75 results about "Wireless sensor node" patented technology

The invention discloses an intelligent seedling greenhouse environment regulation and control system based on the Internet of Things, and belongs to the field of intelligent regulation and control of the Internet of Things, and the system comprises a heterogeneous sensor dynamic networking construction module which is used for deploying self-positioning wireless sensor nodes and obtaining a dynamic networking scheme based on the wireless sensor nodes; the environment data acquisition and preprocessing module is used for acquiring a multi-dimensional environment data set and preprocessing the multi-dimensional environment data set to obtain a complete environment data set; the growth stage self-adaptive control module is used for dynamically regulating and controlling a seedling culture strategy by adopting an environment variable coupling method based on the complete environment data set to obtain a self-adaptive regulation and control scheme; and the virtual feedback simulation module is used for performing calibration regulation and control through a virtual feedback mechanism and an actual acquisition device to obtain an optimized regulation and control scheme.

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 relates to the technical field of wireless sensing nodes, and discloses a self-adaptive sleep scheduling method for a wireless sensing node powered by solar energy. According to the method, energy collection data and environmental parameters of a solar cell panel are monitored in real time, an energy prediction model is constructed to generate an energy supply curve, a dynamic sleep time threshold value is calculated according to the energy supply curve and task load priority weight, a multi-stage sleep mode is generated through a fuzzy decision rule, and time-sharing sleep scheduling of communication, sensors and calculation modules is achieved. Meanwhile, a sleep mode can be switched by detecting the emergency degree change of a task queue, an emergency scheduling strategy is started and a standby power supply is activated when energy is insufficient, and data can be recorded to update model parameters to realize self-optimization iteration. According to the method, the energy utilization efficiency and the working stability of the wireless sensing node are improved, and the problems of unstable solar energy supply and unreasonable task scheduling are effectively solved.

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 disclosure relates to authenticating wireless sensor nodes in a network using physical phenomena. A battery management system (BMS) includes: a battery pack including a plurality of battery cells; and a first node (352) configured to: cause one or more cells of the battery pack to alter current or voltage (310); receiving a first value (316) from a second node (356); comparing the first value with a second value calculated based on the altered current or the altered voltage; and authenticating the second node as a member of the BMS based on comparing the first value to the second value (318).

A battery management system (BMS) includes a battery pack including multiple battery cells and a first node configured to: cause one or more battery cells of the battery pack to alter a current or a voltage, receive a first value from a second node, compare the first value with a second value calculated based on the altered current or the altered voltage, and authenticate the second node as a member of the BMS based on comparing the first value with the second value.

The invention belongs to the technical field of intelligence, and discloses a bridge load test detection system based on wireless transmission. Comprising the following steps: uniformly distributing a bridge into M sections, and calculating a double-field coupling weight coefficient of each section; dynamically selecting a test point according to the double-field coupling weight coefficient of each section; a movable wireless sensor node is adopted to monitor a test point, and structural response data of the test point is collected; constructing a sensitivity matrix through the initial excitation parameter and the structural response data to obtain a corrected target function, and correcting the target function through iterative optimization to finally obtain an optimal excitation parameter; predicting the maximum dynamic load force and the actual dynamic load force; whether early warning is carried out or not is judged 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 obviously improved.

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 invention discloses a 360-degree camera wireless sensor network coverage method for three-dimensional curved surface monitoring, and belongs to the technical field of three-dimensional curved surface monitoring. The method comprises the following steps: S1, acquiring a three-dimensional curved surface scene to be monitored, and constructing a 360-degree camera wireless sensor network coverage model; and S2, optimizing the 360-degree camera wireless sensor network coverage model by adopting an improved hummingbird swarm optimization algorithm, and obtaining a non-dominated coverage scheme based on an optimization result. The coverage scheme of the 360-degree camera wireless sensor network is optimized through guiding foraging, regional foraging and migration foraging of the hummingbird group. And obtaining a non-dominated coverage scheme of the 360-degree camera wireless sensor network through a non-dominated sorting method of the multi-target hummingbird group. The coverage effect of the 360-degree camera wireless sensor network is effectively improved, the coverage rate and the network connectivity are effectively improved, and the energy consumption of the 360-degree camera wireless sensor node is reduced.

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.

The invention belongs to the technical field of coal mine safety monitoring, and relates to a coal mine working face mining-induced stress monitoring system and a construction method thereof.The coal mine working face mining-induced stress monitoring system comprises a plurality of wireless sensing nodes which are arranged in a coal body drill hole in an equal difference mode in the working face trend, and each node comprises a three-dimensional stress sensor, a data acquisition module, a wireless communication module and a power module; the drilling depth extends to a mining influence area according to an equal difference sequence, and the distance between every two adjacent nodes is smaller than or equal to 10 m; the pressure monitoring substation is connected with each wireless sensing node through a wireless network, and a modulation demodulator and a voltage-stabilized power supply are arranged in the pressure monitoring substation to realize signal conversion and power supply; and the system host communicates with the pressure monitoring substations through a wired network, configures a data processing unit, a digital twin model and a human-computer interaction interface, dynamically analyzes stress distribution and generates an early warning signal. According to the method, the full-depth coverage monitoring of the mining influence area is realized for the first time through the equal-difference drilling layout (20m, 22m, 24m... 50m) and the grading of the depth monitoring points.

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 invention addresses the problem of premature node death caused by uneven energy consumption among nodes in a wireless sensor network. From the perspective of grid partitioning, the present invention considers the clustering and cluster head election of wireless sensor nodes, and proposes a method for clustering and cluster head election of wireless sensor nodes based on grid partitioning with energy load balancing. The sensor nodes are divided into different grid regions for clustering, and a dynamic cluster head election mechanism is adopted to reduce the collection of duplicate data. Moreover, the cluster head nodes are evenly distributed through the uniform distribution of the grids, thereby solving the problems of unreasonable clustering and uneven energy consumption of network nodes.

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 discloses a wireless sensor network coverage optimization method and system based on deep reinforcement learning, and the method comprises the steps: obtaining the state information of a wireless sensor network diagram, enabling the diagram to serve as the basic structure and state representation of a decision environment, enabling each node to represent a possible action, and enabling the node to be a possible motion; the wireless sensor network coverage is optimized and reconstructed into the minimum vertex coverage problem, state representation is simplified, the state space scale is reduced, the model convergence speed is increased, the search space is effectively reduced, the minimum vertex coverage problem is solved in combination with a deep learning model, an optimal decision is selected according to a Q value, and the network coverage is optimized and reconstructed. The limitation of a deep reinforcement learning method in the aspect of network topology representation is overcome, the problem of long-range space association between wireless sensor nodes can be more effectively captured, the service life of a wireless sensor network is prolonged, and the monitoring reliability of the wireless sensor is improved.

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 invention relates to the technical field of sensors, and provides a voltage-controlled oscillator, a wireless sensor and a wireless sensor network, and the voltage-controlled oscillator comprises a current conversion module, a first current mirror, a second current mirror, an oscillation generation module and a sensing module. The current conversion module converts an input reference voltage into a reference current; the sensing module outputs a first voltage and a second voltage, and at least one of the first voltage and the second voltage is a variable voltage changing along with the change of a sensing target; the two current mirrors respectively generate a first mirror current and a second mirror current according to the reference current; the oscillation generation module generates a high-level signal according to the first voltage and the first mirror current, generates a low-level signal according to the second voltage and the second mirror current, and determines the duration of the high-level signal and the low-level signal according to the variable voltage. According to the voltage-controlled oscillator, the sensing module is integrated, and an independent sensor for sensing the change of a sensing target in an environment is not needed, so that the energy consumption of a wireless sensor node is reduced.

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 present invention relates to the technical field of the intersection of unmanned aerial vehicle and wireless sensor network technologies, and specifically to a data-driven heterogeneous unmanned aerial vehicle swarm-assisted wireless sensor data collection method, including Step 1: constructing a hierarchical data collection system for heterogeneous unmanned aerial vehicle formations; Step 2: determining the communication coverage range of data collection unmanned aerial vehicles for the wireless sensor network; Step 3: performing the first round of regular hexagon coverage; Step 4: performing the second round of regular hexagon coverage; Step 5: optimizing the initial flight path of communication unmanned aerial vehicles; Step 6: determining the central coordinate information of the task area of data collection unmanned aerial vehicles; Step 7: performing dynamic task allocation for data unmanned aerial vehicle formations; Step 8: adjusting the positions of data collection unmanned aerial vehicles; Step 9: performing dynamic position adjustment for communication unmanned aerial vehicles; Step S10: determining whether the data collection of wireless sensor nodes is completed. If so, loop through Steps S7 to S10 to perform the next round of tasks; if not, end the task.

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.