2 results about "Single indicator" patented technology

The invention relates to the technical field of power equipment detection, in particular to a multi-dimensional evaluation method and device for an insulator shackle corrosion detection model, and aims to evaluate the insulator shackle corrosion detection model based on a detection result of the insulator shackle corrosion detection model by integrating a plurality of evaluation indexes. Carrying out classification and standardization processing on values of the evaluation indexes based on characteristics of the evaluation indexes so as to eliminate dimensional differences and then convert the evaluation indexes into uniform interval values; and the weight value of each evaluation index is calculated by using an automatic weight value optimization method, so that the importance degree of each evaluation index can be objectively quantified, the one-sidedness of subjective weighting is avoided, and the scientificity and credibility of an evaluation result are improved. According to the method, multi-dimensional index information is integrated, limitation of a single index is reduced, scientific and comprehensive evaluation of the insulator shackle corrosion detection model is realized, accurate evaluation of the performance of the insulator shackle corrosion detection model can be realized, and a basis is provided for researchers to further optimize the detection model.

This invention discloses an AI-based early warning method for estuarine wetland degradation risk, belonging to the field of ecological remote sensing monitoring technology. Addressing the shortcomings of existing wetland degradation prediction methods, such as reliance on multi-source data, lack of bidirectional temporal modeling and key-step focusing capabilities, and absence of critical warning mechanisms, this invention uses only MODIS NDVI time series data as the sole data source. It simultaneously captures the forward and backward temporal dependencies of the NDVI sequence through a bidirectional long short-term memory network (BiLSTM) and introduces an attention mechanism to adaptively focus on the key time steps that contribute most to the degradation trend. After model training, multi-step predictions are made for wetland vegetation cover dynamics over the next 6–24 months. Based on this, the degradation rate and acceleration of the predicted sequence are calculated and compared with historical thresholds. When the predicted NDVI consistently falls below the critical degradation threshold and the degradation rate accelerates significantly, a graded early warning signal is automatically issued, particularly identifying critical degradation states about to cross the irreversible inflection point. Using Chongming East Beach in the Yangtze River Estuary as an example, this method achieves a prediction error (MAE) as low as 0.028 when using only the NDVI single indicator and can issue effective critical warnings 6–12 months in advance. This invention has the advantages of simple data acquisition, high prediction accuracy, interpretable attention weights, and strong transferability, providing a new technical means for the protection and management of estuarine wetlands.