681 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 discloses a power transmission and transformation equipment fault early warning system based on online monitoring, and the system comprises the following modules: a multi-parameter sensor module which is used for collecting data in real time; the wireless sensor network module is used for transmitting the data to the data processing center for processing; the data fusion module is used for generating a comprehensive health score of the equipment by adopting a multi-modal data fusion algorithm based on a self-evolution nested network; the trend prediction module introduces an adaptive residual module to carry out trend prediction based on a nonlinear time sequence analysis technology of a generative adversarial adaptive residual network; the early warning threshold value adjusting module is used for dynamically adjusting an early warning threshold value by adopting a self-adaptive evolutionary algorithm of meta reinforcement learning; the maintenance suggestion generation module is used for generating maintenance suggestions; and the power grid dispatching linkage module is used for automatically linking a power grid dispatching system and detecting and adjusting an equipment load. According to the invention, a multi-parameter fusion and generative adversarial network method is adopted, and an adaptive residual module is introduced, so that accurate early warning of faults of power transmission and transformation equipment is realized.

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 provides an energy control method and system for a wireless sensor network. The method comprises the following steps: acquiring working states and state conversion time of a plurality of wireless sensors of the wireless sensor network; inputting the working state and the state conversion time into a pre-constructed energy consumption prediction model to obtain an energy consumption prediction result output by the energy consumption prediction model; according to the energy consumption prediction result, determining a data quality demand, evaluating a data acquisition demand, and obtaining a work demand evaluation result; based on the work demand evaluation result, dynamically adjusting the sleep periods and data transmission parameters of the plurality of wireless sensors; and monitoring the adjusted energy consumption levels of the plurality of wireless sensors, identifying abnormal nodes, and adjusting the connection paths of the plurality of wireless sensors. According to the invention, energy consumption of the sensor can be controlled in real time, abnormal nodes can be identified in time, network connection is optimized, and operation efficiency, stability and reliability of the sensor network are improved.

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 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 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 provides a lightweight encryption authentication method for a wireless sensor network, which comprises the following steps: a gateway node generates a sensing instantaneous key and a user instantaneous key respectively based on a one-way hash function and a preset physical unclonable function, the sensing instantaneous key, the user instantaneous key, a preset sensing long-term key and a user long-term key are respectively sent to the user side and the sensor node; after the user side and the sensor node are registered, the user side performs identity verification on identity information currently input by a user, and performs mutual authentication among the user side, the gateway node and the sensor node based on the identity information, so as to send a sensing hash message authentication code and a user hash message authentication code to the sensor node according to an authentication result; according to the method, the sensor node and the gateway node are subjected to key negotiation authentication to obtain the session key, so that wireless sensor network communication can be carried out based on the session key, and the security authentication problem of the wireless sensor network is effectively solved.

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 relates to the technical field of wireless sensor networks, and discloses an automatic maintenance method for wireless sensor network nodes in a mine laneway, which comprises the following steps: S1, network topology structure design: establishing a wireless sensor network consisting of sensing nodes, relay nodes and gateway nodes, the sensing nodes being distributed in the mine laneway, the relay nodes being distributed in the mine laneway, and the gateway nodes being distributed in the mine laneway; the data acquisition node is used for acquiring environment data, the relay node is used for multi-hop transmission of the data, and the gateway node is used for communicating with a ground monitoring system; s2, a fault detection mechanism: integrating a fault detection module in each node; s3, a self-repairing mechanism: when the node detects a fault, automatically triggering the self-repairing mechanism; s4, performing fault early warning and reporting; and S5, energy consumption management and optimization: the system integrates an energy consumption management module in the node. Through the integrated fault detection module and the self-repairing mechanism, the system can automatically identify and try to repair the fault when the fault occurs, so that the downtime of the system is reduced, and the continuous and stable operation of the network is ensured.

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 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 discloses a liquefied oil product flow management method based on the Internet of Things technology, and the method comprises the following steps: installing a high-precision flowmeter at a key node of a liquefied oil product pipeline, monitoring the flow data in real time, and collecting the historical data; local processing and analysis are carried out on real-time and historical data by using a multi-source domain antagonistic transfer learning algorithm, and the processed data are transmitted to an Internet of Things platform through a self-adaptive multi-hop wireless sensor network. And storing and processing the data through space-time self-correlation analysis, and identifying a space-time mode and abnormal change. And real-time monitoring and prediction are performed in combination with a mixed event triggering prediction control algorithm. A multivariable time sequence decomposition and reconstruction algorithm is utilized to deeply analyze flow data, and a multi-kernel learning algorithm is combined to optimize a flow prediction model, extract key features, identify abnormities and optimize transportation and storage strategies of liquefied oil products. According to the invention, real-time monitoring, automatic management, data fusion and analysis and abnormal early warning of flow data are realized.

A node positioning method for marine environmental monitoring wireless sensor networks is provided, which jointly estimates node positions and path loss factors by considering the node real-time movement and the path loss and absorption effect of underwater communication; transforms an original non-convex problem into a non-negative constrained least squares framework, and finds the optimal solution of marine node positions by two stages of interior point method and block coordinate update. In the first stage, the problem is re-expressed by using the penalty function according to the interior point method to obtain the approximate solution; and in the second stage, the original problem is transformed into a generalized trust region sub-problem, and the approximate solution obtained by the interior point method is used as the initial estimation, the accurate estimation values of the marine node position and path loss factor are obtained by iterative solutions combined with the block coordinate update.

An IoT-based traffic monitoring system, consisting of: a variety of sensor units, each sensor unit comprising a video camera, an infrared sensor, a GPS module and an accelerometer; a local processing unit that is connected to each sensor unit and configured to filter and analyze raw traffic data; a central processing unit configured to receive processed data from the local processing unit, to further analyze the data using machine learning, and to optimize traffic control measures, wherein the central processing unit is configured to dynamically adjust traffic light sequences based on the analysis of real-time traffic conditions, thereby improving the efficiency of traffic flow at intersections. a communication framework that includes a wireless sensor network and a mobile ad-hoc network to transfer data between the sensor units and the central processing unit; a traffic management interface that provides traffic authorities with real-time traffic conditions and control functions; and an end-user application interface to provide real-time traffic information for commuters and service providers.

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 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.

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.

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 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 present invention relates to the technical field of vehicle monitoring methods, and specifically to a vehicle monitoring method and system, comprising the following steps: using a vehicle monitoring system to collect real-time vehicle data including speed, acceleration, and steering angle. In the present invention, by comprehensively utilizing a vehicle monitoring system, a machine learning algorithm, computer vision technology, cloud computing and big data analysis technology, a geographic information system, and a wireless sensor network, all-round monitoring of the real-time driving status of a vehicle, identification and verification of abnormal driving behavior, prediction and analysis of traffic flow, planning of intelligent navigation routes, and sharing of information between vehicles and cooperative driving functions are realized. By collecting vehicle data in real time and identifying abnormal driving behavior, timely discovering and warning of traffic risks, traffic flow prediction and intelligent route planning help drivers avoid congestion points and potential accident risk points, saving driving time, and sharing of information between vehicles and cooperative driving improve driving efficiency and safety.