4Paradigm UED ᱨᱮᱱᱟᱜ Smart Wind Turbine Prediction ᱯᱨᱮᱰᱤᱠᱴᱤᱵ ᱢᱮᱱᱴᱮᱱᱮᱱᱥ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ ᱫᱚ ᱤᱱᱴᱟᱨᱯᱷᱮᱥ, ᱤᱱᱴᱟᱨᱮᱠᱥᱚᱱ ᱟᱨ ᱵᱮᱵᱦᱟᱨᱤᱭᱟᱹ ᱟᱱᱩᱵᱷᱟᱵ ᱰᱤᱡᱟᱭᱤᱱ ᱯᱨᱚᱛᱤᱡᱚᱜᱤᱛᱟ ᱵᱷᱟᱜ ᱨᱮ 2023 A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱨᱮᱱᱟᱜ ᱟᱹᱰᱤ ᱢᱟᱱ ᱟᱠᱟᱱ ᱡᱤᱛᱠᱟᱨ ᱠᱟᱱᱟ᱾
ᱨᱩᱯᱟᱤᱱᱴᱟᱨᱯᱷᱮᱥ· 2023
Smart Wind Turbine Prediction ᱯᱨᱮᱰᱤᱠᱴᱤᱵ ᱢᱮᱱᱴᱮᱱᱮᱱᱥ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ
4Paradigm UED ᱞᱮᱠᱟᱛᱮ
ᱜᱟᱦᱟᱠ: Jianxin Technology Co., Ltd
ᱤᱱᱴᱟᱨᱯᱷᱮᱥ ᱵᱷᱟᱜ ᱨᱮ A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱡᱤᱛᱠᱟᱨ
ᱡᱟᱸᱪ ᱟᱠᱟᱱ ᱮᱣᱟᱨᱰ ᱡᱤᱛᱠᱟᱨ
#141912 ᱞᱟᱜᱤᱫ ᱮᱣᱟᱨᱰ ᱦᱟᱞᱚᱛ ᱡᱟᱸᱪᱰᱤᱡᱟᱭᱤᱱ
Smart Wind Turbine Prediction ᱯᱨᱮᱰᱤᱠᱴᱤᱵ ᱢᱮᱱᱴᱮᱱᱮᱱᱥ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ ᱵᱟᱵᱚᱛ ᱛᱮ
ᱥᱢᱟᱨᱴ ᱩᱤᱱᱰ ᱴᱚᱨᱵᱟᱭᱤᱱ ᱯᱨᱮᱰᱤᱠᱥᱚᱱ ᱫᱚ ᱦᱚᱭ ᱯᱷᱟᱨᱢ ᱠᱚᱨᱮᱭᱟᱜ ᱥᱮᱭᱟᱱ ᱠᱟᱹᱢᱤ ᱟᱨ ᱡᱚᱛᱚᱱ ᱞᱟᱹᱜᱤᱫ ᱛᱮ ᱢᱤᱫ ᱥᱚᱯᱷᱴᱣᱮᱨ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ ᱫᱚ ᱛᱤᱱ-ᱰᱤᱢᱮᱱᱥᱚᱱ ᱵᱷᱤᱡᱩᱣᱟᱞᱟᱭᱡᱮᱥᱚᱱ, ᱵᱷᱟᱨᱪᱩᱣᱟᱞ ᱥᱤᱢᱩᱞᱮᱥᱚᱱ ᱟᱨ ᱰᱟᱴᱟ ᱜᱽᱨᱟᱯᱷ ᱠᱚ ᱵᱮᱵᱷᱟᱨ ᱠᱟᱛᱮ ᱴᱚᱨᱵᱟᱭᱤᱱ ᱨᱮᱭᱟᱜ ᱚᱵᱚᱥᱛᱟ ᱩᱫᱩᱜᱟᱭ ᱟᱨ ᱫᱚᱥ ᱠᱚ ᱞᱚᱜᱚᱱ ᱧᱟᱢ ᱚᱪᱚᱭᱟ᱾ ᱢᱮᱥᱤᱱ ᱞᱟᱨᱱᱤᱝ ᱢᱚᱰᱮᱞ ᱠᱚ ᱫᱚ ᱰᱟᱴᱟ ᱟᱨ ᱢᱚᱰᱮᱞ ᱥᱮᱴᱤᱝ ᱠᱚ ᱚᱨ ᱟᱨ ᱫᱚᱦᱚ (ᱰᱽᱨᱟᱜᱽ ᱟᱨ ᱰᱽᱨᱚᱯ) ᱛᱮ ᱥᱟᱡᱟᱣ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ, ᱚᱱᱟᱛᱮ ᱢᱤᱫ ᱟᱨ ᱵᱟᱝᱠᱷᱟᱱ ᱟᱭᱢᱟ ᱞᱮᱠᱟᱱ ᱴᱚᱨᱵᱟᱭᱤᱱ ᱠᱚ ᱦᱚᱸ ᱛᱚᱯᱚᱞ ᱫᱟᱲᱮᱭᱟᱜ-ᱟ᱾ ᱫᱚᱥ ᱨᱮᱭᱟᱜ ᱦᱩᱸᱥᱤᱭᱟᱹᱨ ᱠᱷᱚᱵᱚᱨ ᱫᱚ ᱜᱚᱴᱟ ᱠᱟᱛᱮ ᱵᱤᱥ ᱴᱤᱯᱤᱡ ᱞᱟᱦᱟ ᱨᱮᱜᱮ ᱮᱢᱚᱜ-ᱟ, ᱟᱨ ᱢᱚᱰᱮᱞ ᱨᱮᱭᱟᱜ ᱴᱷᱤᱠ ᱦᱚᱪᱚ ᱫᱚ ᱮᱭᱟᱲᱮᱜᱮᱞ ᱢᱤᱫ (᱘᱑) ᱯᱨᱚᱛᱤᱥᱚᱛ ᱦᱟᱹᱵᱤᱡ ᱥᱮᱴᱮᱨᱚᱜ-ᱟ᱾ ᱛᱤ ᱛᱮ ᱡᱚᱛᱚᱱ ᱠᱟᱹᱢᱤ ᱨᱮᱭᱟᱜ ᱠᱷᱚᱨᱚᱪ ᱟᱨ ᱡᱚᱠᱷᱚᱢ ᱨᱮᱭᱟᱜ ᱰᱚᱨ ᱠᱚ ᱠᱚᱢᱚᱜ-ᱟ᱾
ᱢᱟᱱᱟᱱ
A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱵᱟᱵᱚᱛ ᱛᱮ
Smart Wind Turbine Prediction ᱯᱨᱮᱰᱤᱠᱴᱤᱵ ᱢᱮᱱᱴᱮᱱᱮᱱᱥ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ ᱫᱚ A' Design Award ᱨᱩᱯᱟ ᱯᱩᱨᱚᱥᱠᱟᱨ ᱧᱟᱢ ᱟᱠᱟᱱᱟ, ᱱᱚᱶᱟ ᱫᱚ ᱥᱩᱱᱫᱚᱨ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱞᱟᱹᱜᱤᱫ ᱮᱢ ᱟᱠᱟᱱ ᱥᱚᱢᱟᱱ ᱠᱟᱱᱟ᱾ ᱱᱚᱶᱟ ᱠᱚ ᱫᱚ ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱠᱚ ᱨᱮᱭᱟᱜ ᱵᱷᱟᱵᱱᱟ ᱟᱨ ᱵᱷᱟᱞᱟᱭ ᱠᱟᱢᱤ ᱡᱷᱟᱞᱠᱟᱣ ᱠᱚ᱾ Smart Wind Turbine Prediction ᱯᱨᱮᱰᱤᱠᱴᱤᱵ ᱢᱮᱱᱴᱮᱱᱮᱱᱥ ᱤᱱᱴᱚᱨᱯᱷᱮᱥ ᱫᱚ ᱠᱚᱞᱟ, ᱣᱤᱜᱭᱟᱱ, ᱰᱤᱡᱟᱭᱤᱱ ᱟᱨ ᱴᱮᱠᱱᱚᱞᱚᱡᱤ ᱨᱮᱭᱟᱜ ᱥᱤᱢᱟ ᱵᱟᱲᱦᱟᱣ ᱟᱨ ᱫᱩᱱᱤᱭᱟ ᱵᱷᱟᱞᱟᱭ ᱞᱟᱹᱜᱤᱫ ᱜᱚᱲᱚ ᱮᱢ ᱠᱟᱱᱟ᱾
ᱰᱤᱡᱟᱭᱤᱱ ᱯᱤᱪᱷᱟ ᱨᱮ
ᱯᱨᱮᱥ ᱠᱤᱴ
ᱰᱤᱡᱟᱭᱤᱱᱟᱨ ᱧᱮᱞ ᱠᱷᱚᱱ ᱠᱨᱮᱰᱤᱴ, ᱴᱤᱢ ᱥᱫᱚᱥᱭ ᱟᱨ ᱴᱮᱠᱱᱤᱠᱮᱞ ᱵᱤᱵᱚᱨᱚᱬ ᱮᱢ ᱟᱠᱟᱱᱟ᱾
- ᱟᱞᱜᱟ ᱜᱩᱱ ᱠᱚ
- Through the visualization of wind turbine models and data graphs, we can quickly pinpoint the fault in wind turbines and efficiently safeguard the operations and maintenance process. Through the “drag and drop” data and model settings, we can meet the custom settings of “same type and same module” and “multiple types and multiple modules” from various machine learning models to hone targeted operations and maintenance trouble-shooting capabilities.
- ᱢᱟᱯ ᱟᱨ ᱯᱮᱠᱮᱡᱤᱝ
- Width 1920 Height 1080
- ᱴᱮᱜ ᱠᱚ
- AI System Design , AI , AI Applications , Machine Learning , Modeling , Big Data
- ᱛᱮᱭᱟᱨ ᱛᱮᱠᱱᱚᱞᱚᱡᱤ
- Using 3D visualization modeling, virtual simulation, surveillance visualization, and other technical means, we intelligently display the physical reality space in the digital system, provide interpretable and traceable machine learning capabilities, and simulate the logic behind users’ real interactive operations in the physical world. Our solution enables enterprise customers to receive fault warnings (on average) 20 minutes earlier, and the overall model accuracy rate has reached 81%.
- ᱰᱤᱡᱟᱭᱱ ᱪᱮᱞᱮᱧ
- It also enables more effective use of wind resources and contributes to the global governance effort to combat climate change.
- ᱜᱚᱵᱮᱥᱚᱱᱟ
- It addresses user pain points of high manual operations and maintenance costs and improves operations and maintenance personnel safety and efficiency.
- ᱯᱨᱮᱨᱚᱱᱟ
- The system is an intelligent operation solution for renewable energy scenarios based on the digital base of Industrial IoT and the prediction capability of machine learning models. It provides decision support for the wind farm's intelligent operation and maintenance, turbine start/stop, and fault analysis.The system provides manageability for power generation capacity and allows stable and continuous output through intelligent, decision-driven solutions.
- ᱯᱨᱚᱡᱮᱠᱴ ᱥᱚᱢᱚᱭ
- The project was completed in Beijing in 2022
- ᱠᱟᱹᱢᱤ ᱦᱚᱨ
- The design language follows the principle of intelligence and efficiency, and it aims to deliver a brand-new experience to users through digital and intelligent interactions.
- ᱴᱤᱢ ᱥᱮᱫᱟᱭ ᱠᱚ
- Design Director：Yun Xu
Design Manager：Wei Wang
Designer：Shaoxiong Bai
Designer：Xinjie Li
Designer：Yanfen Zhang - ᱪᱤᱛᱟᱹᱨ ᱥᱟᱹᱦᱟ
- Design creativity：Yun Xu , Wei Wang
Interface design: Yanfen Zhang
Interaction design：Xinjie Li
Video Copyright：Shaoxiong Bai
ᱥᱯᱚᱱᱥᱚᱨᱰ
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ᱥᱟᱹᱜᱟᱹᱭ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ
ᱢᱤᱫ ᱞᱮᱠᱟ ᱠᱮᱴᱮᱜᱚᱨᱤ ᱠᱷᱚᱱ ᱟᱨᱦᱚᱸ ᱯᱩᱨᱥᱠᱟᱨ ᱧᱟᱢᱟᱠᱟᱱ ᱰᱤᱡᱟᱭᱤᱱ ᱠᱚ ᱧᱮᱞ ᱢᱮ, ᱡᱚᱜᱭᱟᱛᱟ ᱧᱮᱞ ᱛᱮ ᱵᱟᱪᱷᱟᱣ ᱟᱠᱟᱱᱟ᱾
