429 results about "Wireless sensor networking" patented technology

The present disclosure generally relates to a two-stage system for detecting malicious nodes in Wireless Sensor Networks (WSNs), enhancing network security and resilience. The system employs a distributed approach, leveraging Cluster Heads (CHs) and a central server for efficient and accurate detection. Initially, sensor nodes are monitored for comprehensive node and network metrics, statistically ranked by significance in identifying malicious behavior. CHs perform a resource-aware first-stage detection based on their resource weight, filtering potential threats locally. Results are then aggregated at a server for a second-stage analysis using a hybrid Machine Learning (ML) and Deep Learning (DL) approach. This advanced analysis, combined with statistically relevant metrics, significantly improves detection accuracy. By integrating resource-conscious CH operation with powerful server-side ML / DL, this system offers a scalable, energy-efficient, and highly effective solution for securing WSNs against malicious node attacks, surpassing traditional detection methods in both speed and precision.

The invention provides a coal mine goaf coal spontaneous combustion risk intelligent monitoring and early warning method and system, and relates to the technical field of coal mine safety management. A dot-matrix wireless sensor network is deployed in a target area, temperature and gas concentration data are collected in real time, and data processing is carried out through edge calculation. A dynamic characteristic matrix is generated based on transfer learning and a space-time diagram convolutional network, a comprehensive risk index is calculated, four early warning levels are divided, efficient and accurate coal spontaneous combustion risk monitoring and early warning are achieved, and an innovative solution is provided for mine safety management.

The invention discloses an electrical fire real-time monitoring system based on a wireless sensor network, and relates to the technical field of electrical fire monitoring. The cloud monitoring platform is in communication connection with an electrical data acquisition module, an electrical fire abnormity analysis module, an abnormity early warning module, a node management configuration module and a visual monitoring module, and all the modules are in electric signal connection; the electrical data acquisition module is used for acquiring monitoring data of electrical nodes by using a deployed wireless sensor and transmitting the monitoring data to the cloud monitoring platform through a wireless communication technology, and the monitoring data comprises current, voltage, temperature, humidity and leakage current. Multi-parameter fusion monitoring is carried out through the wireless sensors deployed at the key nodes of the electrical equipment, compared with a traditional wired sensor, the wireless sensors do not need wiring, installation is flexible, early signals of an electrical fire can be captured more comprehensively, and the accuracy of fire early warning is remarkably improved.

The invention relates to a wireless sensor network coverage optimization method based on a star-graffiti optimization algorithm, and belongs to the technical field of wireless sensor network coverage optimization. In order to solve the problems of low convergence precision, easy falling into local optimum and convergence hysteresis of a traditional coverage method, the invention provides an enhanced star-graffiti optimization algorithm (INOA) fusing a hyperbolic sine and cosine optimizer and a nonlinear storage strategy. Firstly, a population is initialized through Bernoulli mapping, and the global exploration capability in a high-dimensional scene is improved; secondly, designing a nonlinear convergence mechanism based on hyperbolic sine and cosine, and dynamically balancing global optimization and local development; and a dynamic nonlinear storage strategy is introduced, so that the convergence speed is remarkably increased. The method effectively improves node deployment optimization in a complex environment, has higher adaptability, improves convergence speed, precision and stability, and realizes node efficient coverage and energy consumption balance.

The invention discloses a traditional Chinese medicinal material standard base intelligent irrigation and fertilization method based on the Internet of Things and a decision making system, and relates to the technical field of agricultural informatization. Through multi-source data acquisition and intelligent analysis, the limitation of an existing irrigation and fertilization method is solved, accurate decision making and resource optimization are realized, comprehensive data are acquired by utilizing a soil moisture content sensor, a meteorological station and plant image acquisition equipment, and by combining principal component analysis, a long-short-term memory neural network and a block chain technology, the intelligent irrigation and fertilization method is realized. According to the system, the water and fertilizer requirements are accurately predicted, an irrigation and fertilization scheme is optimized, data credibility and traceability are ensured, meanwhile, real-time control and collaborative operation of equipment are achieved through edge calculation and a wireless sensor network, the resource utilization efficiency is improved, the production cost is reduced, and scientificity and sustainability of traditional Chinese medicine planting are remarkably improved.

The invention relates to the technical field of industrial Internet of Things and wireless sensor networks, in particular to a large-scale industrial fault diagnosis system and method based on Bluetooth MESH, and efficient monitoring and fault early warning of industrial equipment are realized through low-power-consumption sensor nodes, edge computing, a Bluetooth Mesh network and a clustered Mesh tree architecture. The system comprises a client, a server, a gateway, a cluster head and a cluster node, the cluster node comprises a sensor node and a relay node, a lightweight neural network model is built in the sensor node, and edge reasoning and fault detection can be realized locally. Stable communication and real-time cooperation of large-scale nodes are ensured through a decentralized architecture of Bluetooth Mesh, establishment of a dynamic cluster head chain and optimization of an RSSI threshold value. And a low-power-consumption mechanism combining event driving and fixed polling is adopted, so that the energy consumption of the system is remarkably reduced. In-cluster communication loads are reduced through a clustering architecture, and the communication efficiency and the anti-interference capability of the system are improved in combination with channel separation of Mesh and BLE protocols.

The invention discloses a cluster head election method of an improved LEACH algorithm and an improved LEACH routing protocol method, belongs to the technical field of routing soft security of an underwater wireless sensor network, and particularly relates to a cluster head election process of the underwater wireless sensor network. The problems that an existing cluster-based routing protocol neglects the security problem and the security of a cluster head election process is relatively low are solved; the method comprises the following steps that: each node updates own mse according to a trust evaluation result of an aggregation node on the node; calculating a cluster head election threshold value by using an improved cluster head election threshold value function Timp (n) based on the number of times that each node serves as a cluster head node, a priori determined cluster head node proportion and mse; each node compares a random number generated by the node with the cluster head election threshold; and if the random number is smaller than the cluster head election threshold, the node becomes a cluster head node of the current large period. The cluster head election method for improving the LEACH algorithm and the method for improving the LEACH routing protocol are suitable for providing a cluster-based routing protocol.

The invention discloses an unmanned aerial vehicle three-dimensional flight path planning method for wireless sensor network data collection and energy supplementation, and belongs to the technical field of Internet of Things. A network is divided into a plurality of clusters through a density peak clustering algorithm, and a dynamic cluster head selection mechanism based on a routing protocol is designed to optimize the positions of cluster heads. The shortest flight path is determined by adopting a self-organizing mapping network introducing a penetration mechanism. Through a depth deterministic strategy gradient algorithm, the flight height of the unmanned aerial vehicle is dynamically adjusted, and meanwhile, the flight route of the unmanned aerial vehicle is planned, so that the unmanned aerial vehicle can efficiently collect data and supplement energy to sensor nodes in a charging range. According to the method, the distance of the unmanned aerial vehicle for charging the sensor and collecting data is obviously optimized, the energy utilization efficiency is improved, the energy supplement and data collection efficiency of the network is improved, and the network survival time is prolonged.

The present invention relates to the technical field of wireless sensor networks, and to an average consistency time synchronization method based on a multi-hop virtual link and event triggering. According to the method, in the case that a communication delay is present in a wireless network, a least squares estimator is used to restrain the effect of the delay in the relative frequency offset estimation process; clock information of nodes is fully called by means of the multi-hop virtual link for update, thereby effectively increasing the clock synchronization speed; an event triggering policy is used to reduce a transmission frequency between the nodes, thereby effectively reducing communication overhead; and a consistency algorithm is used to update logic clock parameters of the nodes, thereby realizing global consistency of clocks of the nodes in a wireless sensor network. According to the present invention, the effect of the communication delay in the wireless sensor network is taken into consideration, an iterative least squares estimator is used to obtain a high-precision relative frequency offset value to improve the synchronization precision, and by using an interaction mechanism of the multi-hop virtual link and event triggering, the communication overhead is effectively reduced while increasing the convergence speed of a clock synchronization error.

The invention discloses an arbor community construction method suitable for urban landscaping, and relates to the technical field of urban planning, and the method comprises the steps: deploying an environment sensor in a target urban landscaping region, and collecting the environment data of the target region in real time through a wireless sensor network; based on the environment data, adopting a genetic algorithm to carry out adaptability evaluation on a plurality of candidate tree species, and selecting a tree species combination with the strongest adaptability to carry out preliminary community configuration; and a multi-objective optimization algorithm is adopted to carry out optimization configuration on the number, distribution and ecological functions of tree species in the initial community configuration, the performance of the community under different environmental changes is evaluated, and the overall ecological benefits of the community are maximized. According to the arbor community construction method suitable for urban landscaping provided by the invention, the configuration of the tree species is automatically adjusted based on multi-dimensional evaluation of environmental data, tree species adaptability and community ecological benefits, and the community ecological benefits are maximized.

The invention relates to the field of communication transmission, and discloses an anti-interference secure transmission method for a wireless sensor network, which comprises the following steps of: acquiring residual electric quantity, position and communication quality parameters of a sensor node in real time; dynamically selecting a multi-hop routing path based on interference detection, excluding low-electric-quantity nodes, and preferentially selecting high-electric-quantity and low-interference area nodes to form a main path; at least one independent redundant path is generated according to the node electric quantity distribution and the interference intensity, and the relay node electric quantity of the redundant path is higher than a threshold value; monitoring electric quantity change in real time, and triggering a route dynamic reconstruction mechanism; and recovering complete information through a data fusion and verification algorithm. In addition, a charging request protocol is triggered for a low-power key node. According to the invention, by optimizing routing selection, redundancy transmission and energy management strategies, the anti-interference capability and transmission reliability of the wireless sensor network are improved, and the problem of data interruption caused by insufficient electric quantity of nodes is avoided.

The invention discloses a cable defect detection method of a binocular intelligent inspection robot, and relates to the field of image processing, and the method comprises the steps: firstly obtaining the state information of a cable, then carrying out the preprocessing and stereo matching of the image information of the cable, and then constructing a target detection and segmentation model; the target detection and segmentation model is adopted to detect and segment cable defects, and model sample data is updated in real time in an incremental learning mode; fusing the cable state information acquired by the infrared camera, the thermal imager and the wireless sensor network by using a multi-sensor data information fusion model; the cable is maintained and managed based on a detection result; according to the invention, the detection precision and accuracy can be improved, cables of different types and specifications can be handled, the adaptability and stability are ensured, and the real-time performance and accuracy of detection can be improved.

An intelligent grain storage control system based on a wireless sensor network comprises a grain storage environment monitoring subsystem which comprises a plurality of sensor arrays used for monitoring at least one grain storage environment parameter; the grain condition early warning subsystem is used for processing all the grain storage environment parameters by using a deep belief network and a long-short term memory network to obtain a real-time grain condition state; the intelligent control subsystem is used for generating an equipment control instruction according to the real-time grain condition state; and the overrun execution subsystem comprises a plurality of environment control devices, and the environment control devices are used for operating according to the device control instructions to change the environment of the grain depot. According to the invention, the grain storage environment parameters in the grain depot can be more flexibly collected, and the real-time grain condition state is predicted through the grain condition early warning model comprising the deep belief network and the long-short term memory network based on the grain storage environment parameters, so that the grain depot environment can be controlled with high precision, and full protection of grains is realized.

The invention discloses a wireless sensor network node fault diagnosis system based on a multi-scale Bayesian network, and relates to the technical field of wireless sensor networks. The system comprises the following components: a data acquisition and preprocessing module which adopts sensor nodes distributed in a monitoring area to acquire environmental parameters according to a preset frequency, wirelessly transmits the environmental parameters to a data processing unit, performs preprocessing in a self-adaptive mode, and stores the processed data into a database; according to the method, a micro-scale Bayesian network model, a node-scale Bayesian network model and a sub-network three-scale Bayesian network model are constructed, so that cross-scale fault analysis from a component level to a network level is realized, the micro-scale captures fault association of internal components, the node scale evaluates the overall sensor performance, and the sub-network scale analyzes a cooperation state between nodes. The multilevel information interaction and fusion mechanism can comprehensively consider node internal fault propagation, external communication interference and subnet topology influence, and significantly improves the integrity and accuracy of fault diagnosis.

The invention relates to the technical field of wireless communication and intelligent optimization, and particularly discloses a path planning method and system for data collection of a wireless sensor network, and the method comprises the steps: dividing a sensor working process, and defining a time window constraint condition, so as to build an energy consumption model; taking maximization of data collection amount, minimization of energy consumption and minimization of path length as optimization objectives, and constructing a path planning model based on multi-objective Pareto optimization; carrying out global exploration by adopting an improved ant colony algorithm, carrying out local optimization by taking an exploration result as an initial solution of a particle swarm algorithm, and dynamically adjusting pheromone distribution and algorithm parameters through a bidirectional feedback mechanism; and generating an optimal path of the mobile node according to an optimization result, and controlling the mobile node to execute a data collection task.

The invention discloses a multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission trajectory optimization method and system, and the method comprises the steps: 1, building a multi-unmanned aerial vehicle cooperation real-time data acquisition and transmission system model for a large-scale wireless sensor network data acquisition and transmission scene, and constructing a completion time minimization problem; 2, the sensor nodes are clustered, the clustering center is determined to be the position of a data acquisition point, then a task area is divided according to the communication radius between the unmanned aerial vehicle and the base station and the communication radius between the unmanned aerial vehicles, and the acquisition points located in the same area are responsible for the same unmanned aerial vehicle; 3, converting the optimization problem constructed in the step 1 into a new optimization problem about the hovering points, the hovering time, the flight speed and the collection sequence of the multiple unmanned aerial vehicles through trajectory discretization; and 4, solving the new optimization problem converted in the step 3 through a point matching-based trajectory planning algorithm to obtain the flight trajectory of the multiple unmanned aerial vehicles.

The invention relates to a hybrid ad hoc network and a safe and efficient communication method in a large-scale wireless sensor network. The method comprises the steps that a network topology construction module is used for designing a heterogeneous communication protocol with high adaptability based on the distribution condition of large-scale sensor nodes, and seamless cooperative work of multiple communication technologies is ensured; the ad-hoc network optimization module adopts a dynamic ad-hoc mechanism to realize autonomous discovery and network connection between nodes and improve the expansibility and flexibility of the network; the safety communication module is combined with an advanced encryption algorithm and an authentication mechanism to ensure the safety and the reliability in a data transmission process; and the efficient communication protocol module improves the data transmission efficiency and the overall performance of the network through the technical means of signal transmission optimization, interference management and the like. Through cooperative work of the modules, efficient, safe and reliable heterogeneous wireless communication can be realized in the large-scale wireless sensor network, and the requirements of the large-scale wireless sensor network in practical application are met.

The invention belongs to the technical field of rail safety real-time monitoring, and particularly relates to a rail safety real-time monitoring system based on rail excitation and big data analysis. The rail safety real-time monitoring system comprises sensor monitoring nodes, a wireless sensor network and a cloud intelligent processing center. The sensor monitoring node is used for recording rail vibration data when a vibration monitoring device is arranged on a rail and an extrusion excitation effect between wheels and the rail is monitored; the wireless sensor network constructs a wireless sensor network based on a narrowband Internet of Things node and a plurality of sensor monitoring nodes, then forms a rail monitoring network, and uploads all rail vibration data to the cloud intelligent processing center; and the cloud intelligent processing center analyzes the rail vibration data based on a big data analysis algorithm, and determines a rail coping strategy according to an analysis result. The rail monitoring system which is low in cost, high in efficiency, capable of responding in real time, free of line occupation and easy to maintain is developed, and the safety and efficiency of railway operation are improved.

The invention discloses a dynamic topology ad hoc network method based on environmental perception and adaptive optimization, and relates to the field of wireless sensor networks. Parameters (such as temperature, obstacle distribution and node energy consumption) of nodes are collected in real time; a multi-dimensional quantization parameter matrix including an obstacle coverage matrix, an energy consumption distribution matrix and a signal attenuation coefficient matrix is constructed, and path loss, communication delay and node energy consumption are fused and calculated to generate a node weight value by adopting an adaptive weight calculation mechanism, so that the priority of a communication link is dynamically adjusted. Compared with the prior art, the method has the advantages that the self-healing capability and the real-time performance of the network in a complex environment are remarkably improved, the global calculation overhead is reduced, the real-time performance and the expansibility of the system are improved, the node layout and the dynamic optimization of a communication path are realized, and the network load and the stability are balanced.

The invention relates to the technical field of component quality control, and discloses a remote component quality control system based on digital twin and wireless communication. A component data acquisition module of the system obtains multi-dimensional data of a to-be-monitored component through a wireless sensor network, and a component identification code is associated to generate a quality basic data set; the regional association analysis module establishes association mapping of adjacent components according to a spatial topological relation to form a quality association partition map; the environmental response evaluation module analyzes the influence degree of environmental factors on the performance of the component and generates an environmental response analysis report; the defect positioning module identifies problem points and constructs a defect distribution map; and the quality early warning module integrates the problem points and the associated parameters, marks the position of the potential safety hazard, and outputs a quality evaluation and risk early warning report. The system realizes remote and comprehensive control of component quality, and assists in timely finding and dealing with quality problems.

The invention discloses an optimal deployment method for wireless sensor network nodes based on a three-dimensional space, and the method comprises the steps: obtaining a to-be-deployed three-dimensional space model of the wireless sensor network nodes, and segmenting the to-be-deployed three-dimensional space through a KD tree; randomly throwing the movable wireless sensor network nodes to the to-be-deployed three-dimensional space, and obtaining the initial position of each wireless sensor network node; position deployment of wireless sensor network nodes is planned by adopting an ant lion optimization algorithm, a differential variation disturbance strategy is introduced, and the global search capability of the algorithm is improved; the RRT algorithm is used for performing path search, and meanwhile, a cost function is introduced for optimization, so that node movement collision is avoided. The problem of deployment of the wireless sensor network nodes in the three-dimensional space is solved by constructing the three-dimensional environment model, optimizing the particle swarm optimization algorithm, adopting path planning and the like, the search efficiency and convergence precision of the algorithm are improved, collision and interference between the nodes and obstacles and between the nodes are reduced, and the deployment efficiency of the wireless sensor network nodes is improved. The stability, the energy efficiency and the communication quality of the wireless sensor network can be effectively improved.

The invention discloses a wireless sensor network (WSN) target coverage method and device based on a chaos adaptive bat algorithm, and belongs to the technical field of wireless sensor network coverage. Aiming at the problems of limited communication range, poor adaptive capability and the like of the existing WSN, the method comprises the following steps of: initializing parameters, constructing a monitoring relation matrix to determine a fitness value, designing an adaptive inertia weight strategy, updating various parameters of a bat individual, and carrying out operations such as local search, adaptive adjustment of loudness and pulse emissivity and the like. And judging a termination condition to obtain an optimal solution in combination with an elitist strategy and chaos updating. The device comprises a network model construction module and the like and is used for executing the method. According to the method, the adaptive bat algorithm is adopted to establish the target coverage model, the elite strategy and chaos updating are combined, the algorithm is prevented from falling into local optimum, the stability is improved, the sensor node coverage area is effectively increased, the convergence speed is higher, the optimization capacity is stable, and the method is suitable for a WSN target coverage scene.

The invention belongs to the technical field of wireless sensor networks, and discloses a sea rice environment monitoring WSN clustering routing protocol method and system based on multi-parameter perception, and the method comprises the steps: carrying out the simulation construction of a sea rice environment monitoring region, deploying sensor nodes based on the environment, and carrying out the clustering of the deployed sensor nodes through employing an LEACH algorithm; establishing a cluster head rotation comprehensive evaluation function according to the current data of each node after the cluster is established, and dynamically adjusting the weight of a cluster head rotation parameter by using a particle swarm optimization algorithm with a constraint mechanism; selecting an optimal node as a cluster head based on an adjustment result, and screening intra-cluster routing paths by using the average residual energy ratio of the intra-cluster paths to obtain the intra-cluster routing paths; a single cluster is regarded as a node for inter-cluster routing, the stability of a routing path is evaluated by utilizing a cluster head rotation frequency weight, the routing cost is evaluated according to the inter-cluster distance, the inter-cluster routing path is obtained according to the stability and the cost, and the large-scale WSN clustering routing protocol method is completed.

The invention provides a channel navigation condition comprehensive assessment method based on multi-technology fusion, and relates to the technical field of waterway transportation safety monitoring and risk assessment. The method comprises the following steps: acquiring bridge distance, illumination, vibration data and channel clear height information in real time, and acquiring geographic features in combination with satellite remote sensing images; constructing a navigation channel accident influence coefficient and ship navigation risk degree model, and identifying a high-risk area through improved K-means clustering; the game theory is adopted to optimize the weight, and the Mahalanobis distance is combined to realize risk positioning; constructing an evaluation matrix based on the remote sensing image to divide risk levels; a multi-threshold early warning mechanism is set, and information is pushed in real time through a wireless sensor network; and outputting channel planning, scheduling suggestions and safety prompts. The method breaks through the limitation of traditional static assessment, self-created parameters quantify accident influences and ship risks, an optimization algorithm improves weight objectivity and clustering precision, early warning response reaches a millisecond level, risk positioning errors are reduced by more than 40%, and intelligent support is provided for inland waterway safety management.

The invention discloses an evacuation path dynamic planning method and system based on an HPSO-IGWO algorithm, and relates to the technical field of fire safety and emergency evacuation. The system mainly comprises a fire information acquisition module, a data processing coordination module, a path planning module and an evacuation indication module. The fire information acquisition module acquires information such as the position, smoke concentration and temperature of a fire through a sensor, and transmits the acquired data to the data processing coordination module; the data processing coordination module sends the data to the path planning module, so that the evacuation path is planned and optimized by using an HPSO-IGWO algorithm, the path is dynamically adjusted according to the real-time condition of the fire, and the safety and optimality of the evacuation path are ensured; and finally, the path planning module sends the planned evacuation path information to each LED evacuation indicator through the wireless sensor network, and the indicators display the evacuation direction in real time according to the information sent by the server to guide people to evacuate along the planned path.

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 invention discloses an intelligent monitoring system for hydrogen embrittlement detection and an implementation method of the intelligent monitoring system, and relates to the field of material structure monitoring, a strain sensor, a temperature sensor and a hydrogen concentration sensor are installed, and a wireless sensor network is constructed to achieve real-time monitoring of the strain state, the temperature change and the hydrogen concentration of a material; in the data processing stage, monitoring data is smoothed, cleaned and normalized, noise is effectively eliminated, the data quality is improved, the characteristics of the strain rate, the temperature change rate and the hydrogen concentration change rate are extracted, and subdata sets are obtained through recursion division; evaluating the relationship between the characteristics and the hydrogen embrittlement risk by calculating a Pearson's correlation coefficient, and calculating an abnormal score by using an isolated tree algorithm so as to divide risk conditions; creating a RESTfu API interface, regularly generating a hydrogen embrittlement risk report, and implementing corresponding monitoring and maintenance measures according to the risk level; through the method, the equipment safety and the management efficiency are improved, accidents and loss caused by hydrogen embrittlement are reduced, and the overall management efficiency is improved.

The invention discloses a wireless sensor network hierarchical routing optimization method and system based on a quantum dung beetle mechanism, relates to the technical field of wireless communication, and is used for solving the technical problems of unbalanced energy consumption and short network life of a wireless sensor network in a traditional method. The method is technically characterized by comprising the following steps: establishing a wireless sensor network system model; determining a clustering number and dynamically clustering, and establishing a network node dynamic clustering cost function at the same time; a quantum dung beetle group is initialized, parameters are set, the organic matter decomposition degree of all quantum dung beetles is calculated, and the global optimal quantum position of the quantum dung beetle group is determined; the quantum positions of the quantum dung beetles are evolved, and a global optimal quantum position is obtained through a selection mechanism; determining quantum dung beetle mechanism evolution termination, and outputting a current wireless sensor network cluster center; performing dynamic cluster head election on the wireless sensor network; and the wireless sensor network performs stable data transmission and updates the network state at the same time. The energy consumption efficiency of the wireless sensor network is effectively improved, and the service life of the network is effectively prolonged.

The present invention discloses a humidity sensor position optimization method and system based on a wireless sensor network. By constructing a humidity monitoring model based on three-dimensional visualization, humidity sensor distribution information is imported. Within a preset time interval T, humidity monitoring area data is collected. After the monitoring data is serialized, it is imported into an LSTM prediction model to generate prediction data. In combination with the monitoring and prediction data, an isolated forest algorithm is used to identify abnormal data sets and mark the sensors to be tested. For abnormal sensors, adjacent sensors are marked by a monitoring model, integrated into sample data, and linear regression is used to judge the linear relationship. If there is no linear relationship, it is marked as abnormal. Finally, the abnormal equipment is located in the monitoring model and the data is analyzed, a maintenance plan is formulated and the sensor position is optimized. Through the present invention, it is possible to dynamically, quickly and accurately evaluate abnormal sensors, and quickly locate abnormal sensors, effectively improve the accuracy and efficiency of humidity monitoring, and reduce human participation.

The invention discloses a path planning method and system based on a wireless sensor network, relates to the technical field of wireless communication, and is used for solving the technical problem that an optimal planning path is difficult to obtain based on the wireless sensor network due to the fact that the power consumption of a sensor node cannot be reduced in the prior art. According to the technical key points, the method comprises the following steps: collecting communication data between the flight equipment and a ground sensor, wherein the communication data comprises motion data, state data and alarm data of the flight equipment; the motion data comprises position and speed; the state data comprises electric quantity and working duration; the alarm data comprises low electric quantity alarm, obstacle alarm and fault alarm; constructing a channel optimization model based on the optimized air-ground channel throughput; and constructing a path planning model, and solving the path planning model by using a deep neural network algorithm to obtain a flight equipment full-coverage path. According to the invention, full-coverage optimal path planning based on combination of channel optimization of the wireless sensor network and an improved neural network path planning algorithm is realized.