4 results about "Cumulative effect" patented technology

The application discloses a system and method for pile foundation quality detection, and relates to the technical field of pile foundation quality detection, and solves the problems of difficulty in determining the optimal excitation energy range, deviation of damage quantization criteria, and low reliability of detection results, wherein the application randomly distributes excitation pulses with different energies to different excitation areas, ensures that each energy is excited only once in one area and the same area is not continuously used, combines with environment monitoring and correction during resetting, effectively eliminates the cumulative effect of previous excitation and environmental interference, obtains pure damage increment data, identifies the inflection point of the damage increment-energy curve, takes the inflection point energy as the critical excitation energy of the depth, takes the minimum value and the maximum value of all depths to form an adaptive detection energy window, and fundamentally solves the technical problems of difficulty in determining the excitation energy range and contradiction between detection results of the same pile under different energies.

The present application belongs to the technical field of tailings thickening process control, and discloses a tailings thickening machine underflow concentration automatic detection and control system and method; through collecting multi-point turbidity data in the radial direction of the overflow area of the feedwell, early identification of the disturbance of the feed properties is realized; a layered settling model is constructed based on the mud layer interface height and the thickening machine geometric parameters, and the material residence time is accurately calculated; a medicament effect propagation descriptor is created, and the dynamic propagation process of the flocculating agent in the system is tracked; combined with the in-transit cumulative effect evaluation and future concentration prediction, feedforward-feedback composite control is realized; through lagging prediction error analysis, the model parameters are dynamically corrected, and the system performance is continuously optimized. The present method realizes predictive control and accurate adjustment of the underflow concentration, effectively solves the problem of excessive flocculating agent adjustment, and improves the stability and reliability of the tailings treatment process.

The present application belongs to the technical field of tailings thickening process control, and discloses a tailings thickening machine underflow concentration automatic detection and control system and method; through collecting multi-point turbidity data in the radial direction of the overflow area of the feedwell, early identification of the disturbance of the feed properties is realized; a layered settling model is constructed based on the mud layer interface height and the thickening machine geometric parameters, and the material residence time is accurately calculated; a medicament effect propagation descriptor is created, and the dynamic propagation process of the flocculating agent in the system is tracked; combined with the in-transit cumulative effect evaluation and future concentration prediction, feedforward-feedback composite control is realized; through lagging prediction error analysis, the model parameters are dynamically corrected, and the system performance is continuously optimized. The present method realizes predictive control and accurate adjustment of the underflow concentration, effectively solves the problem of excessive flocculating agent adjustment, and improves the stability and reliability of the tailings treatment process.

The application discloses a forest fire risk prediction method based on time sequence characteristics and an integrated model, and relates to the field of forest fire risk prediction. From the data level, the training set random downsampling algorithm is adopted; from the algorithm level, the more minority class prediction model (BRF) and the setting of each model super parameter are adopted; from the evaluation level, the more minority class prediction evaluation index is selected, and the effectiveness of the fire risk prediction model under the unbalanced data is comprehensively evaluated. By extracting the average value and trend value time sequence dynamic characteristics of the meteorological and combustible materials within 16 days, the cumulative effect of the fire risk is effectively described, and the identification accuracy of the potential high-risk fire area is improved. Through the base model combination of XGBoost, LightGBM and balanced random forest and the fusion of the logistic regression meta model, the advantages of different models are integrated, and compared with a single model, the prediction accuracy is improved.