In this paper, we propose 4 theoretical models to deal with the wild hornet crisis. First, we use ORIGIN to visualize the distribution of wild wasps. Using the least square method and the grey system GM (1, 1), we establish a theoretical model to predict the propagation of wild wasps over time and analyze the accuracy of the model. However, the accuracy of our model is not very high, which results from the influence of many factors such as climate and human. Secondly, we use convolution neural network to recognize the images. With the increase of network depth, the accuracy rate reaches a bottleneck, which can help predict mistaken classification. We also use the SIR infectious disease model based on the dataset file provided. In the model, we mark the confirmed giant hornet as the infected state I (infected), mark the nonwild wasp as the removed state R (removed, refractory, or recovered), and mark the unclassified and unverified wild wasp as the susceptible state S (susceptible). A model to predict the possibility of misclassification was established by considering the normal death of wild wasp. Thirdly, by analogy with the SIR model, when the epidemic occurs, people pay more attention to the infected person. Thus, the SIR model will lead to the most likely positive sightings. Then, in order to ensure the timeliness and accuracy, the model must be updated once a year by changing or adding parameter according to local conditions. Finally, by establishing an optimized SIR infectious disease model, we added the factor of the Washington state's control of wild wasps. The analysis shows that the number of infected I (i.e., wild wasps) has tended to zero after 250 days, so it can be proved that the Washington state has eliminated the pest.
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