693 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 belongs to the field of remote monitoring, and discloses a remote monitoring system and method for coal mine production equipment, and the system comprises a multi-sensor integrated monitoring module which employs an integrated multi-sensor node, monitors the multi-dimensional parameters of the coal mine production equipment, collects data in real time through a wireless sensor network, and transmits the data to a central control platform; the cloud computing and big data analysis platform is used for uploading the real-time data to a cloud end, automatically analyzing the running state of the equipment by using a big data analysis and machine learning algorithm, and carrying out fault prediction and early warning; the remote control and maintenance module is used for checking the equipment state in real time and carrying out remote control and debugging; and the high-efficiency data transmission module is connected with the sensor nodes, the equipment and the monitoring platform by utilizing the Internet of Things technology and the 5G communication technology. The operation state of the coal mine production equipment can be accurately monitored in real time, the automation degree is improved, the environment adaptability is enhanced, the operation cost is reduced, and the data transmission efficiency is 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 relates to the technical field of wireless sensor networks, is applied to a soil humidity monitoring scene in agriculture, and particularly relates to a soil humidity monitoring wireless sensor network coverage optimization method based on a chaos adaptive dung beetle optimization algorithm, which comprises the following steps: constructing a soil humidity monitoring wireless sensor network coverage model of a monitoring area; an improved dung beetle optimization algorithm is adopted to carry out coverage optimization on the soil humidity monitoring wireless sensor network coverage model, an optimized coverage result is obtained, and the improved dung beetle optimization algorithm is obtained through improvement of a Logistic chaos operator and an adaptive quasi-reverse learning strategy. According to the method, the coverage rate and robustness of the agricultural soil humidity monitoring wireless sensor network can be improved, local optimum can be avoided while the convergence speed is guaranteed and global search is improved, and the optimal coverage effect is achieved.

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 invention discloses a frequency hopping carrier signal detection method, system and device based on time-frequency analysis and a storable medium, and belongs to the technical field of frequency hopping carrier signal detection. According to the detection method, firstly, a sampling signal is subjected to combined time-frequency analysis transformation (STFTamp; according to the method, a to-be-detected time-frequency graph is obtained through a WVD method, structural elements of different scales are constructed through self-adaptive morphological filtering to process the time-frequency graph, noise and burst signals are removed, influences on to-be-detected frequency hopping signals are avoided, and the detection accuracy is improved. And then, aiming at the residual fixed-frequency signal and sweep-frequency signal interference, Hough transform is used for carrying out feature extraction on a straight line, and a frequency hopping signal is judged according to different time-frequency features of different signals, so that the detection process of the frequency hopping signal is completed. The detection method can be applied to a wireless sensor network and is used for realizing data communication and information transmission between sensor nodes. Through detection and analysis of the frequency hopping signal, the communication stability and reliability of the sensing network in a complex electromagnetic environment can be effectively improved.

The invention relates to the technical field of concrete detection, and discloses a concrete quality rating method and system based on a multilayer neural convolutional network, and the method comprises the steps: building a standardized performance index data set, training the standardized performance index data set through employing a support vector machine algorithm, and building a concrete material resistance evaluation model; environment parameters of a construction site are collected in real time through a wireless sensor network, and the influence of the current construction environment on concrete is judged; and inputting the construction environment parameters into the concrete material performance evaluation model to obtain an optimal concrete material ratio suggestion, uploading the concrete material performance parameters to a cloud server in the concrete use process, and uploading a concrete quality evaluation report to a concrete quality monitoring platform after concrete construction is completed. According to the invention, intelligent evaluation and optimization of the concrete material performance are realized, and the concrete quality and efficiency are improved.

The invention provides a deep learning-based bridge structure erosion degree monitoring method, which comprises the following steps of: for bridge steel samples of different types and parts, characterizing grain boundary structures and grain orientation microstructure characteristics by utilizing an electron back scattering diffraction method, and determining typical microstructure types of crystals; performing preprocessing and feature engineering processing on the obtained large sample data set, extracting chloride ion concentration and microstructure features, and constructing training data of an intercrystalline corrosion prediction model; according to the change rule of the chloride ion concentration and the typical microstructure type of the crystal, a bridge steel intergranular corrosion prediction model is constructed, and training and verification are carried out on a large sample data set; a wireless sensor network is arranged in a bridge steel service environment, chloride ion concentration and temperature and humidity environment parameters are collected in real time, and data needed by model input are obtained through data preprocessing and feature extraction.

The invention provides a method and a system for intelligently controlling noise of a transformer outer cover, and the method comprises the steps: monitoring the operation state data of a transformer in real time through installing a plurality of sensors, carrying out the primary processing of the data on an edge calculation node, transmitting the data to a central server through a wireless sensor network, and extracting features through principal component analysis; an LSTM deep learning model is constructed based on historical data to predict a noise level, load distribution and current are dynamically adjusted according to a prediction result, an energy-saving mode is optimized, filter parameters are automatically adjusted by using an adaptive filter technology in combination with real-time monitoring data, and noise of different frequency bands is suppressed; the development of the user interface supports remote access and manual adjustment of working parameters, regularly evaluates the system performance and updates an intelligent control strategy, effectively reduces the noise of the transformer, improves the energy efficiency and the equipment reliability, and is suitable for transformer noise management in various industrial scenes.

The invention provides an Internet of Things cooperative routing method based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a wireless sensor network model; constructing an energy consumption model; establishing an optimization problem of network routing according to the constructed wireless sensor network model and the energy consumption model; modeling the optimization problem into a multi-agent reinforcement learning problem, and selecting an optimal routing path according to the multi-agent reinforcement learning problem; wherein in the multi-agent reinforcement learning problem, each sensor node is regarded as an independent agent, and each agent learns a strategy for optimizing the overall network performance according to a local state and a reward mechanism. According to the invention, the energy utilization efficiency of the routing protocol is improved, routing with balanced energy is realized, and the network life cycle is effectively prolonged.

The invention discloses an improved DV-Hop positioning method based on a nearest neighbor anchor node path connection relation, and belongs to the technical field of wireless sensor networks, and the method comprises the following steps: network nodes are initialized, and each anchor node calculates the average hop distance of the shortest path from other anchor nodes recorded in S1; according to the method, the path connection relation between the nearest neighbor anchor node of the unknown node and the other anchor nodes in the network is approximate to the path connection relation between the unknown node and the anchor nodes in the network, and the three anchor nodes closest to the linear path connection are screened through the average hop distance value to participate in the positioning of the unknown node. The method for estimating the distance between the unknown node and the anchor node participating in positioning is optimized, and the distance estimation precision is improved; besides, the multi-target particle swarm algorithm is used for replacing the original least square algorithm to realize the positioning of the unknown node, the distance constraint condition is fully utilized, the target function setting is more reasonable, and the overall positioning precision is improved.

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 provides a multi-objective optimization and fuzzy reasoning WSN security clustering method and a related device, and belongs to the field of Internet of Things. The method comprises the following steps: selecting a plurality of candidate cluster heads from a plurality of wireless sensors by the electronic equipment through a multi-objective optimization algorithm; attribute information of a plurality of candidate cluster heads is obtained, and the attribute information of each candidate cluster head comprises a trust index of the candidate cluster head and fitness information corresponding to a multi-objective optimization algorithm; selecting an optimal cluster head from the plurality of candidate cluster heads according to the attribute information of the plurality of candidate cluster heads; wherein the optimal cluster head is used for relaying communication data of the plurality of wireless sensors. Therefore, when the optimal cluster head is selected, the credibility index of the candidate cluster head is considered, and the index represents the security of the candidate cluster head, so that the security of the wireless communication network is improved by the optimal cluster head.

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 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.

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

The 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 agricultural unmanned aerial vehicle wireless energy transmission and information relay method oriented to low-altitude remote sensing data acquisition comprises the following steps: design of an agricultural unmanned aerial vehicle quick charging system, a relay strategy of an agricultural unmanned aerial vehicle, an agricultural unmanned aerial vehicle-infinite sensor network path planning algorithm based on the relay strategy, and a wireless energy transmission and information relay method based on the relay strategy. As an important supplement of aerial remote sensing, unmanned aerial vehicle remote sensing has high feasibility in a low-altitude remote sensing scheme in the agricultural field, (1) an unmanned aerial vehicle intelligent quick charging platform on a high-voltage transmission tower intelligently and quickly charges an unmanned aerial vehicle in the air by using a wireless energy transfer technology, and the problems of short endurance time and frequent battery replacement of the unmanned aerial vehicle are solved; (2) in the sparse wireless sensor network, the unmanned aerial vehicle can replace a wireless sensor network node to monitor some unperceived areas, and can also play a role of a relay node to transmit information, so that energy consumption generated by routing is reduced, the survival time of the whole network system is prolonged, and the system reliability is improved. The problem that the communication range of the unmanned aerial vehicle is limited in the agricultural data acquisition process is solved.

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 relates to the technical field of government affair Internet of Things, and discloses an Internet of Things service data encryption and privacy protection method and device, and the method comprises the steps: collecting real-time data items from government affair Internet of Things equipment through a wireless sensor network through the Internet of Things technology to form a government affair data set, and carrying out the encryption processing; calculating and evaluating the sensitivity delta f of each data item in the government affair data set and a query result corresponding to the data item through a differential privacy algorithm, and adding random noise according to the sensitivity delta f and a privacy budget epsilon, and performing government affair service efficiency, government affair anomaly monitoring, government affair demand prediction and government affair complaint risk prediction analysis on the government affair data set added with the random noise by adopting a machine learning algorithm, providing data service for an analysis result through an API (Application Program Interface), and dynamically adjusting the sensitivity delta f, the privacy budget epsilon and the adding degree of the random noise according to an evaluation result. While data security and government affair privacy are guaranteed, data analysis efficiency and accuracy are improved, government affair management is optimized, and adaptability and expandability are achieved.

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.