Building a cutting-edge hub for intelligent perception

Core architecture and technological advantages of the platform

Low code development and rapid implementation capability: Provides a visual development interface and rich SDK interfaces, supporting developers to complete data collection, model loading, logical judgment, and alarm linkage processes through drag and drop configuration, greatly reducing the threshold for AI applications. Enterprise customers can complete the entire process from model import to on-site deployment within a few days, significantly shortening the product launch cycle. For example, a manufacturing enterprise developed a production line defect detection system through a platform, which completed the configuration of defect classification rules from camera access, YOLOv5 model deployment to defect detection in just 3 days, improving detection efficiency by 70%. In addition, the platform supports a powerful "scene template library" function, with dozens of industry solution templates pre-set, such as device predictive maintenance, unmanned inspection, etc. Users can easily call these templates with just one click and quickly customize and adapt them according to specific needs, thus achieving flexible applications in different industries.

Typical application scenarios and value implementation

Platform ecology and future prospects, Dishan Technology actively builds an open technology ecology, supports integration with mainstream industrial protocols, provides standard API interfaces, and facilitates system integration and secondary development. Through ISO 27001 information security certification and industrial grade EMC testing, it has the ability to operate stably in harsh environments for a long time. Dishan Technology will continue to deepen its efforts in the following areas: introducing generative AI capabilities, exploring the operation of lightweight large models on the edge side, and realizing new functions such as natural language instruction parsing and automatic generation of fault reports. For example, industrial operation and maintenance personnel can ask "current equipment health status" through voice commands, and the system will generate a graphical and textual analysis report based on real-time data. Reinforcement self-learning mechanism: supports continuous incremental training of models on the edge side to adapt to dynamically changing operating environments. For example, in a production line, the model can learn the data features of new equipment or processes and automatically optimize the detection threshold. Expand industry solutions: Deepen the fields of medical care, transportation, agriculture, etc., and create more benchmark applications of "AI+sensor". For example, in the smart port project, the platform will integrate laser radar and visual sensor to achieve automatic container handling and path planning. Build edge AI chip ecology: cooperate with chip manufacturers to develop customized SoC, further reduce the power consumption and cost of edge computing, and promote AI inclusion. Exploring Metaverse Fusion: Real time processing of sensor data through edge AI to support metaverse application scenarios such as industrial digital twins and AR remote collaboration, breaking the boundaries between reality and virtuality.

Market Value and Industry Impact