Optimized Allocation of Resources for Intelligent Construction of Training Venues for Track and Field Teams

Because of the problems of low allocation efficiency and accuracy in the traditional resource allocation model for the intelligent construction of track and field team training venues, this paper proposes a new intelligent construction resource allocation model for track and field team training venues. A distributed data collection method is adopted to select the index data for the intelligent construction of the track and field team training venues to optimize the allocation of resources and the mined resource allocation index through extracting the optimized index characteristics. The goal of the optimization problem is transformed into a single-objective solving problem. The suggested approach is based on the two-level programming concept. The simulation experimental results show higher efficiency and better allocation effect of the proposed scheme compared to other state-of-the-art approaches. Our evaluation and observations suggest that the accuracy of the intelligent construction of track and field team training venues is between 90% and 99% which is higher than two other benchmarked models. Furthermore, the proposed model can quickly reach to an optimal decision.