorithm is used to record and learn personalized preference settings to add personalized components, a progressive interface evolution method is used to carry out progressive evolution processing, an interactive feedback mechanism is introduced step by step, an interactive module is added, and an optimized layout scheme is generated; and constructing a learning library according to the user behavior habits, importing interaction behavior data to perform behavior pre-judgment, adjusting interface elements and a response mode, and generating a touch interaction interface optimization strategy. According to the technical scheme provided by the invention, the man-machine interaction fluency and the user experience of the touch interaction interface are improved.

A calculation method and apparatus for a fractional flow reserve (FFR) of a virtual stent, and a device and a medium. The calculation method comprises: in response to a selection operation for a target blood vessel segment, displaying the selected target blood vessel segment in an interaction interface (S110); and in response to an adjustment operation for a virtual stent that is implanted into the target blood vessel segment, displaying, in the interaction interface, an FFR of the virtual stent after being adjusted (S120).

According to the student personalized learning path generation method based on the neural network, a data acquisition module is deployed on a teaching platform, a learning terminal and an interactive interface, and multi-dimensional data such as student learning duration, interactive frequency and wrong question records are acquired in real time. A knowledge graph is constructed based on a course outline and a prior textbook structure, and a graph neural network GNN is utilized to dynamically adjust association weights of nodes and edges of the graph neural network GNN to reflect weak knowledge areas of students. A Bi-LSTM encoder is constructed for the collected data to generate a behavior feature vector, and the behavior feature vector and a knowledge graph node embedded vector are aligned and fused through an attention mechanism to obtain student personalized multi-modal representation. And taking the representation and the knowledge graph node weight as an environment state, selecting a next knowledge point to be learned as an action space, designing a reward function by integrating multiple indexes, updating a target by using a deep Q network, and training a strategy network to generate a personalized learning path. The trained strategy is deployed as micro-service, a path is displayed according to knowledge module sorting, and related parameters are iteratively updated according to student feedback to improve the recommendation effect.

According to one aspect, a computing system is provided that includes processing circuitry configured to receive input data from multiple interaction modalities of a user, generate a multi-interaction-modality user interaction history from the input data, and extract memories from the multi-interaction-modality user interaction history using a trained memory-extracting generative model. The memories include natural language text descriptions of interactions in the user interaction history generated by the trained memory-extracting generative model. The processing circuitry is further configured to store the memories in file storage having an associated database with a vector search interface configured to receive memory retrieval queries. The computing system may further be configured to receive a user message via an interaction interface, retrieve relevant memories, generate a response-generating prompt with the user message and relevant memories, and use the prompt to generate a response to the user message with a generative language model.