Edge computing technology is an important computer operating system in China. It plays a key role in multi-system fusion and intelligent manufacturing, and can play a key role in training and testing of deep neural networks. The purpose of this paper is to study the application of edge computing technology in the collaborative optimization of intelligent transportation systems based on information and physical fusion. This article sets up monitoring points at different traffic intersections, and applies long-term and short-term memory networks to collect data at each traffic intersection. The DBN-SVR method model was used to detect the traffic flow of some intersections, and the edge computer technology was used to process the information signals generated by the intersections. The other portions of the intersections used traditional monitoring systems. By comparing the work efficiency and utility under the two methods, fitting data is performed, and mathematical statistics and mathematical analysis methods are used to verify the fitted data. The experimental data show that the edge computing technology can help the processing of traffic conditions in the intelligent transportation system integrated with information and physics, which has greatly improved the overall work efficiency of each system. Experimental data shows that intelligent transportation systems that integrate edge computing technology with information physics have improved transportation efficiency by about 20% and urban security by about 35%, which has a great effect on building smart cities and safe cities.
In the era of big data, the global data is growing explosively. The huge growth rate makes data processing and storage difficult, especially in the field of transportation. Based on the above background, this paper aims to study the autonomous coordinated control strategy for the complex process of traffic information physical fusion system based on big data. In this paper, the information physical fusion system is applied to the modern transportation system, and it is used to realize the high integration of computation, communication and control. Realize the independent and coordinated control of the transportation system. This paper proposes an autonomous traffic management mechanism based on multi-agent CPS system. In view of the instability and untimely of the original control strategy, a new traffic optimization control strategy conflict reduction control strategy is proposed. In order to solve the complexity of traffic system, the generation method of CPS autonomous control strategy based on multi-agent is studied and analyzed. Through the evaluation and verification of the conflict reduction control strategy and the online simulation of the incremental data synchronization strategy, it can be seen that the inconsistency ratio curves of message quantity and byte transmission quantity are always kept at a relatively low level, 1% and 2%, respectively. During the whole experiment, the average number of inconsistent messages and byte transmission of the agent are ideally controlled at 1.2 messages / train and 0.5kb/train.
The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.
In recent years, underwater shield tunnels are being developed according to large-scale sections. The problems of large buried depth and high water pressure have posed major challenges to the safety of segmented structures. The load-bearing capacity and damage of segmented structures under high water pressure features have always attracted attention. Based on a machine learning approach to smart grid energy management, this paper proposes a design method for high voltage tunnels in a balanced groundwater environment and tests the capacity of the high voltage tunnels. Based on the high water pressure failure test phenomenon of the large-section shield tunnel of the GIL project, this paper analyzes the failure characteristics and laws of the segment structure under high water pressure conditions. On this basis, an evaluation index for the load-bearing performance of the segment structure is proposed, and control suggestions are given based on the research results. According to the fault characteristics and the section structure law, the section performance evaluation index is proposed, and the control parameter recommendations are given based on the test results. Valuable discoveries and breakthroughs have been made in the failure of the prototype segment structure and the difference in the mechanical properties of the segment structure in the form of the high water pressure tunnel assembly. The research results show that under the condition of staggered assembly of high-voltage tunnels, the maximum dislocation amount of the high-voltage tunnel structure during instability failure is 10 mm, and the bolt strength is improved. The more important aspect is the existence of concave and tenon between the rings. In structure, the maximum stress of the bolts between the rings is only 38.6% of the yield stress at the time of instability failure. This indicates that the distributed concave-convex tenon between the segments not only can control the dislocation of the segments but also can ensure that the longitudinal bolts are well protected. It is safe to ensure the pressure resistance of the high water pressure tunnel.
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