Wonder Heat Supply 난방 공급 클라우드 플랫폼 by Wuxi Hundun Energy Technology Co., Ltd.

Wonder Heat Supply 난방 공급 클라우드 플랫폼 by Wuxi Hundun Energy Technology Co., Ltd. is a highly esteemed winner of the very prestigious 2023 A' Design Award 동상 in 정보 기술 디자인 Competition Category.

동정보 기술· 2023

Wonder Heat Supply 난방 공급 클라우드 플랫폼

by Wuxi Hundun Energy Technology Co., Ltd.

Client: Wuxi Hundun Energy Technology Co., Ltd.

A' Design Award 동상 Winner in 정보 기술 Category

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The Design

About Wonder Heat Supply 난방 공급 클라우드 플랫폼

Wonder Heat Supply는 도시 난방·환기 기업을 위해 개발된 스마트 난방 공급 클라우드 플랫폼입니다. 이 플랫폼은 사물인터넷, 디지털 트윈 기술, 지능형 알고리즘을 결합하여 빅데이터와 배관망 분포를 기반으로 열 네트워크 모델을 구축합니다. 열 수요를 예측하고 자동으로 조절함으로써 각 지역의 실제 필요에 따라 에너지가 배분됩니다. 이 시스템은 비맞춤형 하드웨어를 지원하며, 다양한 기기 구성에 맞추어 표시 콘텐츠를 조정합니다.

The Recognition

About the A' Design Award 동상

Wonder Heat Supply 난방 공급 클라우드 플랫폼, 이 작품은 A' Design Award 동상 수상작으로, 창의성, 독창성, 영민함으로 돋보이는 뛰어난 디자인에 수여되는 높은 권위의 인정입니다. 동상 수상작은 충분히 숙련되고 인상적으로 창의적인 작품으로, 디자이너의 폭넓은 경험, 상상력, 기지를 입증합니다. Wonder Heat Supply 난방 공급 클라우드 플랫폼, 이 작품은 높이 평가받고 감탄과 사랑을 받는 사물로서 두드러지게 전문적이고 혁신적일 것으로 기대됩니다. 이 동상 수상 디자인은 예술, 과학, 디자인, 기술의 모범 사례를 담고 있으며 세부에 대한 주의로 훌륭합니다. Wonder Heat Supply 난방 공급 클라우드 플랫폼, 이 작품은 필요를 훌륭하게 충족하고 욕망에 닿으며 감정을 매료하고 호기심을 불러일으킵니다.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: This is a standardized smart heat supply cloud platform designed on the basis of IoT, digital twin, and intelligent algorithms, which is specially designed for city HVAC companies. Being a self-aware, self-analyzing, self-regulating and breathing heating system, it can establish a heat supply network model grounded on big data and pipeline networks distribution to predict and regulate the heat supply of cities.
Tags: Heat supply cloud platform for city, Heat supply load simulation, Self awareness, Self regulation, Digital twin
Production Technology: It adopts clear policy instruction, simple visual language and full function setting, and designs visual data analysis system and a UI interface designed for managers, facilitating the access of users, remote regulation and dispatching. The system reduces the parameter requirements for hardware devices and adopts non customized configuration, lowering the hardware threshold for access and self adjusting the screen effect and display content based on different hardware configurations of users.
Design Challenge: A key issue in the research is how to give AI the ability of self-analysis and self-control to energy parameters. With algorithmic strategy of the energy model, digital twin and actual operation data, the design team gives AI the ability to self-adjust parameters and optimize energy efficiency. The optimal solution for heat network management saves energy by 10-15 percent, thus reducing carbon emissions. It helps generate more economic and socially environmental benefits of enterprises.
Research: City complexes contain more functional areas of buildings. Compared with the HVAC systems of other single buildings, city HVAC systems need to allocate energy properly based on the actual demand of each area. The traditional mode of manual management and coordination has gradually become unsuitable. Thus, it has become the preferred solution of the design team with the help of AI, IOT and digital twin for collaborative and accurate control.
Inspiration: As one of the most energy-consuming systems in the daily lives of residents, problems such as uneven energy distribution and low energy utilization can be found in city HVAC systems, while the platform is designed to solve the above problems. Through the combination of IoT, digital twin and intelligent algorithm, the design team has given AI a new model of self-awareness, self-analysis and self-regulation of unmanned control, thus helping city HVAC systems to save energy and reduce emissions.
Project Duration: The product was developed in Wuxi, Jiangsu Province in March 2022 and also launched here in August 2022
Operation Flow: With the simplification of operation of system levels, users can freely switch between different animation styles and seasonal scenes based on their preferences, making it user-friendly, 10 industry professionals can use it smoothly when tested for the first time, without training and teaching. Its built-in simulation and load prediction functions can obtain real-time data such as weather and geographic location from the meteorological system and geographic space for real-time display.
Team Members: Jun Xu, Jue Wang, Lan Zhu and Guiying Fu