Perak Antara Muka · 2023
Smart Wind Turbine Prediction Antara Muka Penyelenggaraan Ramalan
- A' Design Award Perak 2023 in Antara Muka
- CLIENT: Jianxin Technology Co., Ltd
- Estimated Number of Design Views: 52,172
- Verification code 5059508A13 · Verify this award
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About Smart Wind Turbine Prediction Antara Muka Penyelenggaraan Ramalan
Smart Wind Turbine Prediction ialah antara muka perisian untuk operasi dan penyelenggaraan pintar ladang angin. Antara muka ini menggunakan visualisasi tiga dimensi, simulasi maya dan graf data untuk memaparkan keadaan turbin serta mengesan kerosakan dengan pantas. Model pembelajaran mesin dikonfigurasikan dengan menyeret dan melepaskan data serta tetapan model, jadi satu atau pelbagai jenis turbin boleh diliputi. Amaran kerosakan disampaikan secara purata kira-kira dua puluh minit lebih awal, dan ketepatan model mencapai 81 peratus. Kos penyelenggaraan manual dan risiko keselamatan dapat dikurangkan.
About the A' Design Award Perak
Smart Wind Turbine Prediction Antara Muka Penyelenggaraan Ramalan ialah pemenang A' Design Award Perak, pengiktirafan yang sangat berprestij yang dianugerahkan kepada reka bentuk bertaraf tinggi, indah dan mengagumkan. Pemenang Perak ialah karya yang amat cemerlang dan sangat kreatif yang menggambarkan kepakaran, bakat dan ketajaman fikiran yang luar biasa pereka bentuknya. Smart Wind Turbine Prediction Antara Muka Penyelenggaraan Ramalan diharapkan menjadi karya yang menakjubkan, contoh teladan, sangat profesional dan sangat inovatif. Reka bentuk pemenang A' Design Award Perak ini ialah ciptaan yang sangat ketara dan dikagumi yang memberi manfaat kepada dan memajukan batas seni, sains, reka bentuk dan teknologi. Smart Wind Turbine Prediction Antara Muka Penyelenggaraan Ramalan ialah ciptaan yang sangat dicemburui yang membangkitkan keinginan, membawa perasaan positif dan mencetuskan rasa hairan serta kagum.
Press Kit
- Unique Properties
- 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.
- Dimensions & Packaging
- Width 1920 Height 1080
- Tags
- AI System Design , AI , AI Applications , Machine Learning , Modeling , Big Data
- Production Technology
- 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%.
- Design Challenge
- It also enables more effective use of wind resources and contributes to the global governance effort to combat climate change.
- Research
- It addresses user pain points of high manual operations and maintenance costs and improves operations and maintenance personnel safety and efficiency.
- Inspiration
- 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.
- Project Duration
- The project was completed in Beijing in 2022
- Operation Flow
- 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.
- Image Credits
- Design creativity：Yun Xu , Wei Wang
Interface design: Yanfen Zhang
Interaction design：Xinjie Li
Video Copyright：Shaoxiong Bai
