Core Viewpoint - Ren Zhengfei's unique management style emphasizes trust and direct engagement with employees, contrasting with typical corporate practices that rely heavily on oversight and control [1][5][9] Group 1: Management Philosophy - Ren Zhengfei has historically avoided traditional office meetings, preferring to visit various locations to gather insights directly from employees [3][5] - The management approach at Huawei is based on trust, allowing local representatives significant autonomy, which can lead to potential issues but is seen as a necessary risk [3][5] - Ren believes in the importance of maintaining trust in employees while also implementing measures to identify and eliminate underperforming staff [5][9] Group 2: Leadership Practices - Ren Zhengfei's practice of visiting frontline employees is aimed at understanding the market and operational realities, which he believes cannot be grasped without being present [7][9] - He has established a culture where personal relationships and internal politics are secondary to performance and results, emphasizing accountability and value contribution [9] - Ren's commitment to being present in challenging environments alongside employees serves to boost morale and reinforce a strong corporate culture [9]

Core Viewpoint - The article discusses the innovative dynamic trust management model developed by Micro Algorithm Technology (NASDAQ: MLGO), which integrates sub-array algorithms with blockchain technology to address the challenges of trust assessment in distributed systems, particularly in the context of IoT, supply chain finance, and decentralized storage. Group 1: Model Overview - The dynamic trust management model utilizes blockchain as the underlying data infrastructure, combined with a distributed computing framework of sub-array algorithms to create a decentralized trust assessment system [1]. - The model divides network nodes into multiple sub-arrays based on geographical location, resource type, or historical behavior characteristics, allowing for independent local trust calculations [1][2]. - The model ensures real-time and reliable trust assessment by dynamically adjusting sub-array members and updating trust values, leveraging blockchain's transparency and immutability for data security [1]. Group 2: Operational Mechanism - The model operates through five core processes: data collection and preprocessing, sub-array division, trust calculation, cross-array consensus, and dynamic updating [2]. - Data is collected from nodes and verified via smart contracts before being stored in a distributed ledger, with key features extracted and historical data weighted down using time decay functions [2]. - Sub-arrays are formed using K-means clustering or geographical hashing algorithms, with dynamic adjustments based on node load and trust value fluctuations [2]. Group 3: Trust Calculation and Consensus - Each sub-array independently runs trust evaluation algorithms to compute local trust values, integrating direct and indirect trust assessments [2][3]. - A modified PBFT consensus mechanism synchronizes trust evaluation results across sub-arrays, reducing communication rounds and computational complexity [3]. - The global trust value is generated by aggregating results from sub-arrays, weighted by their historical reliability [3]. Group 4: Dynamic Updates and Applications - The system triggers trust value updates every 30 seconds, allowing nodes to query their trust scores and adjust interaction strategies accordingly [3]. - The model has applications in various fields, including vehicle networking for enhanced safety and efficiency, e-commerce supply chains for optimized operations, and distributed energy systems for stable energy supply [5]. - As technology evolves, the model is expected to expand into IoT, healthcare, and financial services, integrating with AI and big data to foster innovative trust management solutions [5].