178 results about "Applied artificial intelligence" patented technology

The invention relates to the technical field of power grids, in particular to a metering box remote monitoring method, system and device based on artificial intelligence and a medium. Acquiring electrical parameter data and environmental data of the metering box; performing time domain and frequency domain feature extraction on the data to obtain key features; inputting the processed data into a preset abnormal state detection model; identifying an abnormal state of the metering box based on a model output result; and when the abnormity is identified, alarm information is generated and sent to a monitoring terminal through a remote communication network. Multi-dimensional monitoring data are collected in real time through multiple sensors, feature extraction and anomaly detection are performed by applying an artificial intelligence technology, a model is continuously optimized in combination with an online learning mechanism, and comprehensive sensing of the running state of the metering box and accurate recognition of the abnormal state are achieved. Compared with a traditional monitoring system, the method improves the detection accuracy, reduces the false alarm rate and the missing report rate, improves the fault processing efficiency through the automatic generation of the maintenance suggestions, and provides a powerful guarantee for the operation of a power system.

A computer implemented system and method for generating offer ranges for candidates in an interviewing process is disclosed. The system generates an AI-based interviewer simulating human-based interactions for conducting an ongoing interview with candidates. The system analyzes data associated with candidates obtained during ongoing interview. The system process analyzed responses of candidates to determine contextual attributes associated with responses using ML models. The system automatically generates follow-up interview questions to be delivered to candidates during ongoing interview based on analyzed responses from candidates, by applying AI model to contextual attributes associated with responses. The system generates recruitment scores for candidates based on analyzed responses, contextual attributes, and interpreted non-verbal cues, associated with candidates, using AI model. The system generates offer ranges for candidates based on recruitment scores using AI model. The system provides information associated with selected candidates, and offer ranges generated for selected candidates, to users.

A system determines a finding item template based on a current report's characteristics. The system parses and categorizes text from prior reports into finding items using the template's criteria, displaying categorized phrases in a user interface. This enhances consistency and speed of reporting, allowing users to edit finding items and apply AI feature detection to images.

Dynamic storage location allocation and strategy adjustment system (100) for SAP Extended Warehouse Management (EWM) environments, comprising: a) a real-time warehouse data aggregation module configured to collect SKU attributes, inventory levels and bin location availability from SAP EWM; (b) a dynamic bin allocation engine that applies artificial intelligence algorithms to allocate optimal bin locations based on SKU characteristics, space utilization and operational parameters; c) an adaptive strategy adjustment module that dynamically modifies storage and retrieval strategies in response to real-time storage conditions; (d) a pattern recognition and forecasting module configured to analyze historical trends and predict inventory and demand fluctuations; (e) a warehouse resource optimisation module that aligns decisions on the allocation of storage space with the availability of labour and equipment; f) an SAP EWM integration module that communicates with SAP EWM via APIs or BAdIs to implement changes in real time; and g) a review and feedback learning module that tracks system decisions and results to continuously improve allocation and strategy logic.

The invention discloses an intelligent power grid fault monitoring method and system applying artificial intelligence, and relates to the technical field of power system fault monitoring, and the method comprises the steps: obtaining power internet-of-things monitoring data through a sensor, carrying out the preprocessing of the data, extracting fault-related features based on the preprocessed data through a machine learning algorithm, and carrying out the fault-related features; based on historical data of the power grid, a preliminary fault monitoring threshold range is determined through statistical analysis; and comparing the fault feature vector with real-time monitoring power grid operation state data, dynamically adjusting a fault monitoring threshold value by using an adaptive algorithm based on a comparison result of historical data and real-time state data of the power grid, giving an alarm after an abnormal condition is monitored, and optimizing a fault monitoring mechanism according to feedback of a fault processing result. According to the invention, through data preprocessing, noise removal is realized, the monitoring precision is improved, and the effect of improving the fault detection accuracy is achieved.

The invention relates to the field of artificial intelligence, in particular to an artificial intelligence implementation scheme of a wall building robot. The robot is used for wall engineering of civil buildings and industrial buildings and is the general trend of scientific and technological development. The intelligent wall building robot cannot leave artificial intelligence. Application of artificial intelligence to the wall building robot belongs to emerging frontier science and technology. The invention provides a complete and systematic solution for applying artificial intelligence to the wall building robot, so that the wall building robot can complete wall building intelligently, independently, high-quality and high-efficiency without manual cooperation, 'complicated, dangerous, dirty and heavy 'manual operation of wall engineering is avoided, and the social benefit and the economic benefit are remarkable.

A divergence detection method for branch roads, a model training method, and a device, which can apply artificial intelligence technology to the field of autonomous driving. In the method, image or point cloud data of a surrounding environment of a vehicle can be acquired; and when the vehicle enters a preset range of a fork in a road, whether the vehicle diverges when driving through the fork in the road is determined by means of a machine learning model and on the basis of the image or point cloud data of the surrounding environment of the vehicle. Even in traffic scenarios such as tunnels, urban canyons, elevated roads and small-angle parallel branch roads, a vehicle can still acquire high-quality image or point cloud data of a surrounding environment, and can then determine, by means of a machine learning model, whether the vehicle has a divergence behavior when passing through a fork in the road, thereby helping to effectively detect whether the vehicle has a divergence behavior in more traffic scenarios.

An electronic device may include a communication circuit, a memory, and at least one processor. The memory may store instructions that cause the electronic device to execute an application, determine a property of the application, receive a plurality of data packets for the application from an external device through the communication circuit, determine whether at least one of data throughput information on the external device, state information about a channel transmitting the plurality of data packets, or signal round trip time information satisfies a specified condition, apply artificial intelligence learning-based algorithms to merge and process the plurality of data packets to the plurality of data packets, based on the specified condition being satisfied, and refrain from applying the artificial intelligence learning-based algorithms to the plurality of data packets, based on the specified condition not being satisfied.

The invention discloses a weather forecasting method and system, a readable storage medium and a program product. The weather forecasting method comprises the following steps: downloading a global analysis field and a global forecasting field; operating an artificial intelligence meteorological large model, and outputting global forecast data based on the global analysis field; operating a numerical weather forecast model, and generating a regional meteorological field based on the global forecast field; interpolating the global forecast data into the regional meteorological field to generate an interpolated regional meteorological field; operating the numerical weather forecast model, and generating an initial field and a boundary field of a corresponding region based on the interpolated regional meteorological field; and operating the numerical weather forecasting model, and performing ensemble forecasting based on the initial field and the boundary field. Therefore, the artificial intelligence large model can be applied in the numerical mode, the advantages of the two models are combined, and the accuracy of weather forecast is improved.

The disclosures are directed to processing systems and methods that apply artificial intelligence-based processes to match an input to one of multiple options. In one example, a processor receives input data and, based on inputting the input data to a large language model (LLM), generates graph data that associates each of multiple propositions to one or more entities. Further, based on inputting the graph data to a trained artificial intelligence (AI) model, the processor generates query data characterizing one or more queries. In addition, based on inputting the graph data and the query data to the same or different LLM, the processor generates matching data charactering associations between the multiple propositions and the one or more queries. The processor may then receive a query request, and can match the query request to at least one of the multiple propositions based on the matching data.

Intent-based automation that discovers automatable tasks and / or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI) / machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI / ML models are used to determine which parts of the flows are similar and / or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

A wireless communication method for applying AI / ML functionalities, a method for managing mobility of UE and related devices are provided. The method, performed by the UE, includes sending, by a UE in a network, artificial intelligence (AI) / machine learning (ML) capability information to the network, wherein the AI / ML capability information indicates supported AI / ML functionalities at UE side; communicating with the network to decide applicable AI / ML functionality for AI / ML feature / feature group (FG), at least based on the AI / ML capability information; and performing at least one AI / ML functionality activation at a given time to trigger AI / ML inference for seamless data transmission. With this method, intelligent mobility management can be realized.

A system includes a processor of an ID verification node and a memory of a decentralized digital identifier for communication (DsTC) module on which are stored machine-readable instructions that when executed by the processor, cause the processor of the ID verification node to: receive a verifiable credential associated with a user, wherein the verifiable credential is uploaded by a sender; apply artificial intelligence (AI) / machine learning (ML) to scan biometric image data to verify the sender of the verifiable credential corresponds with the user associated with the verifiable credential; verify the authenticity of the verifiable credential with an issuer of the verifiable credential; in response to verifying that the verifiable credential is authentic, generate an intermediate representation to create a secure true digital ID corresponding to the user; and provide role based access for the user based on the secure true digital ID.

Methods and systems are provided for information retrieval pathway guidance. In particular, the systems and methods provided apply artificial intelligence to novel tasks related to retrieving remotely accessible data such as detecting competency levels of users, generating personalized guidance for individual users based on the retrieval goals and initial competency level of a user, generating custom diagnostic assets for those goals based on current strengths and weaknesses, generating content for custom questions for those diagnostic assets, and dynamically tracking and updating the competency level of the user during the course.

A monitoring system, for a charging pile, wherein the charging pile charges an electric vehicle through a power module, includes an image capture module, configured to capture at least one environmental image signal related to the electric vehicle; and a control module, coupled to the image capture module, configured to apple an artificial intelligence technology to analyze the at least one environmental image signal, to generate an analysis result, and control the power module according to the analysis result.

Technology for applying artificial intelligence to decide when to, and / or when not to, send a consumer of a computer system a communication recommending that the computer system be revised to include a more recent version of at least one of the following: a hardware component (for example, microprocessor(s)) and / or a software component (for example, an updated version of an app). The computer system, that is subject to modernization, may be owned outright by the consumer, or it may be purchased as a service (for example, infrastructure as a service, software as a service, package of cloud services). Some embodiments focus on modernization recommendations specifically tailored to cloud orchestration software that deploys containers.

A class-4 telecommunications switch hosts artificial-intelligence agents that convert live speech to text, translate the text between languages, and synthesize natural speech in real time. The switch proxies calls among public trunks, PBX / media gateways, and cloud ACD / CRM services, embedding diacritic-rich transcripts and user-specific language-model personalization. Deployable at the customer edge, in the PSTN core, or as SaaS, the system supports one-to-one, one-to-many, many-to-one, and many-to-many call patterns. FPGA, ASIC, or SoC accelerators minimize latency and bandwidth, cutting capital cost while improving global voice interoperability and cybersecurity.

A medical data processing system is operable to train an image-based adverse reaction prediction function based on utilizing artificial intelligence to process a training set that includes a first plurality of medical image data. A second plurality of medical image data corresponding to a plurality of individuals is obtained based on the plurality of individuals being identified as candidates for administering of a medical treatment. A plurality of adverse reaction prediction data is generated based on utilizing artificial intelligence to perform the image-based adverse reaction prediction function upon each of the second plurality of medical image data to generate corresponding adverse reaction prediction data of the plurality of adverse reaction prediction data, The plurality of adverse reaction prediction data is processed to partition the plurality of individuals into a first proper subset and a second proper subset.

The invention relates to a thermal-mechanical coupling accelerated aging test system for a new energy automobile PCB assembly. According to the system, through the integration test cabin, the thermal load module, the vibration loading module, the mechanical stress loading module, the data acquisition and monitoring module and the control and analysis system, complex environmental conditions faced by the new energy automobile in the operation process can be simulated. The system can apply temperature shock, vibration load and mechanical stress at the same time so as to realize an accelerated aging test on the PCB assembly in a multi-physics coupling environment. The test data is analyzed by applying an artificial intelligence algorithm, the test parameters are dynamically optimized, and the test efficiency and reliability are improved. The system is suitable for accelerated aging tests of a new energy automobile PCB assembly, a power battery management system (BMS), an electronic control unit (ECU) and the like.

The present invention relates to the technical field of tumor diagnosis, prediction, and evaluation. Specifically, it relates to a tumor prediction system, method, and its application based on tongue image. The system includes a tongue image acquisition module configured to acquire the tongue image of a test sample, and a data processing module configured to obtain the probability that the test sample belongs to the positive category by the following operations. The data processing module predicts the probability that the test sample belongs to the positive category based on the discriminative features on the tongue image obtained by automatic learning. The discriminative features are derived from a single positive tongue image or negative tongue image. The system further includes an output module. It is intended to apply AI deep learning to perform diagnostic prediction for tumors based on tongue images. The tumor prediction system is simple to operate, low in cost, painless, non-invasive, and through a large number of test cases, it has demonstrated that the prediction system is a forward-looking, economic, non-invasive, and effective screening system for tumors.

Methods and systems are described for imaging and content generation. During a road trip, a driver or passenger observes an object or scene from a vehicle. From a perspective of the driver or passenger, the object or scene flows out of view in a relatively short amount of time. Images and video of the object or scene are captured with cameras. A field of view is extended beyond what the driver or passenger can easily observe. The capture is configurable to focus on particular types of objects or scenes. Recordings of billboards and road signs are easily displayed after passing the object. Object identification, gaze determination, and interest determination are provided. Curated content is generated. Applications to extended reality environments are provided. Artificial intelligence systems, including neural networks, and models are utilized to improve the imaging and content generation. Related apparatuses, devices, techniques, and articles are also described.

A method for a driver assistant image customization of vehicle-related images, a data processing circuit, a computer program, a computer-readable medium, and a vehicle, can include, with at least one sensing device of the vehicle, obtaining an input image to be customized. With at least one human-machine-interface of the vehicle or connected thereto an input determining at least one customization scheme to be performed is received. Using at least one data processing circuit of the vehicle applying artificial intelligence the input image is transformed according to the at least one customization scheme into a transformed output image. With at least one smart mirror of the vehicle or a mobile device connected to the at least one data processing circuit the transformed output image is outputted. The at least one customization scheme includes a plurality of different types of adaptation modes.

The invention provides an interpretable postoperative analgesia formula intelligent recommendation method based on a decision tree, and the method comprises the following steps: S1, according to hospital postoperative analgesia case data, determining patient characteristics and analgesia drugs, the patient characteristics comprising continuous variables and discrete variables, the analgesia drugs being the continuous variables, and preprocessing the case data; s2, performing feature construction and feature selection on the patient features, and taking the selected patient features and analgesic drugs as data sources; s3, taking the preprocessed medical record data as a training sample of a decision tree algorithm, and performing analgesia formula model learning to obtain a learned model; and S4, inputting test case data into the learned model for prediction to obtain a final analgesic formula. Through application of the artificial intelligence technology in the field of postoperative analgesia, medical and industrial crossing is promoted, intelligent recommendation of analgesia formula decisions is achieved, and therefore the workload of doctors in the process of formulating analgesia formulas is greatly reduced.

The invention relates to the application of artificial intelligence and advanced statistical methods in a single software tool to monitor and analyze quality issues throughout the life cycle of a product, the whole life cycle being:-zero kilometer; -the effective service life within the warranty period ("on-site warranty period"); -an effective service life not within the warranty period ("outside warranty period"). In addition, the invention further provides an innovative method for monitoring products beyond the warranty period based on the network capture technology and artificial intelligence.

The application provides a big data intelligent ore prospecting operation method based on multi-source geological relationship data, applies artificial intelligence technology, big data technology and geological theory research achievements to the field of mineral exploration technology, collects or constructs detailed knowledge graphs of single ore type or multiple ore types according to different situations, divides ore prospecting target areas into a plurality of unit modules with spatial information as the most basic geological relationship knowledge graph carrier unit, respectively assigns corresponding attribute contents to the unit modules according to spatial positions, description contents and geological knowledge, references existing related ore type knowledge graphs, performs knowledge fusion and disambiguation on the attribute contents in the unit modules, and constructs the geological relationship knowledge graph of the unit module, and obtains the scores of the unit modules or the scores of the different types of ore-forming possibilities of the unit modules according to weights, so that the ore prospecting prospective areas can be efficiently and accurately delineated or the dominant ore can be determined.

A method, computer program product, and computer system carry out a process of user registration. The process of user registration comprises receiving a registering user input of a drawing process of a drawn image into a graphical user interface and monitoring the first drawing process to gather registration metadata relating to the drawn image. The process also comprises providing, by applying an artificial intelligence image analysis method, a description of the drawn image. Further, the process comprises securely storing the description and a set of the registration metadata for later authentication by comparison to a description of a later drawn image and metadata relating to the later drawn image.

The invention discloses a music education method and system based on artificial intelligence, and relates to the technical field of music education, and the music education method based on artificial intelligence comprises the following steps: obtaining music learning data of a user; based on a preset classification mode, performing cloud service matching on the music learning data of the user to obtain a plurality of original storage positions; artificial intelligence is applied to the field of music education, learning data of a user is analyzed through the deep learning model, making of a learning scheme and resource recommendation are achieved, and the learning efficiency and effect are improved. According to the method, historical music learning data is utilized to construct a deep learning model for feature analysis and pattern recognition, so that the learning demand and the learning scheme of a user are predicted, and more accurate learning guidance is provided for the user.

The present disclosure provides an advanced energy management system that optimizes energy storage unit (ESU) deployment and operation using artificial intelligence. Unlike conventional systems that rely on static configurations or manual adjustments, this system dynamically analyzes data from multiple sources to configure and manage ESUs. By employing AI algorithms, the system can adapt to specific application requirements, predict future energy needs, and efficiently manage energy flow across distributed storage units. The system generates and evaluates multiple ESU configurations, selecting the optimal setup based on performance metrics and application-specific parameters. This approach addresses the limitations of traditional ESU deployment methods, potentially improving overall energy efficiency, enhancing reliability, and reducing costs in various applications, from small-scale residential installations to large industrial and grid-level energy storage solutions. The AI-driven system continuously monitors and optimizes ESU performance, adapting to changing conditions and maintaining ultra-high reliability standards.

A media search system is described here that finds existing media based on matching metadata and links them together for orderly viewing. The system uses social media providers and artificial intelligence and / or machine learning and or other public APIs to search available metadata of media stored by each provider to find matching metadata for what a person is looking for in a specified area. The system may apply artificial intelligence (Al) to generate additional criteria to use for the search to find media responsive to the person's request. The system then groups the discovered media according to the found metadata and Al may also contribute suggestions for organizing the discovered media. Once the media is grouped together, the system allows the user to switch between media within the same group. Thus, the media search system makes the wealth of available media more accessible to people looking for a particular event.

A method for creating and applying an AI model (60) for a control device (50) for controlling and / or regulating and / or monitoring a ring rolling process of a workpiece (2), the method comprising: generating a database by executing at least one computer simulation, preferably FEM simulation, of at least one fictitious, partial, or complete ring rolling process based on at least one set of fictitious process parameters; creating the AI ​​model (60) by training a neural network using the generated database; and applying the AI ​​model (60) by inputting process parameters of the ring rolling process of the workpiece (2) into the neural network, wherein the neural network of the AI ​​model (60) generates an output field that is characteristic of the workpiece (2), preferably comprising a temperature and / or deformation distribution of the workpiece (2).