Abstract:

During the processing of ontology modeling multi-value chain collaborative data space knowledge engine in the manufacturing field， it should be aimed at the problem of low efficiency of knowledge management and sharing caused by incomplete product knowledge. It is necessary to analyze the relationship among ontology， attributes and features. This is to complete the ontology modeling method of multi-value chain collaborative data space， including production， sales， supply and service. As one of the key links in knowledge engine application scenarios， the construction of multi-value chain collaborative data knowledge engine ontology in manufacturing industry needs to fully mine the characteristic relationship in data space. The tree integration sale forecast model is constructed based on LightGBM， XGBoost and Random Forest， and a feature correlation analysis method of manufacturing multi-value chain collaborative data space is proposed. On the prediction results of the tree integration model， the correlation between the characteristics and the influence of the characteristics on the sales prediction results are analyzed through the SHAP value. The effect of feature correlation obtained by using SHAP value on sales forecast in tree ensemble model is demonstrated through ablation experiments. The tree-integrated sales forecast model proposed in this paper and the feature correlation analysis method of SHAP value provide reliable theoretical and data support for the automatic screening of ontology and attributes in the modeling process of manufacturing knowledge engine.

Key words: sales forecast, machine learning, knowledge graph engine, data space, multi-value chain