Analysis on the Business Model of Fresh E-commerce------Taking Hema Supermarket as an Example

Enterprises are beginning to involve the fresh produce industry, but most companies have withdrawn from the fresh produce industry due to poor performance. This shows that there are many problems with e-commerce of fresh produce. In particular, the business model of e-commerce for fresh produce is a major factor constraining its development. This article takes Hema Supermarket as an example to analyze its business model. It summarizes the areas that can be used for product control, power distribution system construction, platform operation, etc., and provides reference and reference for the operation of fresh agricultural products

Monitoring power module solder degradation from heat dissipation in two opposite directions

Solder degradation is still a main failure mechanism for power semiconductor modules. This study proposes a monitoring method to detect the relative change in heat dissipation from a module in two opposing directions, affected by the degradation: upwards via the silicone gel and downwards via the solder layer to the heatsink. The method is based on external module package measurements, and a Condition Indicator is defined as the ratio of heat transfer rates in the two directions. The expected response of to the level of degradation is analysed for different module operating points and external environment conditions. The method is demonstrated by experiment

Failure and reliability analysis of a SiC power module based on stress comparison to a Si device

The superior electro-thermal properties of SiC power devices permit higher temperature of operation and enable higher power density compared with silicon devices. Nevertheless, the reliability of SiC power modules has been identified as a major area of uncertainty in applications which require high reliability. Traditional power module packaging methods developed for silicon chips have been adopted for SiC and the different thermomechanical properties cause different fatigue stresses on the solder layer of the chip. In this paper a 2-D Finite Element (FE) model has been developed to evaluate the stress performance and lifetime of the solder layer for Si devices, which has been validated using accelerated power cycling tests on Si IGBTs. The proposed model was extrapolated for SiC devices of the same voltage and current rating using the same solder material and the results show that under the same cyclic power loss profile the induced stress and strain energy in the die attach layer is much higher and concentrates on the die/solder interfacial area for SiC chips. Using the validated stress-based model, the lifetime can be quantified when SiC chips are used. This ability to extrapolate the available power cycling and lifetime data of silicon chips to silicon carbide chips would be a key element for developing reliable packaging methods for SiC devices

Enabling high reliability power modules : a multidisciplinary task

Reliability of power electronic systems is a major concern for application engineers in the automotive and power system sectors. Power electronic modules are one of the main sources of failure in wind energy conversion systems. Power electronic converters used in wind turbine electric drive trains, railway traction, more-electric-aircrafts, marine propulsion and grid connected systems like FACTS/HVDC require reliable power devices and modules. Wide bandgap semiconductors like SiC have demonstrated enlarged electrothermal Safe-Operating-Areas compared with silicon devices. However, the reliability of SiC power modules and packages has been identified as an area of potential weakness. Traditional packaging systems have been developed for Si hence the different thermomechanical properties of SiC cause different stresses in the packaging thereby potentially causing reduced reliability. This paper identifies some of the key areas for the development of reliable power electronic systems using SiC. The focus is on condition monitoring, packaging system innovation and thermomechanical stress analysis as a function of the mechanical properties of Si and SiC. Power cycling experiments and finite element models have been used to support the analysis

Reliability of wind turbine power modules using high-resolution wind data reconstruction : a digital twin concept

This study introduces a Digital Twin (DT) framework for the reliability assessment of wind turbine power modules. Its importance is demonstrated by examining the effect of wind turbulence on the electrothermal behaviour and lifetime of machine side power electronic converters and semiconductor devices of direct-drive wind turbines. To this end, an electrothermal model embedded in a turbine model is established, which tracks the changes in wind speed. Using real-world, 1-sec wind speed data, the real device junction temperature profiles and the fatigue experienced by the semiconductor devices are examined for two 10-min periods. Then, these metrics are compared with the corresponding metrics of the same 10-min periods when the wind speed is assumed constant and equal to the 10-min average value, which is often used in traditional device reliability assessment methods using SCADA data. Based on simulation results, the fatigue experienced by the semiconductor devices due to sudden fluctuations of the wind is found to be significantly higher than the fatigue estimated by traditional reliability assessment methods using the SCADA data. Two methods that attempt to reconstruct the wind spectrum (Random Walk Metropolis-Hastings algorithm) and compress the wind speed data (Discrete Wavelet Transform) are proposed. These and/or other similar methods may be integrated into the DT interface to address the issue of the large volume of data required to be stored in DTs

Seizing the window of opportunity to mitigate the impact of climate change on the health of Chinese residents

The health threats posed by climate change in China are increasing rapidly. Each province faces different health risks. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate and even prevent the achievement of the Healthy and Beautiful China initiatives. The 2021 China Report of the Lancet Countdown on Health and Climate Change is the first annual update of China’s Report of the Lancet Countdown. It comprehensively assesses the impact of climate change on the health of Chinese households and the measures China has taken. Invited by the Lancet committee, Tsinghua University led the writing of the report and cooperated with 25 relevant institutions in and outside of China. The report includes 25 indicators within five major areas (climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement) and a policy brief. This 2021 China policy brief contains the most urgent and relevant indicators focusing on provincial data: The increasing health risks of climate change in China; mixed progress in responding to climate change. In 2020, the heatwave exposures per person in China increased by 4.51 d compared with the 1986–2005 average, resulting in an estimated 92% increase in heatwave-related deaths. The resulting economic cost of the estimated 14500 heatwave-related deaths in 2020 is US$176 million. Increased temperatures also caused a potential 31.5 billion h in lost work time in 2020, which is equivalent to 1.3% of the work hours of the total national workforce, with resulting economic losses estimated at 1.4% of China’s annual gross domestic product. For adaptation efforts, there has been steady progress in local adaptation planning and assessment in 2020, urban green space growth in 2020, and health emergency management in 2019. 12 of 30 provinces reported that they have completed, or were developing, provincial health adaptation plans. Urban green space, which is an important heat adaptation measure, has increased in 18 of 31 provinces in the past decade, and the capacity of China’s health emergency management increased in almost all provinces from 2018 to 2019. As a result of China’s persistent efforts to clean its energy structure and control air pollution, the premature deaths due to exposure to ambient particulate matter of 2.5 μm or less (PM2.5) and the resulting costs continue to decline. However, 98% of China’s cities still have annual average PM2.5 concentrations that are more than the WHO guideline standard of 10 μg/m3. It provides policymakers and the public with up-to-date information on China’s response to climate change and improvements in health outcomes and makes the following policy recommendations. (1) Promote systematic thinking in the related departments and strengthen multi-departmental cooperation. Sectors related to climate and development in China should incorporate health perspectives into their policymaking and actions, demonstrating WHO’s and President Xi Jinping’s so-called health-in-all-policies principle. (2) Include clear goals and timelines for climate-related health impact assessments and health adaptation plans at both the national and the regional levels in the National Climate Change Adaptation Strategy for 2035. (3) Strengthen China’s climate mitigation actions and ensure that health is included in China’s pathway to carbon neutrality. By promoting investments in zero-carbon technologies and reducing fossil fuel subsidies, the current rebounding trend in carbon emissions will be reversed and lead to a healthy, low-carbon future. (4) Increase awareness of the linkages between climate change and health at all levels. Health professionals, the academic community, and traditional and new media should raise the awareness of the public and policymakers on the important linkages between climate change and health.</p

Uneven degradation and condition monitoring of multi-chip power modules for wind turbines

The powertrain conversion system in state-of-the-art wind turbines has developed to a power rating of more than 10 MW. Due to the relatively low current rating of a single semiconductor chip, the large power module in turbine converters still adopts a multi-chip in- parallel setup, counted as the most vulnerable component in the turbine system. Thus, this thesis focuses on evaluating the uneven degradation of multi-chip power modules under realistic conditions and developing field-deployable condition monitoring methods for wind turbine converters. Two kinds of initial defects in power module solder layer, voids and cracks, indeed grow quietly under low-temperature stress cycles, illustrated by computed tomography scanning and finite element analysis. This thesis provides a physics-of-failure tool to estimate such dynamic of defect growth and finds that a void may first transform into a crack then grow more rapidly leading to device failure. At converter level, due to deep temperature cycling calculated from an electrothermal model, the machine side converters of fully and partially rated wind turbines, both consume a large amount of lifetime under the fundamental frequency. When looking inside the multi-chip module, an asymmetrical packaging layout and initial defects can cause years lifetime difference between paralleled devices while the weak one’s further ageing progress will be significantly accelerated. A condition monitoring scheme for detecting such uneven degradation in a multi-chip-inparallel system is proposed in this thesis, based on a core concept - train a network to represent the healthy state and then use its prediction deviation to distinguish faulty conditions. A two-stage neural network method based on only external measurements experimentally achieves a detection rate of over 98%. Furthermore, the feasibility of such a method is improved in three aspects. The labelled data for the network training is generated from an inverter test rig of equivalently emulating uneven degradation. The fibre Bragg grating multi-point sensing technique provides high temperature measuring precision with immunity to electromagnetic interference. The complex operating conditions is also generalised by a deep neural network structure, which achieves an overall accuracy of more than 95% under dynamic thermal conditions encountered in a practical wind speed profile. Finally, based on the same concept, a field-deployable condition monitoring method is proposed to detect the early-stage fault of wind turbine converters using limited and unbalanced SCADA data. A deep neural network with optimised cost function is designed by an unsupervised approach and empowered by an online learning process for long-term real-time anomaly detection. The proposed method shows robust diagnosis results and would predict the converter fault a few days ahead of actual failure