265 results about "Applied artificial intelligence" patented technology

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

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.

The invention relates to a power equipment self-repairing surge protection system based on artificial intelligence, and the system is characterized in that a surge detection module is connected between a live wire and a protection ground wire of power equipment, collects voltage and current data, and transmits the data to an artificial intelligence analysis unit; and the artificial intelligence analysis unit extracts the surge characteristic parameters, generates protection strategy parameters through a deep neural network, and controls the trigger thresholds of all levels of protection devices in the programmable protection circuit. The programmable protection circuit comprises a gas discharge tube capable of adjusting trigger voltage, a metal oxide varistor and a transient voltage suppressor diode, and is used for discharging surges with different energies step by step. The resettable fuse cuts off the circuit when the temperature exceeds a threshold value and recovers conduction after the temperature is reduced. And the controller receives and applies the protection strategy parameter output by the artificial intelligence analysis unit, and dynamically adjusts the trigger threshold value of the protection device, so that the response and protection performance of the system to the surge is improved, and the self-repairing and stable operation of the power equipment are realized.

The invention discloses a smart city comprehensive transportation junction passenger flow control optimization method comprising the following steps: collecting data of a sensor, a camera and a mobile device, cleaning and preprocessing the data, using a data mining technology to identify a passenger flow rule, and using the data mining technology to determine the passenger flow rule; and establishing a passenger flow prediction model based on time sequence analysis, a regression model and a neural network, and training and verifying the accuracy of the model through historical data. The invention provides a passenger flow management and control optimization method for a comprehensive transportation junction of a smart city, and aims to realize accurate prediction and real-time regulation and control of passenger flow by integrating multiple data sources and applying artificial intelligence and machine learning technologies. And according to the prediction result, dynamically adjusting the scheduling schemes of the entrance and exit channels of the transportation hub and the transportation means, providing transfer path suggestions and reducing the transfer time of passengers.

The invention discloses a waterproof composite material production intelligent control system, and relates to the technical field of control systems, the system comprises a data acquisition layer, a standard template generation module, a data processing layer, a Transform-based model core layer and an output layer, the data acquisition layer acquires production data in real time; a data processing standard template is generated through a standard template generation module, standardization processing is conducted on real-time data through a data processing layer, production process modeling and production state analysis are conducted through a Transform model core layer, an analysis result is decoded into a control instruction through an output layer, and production line process parameters are adjusted. The production process is intelligently and optimally controlled by applying the artificial intelligence technology, the intelligent level, the operation efficiency and the product quality of the production line are remarkably improved, meanwhile, the production goals of energy conservation, efficiency improvement and intrinsic safety are achieved through refined management and control and whole-process safety protection, and intelligent and green development of the waterproof composite material industry is promoted.

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.

The invention provides a power distribution network unmanned aerial vehicle infrared inspection method and device based on an edge box, and the method comprises the steps: receiving a path planning adjustment instruction transmitted by a cloud server, carrying out the adaptive path planning, and obtaining an inspection path; controlling the unmanned aerial vehicle to carry out real-time infrared imaging shooting on the power distribution network line and the associated supporting structure according to the inspection path to obtain infrared image data; according to the infrared image data, using an artificial intelligence algorithm model to identify components of the power equipment, and using a YOLOv8 model to identify temperature information of each component; and sending the component parts and the corresponding temperature information to a cloud server so as to judge whether temperature abnormity exists or not and generate an inspection report. The adaptive path planning ensures that effective routing inspection, unmanned aerial vehicle routing inspection and edge calculation can be deeply fused in the shortest time, the data processing pressure of the server is relieved, the time interval from data acquisition to decision support is shortened, and the safety and stability of power grid operation are effectively ensured.

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 gas pipe network leakage early warning, in particular to a gas pipe network leakage early warning system applying artificial intelligence. Comprising the following steps: acquiring sensor data, and processing the sensor data through a data fusion algorithm to obtain fused sensor data; on the basis of the sensor data and the fused sensor data, a fuel gas leakage score is obtained through calculation by adopting a multi-level spatio-temporal evolutionary algorithm; based on the fuel gas leakage score, a fuel gas leakage trend analysis algorithm is used for calculation to obtain a fuel gas leakage trend value; external environment data are obtained, and based on the gas leakage score, the gas leakage trend value and the external environment data, an early warning priority evaluation algorithm is used for calculation to obtain an early warning priority value; and setting a low-level early warning threshold value and a high-level early warning threshold value, dividing early warning levels, and formulating emergency response strategies according to different early warning levels. The technical problems that the change of the leakage risk cannot be captured in time in the trend analysis of the gas leakage and the sensitivity to the score change is insufficient are solved.

Disclosed in the present application is a method for training a speech enhancement network, the method comprising: acquiring a training set, wherein the training set comprises a plurality of training samples, and each training sample comprises sample reference speech, sample comparison speech and mixed speech; performing voiceprint extraction on the sample reference speech to obtain a sample voiceprint vector; performing audio feature extraction on the mixed speech to obtain a sample audio feature; performing, by means of a speech enhancement network, enhancement processing on the basis of the sample voiceprint vector and the sample audio feature, and outputting a predicted audio feature for a sample sound-producing object; on the basis of the predicted audio feature and a comparative audio feature corresponding to the sample comparison speech, determining a training loss value of the speech enhancement network; and iteratively updating weight parameters of the speech enhancement network on the basis of the training loss value until a training ending condition is met. The present application is applied to artificial intelligence, and by means of the present application, a speech enhancement network for suppressing interfering human voices can be trained on the basis of sample reference speech and sample comparison speech which originate from the same sound-producing object, thereby effectively improving the quality of speech enhancement.

The invention relates to the technical field of anesthesia management, in particular to an anesthesia medicine excess brain function influence assessment system applying artificial intelligence, which comprises a data acquisition module, a data preprocessing module, a model training module, a real-time assessment module, a risk early warning module, a treatment suggestion module, a system interaction module and a remote monitoring module. Through the combination of a convolutional neural network and a long-short-term memory network, spatial-temporal characteristics of brain wave and functional magnetic resonance imaging data are extracted, a brain function thermodynamic diagram is generated in real time, and the influence of anesthetic drugs is analyzed; the system is combined with a reinforcement learning algorithm to dynamically adjust the anesthetic dosage, a risk early warning module dynamically adjusts a threshold value through physiological parameters and tolerance of a patient, an expert team can provide medical assistance in real time through a remote monitoring module, and brain function safety of the patient in the operation or anesthesia process is ensured; according to the invention, the safety, accuracy and individuation level of anesthesia management are effectively improved.

The invention relates to a discrete manufacturing factory production scheduling strategy determination system and method, and the system comprises a data collection unit, an analysis processing unit, a scheduling strategy generation unit, a simulation strategy operation unit, an execution control unit, and an adjustment optimization unit which are in communication connection with a user interface unit. According to the method, an optimized scheduling strategy can be quickly generated, waiting time and shutdown time in the production process are reduced, and therefore the overall production efficiency is improved, R1, the future order quantity is analyzed and predicted through big data; r2, applying an artificial intelligence algorithm to generate a preliminary scheduling strategy plan list; r3, selecting a required scheduling strategy plan list; and R4, the progress is monitored in the production process, the production time table is automatically adjusted according to the actual situation, it is ensured that changes are responded in time, and the complexity of production scheduling is simplified by defining the operation process of each step, so that the production scheduling is more standardized.

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 belongs to the field of label and portrait construction of digital human resource management, and particularly relates to a label and portrait construction method based on digital human resource management. Basic data are collected, data labels are defined, and a complete label library is constructed; associating the collected basic data information with definitions in a label library, and performing label assignment; constructing employee portraits and post portraits based on the label data; performing intelligent matching by applying artificial intelligence and data analysis methods through employee and post portrait analysis; a data query function is provided, and a user is helped to obtain information from mass data. By constructing the portraits of the employees and the posts, the enterprise can more accurately match the requirements of the applicants and the posts. Multi-dimensional information such as personal characteristics, capabilities and potentials of employees is comprehensively considered in the employee portraits, and the post portraits reflect skills, responsibilities, experience requirements and the like required by posts.

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.

Disclosed herein is computer technology that provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express.

A method generating an image according to a style is described. The method includes receiving a textual description describing a first image. The method further includes applying an artificial intelligence (AI) model to determine the style of a second image based on the textual description, the first image, a plurality of descriptions, and a plurality of images to generate a suggestion. The style provides a context to the second image. The style is lacking in the first image. The method includes generating the second image with the suggestion according to the style and providing the second image to a client device for display.

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.

An approach is provided for a meeting assist process to receive, over one or more communications networks, a meeting request for meeting assistance, the request including meeting background information, the meeting assist process instantiating an information gathering module to automatically parse the background information to form one or more queries for collecting data across a plurality of data sources, query a plurality of data sources to gather information, and distribute the gathered information automatically to predetermined locations. The meeting assist process further instantiates a meeting artificial intelligence module to monitor the meeting and apply artificial intelligence to automatically detect and track meeting progress, and automatically instantiates a summarization artificial intelligence module upon the meeting ending to generate a meeting analysis including at least a post-meeting summary and sentiment analysis, and automatically distributes the meeting analysis to a shared repository database or a predetermined email list.

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.