684 results about "Device Sensor" patented technology

Provided are a method, a system, and a storage medium for intelligent diagnosis of a device failure based on an IIoT. The method includes: obtaining device-related data of an abnormal device from a total sensor database; predicting failure warning information of the abnormal device and a confidence level corresponding to the failure warning information based on the device-related data; determining a failure processing parameter based on the failure warning information and the confidence level, and generating a failure processing instruction; and sending the failure processing instruction via the device sensor network platform to a maintenance personnel terminal of the device perception and control platform or to a failure shooting device, and controlling the failure shooting device to perform failure shooting.

The invention relates to the technical field of wind power generation equipment data processing. The invention provides a time sequence database data compression method, system and device and a storage medium. The method comprises the steps of dynamically partitioning time sequence data based on a preset time window; hybrid coding compression is performed on each data block, and a composite strategy of difference coding and run length coding is adopted for numerical data; a metadata index of the compression blocks is established, the time range, the data feature statistics and the compression parameters of each data block are recorded, and the metadata index comprises extreme value distribution, variance features and data fluctuation frequency indexes; and dynamically adjusting a compression strategy according to a historical data feature analysis result, predicting data fluctuation modes of different equipment sensors through a machine learning model, and automatically selecting an optimal coding combination and compression granularity for subsequent data blocks. The problems that when wind power plant time sequence data are processed through an existing method, time sequence characteristics are difficult to adapt, the storage cost is high and the query efficiency is low are solved.

The present disclosure relates generally to computer-implemented systems and techniques for leveraging a virtual decentralized database to enable user identity authentication and verification of user activity and associated behaviors on open internet and public web-based and native applications. Techniques are disclosed for user identification profile onboarding using biometrics, frictionless CAPTCHA (Completely Automatic Public Turing Tests To Tell Humans And Computers Apart) processes, passive data capture of physiological and behaviors using device sensors, and cryptographic methods of storing user engagement activity as immutable data files for secure distribution on a decentralized virtual private network. Such technologies operate to establish a high confidence level for user authenticity, communication origin, engagement outcome transparency and clarity of intent for users active on social, commercial, e-commerce and information sharing platforms. More specifically, an unobtrusive passive data capture method of authentication and verification on a secure VPN supports the organic, spontaneous and original experience audiences desire while simultaneously eliminating opportunities for unscrupulous actors to exploit open internet exchanges by inserting fraudulent user accounts, automated bots and other measures that mimic or impersonate live humans, which ultimately undermines user confidence in data safety and erodes mutual trust between people, companies and institutions.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

A method includes: obtaining equipment sensor data from sensors in a time series; obtaining equipment optimization goal data from optimization goals in a time series; obtaining historical data on equipment abnormal events and intervention events; obtaining static equipment input parameters; applying time series model to the equipment sensor and optimization data, historical event data, and static equipment input parameters, to obtain predicted equipment sensor data; optimizing and controlling hardware operation based on the obtained predicted equipment sensor data; and providing predicted actions for abnormal event intervention based on the obtained predicted equipment sensor data. Hardware control, with optical mechanisms, into deep space, with a novel blockchained quantum bit communication network can be included, as changes to existing applications of transformer models, including ring-all reduced training on top of data and model parallelism, novel graph modalities, generic chart generations, generated image enabled search and drop ship, and multi-modal car foundation models.

The invention provides an intelligent line dynamic monitoring system integrating Beidou positioning and AI. The system comprises inspection equipment, a sensor network and central control equipment. The inspection equipment is used for receiving the inspection task sent by the central control equipment, planning an inspection path and executing the inspection task according to the inspection task and the position information obtained through the Beidou receiver, and obtaining an environment image through the visual monitoring equipment in the process of executing the inspection task and sending the environment image to the central control equipment. The sensor network is used for collecting line data of the power transmission line and environment data of the surrounding environment of the power transmission line and sending the data to the central control device. The central control device is used for generating an inspection report according to the environment image, the line data and the environment data, evaluating the health state of the power transmission line based on a line state analysis and prediction model, and generating an early warning notification and an inspection task. Omnibearing and multi-level monitoring of the power transmission line and the surrounding environment thereof is realized, and the inspection efficiency and safety are remarkably improved.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.

The invention discloses a mining mechanical equipment type selection management control platform based on a network, and relates to the technical field of mining mechanical equipment, and the control platform comprises a data collection module, a data processing and analysis module, and a type selection recommendation module. Multi-source heterogeneous data from a mining area geological exploration department, an equipment sensor, a satellite remote sensing image and a meteorological station are ingeniously fused, and the data are effectively integrated together through normalization processing and weight distribution, so that a comprehensive and accurate information basis is provided for subsequent model selection recommendation; the data processing and analysis module further performs cleaning, classification, coding and feature extraction on the data, a quantitative relation model between working condition factors and equipment performance is established, and the intelligent data analysis and processing capability enables the platform to perform quantitative analysis according to actual working condition requirements of a mining area. And equipment models and parameters with high adaptability and lowest full life cycle cost are accurately recommended.

The invention relates to the technical field of virtual reality, in particular to a centimeter-level VR real-scene real-time synchronous rendering method for multi-source data fusion and physical field coupling. Comprising the following steps: eliminating cloud layer interference and expanding Kalman filtering to improve positioning precision through multi-source acquisition of satellite remote sensing data, meteorological data and an equipment sensor; dynamically adjusting process noise by using Kalman filtering, and complementing delay data; constructing a ray tracing and U-Net noise reduction model, carrying out hierarchical loading of textures in combination with dynamic texture management, modeling dynamic illumination and wind field simulation based on a solar declination angle, and realizing scene rendering; a virtual ground is aligned through WebXR, gesture recognition and a force feedback mechanism are integrated, and fingertip curvature and movement speed features are extracted in combination with a Leap Motion sensor. According to the invention, through multi-source data fusion, efficient rendering and accurate interactive design, the authenticity and real-time synchronization capability of the VR scene are significantly improved.

The invention relates to the technical field of sensors, and discloses a detection and management method and system for purification equipment. The method comprises the following steps: carrying out three-phase coupling dynamic modeling on a gas phase flow state, a particle phase movement track and a sensor phase interface characteristic in the purification equipment to obtain a gas mass transfer characteristic parameter; performing response characteristic analysis on a real-time detection signal of a sensor array in the purification equipment to obtain a sensor calibration compensation parameter; based on the sensor calibration compensation parameters and the gas mass transfer characteristic parameters, deposition kinetics analysis of pollutants on the surface of the sensor is carried out, and equipment operation state evaluation data are obtained; and performing spatial layout optimization on the sensor array according to the equipment operation state evaluation data and the gas flow consistency constraint condition to obtain an optimal layout scheme and generate a staged adaptive management control instruction. According to the invention, self-adaptive adjustment and intelligent switching of the sensor management strategy are realized, and the detection performance and reliability of the purification equipment sensor system are significantly improved.

Systems and methods for generating certified images and incident reports are disclosed. An image capture device can be used to capture an image and integrate metadata from camera sensors as well as other ancillary device sensors into the image. The image and its metadata can then be certified upon a check that the image and its metadata are authentic and unaltered. The image and its metadata can then be included in or as a part of an incident or other report describing an incident or event such as an accident or a crime. The image and / or incident report may be maintained at a cloud-based server for viewing, authorized editing, and subsequent distribution.

The invention relates to the technical field of chronic disease information management, in particular to a chronic disease information management method which comprises the following steps: S1, acquiring physiological index data, medication record data and behavioral habit data of a patient through a multi-source data acquisition module; s2, performing feature extraction on the multi-source data by using a data analysis module to generate multi-dimensional health assessment parameters; and S3, dynamically comparing the health assessment parameters with a preset assessment value through a risk early warning module, triggering hierarchical early warning when an abnormal parameter combination is detected, realizing multi-modal data deep mining by using an intelligent analysis module, performing association analysis on wearable device sensor data, an electronic medical record and a medication compliance record, and determining whether the health assessment parameters are abnormal or not. An individualized medication portrait of the chronic disease patient is constructed, parameters such as gene detection and metabolic indexes are combined, through medicine-disease conflict detection and dose self-adaptive calculation, an accurate medication adjustment strategy and a chronic disease patient health behavior guidance scheme are generated, and individual differences are fully reflected.

The invention discloses a traffic transportation emergency command and dispatch system based on big data resources, which is constructed based on the big data resources and mainly comprises a data acquisition module, a big data processing and analysis module, an event early warning and prediction module, an emergency command and dispatch module, an information display and interaction module and the like. Various devices (a sensor, a camera, a GPS and the like) are utilized to comprehensively collect data such as traffic flow, road conditions, weather, events and the like. Acquisition has the characteristics of high frequency and high precision, supports a plurality of data transmission modes and protocols, and can integrate and format the acquired data to guarantee data quality and convenience of subsequent processing. According to the system, effective utilization of traffic data is realized through collaborative operation of all the modules, and the traffic transportation emergency management capability is improved from multiple aspects, including improvement of management efficiency, enhancement of emergency response capability, improvement of decision support, enhancement of information transparency, optimization of resource allocation, reduction of economic loss and improvement of public safety.

A method for controlling hardware includes: obtaining equipment sensor data from a plurality of sensors in a time series; obtaining equipment optimization goal data from a plurality of optimization goals in a time series; obtaining historical data on equipment abnormal events and intervention events; obtaining static equipment input parameters; applying at least one time series model to the equipment sensor and optimization data, the historical event data, and the static equipment input parameters, to obtain predicted equipment sensor data; optimizing and controlling hardware operation based on the obtained predicted equipment sensor data; and providing predicted actions for rare event intervention based on the obtained predicted equipment sensor data.

The present invention relates to gas detection devices, and in particular to volatile substance sensors, such as respiratory alcohol device sensors. A semiconductor gas sensor device according to the present invention includes a laser structure and an optical waveguide resonator formed in the same compound semiconductor including at least one light emission layer and one light propagation layer. The optical waveguide resonator is formed in the light propagating layer, and a majority thereof is separated from a remainder of the light propagating layer. The laser structure is disposed adjacent a portion of the optical waveguide resonator and is arranged to transmit electromagnetic radiation of a particular wavelength band to the optical waveguide resonator arranged to resonate at the particular wavelength band.

The invention relates to the technical field of mobile edge computing, in particular to a wind power plant inspection method and system fused with mobile edge computing. According to the working mode of the inspection robot and the distribution condition of the wind turbine, the meteorological monitoring equipment, the sensor and the micro base station, a wind energy intelligent control inspection network is constructed; based on a wind energy intelligent control inspection network, a double-layer multi-robot auxiliary edge calculation model is constructed, a Lyapunov optimization technology is adopted to solve a long-term energy consumption steady-state constraint problem of a stochastic optimization problem, and the problem is converted into a deterministic optimization problem; and applying a DDPG reinforcement learning algorithm to obtain an unloading decision and a calculation task allocation scheme of the inspection robot. According to the method, the computing resources of the inspection robot are saved, and the data processing efficiency is remarkably improved. By optimizing the routing inspection path and task allocation, the mobile energy consumption and task processing time delay are minimized, so that the overall benefit of routing inspection work is improved, and safe and stable operation of the wind power plant is ensured.

A magnetoelastic torque sensor system comprising a shaft having at least one axial section magnetized in a circumferential direction; and a magnetic sensor device comprising three or four magnetic sensors; the sensor device being arranged in the vicinity of the shaft; the sensor device comprising a first semiconductor substrate having a processing circuit, a second semiconductor substrate having a first magnetic sensor, and a third semiconductor substrate having a second magnetic sensor (S2), each magnetic sensor configured for measuring a magnetic field component; wherein the first, second and third semiconductor substrates are incorporated in a single packaged device; the system being configured for determining a torque exerted upon the shaft, based on the measured field components, or based on pairwise differences thereof. A method of measuring a torque exerted upon a shaft.

The invention discloses an augmented reality and digital product fusion display system, which can identify preset identifiers such as a two-dimensional code, a graphic code and a radio frequency identification tag through an identifier identification module, is integrated on an intelligent terminal or augmented reality equipment, is convenient for a user to operate at any time, improves the use convenience, and improves the user experience. The three-dimensional model database stores appearance structures, internal components and interactive function data, provides comprehensive product information for users and meets diversified requirements, the scene fusion calculation module realizes accurate positioning and reasonable proportion zooming of a three-dimensional model in a real scene by means of a virtual reality fusion degree calculation formula, dislocation and disproportionality are avoided, and the user experience is improved. The interactive display module supports multi-mode interaction modes such as gestures, voice instructions and equipment sensors, rich and natural interactive experience is provided, the real-time interaction function enables a user to see the influence of operation on the model in real time, interactivity and feedback are enhanced, and the digital product display effect and the user experience are integrally improved.

The invention relates to the technical field of intelligent agricultural planting and breeding, and discloses a digital intelligent agricultural planting and breeding combined system which comprises a multi-source sensing module used for collecting environment data, biological sign data and equipment operation data through a planting area sensor, a breeding area sensor and a circular processing equipment sensor; the data verification module is used for dynamically verifying the collected data based on a preset agronomic mechanism model; the hierarchical digital twinning module is used for carrying out collaborative decision making in a three-level architecture of the edge computing node, the local server and the cloud platform and generating an optimization instruction; a virtual-real decision engine module; and a closed loop execution network module. According to the invention, a lightweight twinborn model is deployed through the edge layer to realize millisecond-level equipment control and guarantee timely response of extreme working conditions, the field area layer coordinates planting and breeding material circulation through a dynamic optimization algorithm, and the cloud layer integrates climate data to generate a carbon sink strategy of block chain evidence storage. And the real-time decision-making precision, the resource utilization efficiency and the carbon economic value are improved through three-level cooperation.

The invention relates to the technical field of data management, and discloses a dynamic error management and tracing method for flow line production data, and the method comprises the following steps: collecting multi-source dynamic data in a flow line production process, including equipment sensor data, production parameter data and product quality data; performing real-time dynamic error detection processing based on the multi-source dynamic data, and generating dynamic error characteristic data through a sliding window algorithm and an abnormal value analysis technology; in the dynamic error management and tracing process of flow line production data, equipment sensor data, production parameter data and product quality data are acquired in real time through a multi-source dynamic data acquisition technology, and data integration is performed by using a timestamp alignment algorithm, so that the synchronism and integrity of error detection data are ensured; meanwhile, through real-time dynamic error detection processing, abnormal fluctuation in the production process can be recognized in time, and the real-time performance and reliability of error detection are guaranteed.

A real-time location system comprises sensors and an upstream device communicating with the sensors. Sensor pairs of the sensors include a transmitting sensor and a receiving sensor. Each sensor is assigned as the transmitting sensor in turn, while all other sensors are designated as the receiving sensors and collect signal strength data. The upstream device determines a labelled dataset based on the signal strength data for the sensor pairs and zone labels associated with the signal strength data. The RTLS ML model is trained based on the labelled dataset.

The invention relates to an equipment health monitoring method and system based on multivariate information fusion. The method comprises the following steps: firstly, collecting a multi-element vibration signal from an equipment sensor, and segmenting the multi-element vibration signal to obtain segmented signals; calculating a multi-dimensional characteristic value of each segment of signal in the segmented signals, and integrating the characteristic values to obtain a characteristic matrix; then, inputting the feature matrix into a sparse representation model, constructing a health index fitting function, performing recursive optimization of a residual error, and outputting to obtain a health index fitting result; and finally, performing equipment state evaluation by utilizing a health index fitting result, and monitoring and outputting an equipment abnormal point. Compared with the prior art, the method has the advantages of improving reliability and accuracy, enhancing tendency and monotonicity of indexes, comprehensively extracting multi-dimensional information in vibration signals, improving reliability and sensitivity of anomaly detection and the like.

The invention discloses a power equipment operation fault identification and reasoning method and system, and the method comprises the steps: obtaining historical equipment sensor data, historical audio data and historical video data, and carrying out the preprocessing of the data; the preprocessed features serve as input, historical data serve as output, classification is carried out according to divided time periods, a deep belief network is adopted for model training, and a plurality of models are obtained after training; preprocessing the real-time equipment sensor data, the real-time audio data and the real-time video data, and inputting the data into a plurality of models to obtain a plurality of fault identification results; and if the plurality of fault identification results are consistent, the determined fault information can be obtained, and if the plurality of fault identification results are inconsistent, the probability of occurrence of each fault is obtained through Bayesian reasoning. The method can improve the fault detection precision, and greatly improves the safety and reliability of a power system.

A sensor device (1A; 1B; 1C) for a motor vehicle is provided. The sensor device (1A; 1B; 1C) comprises a sensor housing (3), a sensor optic (2) arranged in an outer wall of the sensor housing (3), and a supply line (6) for a cleaning fluid for cleaning the sensor optic (2). The sensor device (1A; 1B; 1C) includes a lid device comprising a movable lid (5) for covering the sensor optic (2) and a movement mechanism (7; 10, 11) for moving the lid (5), wherein the supply line (6) is arranged at least partially in or on the movement mechanism (7; 10, 11).Alternatively, or in addition to the lid device, the sensor device (1A; 1B; 1C) comprises a cleaning chamber (15) which is formed inside the sensor housing (3) behind the sensor optics (2), wherein the cleaning chamber (15) can be filled with the cleaning fluid via the supply line (6), and the sensor optics (2) is rotatably mounted such that the sensor optics (2) project at least partially into the cleaning chamber (15) in a rotated position for cleaning the sensor optics (2).

The invention provides a wind power plant equipment modeling and operation and maintenance management and control method and device based on digital twinning, and the method comprises the steps: restoring a fan entity based on a 1: 1 three-dimensional modeling technology, integrating multi-source data, including equipment sensor, video monitoring and GIS topographic data, and achieving the real-time mapping of a physical field and a virtual field through the construction of a high-precision digital twinning model. And the intelligent decision support module predicts wind resource distribution by using a dynamic boundary layer model, optimizes a grid-connected control strategy in combination with a virtual inertia simulation technology, and guarantees the transient stability of the power grid in a new energy fluctuation scene. And the full-life-cycle management platform supports full-process management from site selection planning, operation monitoring to fault diagnosis, and realizes multi-service data fusion display and interactive operation and maintenance strategy verification through a three-dimensional visual large screen.

Systems, apparatus and methods to supplement, combine, replace, verify and calibrate in-vehicle and in-device sensors and GNSS systems are presented. A mobile device and a vehicle navigation system share sensor and GNSS information to arrive at an improved navigation solution. For example, a navigation solution computed by a vehicle may rely on a sensor signal from a mobile device. Similarly, a navigation solution computed by a mobile device may use a sensor signal or a GNSS signal from a vehicle.

The invention relates to a vehicle fault prediction method and device and a storage medium, and the method comprises the steps: obtaining multi-source heterogeneous data of a vehicle, the multi-source heterogeneous data comprising at least two types of equipment sensing data, driving data and environment data of the vehicle; performing feature extraction on different data in the multi-source heterogeneous data based on a mode corresponding to each type to obtain a plurality of feature vectors; and obtaining the fault risk of the vehicle in the future time period through a prediction model based on the plurality of feature vectors. According to the method, by integrating the equipment sensing data, the driving data and the environment data, information richer than that of a single data source can be provided, the running state of the vehicle can be comprehensively evaluated, and therefore potential faults can be predicted more accurately.

The invention discloses a hospital intelligent fire-fighting monitoring system and method based on the Internet of Things, and relates to the technical field of the Internet of Things. Spatial position coordinates of fire extinguishing equipment, a sensor and a monitoring camera are acquired; training a fire behavior prediction model; predicting a probability value that the internal image of the hospital belongs to each fire category by using a fire prediction model, judging whether a fire exists, and if so, triggering first-level early warning; acquiring environmental parameters in continuous n1 normal sampling intervals to calculate a comprehensive fire risk value, and if the comprehensive fire risk value is less than or equal to a risk threshold, determining that no fire exists; if the comprehensive fire risk value is greater than a risk threshold value, determining that a fire exists, and triggering second-level early warning; calculating an adjustment coefficient, obtaining a second sampling interval according to the adjustment coefficient, and sampling abnormal environment parameters; the normal environment parameters are obtained, the difference value is calculated, if the difference value is larger than the difference value threshold value, the spatial position coordinates of the sensor serve as the fire point, the fire extinguishing equipment is started, and the fire behavior recognition accuracy is improved.

The invention discloses a thermal power generating unit operation and maintenance decision digital twinning test system. The system comprises a physical layer which is composed of thermal power generating unit equipment, a sensor and edge computing equipment; the data acquisition and transmission layer comprises a plurality of sensor interfaces and transmits the acquired data to a cloud or a local server in real time; according to the data twinborn model layer, sub-models of all devices of the thermal power generating unit are built based on physical modeling, the sub-models are optimized and integrated through a machine learning algorithm, and a digital twinborn model is built; the test and optimization decision layer is used for evaluating an operation and maintenance decision according to a simulation result and selecting an optimal operation and maintenance strategy; and a man-machine interaction layer. The invention belongs to the technical field of generator set operation and maintenance, and particularly provides a method for solving the problems of high data fusion difficulty, lack of real-time performance and verification mechanism, insufficient operation and maintenance optimization capability and the like in the prior art.