4 results about "Single indicator" patented technology

The invention discloses a method for evaluating structural safety by using tunnel health monitoring data, which comprises the following steps of: acquiring monitoring data to obtain real-time monitoring data; performing abnormal data judgment; correcting the abnormal data; calculating a threshold value of a single index; performing single index evaluation by adopting a membership function; tunnel health comprehensive evaluation grading is carried out; calculating a correction coefficient by adopting an entropy weight method; and tunnel health comprehensive evaluation is carried out step by step. According to the invention, rapid and quantitative evaluation of tunnel structure safety is realized, the problems of low manual inspection efficiency, poor quality and incapability of mastering the stress state of the tunnel in the traditional tunnel operation and maintenance process are solved, the purpose of rapidly identifying the most unfavorable stress area of the tunnel can be met, and the tunnel operation and maintenance efficiency is improved. The purpose of providing a large amount of data to support formulation of disease treatment measures when diseases occur can be achieved, the idea of intelligent operation and maintenance is met, the automation degree is high, practicability is high, and the obvious application and popularization value is achieved.

The present invention discloses a method for generating custom indicators based on a real-time stream framework, comprising receiving indicator data to form an indicator data stream; receiving indicator rules to form an indicator rule stream; converting the indicator rule stream into an indicator rule broadcast stream; merging the indicator data stream with the indicator rule broadcast stream, matching indicator data that meets different indicator rules, and determining whether the matched indicator data is single indicator data or associated indicator data; calculating the single indicator data / associated indicator data using a calculation expression in the indicator rule to obtain a single indicator value / associated indicator value; calculating the single indicator value / associated indicator value based on the indicator calculation type in the indicator rule to obtain an indicator value; merging each indicator value and the corresponding indicator name, event association identifier, and indicator time of the indicator data into an alarm indicator and reporting it to a database. The present invention provides a new processing logic based on the Flink framework to realize the generation of custom indicators and alarm behavior.

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.