510 results about "Power sensor" patented technology

Techniques are provided for estimating an auto-correlation matrix for a transceiver that is coupled to a multi-antenna array. A different power sensor may measure the power of the signals received at a corresponding antenna of the multi-antenna array. The signals received at one antenna of each unique pair of antennas may be shifted in phase using a plurality (e.g., three) of different phase settings to generate a plurality of different shifted signals for each unique pair of antennas. Each of one or more power sensors, for each unique pair of antennas, may measure different combined powers based on the shifted signals and the RF signals received at the other antenna of the unique pair. A module may use the different combined powers with the power measured for each antenna to estimate the auto-correlation matrix.

The invention discloses a smart power grid fault early warning and diagnosis system, and relates to the technical field of smart power grids. Comprising a multi-modal data acquisition and fusion module, a deep reinforcement learning early warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrated with multi-task learning, an edge calculation and cloud collaboration module and a system test and verification module, and comprises the following steps: step 1, acquiring multi-source data through a power sensor, an intelligent electric meter and a meteorological station; dimension reduction and feature fusion are carried out on the data by using a depth auto-encoder model; 2, importing the collected data into a deep reinforcement learning early warning module, and dynamically adjusting an early warning strategy through interaction with a power grid operation environment; 3, a fault diagnosis module based on the graph neural network analyzes the topological structure of the power grid and identifies a fault position; 4, learning a model and processing a fault type; 5, edge calculation and cloud cooperative work are combined; and 6, testing, verifying and optimizing the model by the system testing and verifying module.

The invention relates to the technical field of data center energy management, and discloses a flywheel energy storage system control system for data center computing power load energy recovery, which comprises an energy storage unit, a load monitoring unit, a flywheel control unit and an energy distribution unit. The energy storage unit is provided with a plurality of groups of flywheel energy storage devices capable of charging / discharging; the load monitoring unit monitors load data by covering key nodes of computing power equipment through a power sensor; the flywheel control unit collects flywheel rotating speed and power grid frequency fluctuation data; and the energy distribution unit decides charging and discharging based on load data, establishes a load and frequency coordinated alternating control strategy, and dynamically adjusts and predicts the running state of the flywheel. The system adopts time sequence synchronous control, the energy distribution unit comprises multiple modules, the alternate controller supports dual-mode switching and parameter optimization, and the flywheel unit adopts an annular redundancy or star-shaped concentrated structure. The system realizes efficient recovery of computing power load energy and flywheel energy storage optimization scheduling, and improves the energy utilization rate of a data center and the stability of a power grid.

The invention discloses a digital twin-driven bushing operation monitoring and early warning system, which relates to the technical field of digital twin bushing operation monitoring and early warning, and comprises a multi-source acquisition, transmission, analysis, early warning and interaction module, an acquisition module adopts an ad hoc network, a self-powered sensor acquires data in a multi-mode manner, and the sampling frequency is dynamically adjusted. The transmission module guarantees data transmission through LPWAN, satellite communication and quantum encryption, the analysis module builds a multi-scale coupling model to predict the state and the service life, the model can be adaptively adjusted, the early warning module determines the early warning level in combination with a fuzzy decision and a Bayesian network and gives decision suggestions, and the interaction module provides immersive experience and natural interaction based on VR / AR / MR. According to the invention, comprehensive monitoring, stable and safe data transmission, accurate analysis and prediction, scientific and reliable early warning, convenient man-machine interaction, rapid fault diagnosis, cooperation of all modules, real-time and accurate monitoring and early warning, fault reduction, service life prolonging of a sleeve, safety guarantee and benefit and competitiveness improvement are realized through multi-source acquisition.

The invention discloses a preparation method and application of a novel ionized water gel-based moisture power generation material. The material is composed of a carbon coating fabric, ionized water gel and a patch metal electrode. Wherein the nano carbon black is used as a conductive filler of the hydrogel and can enhance electric energy output by promoting functional group dissociation and ion migration. The high-hygroscopicity sea salt in the hydrogel can enhance the capture and transmission capability of the material on water molecules in the atmosphere, and ensures the continuous power output capability of the moisture power generation material. Due to the design of the zigzag dry-wet interface, the wettability between the hydrogel and the carbon black coating can be improved, the moisture absorption rate is increased, the contact area is increased, and therefore the power generation power is improved. The metal patch electrode can ensure the stability of long-term power generation of the material, and can be customized to reduce the contact resistance. A moisture power generation device prepared from the material can be used as a passive energy supply device and is suitable for the fields of self-powered sensors, wearable electronic equipment and the like.

Methods and apparatus for energy management in data storage devices are provided. One such data storage device (DSD) includes a non-volatile memory (NVM), a set of hardware processing engines, and a power sensor to detect a total power consumption of the set of hardware processing engines. A processor is configured to determine an energy-per-processing event value for each of the set of processing engines based on total power consumption measurements and processing event duration values, then control energy delivery to the processing engines based on the energy-per-processing event values in accordance with an energy budget. In some examples, the DSD employs a least-squares procedure to estimate power-per-processing event values so the values can be determined without needing to measure individual power consumption of the processing engines. The power-per-processing event values are converted to energy-per-processing event values based on corresponding processing event durations. A recursive least-squares update procedure is also described.

The invention provides a device and method for testing the reliability of a power sensor, and the device comprises a magnetic field generation unit which is used for providing an alternating current for a Helmholtz coil through a power frequency current source, and generating a controllable magnetic field; the electric field generating unit is used for providing a high-voltage direct current or power frequency alternating current power supply for the parallel-plate capacitor through a high-voltage generator to generate a uniform electric field; the tested unit is used for providing a standard excitation signal through a standard source matched with the to-be-tested sensor, simulating an input condition of the to-be-tested sensor under an actual working condition and collecting output data of the to-be-tested sensor; and the control unit is used for setting test parameters, generating a control instruction according to the test parameters and generating a test result according to the output data. According to the device and the method, by simulating a strong magnetic field and a strong electric field environment and combining application of various measured parameters and an automatic control system, the reliability evaluation requirement of the sensor under the actual working condition is fully met.

The invention discloses an energy storage cluster dynamic optimization control system based on edge computing, which relates to the technical field of micro-grid management, and comprises a data acquisition module, a voltage and current sensor, a power sensor, a temperature sensor, an illumination intensity sensor and the like are utilized for real-time monitoring and transmission optimization of the data acquisition module; power load related data, energy storage state related data and environment variable related data in the micro-grid are collected in real time; the edge calculation processing module performs local processing and dimensionless analysis on the data, and extracts a load change rate, an energy storage state index and an operation environment index; decision suggestions are generated through normalization processing and sorting evaluation of the decision support module in combination with a dynamic load prediction model, and the data processing speed and the decision accuracy are effectively improved. The comprehensive performance evaluation index is calculated and evaluated, self-adaptive adjustment is carried out, it is ensured that the energy storage system quickly responds to sudden changes in a complex environment, and stability and efficiency are improved.

The invention discloses a tool wear state monitoring method based on a power sensor, and the method comprises the steps: collecting sample data of voltage and current values of a main shaft in a machining process of a machine tool in real time, converting the sample data into power signals, and recording a tool wear state corresponding to each piece of sample data; preprocessing the power signals, calculating time domain features and frequency domain features of the preprocessed power signals, extracting frequency band energy features corresponding to tool wear through fast Fourier transform and wavelet packet decomposition to jointly form a feature sample set, and encoding the feature sample set with corresponding tool state types to form a sample set; optimizing the BP neural network based on a grey wolf particle swarm algorithm; high-correlation features are screened out through a Pearson's correlation coefficient method, feature parameters serve as input variables of a BP neural network, the tool wear state serves as output variables, and the tool wear state is judged according to the screened feature parameters. And the recognition efficiency and the recognition precision of the tool wear state are effectively improved.

An adaptive IoT power plug system for intelligent load management and remote control, consisting of: a power plug housing configured to be inserted between a power outlet and an electrical appliance; an embedded sensor array comprising at least one voltage sensor, at least one current sensor, and at least one power sensor and configured to continuously monitor the electrical parameters of the connected device; a microcontroller unit (MCU) operatively coupled to the sensor array and configured to execute a load prioritization technique that dynamically determines power allocation based on real-time consumption data and user-defined priority levels for one or more connected loads; a semiconductor switching module electrically connected to the MCU and configured to selectively interrupt or modulate the power supply to the connected device based on commands from the load prioritization technology; a wireless communication module integrated into the MCU for bidirectional data exchange via at least one wireless protocol selected from Wi-Fi, Zigbee or Bluetooth Low Energy; a user interface that can be accessed remotely via a mobile or web application and enables the configuration of load priorities, real-time monitoring of power consumption, scheduling of device operation, and manual override of switching commands; and a power management subsystem including a power harvesting module configured to provide supplemental power to the system's electronic components, thereby reducing dependence on the main power source; The system autonomously adjusts the power distribution between multiple connected devices to load fluctuations and predefined user priorities in order to optimize energy efficiency and prevent overloading of the circuit.

Techniques are provided for implementing a power sensor calibration process and estimating an auto-correlation matrix for a transceiver with a multi-antenna array. Prior to auto-correlation matrix estimating, the power sensors may be calibrated. A different power sensor may measure the power of the signals received at a corresponding antenna of the multi-antenna array. The signals received at one antenna of each unique pair of antennas may be shifted in phase using a plurality (e.g., three) of different phase settings to generate a plurality of different shifted signals for each unique pair of antennas. Each of one or more power sensors, for each unique pair of antennas, may measure different combined powers based on the shifted signals and the RF signals received at the other antenna of the unique pair. A module may use the different combined powers with the power measured for each antenna to estimate the auto-correlation matrix.

The invention discloses a low-power-consumption communication module for fire monitoring of the Internet of Things, and belongs to the technical field of communication, and the module comprises a low-power-consumption wireless communication module which supports a plurality of network types and has a low-power-consumption working mode; the power management module comprises a low-power-consumption DC-DC converter and a power monitoring chip and is used for converting the output voltage of the battery into stable voltage required by each module and monitoring the electric quantity of the battery in real time; the wake-up control module is provided with two modes of timer wake-up and external interrupt wake-up and is used for controlling the equipment to be switched between a dormant state and a working state; the data processing module is used for receiving the sensor data, analyzing and processing the sensor data, and waking up the wireless communication module for data transmission when needed; according to a low-power-consumption sensor combination and data acquisition strategy, a low-power-consumption digital temperature sensor, an MEMS smoke sensor and a low-power-consumption infrared flame sensor are adopted, and a periodic data acquisition mode is matched, so that the power consumption of a data acquisition link is greatly reduced while the monitoring precision is ensured.

The invention relates to the technical field of unmanned vehicle management, in particular to an unmanned vehicle intelligent monitoring management system, which accurately detects electronic activities in a battery pack through a quantum tunneling effect electric quantity sensor, generates quantum signals, obtains multi-dimensional electric quantity data through processing, and provides a basis for energy management. Tensor analysis and a convolutional neural network are used to carry out deep mining on the multi-dimensional signal data, an energy flow state is monitored in real time, and an abnormal energy flow mode is identified; in combination with an energy flow analysis result, a charging scheduling scheme is generated and optimized by using chaotic mapping and a multi-objective optimization algorithm, and the charging efficiency, the vehicle waiting time and the power grid load are balanced; a charging adaptation model based on a biological neural network is constructed, intelligent charging adaptation decision making and charging equipment operation control are realized through reinforcement learning algorithm training, and the charging flexibility and efficiency are improved.

The invention discloses an intelligent lighting control system based on the Internet of Things and AI prediction, and relates to the technical field of intelligent lighting, and the system comprises a data collection module which is connected with an ambient light sensor, a motion sensor and an electric quantity sensor through an intelligent lamp, collects the state data of intelligent lighting in real time, and sends the state data to a server; by integrating the ambient light sensor, the motion sensor and the electric quantity sensor, the system can sense environment changes and user behaviors in real time, self-adaptive adjustment of lamplight is achieved, user operation is simplified, a user does not need to frequently and manually adjust lamplight, the indoor illumination brightness can be automatically adjusted according to the ambient light intensity, and the user experience is improved. Meanwhile, through application of the AI prediction module, through deep analysis of user behavior modes and environment data, the illumination requirements of the user in different scenes can be accurately predicted, the lamplight state is adjusted in advance, and the user experience is further improved.

The invention relates to a bioelectronic surgical suture for suture tension monitoring, the surgical suture comprises a cannula and a conductive material, the cannula is a hollow capillary tube, and the conductive material is arranged in the cannula through perfusion and is subjected to first laser scanning treatment after perfusion; a micro-nano structure is manufactured on the outer wall of the sleeve through second laser scanning. According to the principle of friction nanometer power generation, the tension of the surgical suture causes the change of the contact area between the suture and tissue and the change of an output electric signal in the collection process, the tension of the surgical suture can be reversely deduced, and the purpose of monitoring the surgical suture tension is achieved. The operation line is a self-powered sensor, and an external power supply is not needed. The operation line can provide different quantitative feedbacks according to different suturing modes and suturing parts, and is combined with clinical suturing experience of doctors to assist in establishing a tension quantitative model, so that the doctors are helped to optimize operation procedures, postoperative complications are reduced, and wound healing is promoted.

The invention discloses an environment index monitoring method and system for a networked storage cabinet, and the method comprises the steps: collecting environment index data in a plurality of storage cabinets through employing a low-power-consumption sensor array, enabling the environment indexes to comprise the temperature, humidity, illumination and gas concentration of the storage cabinets, carrying out the preprocessing of the environment index data, and obtaining a pre-processed environment index data; the preprocessing comprises denoising, normalization and feature extraction; sending the preprocessed environment index data to a monitoring terminal, and dynamically adjusting a communication frequency band in a sending process according to real-time channel quality by adopting a self-adaptive frequency hopping mechanism; based on the received environment index data, the monitoring terminal adopts an offline layered reinforcement learning model to carry out environment anomaly detection, and the model comprises bottom-layer perception, middle-layer reasoning and high-layer decision; and if the detection result is environmental anomaly, constructing a space-time diagram structure of the storage cabinet network, and performing multi-cabinet collaborative analysis by adopting a double-current diagram converter fusion network to realize environmental anomaly correlation analysis among the plurality of storage cabinets.

A communication-enabled power management system is disclosed. The system includes a self-contained receptacle outlet comprising a housing, a power receptacle, a power sensor configured to monitor a plurality of power parameters, a motion detection module, a transceiver, and a processor with a unique identifier. The processor connects the receptacle to a remote controller and allows the user to toggle between power states. The system further includes a predictive analytics module to optimize power usage based on occupancy patterns, and an independent power backup management module for maintaining power during failures. The system also communicates with secondary devices, such as vents and switches, to manage energy distribution and respond to emergency events.

The invention provides a bridge cable force health state monitoring method, device and equipment, and the method comprises the steps: building a target finite element model corresponding to a bridge based on bridge data collected by a self-powered sensor array and an initial finite element model; a space-time diagram corresponding to the bridge is constructed according to the target finite element model, the hidden state corresponding to each node in the space-time diagram is extracted through a gating diagram time sequence convolutional network, the nodes in the space-time diagram represent inhaul cables and supporting structures contained in the bridge, and edges in the space-time diagram represent the physical connection relation and dynamic interaction between the inhaul cables and the supporting structures; constructing a causal graph between the cable clamping stress of the bridge and the environment variable so as to dynamically adjust the early warning threshold value corresponding to the bridge; and monitoring the cable force health state of the bridge based on the hidden state corresponding to each node in the space-time diagram and the dynamically adjusted early warning threshold, and obtaining a health monitoring result corresponding to the bridge. According to the invention, the problem of low monitoring accuracy in the prior art is solved.

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.

The invention relates to a state monitoring and performance evaluation method for a laser wireless energy transmission system, and the method comprises the steps: carrying out the prediction of the output power of a laser device and the irradiation laser power of a photovoltaic array according to the real-time parameters of each energy transmission link, thereby constructing the performance evaluation parameters of a laser wireless energy transmission link, and carrying out the evaluation of the performance of the laser wireless energy transmission link. Extracting a transmission medium part in the evaluation parameters; a transmission medium part is eliminated for laser wireless energy transmission link performance evaluation parameters of each energy transmission link, so that comprehensive performance evaluation of a laser and a photovoltaic array group is carried out; and judging a specific fault link according to a comprehensive performance evaluation result, and judging a laser or a photovoltaic array with a specific fault by switching the laser and the photovoltaic array accessed into the fault link. Compared with the prior art, state monitoring and fault positioning of the laser and the photovoltaic array of the distributed system can be realized on line, additional laser power sensors or other auxiliary devices are not needed, and the system has the advantages of simple structure, on-line identification and the like.

The invention discloses a photovoltaic energy storage system control method and system based on data driving, and relates to the technical field of power grid power control, and the method comprises the steps: collecting photovoltaic environment data, and carrying out the preprocessing; processing the satellite cloud picture data through a pre-trained light spot prediction model, predicting a light spot coverage track, and outputting a fusion feature data set; constructing a power loss prediction curve based on the light spot influence time window and the current deviation value in the fusion feature data set, performing power influence conversion on the coverage rate of the shelter, and generating a power correction amount; performing dual-channel compensation on photovoltaic energy storage to generate a compensation instruction set; performing power adjustment on the photovoltaic energy storage adjacent to the position of the shielding object, and outputting a control completion signal; and after the power sensor receives the control completion signal, the photovoltaic energy storage control effect is judged. According to the invention, through cooperation of the light spot coverage trajectory modeling driven by the satellite cloud picture and the spatial verification of the string current data, the dynamic and accurate calibration of the photovoltaic shielding compensation is realized.

The utility model discloses an electric bicycle battery pack with a voltameter function, and belongs to the technical field of electric bicycles. The electric bicycle battery pack comprises a hollow shell, a display screen, a battery pack accommodated in the shell, an electric quantity sensor used for detecting the electric quantity of the battery pack, a temperature sensor used for detecting the temperature of the battery pack, a current sensor used for detecting charging and discharging of the battery pack, and a controller connected with the electric quantity sensor and the display screen, the temperature sensor and the current sensor are connected with a battery management chip, and the shell comprises a bottom plate, a side plate vertically arranged on the bottom plate and a top plate arranged at the top of the side plate. The method has the beneficial effects that a user can better know the state of charge (SOC) and the state of health (SOH) of the battery, so that safety accidents caused by the fact that the battery pack works under an unhealthy condition are avoided.

In some implementations, a plurality of thermoelectric devices may be arranged to cool one or more components of a downhole drilling component during drilling operations. A current can be applied to one or more thermoelectric devices to generate a cooling effect. The thermoelectric devices can be located and arranged so that they cool some or all of a bottom hole assembly or components thereof, such as one or more sensors, batteries, processors, electrics, and the like. The thermoelectric devices also may be located and arranged to cool sensors, batteries, other downhole components, and / or drilling mud in a wellbore during drilling operations. A plurality of thermoelectric devices may be used to generate electric power downhole from a temperature difference. The electric power may be used to power sensors, processors, charge batteries, and be used by one or more downhole electric components, such as those in a bottom hold assembly. One or more thermal storage devices may be used with the thermoelectric devices to store thermal energy for use when needed. A control system may be used to control the activation and / or deactivation of one or more thermoelectric devices to provide cooling when desired.

The present invention discloses a smart grid fault warning and diagnosis system, which relates to the field of smart grid technology, including a multimodal data acquisition and fusion module, a deep reinforcement learning warning module, a fault diagnosis module based on a graph neural network, an adaptive diagnosis module integrating multi-task learning, an edge computing and cloud collaboration module, and a system testing and verification module. The system comprises the following steps: collecting multi-source data through power sensors, smart meters and weather stations, and using a deep autoencoder model to reduce the data dimension and fuse features; importing the collected data into the deep reinforcement learning warning module, and dynamically adjusting the warning strategy through interaction with the grid operation environment; analyzing the topological structure of the grid and identifying the fault location by using the fault diagnosis module based on the graph neural network; learning the model and processing the fault type; combining edge computing with the cloud for collaborative work; and testing, verifying and optimizing the model by the system testing and verification module.

A sensing system includes a sensor, a wireless transmission system, a wireless receiver system, and a controller. The sensor is configured to determine sensor data, the sensor data associated with a sensing environment. The wireless transmission system is configured to wirelessly couple with the sensor, the wireless transmission system configured for wirelessly providing significant electrical energy to the sensor as wireless power signals and receive the sensor data as in-band data signals encoded in the wireless power signals. The wireless receiver system is operatively associated with the sensor and with which the wireless transmission system couples with the sensor and is configured to receive the significant electrical energy as the wireless power signals from the wireless transmission system. The controller is operatively associated with the wireless transmission system and is configured to decode the in-band signals from the wireless power signals as the sensor data.

The invention discloses a calibration factor estimation and correction method for a radio frequency high-power calibration direct measurement method, which comprises the following steps of: receiving a radio frequency high-power signal, and calculating an equivalent source reflection coefficient of a directional coupler to be corrected; arranging the test end face between the directional coupler and the DUT to detect a loss constant ka; estimation of the calibration factor of the direct measurement method is corrected through a radio frequency high-power standard indicating value measured through an experiment and a power indicating value of the DUT. According to the method, the situation that the error of the calibration factor of a direct measurement method is large at a high frequency is avoided, and the measurement error caused by the high frequency is reduced, so that the high-power measurement accuracy of the radio frequency directional power sensor in a high-frequency environment is improved.

Disclosed is a radio frequency (RF) power sensor having measurement uncertainty of less than about ±0.3%, which is achieved through the use of cross-correlation for reduction of uncertainty. The power sensor may have multiple couplers, multiple detectors, and at least one analog to digital converter. Also disclosed is a method for operating the power sensor to realize measurement uncertainty of less than about ±0.3% due to the uncorrelated nature of the measurements, as well as one or more of identifying the need for power sensor calibration, extending the time between calibrations, and predicting remaining time until calibration is required.

The invention discloses a nano confinement catalytic self-energized biosensor, which comprises an anode, a cathode and an electrolyte, the anode is a CC / CoOOH / AuNPs / GOD biological anode, and the cathode is a CC / AuNPs / specific probe biological cathode. By using the nano confinement catalytic self-energized biosensor provided by the invention, signals can be stabilized, the sensitivity of the self-energized sensor can be further improved, and verification is carried out by taking patulin detection as an example. The problems that a self-powered sensor is unstable in signal, enzyme activity is prone to being affected and the like are solved. The nano confinement catalysis which is high in stability, high in activity, low in cost and environmentally friendly is developed and used for enhancing and stabilizing signals of the self-energized sensor.

Systems, structures, circuits, packages and methods provide low-power devices such as sensors and sensor packages having one or more Wiegand coils that can be used for activation of a sensor or other device, as well as potentially supplying operating power. In some embodiments, one or more Wiegand coils can be located in a sensor package, e.g., as one or more discrete components in the package and / or on formed on or connected to a semiconductor die within the package, for providing wake-up and / or continuous power. The Wiegand coil(s) can be separated from the sensor die in some embodiments. In some embodiments, the Wiegand coil(s) can be constructed on the die itself.

The invention relates to an electric power big data efficient collection and storage system and method based on brain-computer interaction, and the system comprises an electroencephalogram signal collection module which is used for obtaining an electroencephalogram signal of a user through a brain-computer interface, and transmitting the electroencephalogram signal to an edge node in real time; the intention recognition module is used for recognizing the current intention of the user by adopting a pre-training intention recognition model based on the brain wave signal of the user and generating a corresponding electric power data acquisition instruction; the data acquisition control module is used for dynamically scheduling the power sensor nodes in different areas to carry out power data acquisition according to the current intention of the user in combination with the state of the power sensor nodes fed back by the edge nodes in real time; the edge calculation processing module is used for carrying out edge processing and feature compression on the collected power data by adopting edge nodes; and the distributed storage module is used for carrying out distributed storage on the power data subjected to feature compression. Compared with the prior art, the method has the advantages of non-contact rapid instruction input, improvement of response efficiency and the like.