471 results about "Device Sensor" patented technology

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.

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.

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.

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 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.

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.

Sensor-based privacy-event detection for a mounted electronic device is described. In aspects, a security system includes a head assembly removably and magnetically coupled to a mounting device having a magnet. The electronic device also includes a camera module and a sensor disposed within the housing. The sensor detects a magnetic field associated with the magnet when the head assembly is coupled to the mounting device. When a user detaches the head assembly from the mounting device (e.g., to recharge the electronic device), the sensor no longer detects the magnetic field and determines the occurrence of a privacy event, which is used to deactivate the camera module to prevent unintentional recordings during the privacy event.

A dryer appliance and sensor assembly are provided. The sensor assembly may include a sensor housing, an appliance sensor, a wireless transmission tag, and a generator. A magnet rail is statically affixed to a cabinet. The sensor housing may extend within a drum and define a compartment proximate a space for the receipt of clothes. The generator is positioned within the compartment. The drum positions the generator in operable communication with the magnet rail. The appliance sensor may be positioned on the sensor housing. The wireless transmission tag may be positioned within the compartment. The wireless transmission tag may be in communication with the appliance sensor. The wireless tag reader may be in operable communication with the wireless transmission tag.

An approach is provided for traffic estimation / detection based on anomaly detection. The approach involves, for instance, retrieving probe data or other sensor data collected from sensors of devices traveling in a geographic area. The approach also involves aggregating the probe or sensor data into a sequence of frames. Each frame comprises a plurality of spatial cells representing the geographic area at a respective time interval. The approach further involves computing a similarity of the sequence to one or more historical sequences comprising historical frames of spatially and temporally binned historical probe data. The approach further involves determining a classification of a traffic state associated with the probe or sensor data as either a normal traffic state or as a traffic anomaly based on the similarity. By way of example, the traffic state of the probe data can then be estimated / predicted based on the classification.

The embodiment of the invention provides a form generation method, device and system based on AI enhancement and a storage medium, and relates to the technical field of form generation. The method comprises the steps of generating form structure metadata according to a natural language processing technology, a reinforcement learning algorithm and / or a type security verification rule; according to the form structure metadata, the equipment sensor data and / or the user visual behavior data, the rendering priority of each field is determined; and generating a target form according to the rendering priority and the equipment parameters. By generating the form structure metadata which is reasonable in structure and conforms to the type safety specification, the automation degree and reliability of form design are improved. By fusing form structure metadata, device sensor data and user visual behavior data, a rendering strategy can reflect device characteristics and user attention distribution. The target form is generated by combining the rendering priority with the equipment parameters, so that the form layout is adaptively matched with the terminal equipment, and the loading performance and the interaction experience of the form are improved.

The invention discloses a parallel double-filtering pressure-reducing valve redundancy control system with one used and one standby, which comprises a main filtering pressure-reducing valve and a standby filtering pressure-reducing valve which are oppositely arranged, a switching valve, a sensor module and a controller, and is characterized in that the main filtering pressure-reducing valve comprises a first air inlet and a first air outlet which are communicated with each other; the standby filtering pressure reducing valve comprises a second air inlet and a second air outlet which are communicated with each other, the first air inlet and the second air inlet are both communicated with an air source pipeline, the first air outlet and the second air outlet are communicated with an input port of the switching valve, an output port of the switching valve is used for being connected with downstream equipment, and the sensor module is arranged on the main filtering pressure reducing valve. The sensor module is used for monitoring the pressure condition of the main filtering pressure reducing valve, the controller is connected with the main filtering pressure reducing valve, the standby filtering pressure reducing valve and the sensor module, and the controller can automatically switch a gas path flowing through the device between the main filtering pressure reducing valve and the standby filtering pressure reducing valve according to the pressure condition. The system has the advantages of seamless switching, quick response and the like.

The invention relates to the technical field of power communication network security, in particular to a power communication network risk assessment method based on risk genes and a space-time large model. According to the technical scheme, the power communication network risk assessment method based on the risk gene and the space-time large model comprises the following steps that equipment sensor data, meteorological grid data and service topology data of a power communication transmission network are obtained in real time through a multi-source data acquisition module, and space-time alignment and feature coding are executed; a risk gene map is constructed, the risk gene map comprises a three-layer map structure of an equipment vulnerability gene, an environment threat gene and a business dependence gene, and a cross-space-time risk propagation rule is defined; a multi-modal space-time large model is trained, and a framework combining Transform and a space-time diagram convolutional network is adopted. A biological gene concept is introduced into power communication network risk assessment, gene-level fault traceability is achieved, and risk factors have combinability and interpretability.

The invention relates to the technical field of image detection, can be applied to a financial science and technology business system platform, and discloses a tamper-proof loss assessment image traceability method, device, equipment and medium, and the method comprises the steps: obtaining global positioning data, base station positioning data and equipment sensor data of a loss assessment image; performing fusion processing on the global positioning data, the base station positioning data and the equipment sensor data to obtain a dynamic timestamp; a preset device hardware fingerprint and a preset sensor fingerprint are combined to generate a composite fingerprint; performing asymmetric encryption binding on the dynamic timestamp and the composite fingerprint to obtain encryption binding data; performing multiple hash processing on the loss assessment image to obtain hash chain data; and tracing the loss assessment image according to the encrypted binding data and the Hash chain data to obtain a tracing result. Through traceability of shooting information, the phenomenon that the operator does not arrive at the scene for survey and certificate collection and is shot by others can be avoided, the leakage risk of a case is further reduced, and the operation cost of a company is reduced.