Experts’ Assignment Algorithm for Cloudbased Agro-advisory Service Information System (CASIS) using Weighted Sum Model: Piloting CASIS

A Cloud-based Agro-advisory Service Information System (CASIS) uses interactive operating mode where assignment of questions from farmers to experts is done manually. Questions as input to the system are received randomly in a day and experts are supposed to respond within a specified time. The system has 20 experts in its database who respond to farmers questions and it can receive more than 30 questions per day. If there is a significant delay in the responses to a question then the question is reassigned to another expert. Each expert behaves differently when responding to their assigned questions. In order to address the shortcomings, the experts’ assignment algorithm was developed utilizing the respondents’ response probabilities and time of responses. Assignment decision is based on using a model that trains ‘CASIS’ on the determination of best experts. CASIS training algorithm is developed to complement current weakness. The algorithm doesn’t omit experts who respond late but complements them with active ones. The decision boundary is homogeneity and numerical so as to give a single output quickly. The input (x1, x2) and output (y) variables are numeric. The main concept is that the output is generated using linear combination or weighted sum model The algorithm considers response time as best criteria to satisfy the farmers who send the questions. This algorithm provides a great chance of finding a quick answer within a short period of time. Automatic expert assignment is essential to achieve high adoption of the system that satisfies the on-time farmer advisories demand and promote efficiency as well as effective extension services for rural development.

基于云的农业咨询服务信息系统（Cloud-based Agro-advisory Service Information System, CASIS）采用交互式运行模式，当前农民问题向专家的分配流程为人工操作。该系统每日会随机接收农户提出的咨询问题，专家需在规定时限内完成回复。系统数据库中配备20名专职回复农户问题的专家，单日可接收超过30条咨询请求。若某一问题的回复出现显著延迟，则会将该问题重新分配给其他专家。每位专家在处理分配到的问题时，响应风格与效率均存在差异。为弥补现有系统的不足，研究团队基于专家的回复概率与回复时长，开发了全新的专家分配算法。该分配决策依托一款以CASIS为训练基础的模型，用于遴选最佳匹配的回复专家。CASIS训练算法的开发旨在弥补现有系统的短板，该算法不会直接剔除回复迟缓的专家，而是通过搭配活跃专家来优化整体响应效率。决策边界具备同质性与数值化特征，可快速输出单一分配结果。输入变量(x1, x2)与输出变量(y)均为数值型，其核心原理为通过线性组合或加权求和模型生成输出结果。该算法将回复时长作为核心评判标准，以满足提出咨询的农户的需求，可大幅提升在短时间内获取快速回复的概率。自动专家分配机制是提升系统普及率的核心要素，既能满足农户对咨询服务的时效性要求，又可提升整体服务效率，为乡村发展提供优质高效的农业推广服务。