50 results about "Wireless sensor node" patented technology

The invention 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 acquires soil humidity data, illumination intensity data and meteorological parameter data through wireless sensor nodes deployed in a farmland area, and performs timestamp synchronization on the data to ensure the consistency of the data in time dimension. And the communication protocol adaptation module dynamically selects a wireless transmission frequency band according to the synchronous data volume, allocates communication time slots based on the data priority, and generates adaptive transmission channel parameters so as to cope with the communication requirements in the data volume fluctuation and complex environment. The crop state evaluation module receives the transmission channel parameters, analyzes the real-time environment data flow, 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 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 invention discloses a self-powered multi-parameter farmland microenvironment monitoring system, and belongs to the technical field of smart agriculture and Internet of Things. The system comprises a distributed self-powered wireless sensing node array, gateway equipment and a cloud data platform. The sensing node adopts a photovoltaic and soil temperature difference synergistic hybrid energy supply module, integrates temperature, humidity, pH and other multi-parameter sensing probes, and realizes long-distance low-power-consumption transmission through an improved LoRa communication module. And the gateway is used for receiving and fusing the data and uploading the data to the cloud platform through 4G or Wi-Fi. The cloud platform can store and visualize data, spatial modeling is realized by using improved Kriging interpolation, a soil moisture content prediction model and an anomaly detection algorithm are introduced, and intelligent irrigation suggestions are generated in combination with a crop water demand model. The system has the advantages of stable energy supply, high sensing precision, strong communication anti-interference capability, intelligent data processing and the like, can operate unattended for a long time, is widely suitable for scenes of field planting, greenhouse agriculture, scientific research experiments and the like, and provides reliable support for precise and intelligent management of agricultural production.

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.

The invention belongs to the field of fire control, and discloses a park intelligent fire control system based on the Internet of Things, comprising an early warning data collection module; the early warning data collection module comprises a clustering control device and a plurality of wireless sensor nodes; the clustering control device is used for calculating a management period based on the early warning data and clustering the wireless sensor nodes based on the management period to obtain cluster head nodes and member nodes in the next management period; the calculation process of the management cycle comprises the following steps: calculating a change coefficient of fire-fighting data; calculating a next management period based on the change coefficient and the previous management period; the member nodes are used for acquiring early warning data and transmitting the early warning data to the cluster head nodes; and the cluster head node is used for forwarding the early warning data to the clustering control device. According to the invention, effective transmission of early warning data can be effectively guaranteed, and the cruising ability of the wireless sensor node is improved.

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 invention discloses a fire-fighting electrical early warning method and system based on wireless networking, particularly relates to the technical field of electrical fire monitoring, and is used for solving the problem of multi-node data space-time misalignment caused by transmission delay and data packet disorder in an existing wireless early warning system. Electrical parameter data with a local timestamp is acquired through a wireless sensor node, and after a transient characteristic waveform is extracted by a cloud platform, time synchronization verification is carried out by adopting a time service reference node and a dynamic fault-tolerant window mechanism; respectively generating a certainty index and a path conformity confidence index for the data which do not pass the verification through a recurrence plot analysis and transfer entropy algorithm, and finally carrying out fault analysis based on dual-index weighted correction; while the convenience of wireless networking is maintained, the accuracy and reliability of fault early warning are effectively improved through a multi-level data verification and correction mechanism.

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.

A power management integrated circuit includes a reconfigurable converter connected to a rechargeable battery and a control circuit. The reconfigurable converter includes an inductor to accumulate energy based on an input voltage from a thermoelectric device, provides a wireless sensor node connected to an output node with an output voltage based on the input voltage and an internal input voltage of the rechargeable battery, operates in one of operation modes based on an available power provided from the thermoelectric device and a power consumption by the wireless sensor node and drives the wireless sensor node in response to the wireless sensor node consuming a current greater than a maximum current generated by the thermoelectric device. The control circuit to control the reconfigurable converter based on the input voltage, a first voltage of a first switching node, a second voltage of a second switching node and the output voltage.

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.

A wireless sensor network (WSN) and a method of routing in a wireless sensor network (WSN) includes wireless sensor nodes for monitoring an environment and a base station having a WSN manager to which information about the environment is sent from the wireless sensor nodes via multiple hop communication. The method includes responding to an attack on the wireless sensor network by an adversary to locate critical nodes among the wireless sensor nodes by adaptively deploying a sequence of different routing mechanisms, by the WSN manager, to the wireless sensor nodes such that location privacy preservation (LPP) is protected by preventing detection of location of the critical nodes.

The application discloses a kind of wireless sensor network node security state evaluation method and system, it is related to network security technical field, the present application obtains multiple groups of virtual neighbors by each wireless sensor node, and judges based on virtual adjacent coefficient Xx, so that network can flexibly cope with node loss or link failure problem.When node fails, it can be replaced by virtual neighbor in time to replace the lost node, avoids the problem that network interruption is caused by node loss, improves the fault tolerance and reliability of network.In the process of node online state updating, the present application sets timeout duration T, and starts maintenance mechanism, dynamically updates the online state of each node in task path, which avoids the situation that path updating is lagged due to network topology change, makes the network path more flexible, timely response to node failure or fault change.

A smart multi-modal telehealth IoT system for respiratory analysis. Such a system includes a body area sensor network comprised of meshed wireless sensor nodes and advanced machine learning techniques. The system may be used to remotely diagnose a user's respiratory illness and monitor their health.

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 invention relates to the technical field of constructional engineering, and discloses a constructional engineering quality defect real-time monitoring system, which comprises a four-layer architecture system consisting of a sensing layer, a communication layer, a data processing layer and an application layer, and is characterized in that the sensing layer consists of multiple types of wireless sensor nodes consisting of strain gauges, displacement sensors and temperature sensors; deploying at key parts of a building structure; and the communication layer adopts a Mesh ad hoc network technology to realize multi-hop transmission and protocol conversion of monitoring data. According to the real-time monitoring system for the quality defects of the constructional engineering, a sensing layer sensor node is deployed by adopting an improved firefly algorithm module; initial uniformly distributed nodes are generated based on a good point set theory, inertia weight and step length are dynamically adjusted through an adaptive adjustment mechanism in iteration, early-stage large-step-length global search is performed, later-stage small-step-length local optimization is performed, and Gaussian disturbance is applied during convergence stagnation to jump out of local optimization; node uniform distribution is achieved, the coverage rate is increased, blind areas and redundancy are reduced, and data integrity is guaranteed.

The invention discloses a gridding edge terminal wireless sensor network covering and positioning method and system, and the method comprises the steps: building a wireless sensor coordinate system model of a monitoring region based on an actual operation environment of a wireless sensor; configuring the position information of each wireless sensor node as a layout scheme, and constructing a wireless sensor coverage evaluation model to evaluate the network coverage of each wireless sensor layout; optimizing the positions of the wireless sensor nodes based on an evaluation result to obtain an optimal node layout scheme; and based on the optimal node layout scheme, calculating the distance of the unknown node relative to the known reference point, optimizing the estimation of the relative distance between the wireless sensor nodes, and accurately positioning the unknown node. According to the invention, self-organizing network coverage and node positioning can be realized, the dependence on a central node is reduced, and the robustness and expandability of the system are enhanced; through cooperative work of multiple agents, a more accurate node position is found in an uncertain or dynamically changing environment.

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.