503 results about "Wireless sensor network" 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 relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

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 provides an intelligent warehouse management system and method based on big data, and relates to the technical field of warehouse logistics, and the system comprises a data collection module which is used for collecting the electronic tag information, goods location coordinates and environment data of materials in a warehouse in real time through a radio frequency identification reader-writer and a wireless sensor network node, and uploading the information to a central server; the inventory early warning module is used for dynamically classifying the materials in the warehouse in the central server, calculating safety inventory intervals of various materials through historical turnover rate data, and correcting an inventory threshold value in combination with a real-time correction factor; and the positioning navigation module is used for acquiring the real-time positions of the materials according to the new safe inventory interval, and generating a goods allocation electronic map and final path navigation. According to the invention, the warehouse management efficiency and accuracy are effectively improved, the warehouse space utilization is optimized, the warehouse-in and warehouse-out operation level is improved, and visual and intelligent management is realized.

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 relates to the technical field of the Internet of Things, and discloses a wireless sensor network environment monitoring system and a safety protection method based on the Internet of Things, and the system method comprises the steps: collecting sensing node environment data to generate a multi-dimensional feature matrix, and constructing a safety protection strategy set; setting threat parameter types, and establishing a security threat association model by using historical data; extracting real-time attack features and flow parameters, and generating a dynamic protection instruction set and an optimization scheme in combination with the model; and reconstructing a network node control mechanism to generate a target protection scheme, and dynamically and synchronously calibrating an instruction time sequence. The system comprises a node data acquisition module, a security policy generation module, a threat parameter analysis module, a threat modeling module, a risk assessment module, a protection mechanism reconstruction module and a time sequence synchronization module. Through multi-dimensional feature analysis, dynamic threat modeling, adaptive risk assessment and time sequence encryption synchronization, the security protection capability of the wireless sensor network in a complex environment is improved, and the method is suitable for security protection requirements in the field of Internet of Things environment monitoring.

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 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 relates to the technical field of data processing, in particular to a flood disaster-oriented wireless sensor network multi-modal feature fusion system and method, and provides the following scheme: acquiring multi-dimensional sensing data through a wireless sensor network, constructing a multi-modal feature matrix, and inputting a preset intelligent AI model for disaster assessment. The rainfall intensity change trend is dynamically captured through a self-adaptively adjusted time window and a sliding step length in combination with an LSTM prediction model, and the disaster prediction precision is optimized. The problems of multi-source data fusion, poor real-time performance and low precision in a traditional flood disaster early warning system are solved, and efficient and accurate flood disaster prediction and risk assessment are realized.

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.

The invention provides a slope hidden danger monitoring system based on a wireless sensor network, and relates to the technical field of dam slope safety monitoring. The system comprises a wireless sensing node used for monitoring side slope hidden danger characteristic data of a dam side slope area; the area node management equipment is arranged at the central position of a monitoring sub-area of the dam slope area, forms a self-organizing communication network with each wireless sensing node in the area, and is used for collecting and summarizing slope hidden danger characteristic data corresponding to each monitoring sub-area; and the monitoring control center is in communication connection with the area node management equipment and is used for performing data analysis on the slope hidden danger characteristic data to obtain slope hidden danger risk distribution data corresponding to the dam slope area. According to the technical scheme, a stable monitoring network can be formed in a complex terrain area, the comprehensiveness, reliability and real-time performance of dam slope monitoring are ensured, the transmission stability and collection efficiency of monitoring data are improved, and the accuracy of a hidden danger recognition result is ensured.

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 provides a logistics storage life management method based on radio frequency tag identification, and the method comprises the steps: obtaining an article identifier and initial life data stored by a passive radio frequency tag attached to an article from a radio frequency tag reader-writer of a target storage partition in response to an article life evaluation request; corresponding environment parameters, signal intensity and position information are called from the storage module, and abnormal records are read; the calculation module determines a life information set based on the data and reports the life information set to a superior management system; and after verification is correct, generating a service life detection packet for the packaging environment parameters, the signal strength, the position information and the service life adjustment rule of each article, and transmitting the service life detection packet to a storage terminal through a wireless sensor network. According to the invention, precise and automatic management of the service life of the stored goods can be realized, the inventory turnover efficiency is improved, and the deterioration loss is reduced.

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 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 a coal mine area ground surface settlement real-time monitoring method integrating multiple sensors, and relates to the technical field of settlement monitoring. The method comprises the following steps: constructing a heterogeneous sensor network, collecting original monitoring data in real time, and transmitting the original monitoring data to a monitoring module through a wireless sensor network for preprocessing and time-space reference unified processing to obtain multi-source heterogeneous data; performing deep fusion on the multi-source heterogeneous data by adopting a data fusion algorithm to generate fusion information of optimal estimation; calculating settlement characteristic parameters according to the fusion information, inputting the settlement characteristic parameters into a settlement trend prediction model, and determining settlement trend prediction data; comparing the multi-source heterogeneous data and the settlement trend prediction data with multi-level preset thresholds: when any numerical value in the multi-source heterogeneous data exceeds a corresponding threshold interval, or when the settlement trend prediction data exceeds the corresponding threshold interval, triggering early warning information. The real-time monitoring precision of the ground surface settlement of the coal mine area can be improved.

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 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 relates to the technical field of wireless sensor network communication, in particular to a networking method, a networking device, networking equipment and a medium. In the application, when the network node to be accessed is accessed to the network, the candidate node is determined from the accessed network nodes, then the optimal node is determined according to the detection frame signal received by the candidate node, and the optimal node is taken as the father node, so that multiple times of trying to judge whether each candidate node is the father node corresponding to the network node to be accessed to the network are not needed; the time for determining the father node by the corresponding network node is saved, the father node determining efficiency is improved, and the network access efficiency of the network node to be accessed to the network is improved.

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.