96 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 system based on a dynamic cryptographic architecture for securing resource-constrained networks, consisting of: a multitude of wireless sensor nodes, each node comprising a central processing unit configured to enable lightweight encryption for secure and energy-efficient communication in the sensor network, the central processing unit comprising the following: a dynamic cluster selection module configured to regularly rotate the selection of the cluster head based on node performance, node spacing, and network topology; a flexible, lightweight encryption module configured to automatically select encryption parameters using a symmetric block cipher algorithm, with the encryption module dynamically adjusting the complexity based on the available computing resources; a key management module configured to establish a secure connection between the sensor, the cluster head, and the neighborhood access point; an authentication module that includes a two-stage authentication mechanism to secure data transmission between sensor nodes, cluster heads and an access point; a wireless communication interface configured to transmit encrypted data packets with embedded encrypted encryption parameters; and a routing device that acts as an access point and is configured to enable secure communication between the wireless sensor network and the cloud storage or database.

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 field of photovoltaic module management, and discloses a high-altitude photovoltaic module state management system, which comprises a wireless sensor node, a transfer control device and a management device, the transfer control device is used for clustering the wireless sensor nodes, and each cluster comprises a cluster head node and member nodes; the member nodes are used for collecting monitoring data of the photovoltaic modules and transmitting the monitoring data to the cluster head nodes; the cluster head nodes are used for transmitting the monitoring data received from the member nodes to the transfer control device; the transfer 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. According to the invention, the time interval between two adjacent clustering of the wireless sensor node can better adapt to the high-altitude temperature condition, and the monitoring cost can be saved while the monitoring effect is ensured.

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 provides an underground working face spontaneous combustion monitoring system and method.The underground working face spontaneous combustion monitoring method comprises the steps that (1) a target area is divided to determine monitoring points and strain points, and first wireless sensor nodes and second wireless sensor nodes are deployed respectively; (2) acquiring the environment temperature and the combustible gas concentration of each monitoring point on line through a first wireless sensor node, and comparing and analyzing the acquired real-time data and a preset critical value to determine a dangerous area; (3) monitoring the temperature change and the combustible gas concentration change of the dangerous area within the preset time for visual display, and judging whether a fire risk exists in the dangerous area or not; and (IV) waking up the second wireless sensor nodes deployed at the strain points around the dangerous area, evaluating the fire hazard grade, and carrying out graded early warning. According to the method, the monitoring data of the underground working face is comprehensively analyzed, the spontaneous combustion risk is evaluated and graded early warning is performed, and the detection accuracy is improved.

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 invention relates to the technical field of transformer substation comprehensive monitoring, in particular to a WAPI-based transformer substation wireless sensor network system, which comprises at least two wireless sensor nodes and a self-adaptive wireless communication unit, each wireless sensor node is added into the network by automatically acquiring network parameters; the self-adaptive wireless communication unit comprises an ad hoc network module and a spectrum management module; the ad hoc network module is provided with at least one WAPI gateway, and the networking forms comprise LORA, Bluetooth and WAPI; the frequency spectrum management module is used for dynamically adjusting the wireless communication frequency according to the environment change of the transformer substation and the signal interference condition; and each wireless sensor node is used for evaluating the quality of a communication link in real time and determining an optimal communication path, and comprises an optimal transmission protocol and an optimal WAPI gateway configured to support the optimal transmission protocol. Through automatic network configuration, adaptive spectrum management and dynamic network topology adjustment, efficient and reliable data acquisition and monitoring are realized, and the security and environmental adaptability of the network are enhanced at the same time.

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 present invention proposes an energy management method for a self-driven wireless sensor node. The method can calculate the captured environmental energy intensity according to the power consumption state of the energy storage element after power-on initialization, and then determine the working cycle of the working mode of "continuously collecting and sending after power-on until the power is consumed to the low-voltage threshold and re-entering the charging cycle" or "calculating the sleep and wake-up cycle after power-on, and then performing periodic sleep, wake-up, data collection and transmission, and sleep cycle according to the cycle", and select the working mode with the shortest working cycle to maximize energy utilization efficiency.

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.

The present invention discloses an industrial wireless network resource scheduling method based on a multi-weight quality assessment model. The method collects relevant data of the recent network status through the application of a sliding window mechanism by the internal nodes of the network, constructs a multi-weight quality assessment model to calculate the transmission quality of the time slots in the candidate pool; uses a Q learning algorithm of Pareto optimization and adaptive exploration to dynamically adjust the weights; based on the transmission quality of the time slots output by the multi-weight quality assessment model, the 6P protocol is combined to dynamically manage the time slot resources of the nodes, including dynamically releasing and supplementing time slots, maintaining and optimizing the time slot pool, and selecting the most suitable channel combination through a pseudo-random channel selection mechanism. The node uses the selected channel for data transmission and updates the scheduling table. This method utilizes a multi-weight quality assessment model and Pareto optimization to achieve dynamic adjustment of time slots and channels, and ensure high reliability and end-to-end low latency of wireless sensor node data transmission.

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 present invention discloses a passive Internet of Things node medium access control method, system, device and medium, wherein the method comprises: S1, when a wireless sensor node has a data transmission demand, initializing the number of conflicts i=0; S2, updating the priority K of the node data transmission according to the current remaining energy E of the node; initializing the fixed waiting time T1 and the random backoff time T2 of the node according to the priority K; S3, monitoring the channel state, if the channel is idle within the T1 time, executing step S4; S4, the wireless sensor node backs off for T2 time and waits; S5, sending data, if a confirmation signal from a receiving end is received, the data is sent successfully, otherwise, executing step S6; S6, the wireless sensor node updates its own number of conflicts i=i+1, if i is greater than a preset conflict upper limit i max , then the data transmission fails. The present invention solves the channel allocation problem when terminal nodes are simultaneously connected to readers, and can be widely used in the field of passive Internet of Things.