The first is to ascertain the demand for artificial intelligence usage. Based on their own needs, chain owners should actively expand external demand such as upstream and downstream of the industrial chain, adhere to demand orientation, and accurately promote data set construction. The second is to play a traction and driving role. Chain owners should first explore the establishment of benefit distribution and commercial return mechanisms, sort out industry data maps, promote distributed aggregation of industry data, and guide the registration of data sets in the industry to the national data set management service system. The third is to strengthen application feedback. Relying on actual application scenarios, establish data backflow and iterative optimization mechanisms, continuously collect user feedback, promote dynamic data set updates, and continuously improve the adaptability of data to application scenarios. Fourth, strengthen industrial chain collaboration. Give full play to the “clustering” role of chain owners, link upstream and downstream units in the industrial chain, expand the scope of cooperation, and promote the construction of industrial ecology. The fifth is to promote access to facilities. Each chain master unit should actively connect to the national data infrastructure and rely on the facility to promote large-scale circulation and efficient use of data sets. The sixth is to optimize evaluation services. Promote the transformation of data set quality assessment to an instrumentalized and order-based model, shorten the evaluation cycle and reduce the cost of evaluation.

The first is to ascertain the demand for artificial intelligence usage. Based on their own needs, chain owners should actively expand external demand such as upstream and downstream of the industrial chain, adhere to demand orientation, and accurately promote data set construction. The second is to play a traction and driving role. Chain owners should first explore the establishment of benefit distribution and commercial return mechanisms, sort out industry data maps, promote distributed aggregation of industry data, and guide the registration of data sets in the industry to the national data set management service system. The third is to strengthen application feedback. Relying on actual application scenarios, establish data backflow and iterative optimization mechanisms, continuously collect user feedback, promote dynamic data set updates, and continuously improve the adaptability of data to application scenarios. Fourth, strengthen industrial chain collaboration. Give full play to the “clustering” role of chain owners, link upstream and downstream units in the industrial chain, expand the scope of cooperation, and promote the construction of industrial ecology. The fifth is to promote access to facilities. Each chain master unit should actively connect to the national data infrastructure and rely on the facility to promote large-scale circulation and efficient use of data sets. The sixth is to optimize evaluation services. Promote the transformation of data set quality assessment to an instrumentalized and order-based model, shorten the evaluation cycle and reduce the cost of evaluation.