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The purpose of this paper is to analyze and show how to avoid the interference in infrared (IR) temperature field measurement during welding.

First, the hardware used in this experiment is described. In this paper, these interferences are first classified into diffuse and specular reflection based on reflection model, and are restrained, respectively. Finally, IR temperature is calibrated by thermocouple.

The specular reflection is the primary interference which causes high light zone. And it can be transferred out of welding seam when the IR thermography is placed perpendicularly to welding seam.

The paper provides a new IR measure method and detailed analysis about the interferences in applying IR temperature sensing.

Data has become a factor of production. This occurs when history enters the era of big data, in which technologies such as artificial intelligence, cloud computing and blockchain are used to collect, manipulate, mine and process data. Data is a special product of labor, a sub-derivative of other production factors.

The data factor has a dual attribute: being physical (technical) and social. The social attribute of the data factor can not only materialize the technical attribute but also amplify it. In other words, the data has a multiplication effect on the allocation efficiency of other production factors. The social attribute of the data is brought out via the technical attribute as the medium. From a technical perspective, this medium is strongly adhesive, and after being bonded with other factors of production, it will only lead to a physical reaction and not change the nature of other factors.

However, once these two attributes interact with each other, especially when data is combined with capital, the most adhesive factor in the market economy, a series of new social relations will then be produced based on the technical attribute, resulting in significant adjustments in social relations, involving both positive and negative externalities.

Therefore, to get a scientific understanding of the dual attribute and its interaction effects on the data factor, it is necessary to take the following steps. We should promote institutional design that amplifies the positive externality, with a focus on facilitating public data sharing and improving the value of commercial data development. Also, we need to strengthen institutional arrangements that prevent and control the negative externality by emphasizing data supervision based on data types and levels as well as the rule of law.