257 results about "Smart applications" patented technology

The invention discloses a multi-modal knowledge-based automotive body innovative design system, which comprises a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module, a multi-modal data fusion module and a multi-modal data fusion module, wherein the multi-modal data fusion module is used for receiving text data, picture data, a three-dimensional CAD (Computer Aided Design) model file and an engineering symbol expression from an automotive body design process and is used for carrying out feature extraction and semantic alignment on input data of four modals; generating a unified semantic vector representation; the cross-modal knowledge mining and graph construction module is used for extracting multi-level entities and relationships from the unified semantic vector and constructing a dynamically weighted multi-modal knowledge graph; the large-model-driven multi-hop collaborative reasoning module is used for analyzing the multi-modal design requirement of a user, carrying out multi-hop reasoning on a knowledge graph, and outputting design parameter recommendation and an interpretable reasoning chain. The method aims at breaking through the limitation of an existing design system in the aspects of multi-modal processing and shallow semantic understanding, and deep fusion and intelligent application of multi-source heterogeneous design data are achieved.

The subject technology includes an evaluation system for agentic applications. The evaluation system may use one or more evaluation applications to grade the performance of an agentic application based on one or more performance metrics. Scores determined for individual metrics may be combined using a set of weights to tailor the importance of each metric in the overall performance evaluation to a particular industry or application. An optimization engine may improve the performance of target agentic applications that are deficient in one or more metrics by training a portion of the agent application on a training dataset that includes example responses that score well for the one or more metrics where the target applications are deficient.

The invention discloses an intelligent question answering system based on a large language model and a knowledge base, and particularly relates to the technical field of artificial intelligence application, comprising a large language model module, a knowledge base module, a retrieval and matching module, an answer generation module and a user interaction module; according to the method, on the basis of a pre-trained large language model and a structured knowledge base, key entities and semantic intentions in user questions are extracted through a multi-level text analysis technology, and preliminary answers are generated; the system combines domain knowledge in a knowledge base, uses vector retrieval and a graph database to efficiently retrieve related information, dynamically fuses retrieval results and generates answers through an attention mechanism and a gating mechanism, and ensures logic consistency and fact accuracy. The system supports multi-modal input and output, and realizes continuity of multiple rounds of dialogues through a memory network; user feedback data optimizes system performance through strategy gradient, and continuously improves answer quality and field adaptability.

The invention discloses an industry knowledge graph automatic construction method oriented to multi-source heterogeneous data, and relates to the technical field of knowledge engineering. Structured, semi-structured, unstructured and distributed data are supported, and high-concurrency data are cached through Kafka; preprocessing and standardizing, and executing cleaning, field unification, text conversion and format conversion; a pre-training model and a rule engine are adopted to cover entities and relations of multiple industries; designing a dynamic Schema, storing the dynamic Schema in a graph database, and constructing an index; evaluating and correcting from multiple dimensions; and dynamic updating and maintenance, incremental updating based on data change, and support of version management and knowledge service. According to the method, the knowledge graph is automatically constructed through multi-source heterogeneous data, the construction efficiency and quality are improved, cross-domain fusion is supported, dynamic updating is adapted, and multi-industry intelligent application is assisted.

An exemplary system and method are disclosed that is configured to detect cognitive impairment (e.g., early cognitive impairment) or assess cognitive function by analyzing, via machine learning and artificial intelligence analysis, behavioral metadata collected from a smart app during the course when a subject is using a cognitive test instrument for cognitive tests that incorporate motor activity (e.g., drawing or writing). The machine learning and artificial intelligence analysis can execute features associated with the test taker's metadata (e.g., time spent on task or questions, changing answers, referring back to the previous question), drawing qualities (e.g., line straightness, completeness), among others.

The invention provides a data driving and multi-element cloud edge cooperative computing method for intelligent workshop construction, which comprises the functions of multi-protocol heterogeneous equipment sensing, edge node adaptive load balancing, lightweight real-time edge computing processing, cloud centralized management control, industrial intelligent application service deployment scheduling, cloud edge task unloading and the like. The system comprises a data acquisition and transmission middleware, an edge stream processing engine and a cloud service center. The method has the characteristics of real-time performance, low delay, flexibility, expandability, loose coupling, low resource occupancy, high availability and privacy security.

The invention relates to the technical field of edge computing, and discloses a self-adaptive pervasive edge computing task unloading method based on imitation learning, and the method comprises the steps: firstly constructing a task unloading strategy imitation model and an environment adaptability evaluation model, the former is used for learning and simulating a historical task unloading strategy, and the latter is used for evaluating the influence of environment change on the strategy; a deep reinforcement learning algorithm is introduced to train a simulation model, and real-time feedback of an environment evaluation model is combined to continuously optimize and adjust an unloading strategy so as to generate a highly adaptive task unloading decision. According to the method, the task unloading instruction can be intelligently generated and executed according to the real-time edge computing environment state and the task characteristics, the dynamic change of the environment is effectively coped with, and efficient utilization of resources and rapid execution of tasks are achieved. Compared with a traditional method depending on a static rule or a predefined strategy, the task unloading flexibility is remarkably improved, and powerful support is provided for intelligent application in the edge computing environment.

Systems and methods are provided for establishing a network session with a device during which network session data is provided to the device via a network. The device may be connected to an LAN, and portions of data are configured to be selectively provided to the device via the network using a first queue for preferential network traffic or a second queue for non-preferential network traffic. During the network session, the systems and methods may identify characteristic(s) of a portion of the network session data, and determine, based on the characteristic(s), whether to provide the portion of the network session data to the device using the first queue or the second queue. Based on determining to provide the portion of the network session data using the first queue, the systems and method may provide the portion of the network session data to the device using the first queue.

The invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention discloses a detachable glasses leg interface assembly, a connector assembly, a detachable glasses leg and an earphone, the interface assembly comprises an interface assembly body and an interface arranged on the interface assembly body, the interface comprises a magnetic attraction seat and a base, and the magnetic attraction seat is arranged on the base. The detachable structure capable of realizing quick insertion and extraction between the front frame and the glasses legs of common glasses can be realized, so that the front frame and the glasses legs can be produced in a labor division manner, the common glasses and the intelligent glasses legs are promoted to be subjected to batch and systematic production according to a detachable standard, the production efficiency and large-scale application of the intelligent glasses are effectively improved, and the production cost is reduced. The front frames and the glasses legs of various types, which are produced according to detachable standards, can be freely adapted, so that high interchangeability is achieved, social resources are saved, and the economic burden of consumers is reduced; particularly, a more convenient scheme is provided for people with glasses wearing requirements such as myopia, hyperopia and the like to upgrade common glasses into intelligent glasses, and intelligent application upgrade of the common glasses can be expanded.

The invention relates to the technical field of internet data services, and particularly discloses a multi-graph hidden danger prediction method based on retrieval enhancement and full-graph sub-graphs. The method constructs a complete technical closed loop from data preparation, model training to intelligent application by combining external knowledge retrieval, multi-granularity visual perception and hierarchical reasoning, and specifically comprises three core stages: firstly, automatically constructing a high-quality field specific training data set by using a pre-training large model; secondly, specialized cultivation of a lightweight target multi-modal large model is carried out through supervised fine tuning and reinforcement learning alignment; and finally, introducing'full graph-sub graph 'multi-vision input and regulation knowledge retrieval enhancement in a prediction stage, performing hierarchical reasoning, and outputting a structured hidden danger report.

The invention discloses infrared night vision AR (Augmented Reality) intelligent glasses based on multispectral fusion, and relates to the technical field of infrared intelligent application. High-precision scene perception in a low-light / light-free environment is realized by fusing dual-channel data of low-light imaging and infrared thermal imaging and combining cross-modal feature matching and dynamic pose compensation; an environment detail structure is restored by a low-light image enhancement technology, thermal feature targets such as organisms are accurately recognized through thermal imaging target segmentation, and a three-dimensional environment model with space geometric information and target attributes is constructed through heterogeneous feature fusion; and dynamically adjusting the display transparency of the key target in the AR view field through a virtual information layer adaptive mixing mechanism driven by a hot target attribute, and finally outputting a virtual-real fused enhanced view, thereby breaking through the illumination limitation of the traditional AR glasses, and improving the visual effect of the AR glasses. And immersive augmented reality experience with panoramic detail perception and hot target accurate positioning is provided for the user in a dark environment.

The invention discloses a fire-fighting spatial-temporal trajectory data processing and off-line data warehouse construction method based on a stream batch fusion architecture, and relates to the field of digital fire fighting, and the core steps of the method are as follows: after multi-source data are collected by an edge gateway, high throughput distribution and storage are realized by Kafka; real-time optimization such as track cross repair and offset correction is completed through Flink streaming calculation; performing offline batch processing on the historical data by the Spark to generate an analysis result; generating an optimal rescue path through a distributed Dijkstra algorithm in combination with road topology, real-time traffic and fire fighting truck parameters; the method comprises the following steps: realizing mixed storage by adopting ElasticSearch secondary index and HBase total storage; based on hot and cold data hierarchical management, intelligent applications such as fire-fighting commanding and dispatching, battle comment and review and the like are supported. Practice verifies that the method can shorten the fire control time of the main urban area by 60%, significantly improves the fire emergency response capability, and is suitable for large-scale fire track data scenes.

The invention relates to an artificial intelligence application service software development terminal, relates to the technical field of computer software, and aims to simplify and accelerate the design, development, test, deployment and management processes of artificial intelligence application services. The terminal integrates a data processing module, a model development module, a service definition module and a deployment management module, and provides a unified integrated development environment. Through a user-friendly graphical interface, a developer can conveniently import, label and preprocess data; an artificial intelligence model is selected or constructed, and training and evaluation are carried out; defining input and output, an interface protocol and runtime configuration of the model, and packaging the model into a callable service; and the services are deployed to various target platforms. According to the method, the development threshold is effectively reduced, the development efficiency is improved, and wider application of the AI technology is promoted.

The invention discloses an enterprise-level agent system and a data processing method. The system mainly comprises a sensing module, a memory module, a planning module, a Routine agent planning engine, an arrangement engine and an execution module. According to the enterprise-level agent system provided by the embodiment of the invention, short-term memory, long-term memory and enterprise-level memory are combined through a complete closed-loop architecture of the sensing module, the memory module, the planning module and the execution module, and precipitation of personalized and organization-level knowledge is supported; and meanwhile, in combination with a standard business process template provided for an enterprise by a Routine agent planning engine and splitting of a task process by an orchestration engine, role agents are allocated for each sub-task, so that coordination and cross-system orchestration of complex tasks are realized, the construction threshold of intelligent applications is reduced, and the business efficiency is improved.

The invention belongs to the technical field of ancient book digital processing, and discloses a training knowledge-fused ancient book word sense disambiguation deep learning method, which comprises the following steps of: constructing a training knowledge graph; preprocessing ancient book image texts; constructing a deep semantic disambiguation model fusing training knowledge; word sense disambiguation reasoning and result output; and system integration and intelligent application interface design. The high-precision word sense disambiguation method oriented to ancient books and texts is constructed by fusing training knowledge and a deep learning technology, so that the recognition capability of complex semantic phenomena such as polysemy words, ancient and modern heterosemy and common and false characters is remarkably improved, the interpretability and field adaptability of the model are enhanced, the dependence on manual training is reduced, and the training efficiency is improved. The method realizes efficient understanding and intelligent processing of the semantics of the ancient books under the condition of low resources, and has good popularization and application prospects and culture inheritance value.

A hybrid quantum-classical computing system designed to enhance AI applications by integrating classical computational methods with quantum computing power. The system incorporates a classical computing unit and multiple quantum computing units, each specialized for distinct algorithmic tasks. A control module within the classical unit, using predetermined decision logic, manages the distribution of tasks, selectively engaging quantum units for their unique capabilities to optimize efficiency. This scalable architecture allows for significant advances in AI, tackling complex, resource-intensive tasks by effectively harnessing the strengths of both quantum superposition and classical processing.

The invention relates to the technical field of artificial intelligence scheduling, in particular to an artificial intelligence task scheduling method and scheduling system based on big data analysis. The system comprises a data acquisition module, a data processing module, a data prediction module, a scheduling generation module and an anomaly detection module. The data acquisition module acquires performance parameters and resource state parameters of task execution and real-time load data of a task queue in real time; the data processing module performs feature extraction on the acquired data to form a task feature matrix; the prediction module predicts task execution time according to the matrix and outputs a prediction execution time parameter; the scheduling generation module generates an initial task scheduling scheme in combination with the prediction parameters and the real-time load data; and the anomaly detection module monitors task execution anomaly indexes, and dynamically adjusts the initial scheme according to the indexes to obtain an optimized scheduling scheme. The system can adapt to task and resource states in real time, improve scheduling rationality, enhance system operation stability and adapt to complex artificial intelligence application scenes.

Deterministic network architecture and working method for intelligent applications are provided. Generalized service layer obtains task parameters of computing task generated by large model of intelligent application during training, deployment, or inference stage. Mapping adaptation layer determines resource orchestration scheme based on task parameters and determines transmission scheduling scheme based on resource orchestration scheme. Resource orchestration scheme includes target computing domain for completing computing task, and computing resources, storage resources, and communication resources allocated to computing task from target computing domain. Transmission scheduling scheme includes time slots and communication resources for transmitting computing task to target computing domain. Converged network layer transmits computing task to target computing domain based on transmission scheduling scheme. This architecture converges communication resources with computing resources to support co-scheduling of large models, and synchronously designs resource orchestration scheme and transmission scheduling scheme, to prevent transmission scheduling complications caused by new data traffic.

A computer-implemented method for facilitating telemedicine service is provided. The method includes receiving, by a server computer from a client device over a network, a user request for telemedicine services, wherein the user request is received through a chatbot application for facilitating the telemedicine service. The method includes analyzing the user request using an AI application on the server computer, wherein the AI application includes machine learning algorithms configured to interpret the user request. The method includes generating a response to the user request based on the analysis. The method includes transmitting the generated response to the client device through the chatbot application.

The invention relates to the technical field of intelligent early warning, and provides an early warning management method of a video intelligent application system. The method comprises the following steps: collecting multi-channel video data, and generating initial early warning information; pushing the initial early warning information to an operation and maintenance platform for secondary confirmation, and generating a secondary confirmation result; screening the initial early warning information according to a secondary confirmation result, and constructing a target early warning information list which comprises confirmed and unconfirmed early warning information; calling a monitoring record to generate an alarm report according to the confirmed early warning information, and performing rechecking based on the unconfirmed early warning information in combination with the inspection result to generate a rechecking log; and performing early warning management according to the alarm report and the rechecking log. According to the method and the device, the technical problems of false alarm and missing alarm existing in the abnormity identification and early warning confirmation process of the existing video monitoring system are solved, and the technical effects of accurately screening and verifying the early warning information, reducing false alarm and missing alarm and improving the operation and maintenance efficiency and the alarm processing timeliness by combining secondary confirmation and inspection result recheck are achieved.

The embodiments disclose a smart clothing system including a piece of smart clothing having fabric, a plurality of sensors integrated with the piece of smart clothing and coupled to the fabric of the smart clothing, a plurality of actuation devices integrated with the piece of smart clothing and coupled to the sensors of the smart clothing, a mobile device configured to wirelessly communicate via a Bluetooth® protocol with the plurality of sensors and the plurality of actuation devices of the smart clothing with bidirectional send and receive digital signals, and a smart application operating on the mobile device and having a voice command device, wherein the smart application is configured to provide status data of the plurality of sensors and the plurality of actuation devices, and the voice command device is configured to control the plurality of sensor and the plurality actuation devices.

The invention discloses an AI intelligent application integration system based on a container. The system comprises a plug-in store, an application process unit and a plug-in specification, the application process unit comprises an AI large language model, a workflow engine and a container engine; the plug-in store comprises a third-party plug-in for system integration, and the plug-in specification comprises a configuration declaration corresponding to the plug-in, a tool function and a text-based input and output declaration; the AI large language model is used for querying a plug-in store based on natural language input of a user to obtain a plug-in list corresponding to a user task; generating a corresponding execution plan according to the natural language input and the plug-in list; the workflow engine is used for performing task arrangement in response to the execution plan; calling the container engine to execute the plug-in tool function in a sandbox environment to realize safe calling of the plug-in tool function; the system has the advantages of achieving fast system integration and ensuring safe and reliable system integration.

The invention provides a knowledge graph construction method based on parameter calculation logic, and relates to the technical field of graph construction, and the method comprises the following steps: S1, basic data set creation: collecting and sorting domain knowledge data, carrying out systematic classification and organization on the domain knowledge data, and creating corresponding basic data sets for different categories; according to the knowledge graph construction method based on the parameter calculation logic, the knowledge graph with dynamic calculation and reasoning capabilities is realized through parameter calculation logic expression and parameter, formula and index model construction, and the limitation of a traditional knowledge graph in processing a mathematical calculation relation is solved; effective technical support is provided for intelligent decision making in the vertical field, domain knowledge is disassembled into parameters, formulas and attributes based on the first principle by establishing domain knowledge basic data, the knowledge graph is constructed through the entity-relation, and accurate graph reasoning support can be provided for downstream large models and intelligent application.

The invention discloses an intelligent scheduling method and system for concurrent requests of a multi-tenant cloud computer operation management platform, belongs to the technical field of AI intelligent application, and aims at solving the technical problem of how to overcome the defects of resource preemption and low service quality of the multi-tenant cloud computer platform in a high-concurrency scene. According to the technical scheme, the resource utilization efficiency and the service quality guarantee capability of a multi-tenant cloud computer platform are improved, and the method comprises the steps of tenant behavior intelligent analysis: collecting related original data of each tenant in real time, constructing a tenant portrait model, and achieving multi-dimensional priority dynamic evaluation; resource demand time sequence prediction: predicting resource demands of a CPU, a GPU and a memory in a set period through a Prophet prediction model in combination with historical data and a real-time load, establishing a resource demand early warning mechanism, and performing resource pre-allocation in advance; an elastic resource allocation strategy is established, resource sufficiency and shortage scenes are distinguished, dynamic resource quota adjustment is achieved, and response time is shortened; and abnormal behaviors are monitored in real time and rapidly isolated.

The embodiment of the invention provides a code checking method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence application, and the method comprises the steps: obtaining a code file and a project document, and carrying out the content extraction of the project document through a literary and physical agent, the method comprises the steps of obtaining function requirement information and programming specification requirements for a code file, conducting code analysis on the code file through a programming agent according to the function requirement information and the programming specification requirements, extracting a first code block needing to be modified from the code file, and outputting a code suggestion for the first code block, the code suggestions are evaluated through the review intelligent agent and the literary and sports intelligent agent, the evaluation result for the code suggestions is obtained, and the review report file corresponding to the evaluation result is output, so that the comprehensiveness and accuracy of inspection are improved, the inspection efficiency is effectively improved, developers can visually and effectively learn based on the review report file, and the development efficiency is improved. And the programming capability is improved.

The invention discloses an intelligent street lamp system based on renewable energy and a control method, the system comprises a street lamp, a renewable energy power generation module, an energy storage module and an intelligent control unit, and the intelligent control unit comprises an intelligent sensing layer, an intelligent application layer and a cloud platform management layer; the intelligent sensing layer comprises a high-precision environment sensor, a human body / traffic flow detection sensor, an equipment state detection module and a controller; the cloud platform management layer comprises an adaptive dimming control module, an equipment management module, a data analysis module, an AI decision module and a visual monitoring module; the intelligent application layer comprises an intelligent operation and maintenance module, a multi-energy coupling module and a city data service module. By the adoption of the system, the management efficiency, the energy utilization rate and the operation and maintenance intelligent level of urban lighting facilities are improved, renewable energy sources such as solar energy and wind energy are combined, energy is collected through multi-energy coupling, regional cooperative control is optimized, and passive power utilization is achieved in the environment with sufficient natural energy.

The invention provides an intelligent port and navigation AI application system under digital twin enabling, and belongs to the technical field of artificial intelligence application systems. The system comprises an inland river exclusive AI model module, a strong noise voice interaction module, an instruction-scene mapping interaction module and a digital twinborn fusion module. And the inland river exclusive AI model module is used for realizing real-time sensing and accurate decision-making of the navigation channel situation. According to the system, an inland river exclusive AI model module, a strong noise voice interaction module, an instruction-scene mapping interaction module and a digital twinborn fusion module cooperate with each other; real-time sensing and accurate decision making of channel situations, effective voice interaction in a strong noise environment, accurate mapping of voice instructions and virtual scenes and fusion of a high-precision channel model, meteorology and monitoring are achieved, and the problems that traditional inland river shipping management is low in efficiency, inconvenient to interact and poor in scene restoration degree are effectively solved through the design. And the inland river shipping efficiency is improved, the navigation safety is guaranteed, and the operation cost is reduced.

The invention discloses a desktop and application intelligent management method and system based on AI desktop cloud, and belongs to the technical field of AI intelligent application, and the method comprises the following steps: data collection and preprocessing: collecting application use data of a user in a desktop cloud environment, and carrying out cleaning, conversion and integration; user grouping based on AI: analyzing the preprocessed application use data by using a machine learning algorithm, extracting user behavior characteristics, dividing users into different groups and generating group portraits; personalized recommendation and management: providing personalized application recommendation and desktop management strategies in combination with an AI recommendation algorithm based on the user group portrait and the historical use data of the individual user; and abnormal monitoring and early warning: setting various index thresholds of application management, monitoring application use data in real time, and giving out early warning when the indexes exceed the thresholds. According to the invention, user grouping, personalized recommendation management and abnormity early warning are realized, and the intelligent level of application management and the user experience are improved.

The present invention relates to an artificial intelligence application development system for enabling easy development of an artificial intelligence application on the basis of graphics. The artificial intelligence application development system according to the present invention comprises: an IDE providing device (1) for providing an IDE through which a developer can develop an application in a graphic base; and a storage device having a node library and a project library. The IDE providing device (1) includes a user interface (20) which enables a developer to generate a flow graph by using nodes and edges, wherein the user interface (20) includes a node calling means for enabling the developer to call the nodes to a workspace and an edge connection means for connecting the edges for setting a process flow or a data flow between the nodes. In addition, the IDE providing device (1) comprises a hyperparameter engine (13) for setting hyperparameters for the nodes and a flow-graph engine (12) for interpreting and executing the flow graph.