信任管理

Search documents
任正非为啥经常独自出差,难道这就是他的管理方式?懂的自然懂
Sou Hu Cai Jing· 2025-10-05 06:51
众所周知,任正非经常一个人出差,他曾被网友拍到独自在机场乘坐摆渡车的照片。 其实任总这样做已经很多年了,华为员工在机场看到老板拖着行李箱候机的次数不知多少,作为世界500强企业的总裁,像任正非这种作风的确实不多见。 在很多人想来,管理十多万员工大企业的老板只要坐在豪华办公室"日理万机"就行了,听下属的汇报,跟员工开会,只是发号施令就已经很忙了,哪有多少 时间去跑市场一线呢。 而且出于安全或场面的需要,知名企业家出门都是前呼后拥,动静非常大,这样也不适合随意去一个地方。 那么任正非为啥与众不同呢? "前十年几乎没有开过办公会类似的会议,总是飞到各地去,听取他们的汇报,他们说怎么办就怎么办,理解他们,支持他们……" 这是《一江春水向东流》中的一句话。 在华为公司成立之初,任正非可谓甩手掌柜,是听任各地"游击队长"们自由发挥的。但扪心自问,如果换成自己当老板,会这样管理队伍吗?这样做能放心 吗? 各地的代表处有相对独立的管理权,雇佣多少员工,发多少工资和奖金,怎么做业务等,处处都有漏洞,所谓管理,就包含了监控、控制,所以管理的成本 会非常大。 而任正非是基于信任去管理的,他很愿意相信人,但谁也不可能百分百正确,难免 ...
微算法科技(NASDAQ: MLGO)结合子阵列算法,创建基于区块链的动态信任管理模型
Cai Fu Zai Xian· 2025-09-16 02:34
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].