The sampling method for real-time and high-speed changing streaming data is prone to lose the value and information of a large amount of discrete data, and it is not easy to make an efficient and accurate streaming data valuation.The SDSLA (Streaming Data Drilling Sampling Method Under Limited Access) sampling method based on mineral drilling exploration can streaming data valuation containing many discrete data in real-time, but when the range of discrete data in streaming data is irregular, it has low sampling accuracy for discrete data.Based on the SDSLA algorithm, we propose a dynamic drilling sampling method SDDS (Streaming Data Dynamic Drilling Sampling).This method takes well as the analysis unit dynamically changes the size and position of the well, and accurately predicts the position and range of discrete data.A new model SDVEM (Streaming Data Value Evaluation Model), is further proposed for data valuation, which evaluates the sample set from discrete, centralized, and overall dimensions.Experiments show that the method proposed in the paper uses neural network training and testing with a small sampling rate to obtain accuracy, recall, and F1 scores above 90%, which is higher than that of the SDSLA algorithm.In summary, the SDDS sampling method is beneficial to the training neural network models and evaluating the value characteristics of streaming data, which has essential research significance in big data valuation.

The big data sampling method for real-time and high-speed streaming data is prone to lose the value and information of a large amount of discrete data, and it is not easy to make an efficient and accurate evaluation of the value characteristics of streaming data. The SDSLA sampling method based on mineral drilling exploration can evaluate the valuable information of streaming data containing many discrete data in real-time, but when the range of discrete data is irregular, it has low sampling accuracy for discrete data. Based on the SDSLA algorithm, we propose a dynamic drilling sampling method SDDS, which takes well as the analysis unit, dynamically changes the size and position of the well, and accurately locates the position and range of discrete data. A new model SDVEM is further proposed for data valuation, which evaluates the sample set from discrete, centralized, and overall dimensions. Experiments show that compared with the SDSLA algorithm, the sample sampled by the SDDS algorithm has higher evaluation accuracy, and the probability distribution of the sample is closer to the original streaming data, with the AOCV indicator being nearly 10% higher. In addition, the SDDS algorithm can achieve over 90% accuracy, recall, and F1 score for training and testing neural networks with small sampling rates, all of which are higher than the SDSLA algorithm. In summary, the SDDS algorithm not only accurately evaluates the value characteristics of streaming data but also facilitates the training of neural network models, which has important research significance in big data estimation.