Implementation of Asset Inventory Model Based on Intelligent Algorithm

Asset inventory is an important part of internal management in an organization, and it is of great significance for the smooth operation and normal development of the organization. The traditional manual inventory method has problems of low inventory efficiency and low accuracy. The asset inventory model based on intelligent algorithms can overcome the shortcomings of traditional methods and improve inventory efficiency and accuracy. This article introduces the implementation process of an asset inventory model based on deep learning algorithms. Firstly, the YOLOv4 algorithm is used for object detection to obtain the position and category information of the target object in the asset image. Then, the Faster R-CNN algorithm is used to locate the target, further improving accuracy. Finally, a neural network model trained on large-scale data is used to classify, recognize, and count assets. Using the actual data, this paper constructs the optimal portfolio model based on different risk measures and termination conditions: the maximization model of unit risk return, the VaR risk control model, and the risk return preference model. Genetic algorithm and Simulated annealing algorithm are used to optimize the model. Through experimental verification, the recognition rate of this model reaches over 90%, and the inventory efficiency and accuracy are greatly improved compared to traditional manual inventory methods. In summary, asset inventory models based on intelligent algorithms have unique advantages in inventory efficiency and accuracy, and have broad application prospects in the future.