2917 results about "Output device" patented technology

The invention discloses a power plant intelligent maintenance method and system based on multi-modal dynamic graph learning. The method comprises the following steps: acquiring structured sensor data, unstructured data and equipment physical topology data of equipment operation in real time through a multi-source sensor cluster and an industrial terminal; the method comprises the following steps: preprocessing multi-modal data, and fusing multi-modal features by using a double-flow Transform architecture and a gated attention mechanism to generate a joint embedded representation; constructing a dynamic causal graph based on equipment physical topology data and sensor time sequence characteristics, updating an edge weight through a GraphSAGE algorithm, fusing domain rule constraints, and outputting equipment state information; generating a maintenance strategy through an improved near-end strategy optimization algorithm according to the state and the equipment health index; and finally, the maintenance strategy triggers third-level early warning of the DCS through an OPC UA protocol, and a maintenance instruction is accurately issued. According to the method, the defects of a traditional method in the aspects of data fusion, fault modeling and decision making are overcome, and the safety, the economical efficiency and the operation and maintenance intelligent level of power plant equipment are remarkably improved.

The invention relates to the technical field of digital factories, and discloses an equipment real-time monitoring and fault prediction method and system based on a digital factory. The method comprises the following steps: carrying out iterative filtering interpolation processing on equipment operation data collected by a multi-modal sensor in a digital factory to obtain smooth time sequence data, and constructing a multi-modal feature matrix based on the smooth time sequence data; inputting the multi-modal feature matrix into a joint training generalized Siamese-like wide learning network for model training to obtain an equipment state monitoring model; executing system-level state evaluation and subsystem-level fault prediction calculation based on the equipment state monitoring model, and generating an equipment operation and maintenance strategy; according to the equipment operation and maintenance strategy, a fault risk heat map is generated, a digital twin visualization system is constructed, and an equipment maintenance scheduling scheme is output, the production efficiency and equipment health management are balanced, and collaborative optimization of a maintenance plan and a production plan is achieved.

The invention relates to the technical field of mode recognition, in particular to a transformer substation equipment temperature measurement method and system based on visible light guidance. Firstly, a visible light image, an infrared thermogram and vibration spectrum data are synchronously collected, a dynamic compensation coefficient matrix is constructed through illumination intensity analysis, the dynamic compensation coefficient matrix is applied to the infrared thermogram, and illumination interference compensation is achieved. And then, inputting the corrected infrared thermogram and visible light image into a pre-trained heterogeneous atlas matching model, and identifying the category and three-dimensional coordinates of the target equipment. And based on the equipment category information, calling a standard temperature field template in the digital twin model library, establishing a heat conduction path model in combination with the vibration spectrum data, and generating a temperature field prediction reference map of the equipment. And finally, through pixel-by-pixel deviation analysis, inputting the temperature deviation value, the image features and the vibration data into the probability graphic model, and outputting equipment fault information. The method can realize high-precision temperature measurement and fault diagnosis.

The invention discloses an intelligent management system and method applied to a radio frequency energy output device, and belongs to the technical field of intelligent management of the radio frequency energy output device.An integrated sensor array is deployed on a radio frequency host and a hand tool electrode, and load voltage, output current, electrode temperature and tissue impedance are synchronously collected; a dynamic thermal impedance collaborative analysis model is constructed after time synchronization alignment, a three-dimensional thermal field simulation map is generated, and a thermal accumulation trend is predicted; a double-layer control framework is constructed, a first control layer generates a dynamic power adjustment rule base based on a preset treatment target and a safety threshold value and outputs an instruction, a second control layer receives the instruction and adjusts output parameters in real time, and a strategy is adjusted by combining a model prediction result in the execution process; meanwhile, the operation state is continuously monitored, a fault classification model is constructed for anomaly detection, a fault source is positioned, and a visual report is generated; and finally, constructing a mapping relation based on historical data, and generating an energy control scheme adapted to individualized requirements through iterative optimization.

A video output device includes a projector module configured to output a content image along a radiation direction, a first rotating device configured to rotate at least a portion of the projector module about a first axis that is parallel to the radiation direction, a second rotating device configured to rotate the projector module about a second axis intersecting the first axis, a substrate electrically connected to the second rotating device, and a connector electrically connected to the substrate through a first wire, electrically connected to the second rotating device through a second wire. While the projector module rotates about the second axis, the connector is configured to prevent the first wire and the second wire from being twisted.

The invention discloses a power generation equipment state fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: actively injecting a mechanical excitation signal of a preset frequency spectrum into a key part according to a physical topological structure of power generation equipment, and carrying out the fusion to generate a time-space-frequency three-dimensional data volume; inputting the three-dimensional data volume into a physical embedded variational auto-encoder, and outputting an equipment state pure feature tensor; inputting the pure feature tensor into a graph space-time causal reasoning network to generate a fault propagation causal graph with probability weight; performing multi-agent diagnosis on the fault propagation causal atlas, and outputting a fault diagnosis report which has a credibility interval and comprises fault positioning and root cause analysis; and mapping the fault diagnosis report to the digital twin of the equipment in real time, and outputting a self-adaptive maintenance strategy sequence which minimizes the expected value of the whole life cycle operation and maintenance cost. According to the embodiment of the invention, the accuracy and anti-interference capability of fault diagnosis can be improved, and the operation and maintenance cost can be effectively reduced.

The invention provides a method for improving the image recognition precision of a new energy station, and relates to the technical field of image recognition, and the method comprises the steps: obtaining monitoring image data and environment parameter data, and carrying out the image preprocessing based on an environment impact factor and an image degradation score; extracting image features by using a deep convolutional neural network, and extracting time sequence correlation features by using a recurrent neural network; and feature optimization is realized through feature stability evaluation and dynamic calibration, and finally the working state and abnormal early warning information of the equipment are output. According to the method, the precision and robustness of new energy station image recognition can be effectively improved, and the interference of environmental factors on a recognition result is reduced.

Utility locators for providing information about hidden or buried pipes or other cavities are disclosed. A locator may be configured to provide positional information associated with the buried utility based on signals received from a line trace signal and a sonde signal and present the positional information on a visual display or other output device.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention provides a robot intelligent inspection method and system based on multi-source data fusion, and relates to the technical field of intelligent inspection, and the method comprises the steps: carrying out the data collection of an inspection region based on an inspection robot; adjusting the enhancement parameter of the equipment appearance image, highlighting the equipment operation sound in the inspection area sound, correcting the equipment temperature distribution, and compensating the area gas concentration; extracting defect features in the equipment appearance image, capturing sound features in equipment operation sound, extracting temperature features in equipment temperature distribution, and learning gas concentration features corresponding to regional gas concentration; and performing space-time fusion on each feature to generate a space-time fusion inspection feature, outputting an equipment fault diagnosis result and adjusting a current inspection path of the inspection robot, thereby cooperatively collecting multi-dimensional information of the equipment, optimizing data quality through adaptive processing, mining associated features through space-time fusion, and adjusting the inspection path in combination with intelligent decision. Fault diagnosis reliability is improved, and safe and stable operation of equipment is guaranteed.

A system configured to generate dynamic visual feedback by synchronizing smart lights with a rhythm and energy levels of music being output. A device may translate audio samples to luma (brightness), chroma (color), and location data using a low-latency algorithm. For example, the device may divide the full band audio data into separate frequency bands based on the number of output devices and control a brightness for an output device by tracking energy within a corresponding frequency band. The color and location may be controlled based on a number of beats per minute, direction, and / or the like. In addition to controlling external lighting via a smart home hub, the device can control LED light(s) on the device via an LED controller. To further reduce latency, the device can directly communicate between an audio processor, the smart home hub, and / or the LED controller using low level hardware abstraction layers (HALs).

The invention relates to the technical field of industrial manufacturing digital management, in particular to a machine vision production line performance evaluation and optimization management system, which comprises a data acquisition module for triggering a high-speed industrial camera array, a vibration sensor and an RFID reader through a central synchronous controller to synchronously acquire product images, equipment operation and material circulation data; the data processing and fusion module extracts product quality features based on CNN, and fuses multi-modal data through time sequence alignment normalization and an attention mechanism; the dynamic efficiency evaluation module calculates OEE, FPY and a production line balance rate in real time by means of a deep neural network; the optimization strategy generation module is used for reinforcing the learning agent to output optimization instructions such as equipment parameter adjustment; and the control execution module converts the instruction into an industrial protocol format, issues the instruction to the PLC, and verifies the effect to form a closed loop. According to the method, the data relevance and the evaluation real-time performance are improved, the dynamic state of the adaptive production line is optimized, and the efficiency improvement is facilitated.

A method that includes determining a set of one or more reverberation parameters of an extended reality (XR) environment in which a user is to participate, determining whether an audio source device is wirelessly communicatively coupled to send audio to an audio output device, in response to determining that the audio source device is not wirelessly communicatively coupled to the audio output device obtaining a microphone signal produced by a microphone of the audio source device, producing a reverberant audio signal from the microphone signal according to the set of one or more reverberation parameters, and sending the reverberant audio signal to drive a speaker driver of the audio source device.

An example system includes: a first large language model; a memory storing instructions and one or more processors operable to execute the instruction to: receive a natural language text string from a client device, where the natural language text string comprises a formal rule expressed in natural language; input the natural language text string and a control string into a large language model (LLM), where the control string is configured to cause the LLM to generate a formal expression representing the formal rule of the natural language text string; output, by the large language model, the formal expression to an output device; and evaluate a consistency of the formal expression using a test input.

A patient data charting device configured to automatically capture electronic patient care record (ePCR) data from a caregiver is provided. The device includes a memory storing an ePCR including a plurality of data fields, an output device, a microphone configured to acquire speech, and a processor. The processor is configured to convert the speech to text, identify a first value of a data field of the plurality of data fields based on the text, populate the first data field with the first value, generate a prompt that requests a second value of a second data field of the plurality of data fields based on the first data field, and present the prompt to the caregiver via the output device.

The invention discloses a PLC register point location address determination method and device, equipment, a medium and a product, and the method comprises the steps: obtaining a target data packet according with a monitoring rule from data packets sent by PLC equipment to output equipment; based on the filtering condition of the target parameter, filtering out target analysis content from the analysis content of each target data packet; wherein the analysis content of at least part of the target data packet comprises a data value and a register point location address for storing the data value; any target parameter is a parameter displayed by the output device; the register point location address in the target analysis content is the point location address of the target parameter. Real valuable information can be extracted from numerous devices and sensors, accurate acquisition and real-time feedback of data are ensured, a reliable basis is provided for deeply analyzing a production process, capturing key performance indexes and implementing fine control, and core information is also provided for realizing intelligentization, optimization and transparency of the production process.

Systems and methods for stereotactic localization of components include a system for stereotactic localization of a component during a surgical procedure. In some cases, the system includes one or more of an instrument, a plurality of markers disposed on the instrument, one or more sensors configured to detect a position of each of the markers, and an output device. In some cases, the system is configured to calculate a digital 3-dimensional array based on the position of at least some of the plurality of markers, compute a location of the instrument, and communicate the location of the instrument to a user of the system through the output device. Other implementations are described.

The invention discloses a full-equipment energy consumption optimization management system in a building, and relates to the technical field of intelligent control, and the system is provided with a multi-modal data collection module which collects building environment parameters and equipment operation data through a distributed sensor network, and generates a multi-modal sensing matrix; an environment state prediction module is set to construct a dynamic knowledge graph based on a multi-modal sensing matrix, a graph attention network is used, an energy flow matrix of equipment topology is output, micro-environment state prediction data is generated through prediction of a finite element solver, a scheduling scheme solving module is set, a mixed integer programming model is constructed based on the micro-environment state prediction data, and a scheduling scheme is set. And solving the mixed integer programming model to obtain a scheduling scheme of the energy consumption equipment, setting a control instruction optimization module, and driving the edge execution equipment to operate based on the scheduling scheme. The global scheduling optimization of the whole building energy consumption equipment is realized, the energy consumption is effectively reduced, and the environmental comfort is improved.

The invention discloses an AI data scheduling distribution system and method based on cloud computing, and relates to the technical field of intelligent cloud computing, and the system comprises an AI data collection module, a feature extraction module, a decision parameter generation module, a scheduling distribution module and a visual feedback module. The AI data acquisition module acquires equipment video data and temperature data, the feature extraction module extracts video temperature features, the decision parameter generation module outputs equipment vibration intensity and temperature rise rate, and the scheduling distribution module calculates equipment shutdown probability and node load score and triggers a shutdown instruction and a task migration decision. And the visual feedback module maps the equipment health state and node load distribution, and optimizes the feature weight. Through multi-modal data fusion and a closed-loop feedback mechanism, accurate early warning of equipment abnormity and dynamic optimal allocation of resources are realized, visual decision assistance and adaptive weight adjustment are combined, equipment safety and cluster load balancing efficiency in a cloud computing environment are improved, and manual intervention requirements and operation and maintenance cost are reduced.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

A content output device includes a display controller for controlling output of a content to a display device provided for each of seats of a moving object, and an identification unit for identifying each of occupants seated in the seats of the moving object, and the display controller is adapted to: in accordance with a content output instruction from one occupant seated in one seat, connect to an account of the one occupant, the account being related to content viewing, and output a content to be provided to the account to one display device that is the display device for the one seat; and stop the output of the content to the display device for the one seat while maintaining the connection to the account when the one occupant leaves the one seat.

The invention discloses an unattended substation autonomous operation and maintenance management platform based on multi-modal data fusion. Multi-source data such as equipment state, intrusion behavior, flood prevention safety and environmental parameters are acquired through a multi-modal data sensing module; the cross-modal space-time fusion module performs timestamp alignment, space coordinate mapping and feature level fusion on the data to generate a unified representation vector; the intelligent analysis module outputs analysis results such as equipment fault probability and intrusion threat level by using a multi-task parallel deep learning model; the autonomous decision-making module dynamically generates an operation and maintenance strategy set based on the rule base and the knowledge graph; the execution driving module converts the strategy into an executable instruction and issues the executable instruction to the edge computing node; the visual interaction module realizes strategy verification through a three-dimensional digital twinborn body; and the edge computing node drives an execution mechanism to complete specific operation and return a result. According to the invention, multi-source data fusion, intelligent analysis and autonomous decision making of the unattended substation are realized, and the operation and maintenance efficiency and reliability are improved.

The invention discloses a tunnel electromechanical equipment identification method and system based on Retinex-DCE-YOLOv5s, and the method comprises the steps: S1, obtaining a tunnel electromechanical equipment image collected in a vehicle inspection process; s2, carrying out brightness enhancement processing on the obtained equipment image by adopting Retinex-DCE; s3, constructing a YOLOv5s network, replacing common convolution except the first layer of convolution in the backbone network of the YOLOV5s with CoordConv convolution, introducing a CBAM attention mechanism at the tail end of the backbone network, and adding a detection head at a Head layer of the YOLOV5s to obtain an improved YOLOv5s network model; s4, inputting the equipment image subjected to brightness enhancement processing into an improved YOLOv5s network model for training to obtain a trained detection and recognition model; and S5, inputting a to-be-processed equipment image into the detection and identification model for processing, and outputting the equipment image, the position of the equipment image and the type of the equipment.

A computer system outputs audio content via one or more audio output devices. If the audio content includes information that enables spatialization of the audio content, the system outputs the audio content in a simulated three-dimensional environment, including, if the audio content corresponds to a first category of content, causing the one or more audio output devices to simulate production of the audio content in a first virtual space, and if the audio content corresponds to a second category of content, causing the one or more audio output devices to simulate production of the audio content in a second virtual space that has different simulated acoustic properties than simulated acoustic properties of the first virtual space.

The invention relates to an audio control system and method based on a multi-area framework and a vehicle, the system comprises a central controller and a plurality of area controllers connected with the central controller, the central controller comprises a first Ethernet module, and each area controller comprises a second Ethernet module, a first microcontroller and a power amplifier driving module. The central controller is configured to carry out data protocol packaging on the first audio data and then transmit a first Ethernet protocol data packet to each area controller. The first microcontroller is configured to receive a first Ethernet protocol data packet transmitted by the central controller, analyze the first Ethernet protocol data packet, and transmit first audio data to the power amplifier driving module. The power amplifier driving module is configured to amplify the first audio data and then drive the audio output device to output the amplified first audio data. The problems that an existing audio control system is complex in communication link, too long in wire harness and the like can be effectively solved.

The invention discloses a multi-mode self-learning diagnosis and early warning method and system for a power distribution system, and relates to the technical field of intelligent operation and maintenance of power systems. According to the method, a visible light image, an infrared thermal image, a sound signal, an environmental parameter and an electrical parameter of the power distribution equipment are collected through a multi-mode sensor, and time synchronization and feature extraction are carried out; multi-modal features are constructed into a hypergraph structure, and cross-modal feature fusion is realized by using a graph attention mechanism; adopting a strategy of combining self-supervised pre-training and supervised fine tuning to carry out multi-objective joint optimization on the fused features to obtain a self-learning diagnosis model; the system inputs multi-modal data in real time during operation, outputs an equipment operation state and a health degree index, and realizes early warning and fault alarm through threshold comparison. According to the method, deep fusion and adaptive learning of multi-source information can be realized, high-precision diagnosis, real-time early warning and online updating capabilities are realized, and the intelligence and safety of a power distribution system are remarkably improved.

Certain aspects of the disclosure provide systems and methods for parsing documents using machine learning. For example, a method may include receiving a document of a target document type and in a target human language. The method may include parsing the document with a specialized large language model (LLM) focused on parsing only the target document type in the target human language. The LLM may be trained using a set of training documents of the target document type. Each training document may be a résumé downloaded at least once, and has a completion score equal or greater than a threshold value. The method may include generating parsed content of the document including content extracted from the document. The method may include populating a plurality of predefined fields with the parsed content of the document. The method may include outputting the populated predefined fields to an output device.

The invention discloses a fusion multiply-add operation circuit compatible with multiple floating-point number formats, and the circuit comprises an input device which is used for obtaining three floating-point numbers and control information in a fusion multiply-add operation instruction; the input decoding device is used for analyzing the control information and extracting the symbol, the index and the mantissa of each floating-point number; the multiplication correlation device is used for calculating an initial index, a mantissa product and an initial symbol; the addend aligning and shifting device is used for completing addend mantissa aligning and shifting; the additive operation correlation device is used for calculating an addition result, a symbol, a normalized shift bit number and a direction flag bit according to the mantissa product sum; the original result processing device is used for generating an initial rounding bit, a normalized shift pasting bit and intermediate result data; and the final result output device is used for outputting a final operation result compatible with the IEEE-754 or Point floating-point number according to the rounding information. The circuit area and power consumption are remarkably reduced through hardware integration, and meanwhile the calculation efficiency is improved.

A system and method for inferring device types. A method includes selecting a device type inference model from among a plurality of device type inference models based on a manufacturer of a device, wherein each device type inference model corresponds to a respective manufacturer and is trained using training data of devices manufactured by the respective manufacturer, wherein each device type inference model is trained to output a device type prediction; and determining an inferred device type for the device, wherein determining the inferred device type for the device further comprises applying the selected device type inference model to a plurality of features, wherein the plurality of features is extracted from device activity data indicating ports used by the device and at least one volume of traffic communicated via each port used by the device.

An electronic device and method for tailgating detection is provided. The electronic device receives data from an access control system for a physical entry point. When an event that indicates an access grant is detected, entry information for a period is collected. The information includes a set of authentications provided by the access control system. The electronic device receives a video from at least one imaging device, extracts object detection information from the video, and determines cost information based on a comparison of a time of each entry and a time of each authentication. Thereafter, the electronic device determines a correspondence between a subset of the entries and the authentications based on the cost information. The electronic device detects, from entries included in the object detection information, one or more entries that are different from the subset as tailgating entries. An output device renders tailgating information based on such entries.