32 results about "Wireless sensor node" patented technology

The invention discloses a coal mine goaf fire monitoring method and system based on multi-source data fusion, and relates to the technical field of coal mine goaf fire monitoring, and the method comprises the steps: collecting the concentration, concentration and air pressure of each wireless sensing node in a coal mine goaf in real time; according to the concentration of each node, a dual-parameter dynamic coupling gradient field traceability algorithm of fusion / gas expansion and chemical reaction mechanism is adopted to calculate a fire source coordinate; performing anomaly detection according to the concentration of each node, and positioning an air leakage point by adopting a three-dimensional oxygen concentration gradient field positioning algorithm and combining air pressure compensation for an oxygen concentration anomaly point; based on goaf point cloud data, a goaf three-dimensional grid model is constructed, and a fire source spreading path, an air leakage channel and gas concentration distribution are dynamically and visually displayed in combination with multi-source monitoring data and fire source and air leakage point positioning results. The goaf fire prevention and control system can achieve all-around prevention and control of goaf fire.

Embodiments include systems and methods for adaptive monitoring of operational technology networks. In some embodiments, the method includes collecting multi-modal time series data from a plurality of wireless sensor nodes deployed near at least one operational technology asset, aligning and fusing the time series data using dynamic time warping, extracting at least one feature and at least one dependency from the fused time series data, generating, based on the extracted feature and dependency, a real-time anomaly score using a trained machine learning model, determining, based on the real-time anomaly score, at least one anomaly regarding the operational technology asset, and presenting a visualization of the anomaly at an interactive user interface.

The present invention relates to the field of wireless environmental monitoring. Provided are a wireless environmental monitoring method and system based on the Internet of Things. The wireless environmental monitoring method based on the Internet of Things comprises: S1, deploying a plurality of wireless sensor nodes for collecting environment parameters, which comprise temperature, humidity, and air quality; S2, the sensor nodes transmitting collected data to a gateway device by means of low-power wireless communication technology; and S3, the gateway device performing preliminary processing on the received data, and transmitting the data to a cloud server by means of the Internet. The stability and anti-interference capability of data transmission are ensured by using Zigbee communication technology, adaptive frequency-hopping technology, and a dual-antenna system. In terms of data processing, the system uses edge computing technology to perform partial data processing at the gateway device, thereby reducing the computational pressure on the cloud server. Moreover, the cloud server uses a distributed computing architecture, thereby achieving efficient processing of large-scale data and providing good scalability.

The application relates to the technical field of precision agriculture monitoring, and discloses a precision agriculture monitoring system based on adaptive wireless communication. The system comprises a multi-source environment acquisition module, a communication protocol adaptation module and a crop state evaluation module. The multi-source environment acquisition module obtains soil humidity data, illumination intensity data and meteorological parameter data through wireless sensor nodes arranged in farmland areas, and synchronizes the timestamps of the data to ensure the consistency of the data in the time dimension. The communication protocol adaptation module dynamically selects a wireless transmission frequency band according to the synchronous data volume, allocates communication time slots based on data priority, and generates adaptive transmission channel parameters to cope with the communication requirements under the conditions of data volume fluctuation and complex environment. The crop state evaluation module receives the transmission channel parameters, analyzes real-time environment data streams, and divides the current crop growth state into a growth standard state, a growth lag state or a growth fluctuation state. The system provides support for fine management of agricultural production.

The invention provides a self-powered wireless sensor node energy efficiency control method and system for an indoor weak light scene, and belongs to the technical field of self-powered wireless sensors of the Internet of Things. Comprising the following steps: constructing an energy collection and energy perception calculation model which comprises an energy collection module, an energy perception calculation module, an energy prediction module and an energy management module; the energy sensing calculation module dynamically adjusts the capacitor capacity by using a temperature correction model in the process of calculating available energy according to the super-capacitor voltage; the energy prediction module performs prediction by adopting a mixed entropy weight rolling adaptive energy prediction algorithm based on low energy density and high-frequency fluctuation characteristics of an indoor weak light scene, and dynamically adjusts a prediction weight according to an energy fluctuation condition by distinguishing situations, controlling dormancy and quantifying energy fluctuation. Balancing the indoor energy prediction precision and the energy consumption in the energy collection process; and the energy management module performs energy efficiency control based on the model, calculates an energy prediction value of the next time slot and performs working mode control of the node.

A sensing system includes a first wireless sensor device configured to generate sensor data on an object of interest and communicate with a first wireless node, the first wireless node configured to communicate with the first wireless sensor device and a section node, and the section node configured to communicate with the first wireless node and a server, and the server. The first wireless node is located within a communication range of the first communication interface of the first wireless sensor device and is configured to receive the generated sensor data. The first wireless node analyzes the received sensor data to determine whether events relevant to the object of interest have occurred.

The present application belongs to the field of photovoltaic module management, and discloses a high-altitude photovoltaic module state management system, comprising a wireless sensor node, a relay control device and a management device; the relay control device is used for clustering the wireless sensor node, each cluster comprising a cluster head node and a member node; the member node is used for collecting monitoring data of the photovoltaic module and transmitting the monitoring data to the cluster head node; the cluster head node is used for transmitting the monitoring data received from the member node to the relay control device; the relay control device is also used for forwarding the monitoring data received from the cluster head node to the management device; and the management device is used for monitoring the state of the photovoltaic module based on the monitoring data. The present application makes the time interval between two adjacent clusterings of the wireless sensor node more adaptable to the temperature condition of high altitude, and can save the monitoring cost while ensuring the monitoring effect.

The invention is suitable for the technical field of mechanical equipment state monitoring and fault diagnosis, and provides a mine main conveying chain equipment bearing fault embedded diagnosis system and method.The mine main conveying chain equipment bearing fault embedded diagnosis system comprises a tunable piezoelectric energy collecting device which is installed on a bearing seat or a carrier roller shaft and used for converting vibration mechanical energy of a bearing into electric energy, a voltage signal representing the vibration state of the carrier roller bearing is synchronously output; the embedded wireless sensor node is electrically connected with the tunable piezoelectric energy collecting device and is used for managing the collected energy and processing and wirelessly transmitting the voltage signal; and the fault diagnosis module receives the voltage signal and performs feature extraction and fault classification on the bearing state based on a convolutional neural network algorithm. According to the invention, sensing and energy supply integration, frequency adaptive matching, high-quality signal acquisition and lossless embedded installation are realized, and a reliable, long-term and self-energy-supply bearing state in-situ monitoring and intelligent diagnosis scheme is provided for rotating mechanical equipment in various mine main transportation chain equipment.

The application provides a double-in double-out coal mill coal quality measurement polling method and system, relating to the field of industrial equipment state monitoring and wireless communication. The method comprises: receiving the rotation angle of the wireless sensor node relative to the stationary base station, calling the channel quality mapping table to predict the expected channel quality of each angle interval in the next rotation period; dividing the rotation period into fixed time slots, matching the expected signal-to-noise ratio with the priority weight, and dynamically adjusting the coding scheme and transmission power of the node; receiving the three-axis vibration signal sent by the node in the fixed time slot, generating a feature vector through the lightweight one-dimensional convolutional neural network in the node and uploading; constructing a state matrix based on the feature vectors of all nodes and inputting it into the fully connected neural network layer for material level inference. The application solves the problems of low wireless communication reliability and unsynchronized time of multi-node data in a rotating metal sealed environment, and realizes continuous and reliable monitoring of the material level of the coal mill and accurate fusion of multi-source data.

This invention relates to the field of transformer condition monitoring technology, specifically a transformer condition monitoring and protection system utilizing a wireless sensor network. The system includes a sensor optimization deployment module for determining the optimal physical installation location of each wireless sensor node within the wireless sensor network based on thermal imaging analysis of the transformer; and a data acquisition module for collecting transformer condition data through multiple wireless sensor nodes deployed at the optimal physical installation locations. This condition data includes at least temperature data, vibration data, and electrical parameter data. This invention obtains a continuous temperature change map of the transformer through thermal imaging analysis and employs algorithms for calculating the deflection angle and distance. Using the hotspot area with the largest temperature change rate as a benchmark, and combining this with the physical offset of the thermal field propagation, the optimal sensor installation location is calculated, overcoming the shortcomings of traditional manual sensor placement that relies on experience.

The invention relates to the technical field of battery safety, in particular to an electric tricycle lithium battery thermal runaway early warning method and system, and the method comprises the steps: collecting various physical state information of a battery module through one or more self-powered wireless sensor nodes disposed in a lithium battery pack; the physical state information, the vehicle working condition information and the charging process information from the intelligent charging pile are uploaded to a cloud analysis platform through the vehicle-mounted communication terminal; the cloud analysis platform performs short-term early warning analysis, long-term trend prediction and user behavior analysis based on the received multi-source data to obtain analysis results respectively, performs weighted fusion calculation on the analysis results, and outputs a comprehensive risk score; and starting a grading early warning mechanism according to the comprehensive risk score, and sending corresponding early warning information or control instructions to a user terminal, a motorcade management platform, an intelligent charging pile and / or a fire rescue system through a cloud analysis platform. Therefore, the problems of high false alarm rate, early warning lag and the like are solved.

The application discloses a kind of based on intelligent group of supporting roller belt conveyor state monitoring and speed regulation system, including integrated embedded in supporting roller internal wireless sensor node;Wireless sensor node includes energy collection module, energy management module and wireless sensor node module;Energy collection module is used to convert the mechanical energy of supporting roller rotation into alternating current energy;It includes stator assembly and rotor assembly;Stator assembly is fixed to supporting roller shaft, and rotor assembly is fixed to supporting roller bearing seat;Energy management module is connected with energy collection module, for the management of alternating current energy and power supply for wireless sensor node module;Energy management module includes H bridge circuit output positive or reverse direct current;The output end of H bridge circuit is connected with the stator assembly of energy collection module, for the positive or reverse direct current that stator assembly is imported;By this, the same set of electromagnetic mechanism can be intelligently switched between power generation mode and electric mode.

The application discloses a new node position prediction method based on hybrid particle swarm optimization and grey wolf optimization, comprising the following steps: determining a room or an area where a target wireless sensor node is located; measuring received signal strength at different positions away from the target wireless sensor node; filtering the signal strength obtained by using a Gaussian filter; fitting the obtained signal strength with distance, constructing a signal strength-distance relationship model and evaluating the model to select a model with the best fitting degree; dividing the room or the area to be positioned by using a dichotomy method, determining and narrowing a reference node selection area; limiting the position of a reference node deployment; selecting a reference node with strong and stable signal strength by using a weighted distance strategy; and positioning and optimizing the position of the target wireless sensor node by using a hybrid PSOGWO algorithm based on the obtained data. The application improves the positioning accuracy of the target node and also improves the positioning efficiency.

The application relates to the technical field of intelligent traffic and infrastructure monitoring, and particularly discloses a highway infrastructure intelligent monitoring and management system based on wireless communication. The system comprises a wireless sensor node cluster, a relay coordination node and a network management server. The system combines a hybrid routing mechanism of distributed greedy forwarding and centralized optimal path calculation, and dynamically adjusts a routing strategy according to data service priorities, so that balanced distribution of network load and energy consumption is realized, low-delay and high-reliability transmission guarantee is provided for high-priority service data packets, the network life is prolonged, and the timeliness of key information reporting is improved. The system combines a hybrid routing mechanism of distributed local decision and centralized global optimization, and fundamentally solves the problems of energy consumption hotspots and network segmentation caused by static networking.

The invention discloses a wireless sensor network data transmission method based on a minimum convergence delay tree. The method comprises the following steps: S1) randomly initializing a data aggregation tree formed by wireless sensor nodes; s2) calculating the minimum convergence time delay of each node in the aggregation tree according to the direction from bottom to top; s3) calculating the latest transmission time slot of each node in the aggregation tree according to the direction from top to bottom; s4) traversing each node in the aggregation tree from top to bottom, and judging whether the node needs to switch a father node to complete the improvement of the data aggregation tree; and S5) after the data aggregation tree is improved, calculating according to the step S2 again to obtain the minimum aggregation time delay of the final aggregation node. According to the wireless sensor network data transmission method based on the minimum convergence time delay tree, not only is the degree of each node considered when the data convergence tree is constructed, but also the influence of the descendant node of the node on the convergence time delay is considered, so that the convergence time delay of data in the WSN is greatly reduced.

The invention discloses an energy storage system battery equalization method and device and a storage medium, and the method comprises the steps: collecting multi-dimensional state data in real time through an integrated low-power-consumption wireless sensor node for any single battery in an energy storage system, and asynchronously uploading the multi-dimensional state data to a central processing unit through a self-organizing wireless network; the central processing unit analyzes the multi-dimensional state data based on the dynamic evaluation model, determines target single batteries needing energy exchange and determines an energy exchange path and a corresponding exchange rate according to the residual capacity, the thermal state and the load distribution of each target single battery; and distributing an energy exchange instruction and an adaptive rate regulation factor to each target battery monomer based on the energy exchange path and the corresponding exchange rate, so that each target battery monomer performs energy exchange based on the energy exchange instruction and the corresponding adaptive rate regulation factor. The method can effectively reduce the power consumption of the energy storage system, reduces the communication burden, and achieves the asynchronous cooperative control of global balance.

The invention relates to the technical field of intelligent traffic and infrastructure monitoring, and particularly discloses a road infrastructure intelligent monitoring and management system based on wireless communication. The system comprises a wireless sensor node cluster, a relay coordination node and a network management server. Through a hybrid routing mechanism combining distributed greedy forwarding and centralized optimal path calculation and dynamically adjusting a routing strategy according to a data service priority, balanced distribution of network load and energy consumption is realized, and meanwhile, low-delay and high-reliability transmission guarantee is provided for a high-priority service data packet; therefore, the service life of the network is prolonged and the timeliness of key information reporting is improved. According to the system, through a hybrid routing mechanism combining distributed local decision and centralized global optimization, the problems of energy consumption hot spots and network segmentation caused by static networking are fundamentally solved.

The invention belongs to the technical field of cold region engineering material testing, and particularly relates to an intelligent potato drip irrigation and fertilization system based on the Internet of Things. The multi-dimensional environment sensing module comprises wireless sensor nodes deployed in a field and used for monitoring soil humidity, temperature, EC value and pH value, a meteorological collection platform used for monitoring air temperature and humidity, illumination and rainfall, and unmanned aerial vehicle remote sensing equipment used for obtaining multi-spectral images of crop canopies; the device is used for acquiring a digital elevation model (DEM); the requirement for high automation of a large farm can be met, and the device can adapt to a low-automation planting scene of a small farm. For farms with high automation degree, full unattended operation can be realized; and for small farmers, simplified interfaces and instruction guidance are provided, and the operation threshold is reduced.

The application belongs to the technical field of intelligence, and discloses a bridge load test detection system based on wireless transmission; the system comprises the following steps: uniformly distributing a bridge into M sections, and calculating the double-field coupling weight coefficient of each section; dynamically selecting a test point according to the double-field coupling weight coefficient of each section; monitoring the test point by using a movable wireless sensor node, and collecting the structural response data of the test point; constructing a sensitivity matrix by using initial excitation parameters and the structural response data, obtaining a modified objective function, and optimizing and modifying the objective function through iteration to obtain optimal excitation parameters; predicting the maximum dynamic load force and the actual dynamic load force; judging whether to perform early warning according to the predicted maximum dynamic load force and the predicted actual dynamic load force; and the accuracy and efficiency of the bridge load test are significantly improved.

The invention relates to a wireless network coverage system and method based on an adaptive purse conquering algorithm, and belongs to the technical field of wireless network coverage. The problems that in the prior art, when a network deterministic deployment method is applied in a complex coverage scene, coverage rate improvement is limited, stability is insufficient, early-stage global search is insufficient or later-stage fine adjustment is insufficient are solved. The system comprises a parameter initialization module, a coverage modeling module, an optimization calculation module, a coverage evaluation module and a result output module. The system can automatically deploy and optimize the wireless sensor nodes under the condition of different node scales, realizes high-coverage deployment when the number of the nodes is sufficient, and can still maintain relatively high search capability when the number of the nodes is limited, so that relatively high network coverage is realized, coverage holes and redundant coverage are reduced, network energy consumption is reduced, and the system is suitable for large-scale application. And the deployment efficiency and the operation stability of the wireless sensor network are improved.

This invention belongs to the field of power electronic equipment testing technology and discloses a method and system for testing, protection, and monitoring of energy storage converters based on wireless communication. The method includes: establishing local timestamps for wireless sensor nodes; using a digital twin model to predict the switching action times of power devices to generate a strong interference time window, controlling wireless communication to remain silent within the strong interference time window and transmitting data outside the window; extracting channel state information from the physical layer of the wireless signal, analyzing the spatial propagation changes of the channel state information relative to the reference state, and identifying physical environment faults inside the energy storage converter; broadcasting anomaly vectors through a distributed network among wireless sensor nodes, and triggering circuit breaker action based on the consensus voting results of multiple nodes; comparing the measured data transmitted via wireless communication with the theoretical trajectory and dynamic safety envelope interval calculated based on the digital twin model to verify the changing trend of the measured parameters and ensure the synchronization accuracy of time and the integrity of the data.

This application belongs to the field of low-power control technology for wireless sensors. It provides a low-power control method for rapid response of wireless sensors in aircraft landing gear. The embodiments of this disclosure employ multi-mode dynamic switching and module collaborative control technology, significantly reducing the average power consumption of battery-powered wireless sensor nodes to the microampere level. Data transmission performance is excellent, with end-to-end latency strictly controlled within 50 milliseconds, meeting the high requirements for millisecond-level real-time monitoring during critical phases of the aircraft, ensuring timely and accurate flight data. Real-time data analysis at the data sampling end, combined with the operating mode, intelligently optimizes and reduces the amount of transmitted data while maintaining system control accuracy, providing reliable data support for higher-level systems. The introduction of a predictive scheduling mechanism and a dual-threshold triggering strategy achieves zero false alarm rate and zero false alarm rate, complying with CCAR-25-R4 airworthiness standards, ensuring high reliability and safety of the monitoring system.

The invention discloses a belt conveyor state monitoring and speed regulating system based on an intelligent carrier roller group. The belt conveyor state monitoring and speed regulating system comprises wireless sensor nodes embedded in carrier rollers in an integrated mode. The wireless sensor node comprises an energy collection module, an energy management module and a wireless sensor node module; the energy collection module is used for converting the mechanical energy generated by rotation of the carrier roller into alternating current electric energy; the motor comprises a stator assembly and a rotor assembly. The stator assembly is fixed on the carrier roller shaft, and the rotor assembly is fixed on the carrier roller bearing seat; the energy management module is connected with the energy collection module and is used for managing the alternating-current electric energy and supplying power to the wireless sensor node module; the energy management module comprises an H-bridge circuit for outputting forward or reverse direct current; the output end of the H-bridge circuit is connected with a stator assembly of the energy collection module and is used for supplying forward or reverse direct current to the stator assembly; therefore, the same electromagnetic mechanism can be intelligently switched between the power generation mode and the electric mode.

This invention belongs to the field of wireless sensor network data acquisition, specifically relating to a method for optimizing the energy consumption of UAV-based wireless sensor network (WSN) data acquisition in urban environments. The method includes constructing a UAV-based wireless sensor network data acquisition system in an urban environment. In this system, all wireless sensor nodes are divided into multiple clusters, and each cluster selects a cluster head. All data within the cluster is sent to the cluster head, which then uploads the data to the UAV. Based on the impact of buildings on communication distance within the coverage area of ​​the wireless sensor network data acquisition system, the KPP-LEACH clustering algorithm is used for cluster management and cluster head election of wireless sensor nodes within the coverage area. After selecting the cluster head, optimization objectives are constructed to minimize the UAV's flight path and the total energy consumption of data transmission from the cluster head node. The order in which the UAV accesses the cluster head node, the hovering point for UAV data acquisition, and the UAV's flight path are determined. This invention can effectively reduce the total energy consumption of UAV-based wireless sensor network data acquisition systems in urban environments, improve network stability, and extend network lifespan.

The invention belongs to the technical field of equipment state monitoring and fault diagnosis, and relates to a belt conveyor carrier roller fault diagnosis method based on vibration and temperature data fusion, which comprises the following steps of: driving a plurality of wireless sensor nodes deployed along a line through a unified synchronous pulse to carry out synchronous data acquisition, and generating a multi-element sensing data flow; reconstructing a space-time vibration data graph and a space-time temperature distribution graph according to the node physical position information, and identifying and filtering common-mode vibration interference to obtain a differential vibration signal set; the differential signals are subjected to mode recognition, spatial propagation parameters of abnormal fluctuation are extracted, a fault physical source is reversely traced and locked accordingly, and positioning of the carrier roller is achieved; according to the method, the defects that traditional single-point monitoring is prone to being interfered by global vibration and propagation paths are difficult to identify are overcome, the fault identification accuracy and positioning precision are effectively improved, and misinformation and maintenance resource waste are reduced.

A remote monitoring network for actuating Internet of Things (IoT) devices may comprise a mesh network of wireless sensor nodes positioned proximate to fluid tanks. Each sensor node may comprise a wireless communication device configured to acquire fluid level measurements from the fluid tanks. A gateway device connected to the mesh network may be configured to transmit data from the sensor nodes to a cloud-based admin network. A temporary node may be configured to temporarily integrate with the mesh network to provide access to sensor data without internet connectivity. A device command module may be configured to receive the fluid level measurements from the sensor nodes. The device command module may compare the fluid level measurements against threshold values. The device command module may calculate depletion rates based on historical fluid level data. The device command module may transmit control commands to IoT devices based on the calculated depletion rates.

This invention discloses a method for detecting the attachment status of an ultrasonic sensor based on multi-source feature fusion, belonging to the field of sensor data processing technology. It includes acquiring ultrasonic signals and auxiliary motion and pressure data through dual channels at a wireless sensor node. After time synchronization and framing, time-frequency domain features are extracted and normalized to construct a feature vector, which is then uploaded to the terminal. The terminal obtains a signal quality index through weighted fusion, performs time smoothing and trend calculation, and combines rule-based judgment and priority arbitration to output the status of normal, poor contact, loose, or detached sensors, triggering an alert. This solves the technical problem of accurately determining the sensor attachment status in real time and quantitatively assessing long-term stability and potential anomalies. By fusing the time-frequency features of the ultrasonic signal with auxiliary sensor data, it can accurately distinguish between four attachment states: normal, poor contact, loose, and detached. It also achieves potential anomaly prediction and proactive early warning based on the stability decline trend and negative SQI changes.

The invention belongs to the technical field of wireless communication networks, and particularly relates to a monitoring data transmission method and system based on a wireless network, and the method comprises the steps: collecting the internal force data of a steel support and the electromagnetic interference data of a channel environment; determining the initial sending probability of each wireless sensor node; constructing a historical time window sliding queue of the wireless sensor nodes, and determining an interference time sequence smoothing factor and a final sending probability; and performing access control and data transmission scheduling on the wireless sensor nodes by using the optimized p-persistent carrier monitoring multi-channel access algorithm. According to the invention, by analyzing the numerical value change characteristics of the steel support internal force data and the electromagnetic interference data, the transmission probability of the wireless sensor node is adaptively optimized, continuous strong mechanical electromagnetic interference is effectively avoided, the network anti-interference capability is enhanced, and the low-delay, safe and stable transmission of the engineering high-risk early warning monitoring data is ensured.

The invention relates to a piezoelectric energy collection system based on charging management, and the system comprises a piezoelectric element, an interface rectification module, a charging management module, a DCDC module, and a load end which are connected in sequence, and the charging management module comprises a charging management main circuit and a charging management control unit. The charging management control unit comprises a charging protection controller, a capacitance expansion controller and a fast and slow charging controller; the charging management control unit is used for controlling the charging management main circuit; the interface rectification module comprises a piezoelectric energy collection interface, a rectification circuit and a synchronous switch controller; the DCDC module comprises a DCDC converter and a load adjusting controller, the scheme can realize efficient energy storage and protection of the super capacitor, automatically expand the energy storage capacity, and intelligently switch a fast charging mode and a slow charging mode according to a voltage difference, so that input ripples are suppressed, the input voltage of the DCDC converter is stabilized, the overall energy conversion efficiency and the load adaptability are improved, and the energy storage capacity of the super capacitor is improved. The system is suitable for long-term autonomous energy supply of the Internet of Things and wireless sensor nodes.