In recent years, with the rapid growth of the number of cars, the problem of "difficult parking" has become increasingly prominent, especially in the parking lot. It often takes a long time for drivers to find a free parking space. Low parking efficiency seriously affects the driver's parking feeling, which is an urgent problem to be solved. In view of this phenomenon, based on the analysis of the internal structure of the parking lot, this paper abstractly establishes the parking guidance data model, comprehensively considers the restrictive factors affecting the driver's parking psychology, then uses the improved Dijkstra algorithm to optimize the guidance data model, and finally finds out the optimal parking path, which greatly improves the parking efficiency and parking feeling.
KEYWORDS: Data modeling, Statistical modeling, Principal component analysis, Performance modeling, Systems modeling, Process modeling, Control systems, Matrices, Lithium, Data acquisition
Process controller performance evaluation systems have been widely used in modern industries with increasingly complex control objects. However, there is a prerequisite for their implementation: the models obtained from the system identification are all from the same stable operating conditions, because when the operating conditions are not unique, the performance evaluation strategy cannot determine whether the controller performance degradation is due to real uncertainties or changes in the operating conditions. Therefore, it is necessary to identify the corresponding model according to the specific operating conditions and use it to solve the optimization problem to obtain a specific performance benchmark for accurate performance evaluation. In this paper, we propose an online data modeling strategy based on an improved just-in-time learning algorithm, which proposes an integrated learning framework for similar sample selection using a variety of different similarity measures in the selection of local modeling neighborhoods. Then, the local prediction models are trained, and finally, the final results are obtained by integrating the strategy to synthesize multiple local prediction models. The effectiveness of the proposed algorithm in this paper is demonstrated by Wood-Berry simulation experiments.
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