Rules of incidental operation risk propagation in metro networks under fully automatic operations mode

The frequent interruptions of network operation due to any incident suggest the necessity to study the rules of operational risk propagation in metro networks, especially under fully automatic operations mode. In this study, risk indicator computation models were developed by analyzing risk propagation processes within transfer stations and metro networks. Moreover, indicator variance rules for a transfer station and different structural networks were discussed and verified through simulation. After reviewing the simulation results, it was concluded that under the impacts of both sudden incident and peak passenger flow, the more the passengers coming from platform inlets, the longer the non-incidental line platform total train operation delay and the higher the crowding degree. However, train headway has little influence on non-incidental line platform risk development. With respect to incident risk propagation in a metro network, the propagation speed varies with network structure, wherein an annular-radial network is the fastest, a radial is moderately fast, and a grid-type network is the slowest. The conclusions are supposed to be supports for metro operation safety planning and network design.

Enterprise Financial Leverage and Risk Assessment Based on Mobile Payment under Artificial Intelligence

To better promote the healthy and long-term development of corporate financial management, the basement is established on the perspective of artificial intelligence (AI). Initially, based on the theories of modern mobile payment (MP) and corporate financial leverage, the corresponding data set is obtained through the questionnaire method as the research data. The reliability coefficients obtained after the test are all above 0.65, indicating that the reliability and stability of the entire data are relatively good. Besides, it is also found from the data of the questionnaire that some residents believe that MP will bring harm such as information leakage. Next, a new multilevel evaluation analysis method is introduced. After evaluating the financial management risk, operation risk, and network security risk existing in enterprise MP, it is found that the financial management risk accounts for the largest proportion of the three, with a risk weight of about 0.54, and the capital risk occupies the main position in the financial management risk. Finally, through the analysis of the risks existing in the whole operation process of the enterprise, it is found that about 50% of the financial management risk of the enterprise in the market belongs to the advanced risk, about 30% of the operational operation risk belongs to the low risk, and about 20% of the network security risk belongs to the advanced risk, which indicates that the financial management risk and network security risk are the top priority of the enterprise MP risk. Although the operational operation risk belongs to the low risk, it cannot be ignored. Subsequently, feasible suggestions and opinions are put forward on these phenomena from the perspectives of the government, enterprises, and residents. Therefore, there is great reference significance for the current financial risk assessment of enterprises based on MP.

Risk Chain and Key Hazard Management for Urban Rail Transit System Operation Based on Big Data Mining

With the promotion of the national transportation power strategy, super large operation networks have become an inevitable trend, and operational safety and risk management and control have become unavoidable problems. Existing safety management methods lack support from actual operational and production data, resulting in a lack of guidance of fault cause modes and risk chains. Large space is available to improve the breadth, depth, and accuracy of hazard source control. By mining multisource heterogeneous operation big data generated from subway operation, this study researches operation risk chain and refined management and control of key hidden dangers. First, it builds a data pool based on the operation status of several cities and then links them into a data lake to form an integrated data warehouse to find coupled and interactive rail transit operation risk chains. Second, it reveals and analyzes the risk correlation mechanisms behind the data and refines the key hazards in the risk chain. Finally, under the guidance of the risk chain, it deeply studies the technologies for refined control and governance of key hidden dangers. The results can truly transform rail transit operation safety from passive response to active defense, improving the special emergency rail transit operation plans, improving the current situation of low utilization of rail transit operation data, but high operation failure rate, and providing a basis for evidence-based formulation and revision of relevant industry standards and specifications.

Reliability and Characteristic Analysis Considering the Circuit Structure and Operation Risk of Forward Converters

The forward converter is divided into two circuit topologies depending on the method of resetting the energy stored in the magnetizing inductance of the transformer: a single-ended structure using a reset circuit and a double-ended structure called a two-switch forward converter and, accordingly, a difference occurs in the operational mode. Generally, forward converters are designed with low power of less than 500 W and are sold at low prices, so it is common to replace them in case of failure. However, in terms of environmental conservation through resource saving, it is desirable to design with as high reliability as possible. In this paper, reliability is analyzed for the two representative circuit topologies of forward converters. The conventional PCA (part count failure analysis) method determines the failure rate only by the number of circuit components constituting the converter and the correlation between the components, but the FTA (fault-tree analysis) method can additionally consider the operation risk of the converter according to the circuit configuration and operation. Therefore, it is possible to analyze the failure rate according to the operating characteristics of the two converters from the difference in the failure rate obtained by the two methods. In addition, the two converters were manufactured as a 300 W prototype, respectively, and the volume, weight, and power loss were compared, and the economic feasibility was analyzed based on the cost model.

SORA Methodology for Multi-UAS Airframe Inspections in an Airport

Deploying Unmanned Aircraft Systems (UAS) in safety- and business-critical operations requires demonstrating compliance with applicable regulations and a comprehensive understanding of the residual risk associated with the UAS operation. To support these activities and enable the safe deployment of UAS into civil airspace, the European Union Aviation Safety Agency (EASA) has established a UAS regulatory framework that mandates the execution of safety risk assessment for UAS operations in order to gain authorization to carry out certain types of operations. Driven by this framework, the Joint Authorities for Rulemaking on Unmanned Systems (JARUS) released the Specific Operation Risk Assessment (SORA) methodology that guides the systematic risk assessment for UAS operations. However, existing work on SORA and its applications focuses mainly on single UAS operations, offering limited support for assuring operations conducted with multiple UAS and with autonomous features. Therefore, the work presented in this paper analyzes the application of SORA for a Multi-UAS airframe inspection (AFI) operation, that involves deploying multiple UAS with autonomous features inside an airport. We present the decision-making process of each SORA step and its application to a multiple UAS scenario. The results shows that the procedures and safety features included in the Multi-AFI operation such as workspace segmentation, the independent multi-UAS AFI crew proposed, and the mitigation actions provide confidence that the operation can be conducted safely and can receive a positive evaluation from the competent authorities. We also present our key findings from the application of SORA and discuss how it can be extended to better support multi-UAS operations.

Analysis on Operation Risk of Continuous Sunken Tunnel on Expressway

Based on the number of 519 tunnel risk incidents that occurred in the two tunnels of the Qifu Tunnel and the Zhongcun Tunnel during the period from 2018 to 2019 of Guangming Expressway, this paper studies the temporal and spatial characteristics and the risk distribution of the sunken continuous tunnel during the operation period of the expressway. Type of risk event. The results show that January, May, June and September of the year, as well as 14:00–16:00 and 16:00–18:00 during the day are periods of high tunnel risk; at the entrance section of continuous tunnels, Compared with other locations, the number of risk events in the transition section and the open section with sudden environmental changes and gradient changes is more; the types of risk events include safety hazards, roadblocks, vehicle failures, rear-end collisions, and equipment failures. The main types are vehicle failures. There are certain differences in the east-west direction. There are more vehicle breakdowns in the east-bound direction, more roadblocks in the west-bound direction, and more rear-end collisions in the east-bound direction. The main types of risk events are cars and trucks. Both cars and trucks have major risk event types. It is a vehicle failure. In rear-end collisions, small cars account for 65% of the risk models; risk identification methods include gun patrol discovery, road administration reporting, etc., of which gun patrol discovery is the most important identification method, accounting for 65% of the total. Through the analysis of the risk event characteristics of the sunken continuous tunnel of the expressway, it provides reference opinions for perfecting the research deficiencies in related fields in our country.