2176 results about "Smart device" patented technology

XCB poly-radio devices as finders, locators, scanners, sensors, and Bluetooth (BT) radio topology reporters as part of a global lost-and-found services system. XCB devices function as “smart objects” and combine dual transceivers operating in the telecommunications and the low power ISM radio spectra, with default power management overrides under local control of the BT modem. “Familiar zones” are defined, and a “familiarity index” versus a “strangeness index” based on meta-analysis of BT radio contact logs are used to assign location. Location, in concert with other sensor data, is coupled to conditional logic rules programmable by the user or the system administrator. Locations, determined by monitoring familiar and unfamiliar BT radio topology, trigger executable commands to smartphones, to smart devices generally, and to remote machines, and can for example, generate alerts to a user if radiotagged assets, children or pets have strayed, are lost, or are left behind. When tied to cloud predictive services or increased edge computing capacity, incipient loss of radiotag objects can be detected before an owner knows the smart object is lost. In another embodiment, XCB devices having a novel chip architecture for improved power management of cellular and MCU circuits mediated by BT radio modem that achieves “situational awareness” by monitoring the BT signal environment around the device.

The invention provides a multi-source data real-time fusion processing method and system for a mobile intelligent device, and relates to the technical field of data processing.The method comprises the steps that 1, multi-dimensional original data streams are collected in real time through a heterogeneous sensor array integrated by the mobile intelligent device, data streams of different sensors are aligned by applying a space-time synchronization mechanism, and the data streams of different sensors are obtained; generating an original data set with consistent time and space; 2, dynamic interpolation compensation operation is executed on the original data set, and a dynamic calibration framework is constructed based on the internal topological relation of the data flow to form a dynamic sensing domain; and generating an evolution sequence according to the data unit evolution behavior of the domain boundary, generating a space correction value through the evolution sequence and the offset feature of the preset reference, and generating preprocessed data fused with the space correction value in combination with real-time data correlation analysis. According to the method, dynamic adjustment is triggered through anomaly detection, the fusion parameters are updated through the sliding window, and real-time efficient fusion processing of multi-source data of the mobile intelligent device is achieved.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent device communication method and system based on the Internet of Things. The method comprises the following steps: acquiring spectrum occupation data, electromagnetic interference mode data and terminal equipment energy state data of a plurality of communication channels in an industrial environment through a preset distributed spectrum sensing network; performing adaptive spectrum analysis processing on the spectrum occupancy data and the electromagnetic interference mode data to obtain channel quality evaluation data including interference source characteristics and channel attenuation characteristics; and according to the channel quality evaluation data and a pre-acquired historical channel performance memory bank, performing priority ranking on the available communication channels to obtain dynamic channel priority data based on time continuity. The spectrum resource allocation condition in the industrial environment is monitored in real time through the distributed spectrum sensing network, the current interference can be identified, and the interference can be pre-judged through an advanced prediction model.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a linkage method and system of an apartment network and intelligent equipment, and the method comprises the steps: collecting network operation data and equipment operation data of a target apartment in real time; based on the equipment operation data, determining currently achievable candidate service scenes of the target apartment; fusing the network operation data and the equipment operation data into a joint operation map of the target apartment through the space-time diagram attention network; a multi-agent depth deterministic strategy gradient algorithm is adopted to carry out network resource allocation and equipment control decision making on the joint operation map, scene adaptive optimization is realized in combination with candidate service scenes, and a linkage strategy of a target apartment is obtained; and generating a decision instruction based on the linkage strategy, and adopting the decision instruction to realize remote control of the network equipment and the intelligent equipment. Through deep fusion of the space-time diagram attention network and multi-agent reinforcement learning, dynamic collaborative management and control of the network and equipment are realized, and the resource utilization rate and the service response speed are improved.

The invention relates to the field of hospital patient safety management, and particularly discloses a hospital patient safety management system and method based on data analysis, and the method comprises the steps: firstly obtaining the hospitalization record of a patient, the heart rate values, collected by an intelligent device, of the patient at a plurality of preset time points, and the blood pressure values, collected by the intelligent device, of the patient at a plurality of preset time points; and then feature extraction and correlation analysis are carried out on the three by using a deep learning technology, and finally whether a patient safety early warning is sent out is judged through a classifier, so that the patient nursing quality is improved, and the response speed to the health condition of the patient is improved.

A wireless smart wearable device includes a first portion and a second portion that can communicate with each other wirelessly. The first portion and the second portion respectively includes a first processor and a second processor, a first wireless communication module and a second wireless communication module, a first camera and a second camera, a first image acquisition module and a second image acquisition module, and a first clock and a second clock. The first image acquisition module transmits a first hardware trigger signal based on the first clock to the first camera, and the second image acquisition module transmits a second hardware trigger signal based on the second clock to the second camera. At least one processor enables the first wireless communication module and the second wireless communication module to continuously perform wireless communication with each other and / or wireless communication between both of them and a smart device, and determines a clock difference to achieve synchronization between the first and second hardware trigger signals.

Various embodiments described herein relate to systems, methods, and non-transitory computer-readable media structured to perform server-to-device secure data exchange using a device access token. In an embodiment, a smart device receives, from a requestor entity provided to the smart device, an account data provisioning request for an account. Based on the account data provisioning request, an account identifier for the account is determined. In some arrangements, the account identifier comprises or is associated with a device access token. Based on the device access token, a data element associated with the account is determined. In some embodiments, the data element is accessible to the requestor entity only if it is not access-restricted based on the device access token. Based on the data element, an executable graphic rendering instruction is generated. The executable graphic rendering instruction is executed, which includes generating and displaying, on a user interface of the smart device, a dynamic account status indicator relating to the account.

The invention discloses a source-load interaction method based on an electricity utilization information acquisition system, and relates to the field of power distribution network operation and optimization. The method comprises the following four steps: S1, power consumption data acquisition: dynamically acquiring user power consumption, distributed energy output and power grid state data through intelligent equipment, and ensuring real-time accuracy; s2, load characteristic analysis: constructing a quantitative model to analyze characteristics such as a user load elastic coefficient and adjustable potential, establishing a supply and demand balance evaluation system in combination with distributed energy fluctuation, and determining peak and valley time periods and gaps; s3, generating an interactive strategy, and generating and issuing dynamic strategies such as electricity price excitation, load regulation and energy scheduling based on an analysis result; and S4, executing and feeding back the strategy, monitoring the execution effect in real time, and optimizing the strategy through a closed loop mechanism. According to the method, the problems of staticization, single mode and low efficiency of a traditional method are solved, the power grid supply and demand balance capability, the renewable energy consumption rate and the strategy accuracy are improved, and low-carbon and intelligent operation of the power distribution network is supported.

The invention is suitable for the technical field of people flow density detection, and provides a scenic spot people flow density detection and early warning method and system, and the method comprises the steps: collecting real-time passenger flow data through an intelligent device disposed at a key node of a scenic spot; preprocessing the collected data and then constructing a multi-dimensional feature analysis model, wherein the multi-dimensional features comprise people flow composition features, behavior pattern features and environment coupling features; constructing a sample set based on feature vectors output by the multi-dimensional feature analysis model, outputting the sample set to a deep learning model to calculate a people flow density index, and triggering graded early warning when the people flow density index exceeds a first preset threshold value; generating multi-language voice early warning information according to the graded early warning, and performing regionalized broadcasting through directional sound field equipment; and when the people flow density index exceeds a second preset threshold value, generating a tourist shunting scheme including optimized path planning, thereby effectively improving the accuracy of people flow density detection and the timeliness of early warning.

The invention relates to the technical field of intelligent equipment management, in particular to an intelligent protective glasses equipment management method and system based on a cloud platform. The method comprises the following steps: monitoring corresponding dial scanning data, gas monitoring data and equipment external environment data in real time through a corresponding sensor built in intelligent protective glasses equipment, and uploading the data to a cloud platform; acquiring an equipment dial plate scanning use frequency, and performing dial plate scanning fault analysis and gas reading abnormity evaluation to obtain an equipment dial plate scanning fault probability and an equipment gas reading abnormity index; and carrying out equipment intelligent management analysis on the intelligent protective glasses equipment based on the equipment dial scanning fault probability and the equipment gas reading abnormal index so as to generate a cloud platform equipment abnormity maintenance management plan corresponding to the intelligent protective glasses equipment. According to the invention, comprehensive equipment state monitoring and accurate maintenance management work can be provided for the intelligent protective glasses, so that the use performance and user experience of the intelligent protective glasses can be improved.

The present application relates to the field of autonomous driving, and in particular relates to an occupancy grid prediction method, an occupancy grid prediction apparatus for implementing the method, a computer storage medium for implementing the method, and a smart device having the occupancy grid prediction apparatus. The method comprises: acquiring a pure visual image of the surroundings of a vehicle body; inputting the pure visual image into an occupancy grid prediction model; and using the occupancy grid prediction model to determine an occupancy result of an object in the pure visual image as a model output. The model output is an occupancy result in a cylindrical form, and the occupancy grid prediction model is constructed on the basis of a training dataset comprising sample images and truth value information of the sample images. The truth value information is directly generated from point cloud data or converted from occupancy information in a voxel form generated on the basis of the point cloud data.

A system to capture a two dimensional digital source image of a scene by a user, including a smart device having a memory device for storing an instruction, a processor in communication with the memory and configured to execute the instruction, a digital image capture device in communication with the processor, said processor configured to capture a first two dimensional digital source image of the scene, said processor configured to execute an instruction to generate a second two dimensional digital image of the scene from said first two dimensional digital image of the scene via a camera angle rotation of between 1-180 degrees of said first two dimensional digital image of the scene, and a display in communication with the processor, the display configured to display a multidimensional digital image.

The invention provides a smart home control method, a readable storage medium and a smart home system. The method comprises the steps that character characteristics of target home equipment are set at least according to equipment functions of the target home equipment, the character characteristics comprise character dimensions, and the character dimensions comprise at least one of gentle, cold, humor and fine; according to at least part of the voice data, the behavior data and the indoor environment data of the user, the emotional state of the user is determined, and the behavior data comprises use condition data of the user for the multiple intelligent devices; analyzing the emotional state and the character traits by using a large model, and at least determining an interaction strategy of the target home device, the interaction strategy comprising a response style and response content; and under the condition that the emotional interaction function of the target home equipment is started, at least controlling the target home equipment to interact with the user according to the interaction strategy. According to the invention, the personification and intelligence level of the smart home equipment is improved.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention discloses a multi-person voice separation and recognition system and method for real-time voice interaction, and belongs to the technical field of voice signal processing. The technical principle comprises the following steps: segmenting streaming voice through an audio acquisition module and storing the streaming voice into a circular queue; the preprocessing module converts the wav file into a standard wav file; the voice separation module adopts a SepFormer model to separate a speaker track; the voice recognition module executes end-to-end transcription on the voice through a Paraformer model; and the result management module binds the speaker number and the text and then performs structured output. The multi-thread asynchronous architecture enables acquisition, separation and identification to be processed in parallel, and low delay is realized by combining a circular queue; the SepFormer and the Paraformer are cooperated to improve the separation precision and the recognition accuracy of the overlapped voices; jSON / database multi-format output is supported, and the interaction requirements of real-time conferences and intelligent devices are met.

The invention discloses a Suzhou evaluation cavity genre classification method, which comprises the following steps that: each intelligent device encapsulates a local data set and a cavity feature to be identified into a request, submits the request to a multi-mode federal RAG module, retrieves external knowledge through a large model, realizes semantic association analysis and outputs a cavity feature identification result. And the device obtains global parameters of the graph neural network from the federal learning central server to complete initialization. A local operation deep reinforcement learning module screens a high-quality sample subset training graph neural network and uploads parameters; and the server aggregates and generates new global parameters and pushes the new global parameters. And repeating the process until the model converges, issuing a final parameter by the server, reasoning local cavity data by each device according to the final parameter, and outputting a genre category. According to the method, the characterization limitation of a traditional method on complex artistic features such as dialect voice and branch tone retune accompaniment is solved, and the data island dilemma and privacy risk of centralized model training are broken through.

The invention discloses an ultra-low power consumption pyroelectric infrared human body detector and a networking control method thereof. The detector comprises a detection module, a control module, a power supply module, a communication module, an interaction module and a dynamic power consumption management module, human body infrared radiation is detected based on the pyroelectric effect, and ultra-low power consumption operation is achieved by dynamically switching working modes. The networking control method is based on a BLE Mesh protocol, a self-adaptive topology management algorithm is used for optimizing routing, and efficient networking with intelligent equipment is achieved. The system solves the problems of high power consumption, large networking delay, high false alarm rate and the like of a traditional detector, and is suitable for the fields of smart home, intelligent security and protection and the like.

The invention discloses a multi-mode interactive smart home central control screen system and method based on a large model. The system comprises an instruction receiving module, an instruction processing module, a strategy generation and optimization module, an instruction sending and executing module, a state feedback and monitoring module and a learning and adapting module. According to the invention, multiple interaction modes such as touch control, voice, gesture and visual identification are integrated together to form a comprehensive multi-mode interaction system, the multi-mode interaction system is applied to the intelligent home central control screen system, a large model technology is adopted, intelligent services of the intelligent home central control screen system are realized, all intelligent devices at home can be managed in a unified manner, and the intelligent home central control screen system is more intelligent. The remote controller or the mobile phone APP of each device does not need to be operated respectively, more intelligent and personalized services are provided for the intelligent home central control screen system, and efficient, intelligent and personalized control and management of the intelligent home devices are achieved.

A semantic sensing system includes a processor, a memory and at least one sensing element, wherein the processor is configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards a first endpoint supervisor in relation with the affirmative and non-affirmative determinations.

A system and method for a peripheral device workspace cloud orchestrator server to receive environmental telemetry data associated with a plurality of device nodes within a peripheral device workspace at a location that includes an anchor information handling system node with a hardware processor to execute computer-readable program code of a telemetry ownership orchestrator module to determine to offload a portion of environmental telemetry data collection and aggregation functions to be transitioned to a smart device node within the peripheral device workspace from the anchor information handling system node when a resource utilization level at the anchor information handling system node is determined to reach a utilization threshold, and transmitting instructions to the anchor information handling system node to offload the portion of environmental telemetry data collection and aggregation functions for a subset of the plurality of peripheral device nodes in the peripheral device workspace to the smart device node.

The invention discloses a micro-water density monitoring method and system based on multi-sensor fusion compensation, and relates to the field of power equipment safety. According to the micro-water density monitoring method based on multi-sensor fusion compensation, parameters are collected in real time through a micro-water collector composed of a ceramic heater, a humidity-sensitive capacitor collection sheet, a dew point sensor, a pressure sensor and a temperature sensor; a signal conditioning circuit and an analog-to-digital conversion module are integrated by adopting a four-layer PCB (Printed Circuit Board), and the micro-water collector is heated for multiple times by a main controller; carrying out digital filtering processing, cross validation operation and operation of eliminating influence of environment temperature on the acquired parameters; the measurement precision is improved through a sensor self-calibration mechanism, finally standardized micro-water density data are output, and an early warning function is triggered based on a multi-level threshold value. According to the invention, the problems of low precision and poor reliability of the measured micro-water density in the prior art are solved. Therefore, the accuracy of micro-water density measurement under complex working conditions is effectively improved, and data support is provided for predictive maintenance of intelligent equipment.

The invention relates to the technical field of intelligent capability evaluation, and particularly discloses a method and system for evaluating a talent in a tipped point based on the Internet of Things, and the method comprises the steps: analyzing production parameters, and calculating the stability of production equipment; determining an evaluation value containing a time label according to the stability; obtaining a worker matched with the production equipment and a matching time period according to the information collector; and for any worker, obtaining all production devices matched with the worker and matching time periods of the production devices, reading evaluation values of the matching time periods, and accumulating the evaluation values to obtain a final evaluation value. Operation parameters of the production equipment are analyzed, whether the operation parameters are stable or not is judged, related workers are obtained according to an information collector installed on the production equipment, the workload of the workers is determined according to whether the information collector is stable or not, and the process is completed by intelligent equipment installed on the production equipment. And the method is more objective, more real and extremely high in quantization degree.

The invention discloses a gout diet education system based on multi-modal interaction and an intelligent teaching aid. The system comprises a registration module, an intelligent recommendation module, a multi-modal interaction module, a diet recording module, a health monitoring module and a cloud server. The registration module receives personal information input by a user through intelligent equipment; the intelligent recommendation module generates a preliminary personalized diet scheme based on personal information by using a deep learning algorithm in combination with a medical knowledge base and a gout diet management guide; the multi-modal interaction module displays gout diet education content in a multi-modal mode; the diet recording module records diet data in real time and provides instant feedback according to the diet data and the preliminary scheme; the health monitoring module collects physiological indexes through a wearable sensor and synchronously uploads the physiological indexes and diet data to the cloud server; and the cloud server generates a diet and health association result and transmits the diet and health association result to the intelligent recommendation module to optimize the preliminary scheme. The system can provide quasi diet management support for gout patients.

Systems and methods are disclosed for validating transactions executed by Internet of Things (IoT) devices leveraging non fungible tokens (NFTs) stored on a blockchain. The system generates a first NFT paired with metadata associated with an IoT device, a second NFT paired with metadata associated with a transaction requested by the IoT device, and a third NFT merging the first and second NFTs together. The NFTs are used in validation processes to ensure an IoT device and / or a transaction requested by an IoT device are valid. The NFTs are bound to smart contracts assigned one or more rules related to validating an IoT device and / or transaction requested by an IoT device. A transaction requested by an IoT device executes based on the validation processes.

A method for a system includes receiving with a first transceiver of an identity reader device, an ephemeral ID from a first smart device, outputting with the first transceiver, identity reader data to the first smart device, wherein the identity reader data comprises a first identifier and a challenge, receiving with the first transceiver, responsive data from the first smart device, wherein the responsive data comprises token data and contact tracing status of the first smart device, determining in a processor of the identity reader device, whether the first smart device is authorized in response to the ephemeral ID or the token data, and the contact tracing status, and directing with the processor, a peripheral device coupled to the identity reader device to perform a user-perceptible action in response to the processor determining that the first smart device is authorized.

Methods and systems for automatically mitigating risks of water damage to a property caused by insurance-related events are provided. A smart home controller and / or insurance provider remote processor may analyze data received from a smart device disposed on, within, or proximate to a property associated with monitoring water flow, as well as data received from an insurance provider. If it is determined that an actual or potential risk of water leakage exists, the smart home controller or remote processor may automatically issue commands to the smart device to take actions that mitigate the risk of water damage. The smart home controller may transmit information about the actual or potential water leakage risks and any mitigative actions to an insurance provider. The insurance provider remote processor may interpret the transmitted data and perform insurance activities, such as providing a discount or premium, and / or adjusting an insurance policy associated with the property.

The invention relates to the technical field of driving, in particular to a driving risk interaction prompting method, intelligent equipment and a readable storage medium, and aims to solve the technical problem of how to ensure that a driver can timely, accurately and effectively receive a risk prompt of a driving risk scene. In order to achieve the purpose, a driving risk scene of the intelligent equipment is obtained based on environment perception information of the intelligent equipment; obtaining a visual focus prediction result of the driver based on the driver state of the driver of the intelligent equipment; obtaining a channel availability evaluation result of at least one prompt channel of the intelligent equipment based on the prompt channel state of the intelligent equipment; according to a visual focus prediction result and a channel availability evaluation result, risk prompting is performed on a driving risk scene, so that the situation that the risk prompting is ignored in an unconcerned area of a driver is effectively avoided, and the risk prompting can be adaptively, timely, effectively and accurately perceived and transmitted by the driver; therefore, the driving safety of the intelligent equipment is effectively improved.

A variety of smart inference systems and robotic control devices are disclosed. A device networking system interconnects mobile devices, robotic devices, and others. A robotic controller secures and manipulates a controlled device user interface. A smart device system interprets data based on a plurality of models operating on a plurality of runtimes based on a plurality7 of capabilities. A smart payment processor system assigns payments accounts automatically as a function of a semantic matching between one or more inferred semantic identities. A robotic emulation device captures and analyzes a video signal from a target device and transmits manipulation signals emulating a peripheral input device. A system of carts are physically coupled in a charging configuration. A conveyor is configured to couple with another conveyor and having sensors to detect weight, size, location or other aspects of conveyed items, and implementing semantic analysis for management and augmentation.

This application relates to the field of intelligent driving technologies. A recognition model training method includes: obtaining a first image captured by a first photographing component, where the first photographing component corresponds to a first angle of view; generating, based on the first image, a second image corresponding to a second photographing component, where the second photographing component is determined based on the first photographing component, and the second photographing component corresponds to a second angle of view; determining, based on first three-dimensional information of a first object in the first image, second three-dimensional information of the first object in the second image; and training a recognition model based on the second image and the second three-dimensional information of the first object, where the recognition model is used to recognize an object in an image captured by the first photographing component.