2results about How to "Real-time forecast" patented technology

This invention discloses a green and ecological planting method for daylilies, belonging to the field of agricultural planting technology. It addresses the technical problems of excessive reliance on chemical fertilizers and traditional mulching in current daylily cultivation, leading to soil ecological imbalance and low fertilizer utilization. The method includes: applying organic fertilizer made from cow and sheep manure and Asteraceae plant residues through aerobic composting, using a layered strip application method. First, fully decomposed organic fertilizer is applied as a base fertilizer layer in the planting furrow, followed by semi-decomposed organic fertilizer that has completed the main fermentation stage but not the later aging stage as a middle layer fertilizer; after land preparation and ridging, wood ash is spread on the ridge surface, followed by the laying of black fully biodegradable mulch film; finally, holes are made in the mulch film for daylily seedling planting. This method is mainly used for sustainable ecological planting of daylilies, improving soil fertility and structure, regulating soil pH and providing potassium, conserving moisture and suppressing weeds while avoiding residual pollution, thereby increasing daylily yield and quality while reducing environmental impact.

The application provides a battery state of health prediction method, device, equipment and storage medium, including: obtaining charging voltage data of a to-be-tested battery; extracting a health index for indicating the state of health of the battery from the charging voltage data located in a target voltage interval; the health index includes at least two of the following: equal pressure difference charging duration, equal pressure difference charging energy, voltage average, power spectral density value, Euclidean distance and Manhattan distance; the health index is fused in a weighted fusion manner to obtain a fused health index; the fused health index is input into a trained prediction model to obtain a battery state of health prediction result corresponding to the to-be-tested battery; the prediction model is constructed by integrating learning framework and weighting and combining multiple support vector machine models optimized by a swarm intelligence optimization algorithm; thus, the battery state of health can be predicted in real time, with high precision and high efficiency.