Agriculture is unique in that the industry is subject to a certain level of uncertainty due to seasonal and other factors, and its risk level is significantly higher than that of other industries. Therefore, it is necessary to establish an appropriate financial early warning model to predict, analyze, and control its financial risks. The article uses a genetic algorithm and support vector machine-based economic forecasting model for agribusinesses to adapt its own pollutant weights in a practical application environment to improve the forecasting accuracy. This model first uses a genetic algorithm to train a feature weight vector of current data so that the weights are adapted to the current prediction problem and then uses this feature weight vector to apply to SVM model training. It is found that 62.79% of the listed agricultural companies are in warning status, and their development is not optimistic. The article provides new ideas for the subsequent research on financial warning methods and also expands the boundaries of theoretical research for the research system of financial warning problems and enriches the experience and evidence of practical research.
With the rapid development of China itself, the supply chain system has become an effective tool to enhance economic competitiveness, and the intelligent supply chain system integrates innovative technologies such as the Internet, the Internet of Things, cloud computing, and big data into the industrial supply chain management. The ant colony algorithm shows super flexibility and robustness at the level of optimizing many combination problems; the intelligent supply chain architecture of agricultural products is essentially the rational allocation of resources, which is a dynamic combination of resources and tasks that combines a variety of combinations to select the most appropriate and optimal performance. In this paper, the architecture of the intelligent supply chain of agricultural products is studied in combination with the improved ant colony algorithm, and some improvement strategies are adopted to avoid falling into local optimization or local convergence in order to avoid falling into local optimization or local convergence. The improvements of the algorithm mainly include the following: improving the path search of individual ants, improving the pheromone update strategy, improving the selection probability, and improving the dynamic growth mechanism of ants. In this paper, the VRPTW mathematical model, the dataset TSPLIB, and the operation data of the intelligent supply chain of agricultural products are used for test verification; the results can be obtained by verifying the results of the experiment closer to the optimal solution, and the efficiency of the algorithm is significantly improved. Therefore, through the improvement and optimization of the ant colony algorithm, it is very suitable for solving and optimizing the intelligent supply chain system of agricultural products.
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