To regulate regional water resources, it is essential to identify the relationships among the elements that influence water conservation. Taking the Beijing‐Tianjin‐Hebei urban agglomeration as the study area, the authors applied a new method in combination with a geodetector model and spatial elastic coefficient trajectory model to reveal factors controlling water conservation and to identify relationships among the elements driving water conservation, in which the water conservation capacity and its spatial distribution were achieved using an Integrated Valuation of Ecosystem Services and Tradeoffs model. The authors selected precipitation, potential evapotranspiration, temperature, land use, maximum burial depth of soil, plant‐available water content, soil‐saturated hydraulic conductivity, percentage slope, gross domestic product, and population as the spatial driving factors, which measured the influence on the distribution of water conservation capacity on the whole region, plateaus, mountains, and plains, respectively. On the basis of previous research results, the authors selected precipitation, potential evapotranspiration, and land use as time‐driven factors. The results indicated that the strong water conservation capacity was reflected primarily in the Yanshan and Taihang Mountains and the eastern coastal areas. The water conservation capacity of the entire region, mountains, plateaus, and plains was affected mainly by the soil‐saturated hydraulic conductivity, plant‐available water content, precipitation, and precipitation, respectively. Each driving factor exhibited a clearly interactive influence on the spatial distribution of water conservation in terms of space and time.

With the continuous development of unmanned aerial vehicle (UAV) technology, the advantages of employing multiple UAVs to collaboratively perform complex tasks have become increasingly evident and have attracted significant attention from the industry. Traditional deep reinforcement learning models face challenges such as difficulty in convergence and long training times, which hinder their direct application to multi-UAV cooperative systems. In response to these issues, this study proposes an improved multi-agent proximal policy optimization algorithm (AS-MAPPO). This algorithm enables agents to share local observation information through communication, incorporates an attention mechanism to highlight important information, and introduces an exploration reward function. These enhancements allow agents to learn superior strategies in complex environments, significantly improving the efficiency and success rate of multi-UAV cooperative target search tasks. Simulation results demonstrate that the proposed algorithm outperforms MAPPO and MADDPG in terms of task success rate and execution time.

An accurate and stable reservoir prediction model is essential for oil location and production. We propose an predictive hybrid model ILSTM-BRVFL based on an improved long short-term memory network (IAOS-LSTM) and a bidirectional random vector functional link (Bidirectional-RVFL) for this problem. Firstly, the Atomic Orbit Search algorithm (AOS) is used to perform collective optimization of the parameters to improve the stability and accuracy of the LSTM model for high-dimensional feature extraction. At the same time, there is still room to improve the optimization capability of the AOS. Therefore, an improvement scheme to further enhance the optimization capability is proposed. Then, the LSTM-extracted high-dimensional features are fed into the random vector functional link (RVFL) to improve the prediction of high-dimensional features by the RVFL, which is modified as the bidirectional RVFL. The proposed ILSTM-BRVFL (IAOS) model achieves an average prediction accuracy of 95.28%, compared to the experimental results. The model’s accuracy, recall values, and F1 values also showed good performance, and the prediction ability achieved the expected results. The comparative analysis and the degree of improvement in the model results show that the high-dimensional extraction of the input data by LSTM is the most significant improvement in prediction accuracy. Secondly, it introduces a double-ended mechanism for IAOS to LSTM and RVFL for parameter search.

In the transmission and distribution system, the high-energy consumption of the low-voltage circuit breaker easily leads to the decrease of the distribution efficiency of the system. Therefore, on the basis of calculating the internal power consumption of circuit breakers, combining with several factors affecting the energy consumption of circuit breakers, the low-energy consumption mathematical model of circuit breakers is built, and an improved whale optimization algorithm (LA-WOA) is proposed for optimization. The Lévy flight mechanism and the adaptive weight factor are introduced into the traditional whale algorithm to enhance the global and local search ability of the algorithm and also improve the convergence speed and accuracy of the algorithm. The test function simulation experiment proves that the improved algorithm has better performance than other algorithms. Furthermore, the improved LA-WOA algorithm is used to iteratively solve the low-energy consumption mathematical model of the circuit breaker. The experimental results show that the improved algorithm obtains a lower-energy consumption value, which can effectively improve the design efficiency and accuracy of the circuit breaker.