机器学习在社区流浪猫管理投喂方案中的应用

Community Stray Cat Feeding Scheme based on Machine Learning

为了对流浪猫的进食量有更好的把控,保证爱猫人士所送猫粮尽可能不被浪费,同时希望在完成短期流浪猫救助任务的同时不影响小区居民的正常生活,本文提出了一种有关社区流浪猫的管理与喂食的方案。方案结合“抓捕-绝育-放归”模式进行,主要围绕流浪猫投喂装置的投放地点选择以及当日猫粮投放量的预估展开。其中,使用相关性分析法和线性回归法预测猫粮的当日投放量,投放地点流浪猫数目预测采用k近邻算法。方案能够较为准确地判断出受救助的流浪猫数量并预测出次日猫粮投喂量,基本可以满足实际需要。

In order to better control the amount of stray cats,ensure that the cat food sent by cat lovers is not wasted as much as possible,and hope to complete the short-term stray cat rescue task without affecting the normal life of community residents,this paper proposes a scheme for the management and feeding of stray cats in the community.The scheme is suitable for combining TNR mode,mainly focusing on the selection of the feeding location of the stray cat feeding device and the estimation of the cat food delivery amount on the day.Among them,the correlation analysis method and linear regression method were used to predict the daily delivery of cat food,and the K nearest neighbor method was used to predict the number of stray cats at the feeding site.The scheme can accurately determine the number of rescued stray cats and predict the amount of cat food feeding the next day,which can basically meet the actual needs.