What are the difficulties in the application of industrial big data?

Difficulties in the application of industrial big data include:

First, the application of big data technology is difficult, and there are problems such as insufficient data, low signal-to-noise ratio of data and high difficulty in data analysis.

Second, big data brings new challenges to information security. For example, industrial big data has increased the risk of privacy leakage, put forward higher requirements for existing storage and security measures, and big data is being applied to new attack means.

The third is to create intelligent new products, including intelligent application software, intelligent basic equipment, intelligent independent products, smart wearable products and smart home products.

Fourth, the innovation of intelligent application systems, such as intelligent manufacturing, intelligent logistics and intelligent enterprise application systems.

At present, industrial big data is widely used in product innovation design, product fault diagnosis and prediction, supply chain analysis and optimization, product sales prediction and big data marketing, production planning and scheduling, product quality management and analysis. "Data is the blood of the industrial Internet." He You described the interaction between big data and industrial Internet.

However, due to the high value density of industrial big data, the variety of data types, the coexistence of multi-source heterogeneous institutional data and unstructured data, the requirements for data processing are very high, and the data relationship and correlation are extremely complex. How to transform the statistical analysis ability of enterprises into big data analysis, prediction and decision-making ability, and promote the upgrading and transformation of traditional industries and industrial integration is the core key issue that needs to be solved at present.