2135 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 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.

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.

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.

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.

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 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.

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.

The invention discloses a water conservancy intelligent Internet of Things method and system based on artificial intelligence. The method comprises the steps that state data of water conservancy equipment are collected in real time through an on-site sensor and wirelessly transmitted with low power consumption; the edge computing node analyzes the data in real time based on the hydrological model to obtain preliminary abnormal diagnosis information of the equipment; carrying out cryptographic algorithm encryption processing on the data by adopting a hardware platform carrying a water conservancy gap OS; fusing and analyzing data by adopting an end-edge-cloud three-layer collaborative fusion analysis mechanism, and constructing an equipment health intelligent diagnosis model to identify the abnormal state of the equipment in real time; the video AI model analyzes a field equipment operation video in real time to obtain a visual diagnosis result; and the intelligent management platform is used for outputting equipment abnormity alarm information and automatically forming an equipment operation and maintenance scheme. Accurate sensing and efficient fault handling of the water conservancy equipment operation state are achieved, and the water conservancy operation and maintenance intelligent level is improved.

The invention belongs to the technical field of data analysis and processing, and particularly relates to an equipment state monitoring method based on a time sequence synchronous compression mechanism, and the method comprises the steps: 1, a monitoring terminal collects vibration type, acoustic type and acceleration type signals on a unified time baseline of Beidou time service signals, and registers a second sequence number and a frame sequence number in a unified fragment index table; 2, inputting an original time sequence data packet, constructing a phase grid frame by a phase locking ring according to a reference phase rail, extracting road-level flag event bits frame by frame, and fusing the road-level flag event bits into frame-level flag event bits; and step 3, outputting an equipment state label according to a mutual exclusion priority rule only on the basis of the synchronous compression time sequence packet and the coupling mark, the mode instruction and the abnormal fragment mark in the unified fragment index table in the identification window. The method is obviously superior to the prior art in the aspects of time sequence alignment precision, pattern recognition certainty, data compression efficiency and health state judgment reliability.

Dynamic outputs for inducing interactions with autonomous vehicles is described. In one or more implementations, a system includes an output device configured to produce audible signals in an environment outside an autonomous vehicle, and at least one processor operatively coupled to the output device. The processor is configured to detect one or more characteristics of the environment, dynamically generate, based on the characteristics, an audio output for inducing human interactions with the autonomous vehicle, and cause the output device to transmit the audio output as the audible signals produced in the environment for facilitating a human interaction with the autonomous vehicle.

The invention discloses an LSTM electric power Internet of Things ring main unit optimization method, system, device and medium, and belongs to the technical field of electric power Internet of Things management and data analysis, and the method comprises the steps: collecting communication state and network environment data in real time, and generating a multi-dimensional feature matrix through dynamic standard score standardization and derivative feature calculation; dynamically adjusting an LSTM forgetting gate weight based on the bandwidth fluctuation index, and outputting an equipment performance score; constructing a dynamic scoring algorithm and triggering a differentiated maintenance instruction in linkage with a multi-threshold scene strategy; a closed-loop feedback mechanism is adopted to screen high-deviation data, new scene training is enhanced through a dynamic loss function, and minute-level hot updating of the model is achieved; the method solves the problems of static threshold misjudgment, poor environment adaptability of the prediction model and single decision-making mechanism, remarkably improves the feature quality, the equipment evaluation precision and the resource allocation rationality, and strengthens the intelligent operation and maintenance capability of the system.

A method of maintaining the security of an authentication session comprises operating a camera device to continuously capture a view of an environment of a computing device, performing face detection to detect all faces in the environment, performing comparisons between faces detected in the environment and faces of a set of authorized users, at a first time, detecting a first face of an authorized user of the set in the view of the environment and, in response, allowing the authorized user to operate the computing device, and at a second time after the first time, one of failing to detect the first face or detecting a second face that is not of an authorized user of the set in the view of the environment, and, in response, at least partially blocking operator access to input and output devices including blocking a display of the computing device.

The invention provides an ore particle granularity detection device and method based on a flexible vibration disk, and the device comprises the flexible vibration disk which is used for vibrating an ore material at a certain frequency and amplitude, so that the ore material is uniformly distributed and is in a proper posture; the image acquisition device is used for continuously shooting the ore materials on the flexible vibration disc to obtain ore images; the image processing device is used for carrying out preprocessing, segmentation, feature extraction and particle size analysis on the ore image and outputting the ore particle size distribution condition and an accumulative distribution curve; and the result output device is used for displaying the ore image and the granularity analysis result. According to the invention, while material accumulation is prevented, real-time, accurate and efficient detection of attitude distribution of ore materials is realized, and the method has the advantages of low cost of detection equipment, easy maintenance, strong interference resistance, high accuracy and stability of detection results, and the like.

Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

An example may provide at least one first generative machine learning model (GMLM) instruction and an intent to a GMLM. The at least one first GMLM instruction is to cause the GMLM to use the intent to generate first GMLM output. The first GMLM output includes GMLM-generated output sections. A device may provide the first GMLM output including the GMLM-generated output sections and at least one second GMLM instruction to the GMLM. The at least one second GMLM instruction is to cause the GMLM to use the intent, the GMLM-generated output sections, and a first data set to generate second GMLM output including at least one first digital element. A device may validate the second GMLM output by comparing the at least one first digital element to at least one second digital element. The at least one second digital element is accessible via a second data set.

The invention relates to the field of image processing, and discloses an infrared light shadow interaction system and method. The system comprises an image input device, an interaction main platform, an infrared interaction device and a video output device, wherein the image input device is used for acquiring a contour image drawn by a user; the interaction main platform is used for preprocessing the contour image to obtain a mask combination image, and then performing layer synthesis on the mask combination image and a preset static ancient painting material to obtain a multi-layer image; the infrared interaction equipment is used for capturing a laser point track of a user operation point; and the video output equipment is used for displaying the multi-layer image and binding the coordinate information of the laser points with the coordinate position of the mask combination image so as to control the position of the mask combination image in the multi-layer image based on the coordinate information of the laser points. According to the application, an interaction system with user input, intelligent processing and dynamic display feedback is constructed, and a whole-process closed loop from user creation, image processing and immersive interaction is realized.

An audio output system comprising: an audio output device configured to output different pieces of audio content in accordance with sound figures; a user device configured to control the audio output device through an application; and a shadow device connected to the user device and the audio output device through communication networks, respectively, and configured to store control information for the user device to control the audio output device, wherein the control information includes current state information that represents a current state related to an output of the audio content of the audio output device and specific state information for changing the state of the audio output device to a specific state desired by a user, and wherein the user device is configured to be provided with the current state information that represents the current state from the shadow device and to provide the specific state information for changing the state of the audio output device to the specific state to the shadow device.

The embodiment of the invention discloses a display device and a screen projection display method, and the method comprises the steps: after receiving a screen projection connection instruction sent by a terminal device, sending a starting notice to a player through a screen projection service, and enabling the player to build a data pipeline based on the starting notice. And controlling the player to receive the code stream data sent by the terminal equipment after the data pipeline is established, and processing the code stream data by using the data pipeline when the player receives the code stream data to obtain a video picture and an audio. The display displays a video picture, and the audio output device plays audio. According to the embodiment of the invention, data transmission is directly carried out through the terminal equipment and the player, so that communication delay caused by inter-process communication is reduced, the received code stream data is directly processed after the player establishes the data pipeline, cache is reduced, and the delay time of screen projection display is shortened.

The invention relates to the technical field of intelligent interface generation, in particular to an interactive interface generation method and system, computer equipment and a medium. The method comprises the following steps: receiving a multi-protocol interaction request, and extracting natural language context information carried in the multi-protocol interaction request through a protocol processing layer; according to the natural language context information, executing cue word processing to generate interface generation trigger judgment; when the interface generation trigger judgment indicates that an interface needs to be generated, generating an interface mode definition instance based on the natural language context information and a preset cue word template; and according to the interface mode definition instance, generating an interactive interface through a rendering output device. In this way, the technical problem that in an existing interface generation technology based on natural languages, it is difficult to collaboratively optimize the interface dynamic adaptation capacity and the system expansibility is solved, and the intelligent level, cross-platform compatibility and service response agility of interface generation are improved.

Apparatus and methods are described for use with a bodily emission of a subject that is disposed within a toilet bowl. While the bodily emission is disposed within the toilet bowl, light is received from the toilet bowl using one or more light sensors. Using a computer processor, intensities of at least two spectral bands that are within a range of 530 nm to 785 nm are determined, by analyzing the received light, and a ratio of the intensities of the two spectral bands is determined. In response thereto, the computer processor determines that there is a presence of blood within the bodily emission. The computer processor generates an output on an output device, at least partially in response thereto. Other applications are also described.

The invention provides an industrial scene automatic monitoring method, system and product based on multi-modal data, and relates to the technical field of industrial scene monitoring, and the method comprises the steps: obtaining environment multi-modal data in an industrial scene, carrying out the anomaly detection of each device in the industrial scene based on the environment multi-modal data, and obtaining the abnormal data of each device in the industrial scene; outputting an environment perception result in a natural language form; obtaining operation data corresponding to each device based on the environment perception result, obtaining a real-time correlation coefficient between different operation parameters of each device during operation, and outputting a parameter correlation result during device operation in a natural language form; and aligning time information of the environment multi-modal data and the operation data, describing an industrial scene state based on an environment perception result and a parameter association result, and outputting an industrial scene monitoring result including an environment state, an equipment state and an overall trend in a natural language form. According to the invention, the problems of low efficiency, easy omission and the like caused by dependence on workers in industrial scene monitoring are solved.

The invention discloses an equipment invariance enhanced multi-modal deep learning model and application thereof, and the model comprises an input module, a coding module, a modal fusion module, a classification module, an equipment adversarial branch module, an output module and a final total loss function. Extracting features through a coding module to obtain audio features and text features; after the audio features and the text features are processed by the modal fusion module to obtain final joint representation, the classification module processes the final joint representation to obtain classification results and corresponding probabilities, and the classification results and the corresponding probabilities are output by the output module; the equipment confrontation branch module enables an audio encoder to confront an equipment classification head in a training stage; and finally, introducing the total loss function in a training stage to optimize the model. The model provided by the invention has the capability of realizing high-accuracy identification on various respiratory system diseases on the premise of not depending on acquisition equipment of a specific brand, and shows excellent generalization performance and robustness in multi-equipment and multi-center data.