Assessment of village resilience in flood storage areas based on complex networks: From the perspective of safety and low-carbon development

Abstract

This study has built a modified gravity model to quantify the connection strength between villages, and complex networks were used to analyze and deconstruct the resilience characteristics of network structure and simulated resilience change characteristics under different scenarios including climate change, and identified the network core of the villages. The improvement of the resilience levels of rural systems under changing environments is an important sustainable development. The objective of this study is to provide a basis for the construction of rural emergency disaster prevention in flood storage areas and provide a reference for sustainable rural low-carbon development of village system. The results show that (1) from the perspective of spatial patterns of rural networks, there are obvious spatial differences in Mengwa rural network, showing a spindle-shaped spatial structure with dense middle and sparse on both sides. (2) From the perspective of structural resilience characteristics, the network has significant hierarchy and agglomeration, high local transmission and no obvious characteristics of homogeneity or heterogeneity. Amongst them, the core node has a prominent position, strong control ability and significant correlation effect. (3) The results of scenario simulations on rural network resilience show deliberate disturbance can significantly reduce network resilience compared with random disturbance. A large number of high-energy consumption and low-efficiency phenomena occur earlier in the network, leading to the collapse of the network.

Research on active safety monitoring and early warning system for lithium ion battery energy storage power stations based on wireless sensor networks

“…(1) Power supply voltage: 3.0V-5.5V, another power supply method is powered by data cable; (2) Single line interface mode, enabling bidirectional communication with microprocessors using only one line; (3) Support multi-point networking. Multiple DS18B20 can be used in parallel to achieve multi-point electrical measurement; (4) No peripheral devices are required during operation, and all sensor components and conversion circuits are integrated internally; (5) The measured electrical range is -55 ℃~+125 ℃, with an accuracy of up to ± 0.5 ℃; (6) The resolution is 9-12 bits, achieving high-precision electrical measurement; (7) The resolution determines the conversion rate, and the electrical can be converted into numbers within a maximum of 750ms; (8) Negative pressure characteristic: When the power line is connected in reverse, it will not cause damage to the chip, but it cannot work properly. The electrical parameters collected mainly include three-phase voltage, three-phase current, frequency, power, phase angle, power factor, etc.…”

“…the research on knowledge learning based health monitoring and warning for energy storage power plants aims [5][6] to design a health monitoring and warning system based on kernel density estimation and Long Short Term Memory (LSTM) model for the individual health problems of energy storage power plant systems in operation. The system consists of two parts, one is a health assessment module based on kernel density estimation [7], This module can conduct periodic inspections of all energy storage batteries inside the energy storage station. Based on historical data collected by BMS, the health status of individual batteries can be checked in a timely manner using kernel density estimation methods.…”