1. On-site data silos
Traditional siloed system development leads to isolated and scattered data between systems, lacking circulation and integration, making data development and application processes tedious or even difficult, failing to reflect data value. Requires more holistic optimization.
2. Partial data missing
Partial data is missing, and a lot of data has not been collected or is not complete enough, resulting in the inability to carry out subsequent work and a large amount of data not being utilized. Need for more comprehensive management
3. Low analysis efficiency
Numerous processes, devices, and device parameters, complex data, high dependency on data reliability, challenging analysis, high requirements for analysis interpretability, low efficiency of traditional sampling. Need for more efficient analysis.
4. Reliance on manual experience
Analysis traditionally relies on manual experience, accuracy difficult to guarantee. Need for more precise judgment.
