7results 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 invention discloses an air flow prediction method of an engine, a vehicle, a storage medium and a program product. The method comprises the steps that the current working condition and operation state data of an engine are obtained, and the operation state data are used for describing the operation state of the engine at the current moment; the total air inlet flow of the engine is determined based on the current working condition, and the total air inlet flow is used for representing the theoretical maximum air inlet amount of the engine under the current working condition; the operation state data are input into an air flow prediction model, the air flow prediction model is used for predicting the air inlet flow under the current working condition, a prediction coefficient is obtained, and the prediction coefficient is used for reflecting the ratio of the actual air flow to the total air inlet flow; and determining the target air flow of the engine based on the total air inlet flow and the prediction coefficient. The technical problem that the prediction accuracy of the air flow of the engine is low in the related technology is solved.

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.

The application discloses a numerical control lathe tool wear monitoring method and system, the method comprises the following steps: real-time monitoring of numerical control lathe tool running process data; according to the running process data, the corresponding preset running parameter threshold is matched, when the preset running parameter threshold is monitored, the numerical control lathe tool running process data is transmitted to the edge computing node; using the edge computing node, according to the running process data, the corresponding preset running parameter combination is matched and the running parameter screening is completed; based on the screened running parameter, the key feature selection and extraction are carried out by using the feature selection algorithm, the optimal feature set is obtained and uploaded to the cloud platform; in the cloud platform, according to the running process data, the corresponding tool wear prediction model is matched, the optimal feature set is input into the matched tool wear prediction model, the wear value, the wear degree and the life prediction result are obtained, and the early warning decision and the early warning prompt are carried out. The application realizes efficient, accurate and real-time tool wear monitoring under complex working conditions.

A multi-source data fusion and ensemble learning-based method for dynamic prediction of pipeline corrosion includes: constructing a four-source corrosion database, including a laboratory accelerated corrosion database, a simulated corrosion database, a literature database, and a real-sea corrosion database; constructing a two-layer ensemble learning prediction network, consisting of a first-layer primary learner layer and a second-layer meta-learner layer. The first-layer primary learner layer trains on each of the four databases to obtain multiple primary prediction models, while the second-layer meta-learner layer is trained using the prediction results of these primary models as input, outputting the final corrosion rate prediction value; and establishing a database optimization and improvement mechanism, which triggers fine-tuning of the meta-learner and the corresponding primary models when new data is collected from the real-sea test bench. Through the two-layer ensemble learning and database update mechanism, the complementary advantages of multi-source data and the dynamic adaptation of the model are achieved, significantly improving the accuracy and stability of corrosion rate prediction.

This application relates to the field of desulfurization control technology in coal-fired power plants, and particularly to an intelligent slurry supply method, device, and equipment for pH prediction control of desulfurization absorption towers. The method includes: extracting features from historical operating time-series data of the current coal-fired power plant desulfurization system to generate an operating state vector; predicting future pH values ​​based on the operating state vector and calculating the deviation between the predicted pH value and the target pH setpoint to generate a pH difference encoding vector; fusing the operating state vector and the pH difference encoding vector, and inputting the joint feature vector into a preset slurry supply prediction model to obtain the predicted slurry supply volume for the current coal-fired power plant desulfurization system at the next moment; and adjusting the slurry supply volume of the desulfurization absorption tower based on the predicted slurry supply volume. This solves the problems of existing slurry supply volume algorithms failing to effectively integrate and control the target pH value and the frequent large-scale opening and closing of slurry supply valves, thus improving the accuracy of slurry supply volume prediction and pH control.

The invention discloses an indoor environment coordination control system and method for personalized thermal comfort and building energy saving. The indoor environment coordination control system comprises a physiological parameter acquisition module, an overall environment adjustment module and a local thermal comfort adjustment module. The physiological parameter acquisition module uses an infrared camera to obtain the temperature of a human face key area in a non-intrusive manner, and predicts the human body thermal sensation through a personal thermal comfort model in combination with the environment temperature and humidity. And the overall environment adjusting module adopts a fuzzy control algorithm to determine a temperature set point and a wind speed gear of the heating ventilation air conditioner according to the thermal inductance predicted value and the environmental parameters, and indoor overall environment adjustment is implemented. The local thermal comfort adjusting module adjusts the temperature and speed of the air flow at the outlet of the personal thermoelectric comfort device through a gear control algorithm, carries out rapid compensation on the local environment, and adjusts the personal thermal inductance to a comfort range. The cooperation of the heating ventilation air conditioner and the personal thermal comfort device in space, time and target is realized, and the operation energy consumption of the building is remarkably reduced while the personalized thermal comfort is guaranteed.