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With the development of intelligent sports in China and the rapid improvement of the strength of colleges and universities, the reform of traditional football players’ header shooting training methods is becoming more and more urgent in order to solve some problems in the development of sports and speed up the intelligent training of Chinese football players. Based on this, this paper studies the biomechanical analysis and training method based on the integration of header strength data of football players. A dynamic header tracking model of football players based on a local search algorithm is designed. The data collection is realized from the aspects of athletes’ header shooting training, skill improvement, physical consumption, and trajectory. The biological data of header shooting power is comprehensively analyzed and evaluated by using a local search algorithm. The results show that the training system based on a local search algorithm has the advantages of high feasibility, high data accuracy, and fast response speed. It can effectively conduct accurate guidance and improve the shooting accuracy according to the biological characteristics of header shooting intensity. This paper studies the biological analysis and training method of header strength of football players based on a local search algorithm. This has certain reference significance for accelerating the construction of intelligent training of Chinese football players.

The Daihai Lake, the third largest lake in Inner Mongolia Autonomous Region, is the cornerstone to maintain the ecosystem balance in this region, which is facing some problems including size shrinking, water quality declining and biodiversity decreasing largely in recent years. In order to quantify the N purification amount of submerged plants, Stella software was used in this study to construct a nitrogen dynamic model to simulate the nitrogen cycle process in the Daihai Lake and the participation of submerged plants in this cycle process. The results showed that based on the submerged plant growth area in 2019 in the Daihai Lake, the N uptake by submerged plants this year was 5.13t, accounting for 4.8% of all exogenous pollution (107.895t), Moreover, our model also predicted that the purification capacity of the restored submerged plants with a large area of 9.91 km2 in the Daihai Lake was significantly higher than before restoration. And the N pollution load of 107.892t in the Daihai Lake could be purified by this stored pattern in 12 years, while during this process a regular cleaning of submerged plant residues was required. Therefore, only large area restoration of submerged plant would benefit for improving water quality.

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