As the main means of transportation for urban residents, the number of motor vehicles is increasing year by year. With the continuous development of society and the gradual improvement of people’s quality of life, automobiles have gradually become an indispensable means of transportation in people’s lives, resulting in increased traffic flow. However, the old traffic system is still unable to cope with the rapid growth of traffic pressure, and traffic congestion and various accidents occur frequently, which is a huge test for the contemporary intelligent traffic system. With the gradual development of society, more and more researchers are devoted to intelligent transportation systems, which make the development of target detection technology based on video image processing more and more rapid. The primary problem in embedding digital video in applications is that the complexity of video encoding and decoding far exceeds that of simple image and audio compression and decompression. Digital video can take various forms and formats. Developers need to support complex configurations and various aspects, such as different resolutions/display sizes, different bit rates, real-time issues, and even the reliability of the video source. Intelligent transportation achieved a lot of results. However, there are still some deficiencies in precision and robustness. At the same time, the improvement of video image processing technology gives us a new idea. To further improve the intelligent traffic system, provide accurate data information for all departments, and improve the traffic situation, this study, based on video image processing technology, combined with the three-frame difference algorithm, calculates and studies the data of illegal parking at a certain intersection. The calculated false detection rates for Y2 are 1.1%, 0.9%, and 2.4%, and the leakage rates for Y1 are 2.4%, 1.9%, and 4.7%, respectively. This shows that the algorithm has high accuracy for vehicle parking detection data and can collect information quickly and effectively. Applying the algorithm to the detection of other vehicles can provide efficient services for relevant traffic departments and public security departments and relieve traffic pressure. The image processing technology is a process of analyzing and processing images through certain computer technology to achieve the desired results. The scheme in the article realizes background extraction, image filtering, image binarization, morphological transformation, vehicle detection and segmentation, shadow detection, etc.
In order to effectively solve the problem of traditional environmental monitoring system due to high sensor cost, difficult deployment, and high maintenance cost, the node design and implementation of a wireless sensor network-based environmental monitoring system are proposed. Simulation experiments show that the time-consuming running time is 14.210361 s. After adding the action force of the grid point on the node, the running time is 11.257740 s, and the operation efficiency of the algorithm is significantly improved. The improved virtual force algorithm optimization improved node coverage by 5.2%.The system is easy to deploy, reduces the development and maintenance cost, and can obtain data or monitor through wireless communication. It is convenient to use and maintain.
Due to its large network scale, open communication environment, unstable wireless network, and other characteristics, it is extremely vulnerable to attacks and causes security problems, resulting in the collapse of the Internet of Vehicles system. The application of the Internet of Vehicles is becoming more and more extensive, but there are still problems such as information security and privacy leakage in the Internet of Vehicles. Through the analysis of the security threats and privacy protection requirements faced by the Internet of Vehicles system, this paper mainly studies information security, vehicle identity privacy, and location privacy in the process of Internet of Vehicles wireless communication. Therefore, it is urgent to conduct research on the information security and privacy protection issues of the Internet of Vehicles. This paper discusses the research on the security and privacy protection of the consensus algorithm for the Internet of Vehicles based on wireless sensors, compares and analyzes the wireless sensor data privacy protection protocols based on sharding technology, Tongtai encryption technology, and perturbation technology, and selects an optimized Kalman consensus filter. The algorithm is applied to the node information exchange of the sensor network, and two filters (low pass and band pass) are used to unify the observations and covariance of the network. Estimation of the sensor network state with and without data packet loss, the effect of system estimation error under different packet loss rates, data privacy protection algorithm performance, vehicle network data communication volume, and confusion factors on algorithm efficiency and the node energy consumption was compared and analyzed. Based on the application of wireless sensors, the estimation error and inconsistency estimation error of the algorithm in this paper finally converge to about 0.5, and both can maintain good stability and have good robustness. In addition, the communication volume of the algorithm in this paper is about 30% of the SCPDA algorithm. The Kalman consensus filtering algorithm reduces the amount of confusing data sent, improves privacy protection, and also achieves lower communication overhead.
A soft robot is a kind of robot designed to simulate mollusks. It has the characteristics of degrees of freedom, strong adaptability, and high flexibility and safety. The main purpose of this paper is to study the intelligent education assistance of soft robots and then combine the application of improved genetic algorithm and the Internet of Things technology in soft robots to improve its performance and effect. Therefore, this paper designs the optimal guidance strategy through the NSGA genetic algorithm and then combines the improved genetic algorithm and the application of the Internet of Things technology in the flexible actuator and FEA actuator of the soft robot. In order to optimize the performance of the IoT-assisted intelligent education software robot based on the improved genetic algorithm, the genetic algorithm simulation test experiment, the rolling motion simulation experiment of the bionic software robot, and the inflating and exhausting experiment of the base section of the software robot are designed and analyzed. Through the analysis of the data obtained from the experiment, this paper finally designs a set of controlled experiments to verify its teaching ability. The experimental results show that the students’ evaluation of the IoT intelligent education software robot education method based on the improved genetic algorithm designed in this paper is 16.31 points higher than that of the traditional education method. Compared with the traditional teaching, the scores of the students after the IOT intelligent education software robot teaching based on the improved genetic algorithm is 11.16 points higher.
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