随着大量分散式农村污水处理设备在我国投入使用，其在运行成本、监管、控制等方面的诸多问题愈发明显，亟需开发结合农村特点的管控体系，以期保证分散式污水处理设施的高效运行。本研究基于循环富氧生物膜装置，深入分析了装置的运行规律与控制参数，设计开发了装置的光伏供能系统、基于透明度的处理效果快速检测技术和装置的智能管控系统，并在多个场景下对各个系统进行了考察与经济性分析，验证了其实用性与经济性。对循环富氧生物膜装置进行了深入的应用研究，在确定出水水质达标的前提下，探索了其核心控制参数的优化方向，确定了装置的适宜运行温度为10 ~ 15℃以上。设计了光伏供能系统用以减少市电用量以及在冬季低温条件下对装置进行加热保温，在非冬季时能够降低74.9%的运行成本，冬季低温时能够维持装置在适宜温度稳定运行并降低处理体系21.1%的运行成本。为实现分散式污水设备的低成本、自动化水质检测，明确了以透明度作为检测指标，研究了我国生活污水透明度与水质指标的关系。基于计算机视觉技术开发了水样透明度检测算法，并进行了统计学验证，在15 ~ 100 cm的量程内算法在验证数据上的MAE为5.857、R2为0.870，满足工程使用要求。根据检测算法要求开发了水样透明度检测盒，并对其检测稳定性进行测试，误差范围为-8.4% ~ 4.6%。在不同的实际场景下实地验证，证明开发的基于透明度的生活污水处理设备效果检验技术能够长时间稳定运行，自动检测水样的透明度，并在出水水质不达标时及时进行反馈。根据云端计算、网关传输控制、本地感知的三层体系设计了分散式农村污水处理设备的智能管控体系并完成了体系搭建，能够实时展示不同设备的处理水量、出水水质、用电量等运行数据，通过云服务器并行管控多台装置，具有较大应用前景。基于机器学习模型开发了针对循环富氧生物膜装置的自动控制模型，能够根据来水情况智能调控装置循环富氧比。模型在验证数据上的MAE为0.731、R2为0.992，拟合结果较好，又通过实际装置验证了模型的准确性。整合已开发功能设计了两种智能管控系统的自动运行模式并进行了实际验证，在基于水量的智能管控模式下能够降低装置能耗约25.0%，在基于水量与水质的模式下能够降低装置能耗约43.3%。

With a large number of decentralized rural sewage treatment devices being put into use in China, its many problems in terms of operating costs, supervision, control, etc. are becoming more and more obvious, and there is an urgent need to develop a management control system that combines the characteristics of rural areas with a view to ensuring the efficient operation of decentralized wastewater treatment facilities. Based on the circulating oxygenation biofilm equipment, this study analyzed the operation rules and control parameters of the device, designed and developed the photovoltaic energy supply system, the rapid treatment effect detection technology based on transparency, and the intelligent management control system of the device, then examined and economically analyzed each system in multiple scenarios to verify its practicality and economy.In-depth applied research on the circulating oxygenation biofilm equipment was carried out, and the optimization of its core control parameters was explored under the premise that the quality of the effluent meets the standard, and the suitable operating temperature of the device was determined to be above 10 ~ 15℃. A photovoltaic energy supply system was designed to reduce the amount of utility power and heating and insulation of the device under low temperature conditions in winter, which can reduce the running cost by 74.9% in non-winter time, and maintain the device at the appropriate temperature for stable operation in low temperature in winter and reduce the running cost of the treatment system by 21.1%.In order to realize the low-cost, automated water quality testing of decentralized wastewater equipment, the transparency as a testing indicator was clarified, and the relationship between the transparency of domestic sewage and other water quality indicators in China was studied. The water sample transparency detection algorithm was developed based on computer vision technology and statistically validated, and the MAE of the algorithm on the validation data was 5.857 and the R2 was 0.870 in the range of 15.0 ~ 100.0 cm, which can meet the engineering using requirements. A water sample transparency detection box was developed according to the requirements of the detection algorithm, and its detection stability was tested with an error range of -8.4% ~ 4.6%. Field verification in different real-world scenarios proved that the developed transparency-based domestic wastewater treatment equipment effect inspection technology can operate stably for a long time, automatically detect the transparency of water samples, and provide timely feedback when the effluent water quality does not meet the standards.Based on the three-layer system consists of cloud computing, gateway transmission control and local perception, the intelligent management control system of decentralized rural sewage treatment equipment was designed and built, which is capable of real-time displaying the operation data of different equipment such as water treatment capacity, effluent quality and energy consumption, and controlling multiple devices in parallel through the cloud server, which has wide application prospects. With the help of machine learning, an automatic control model for the circulating oxygenation biofilm equipment was developed, which can intelligently regulate the circulating oxygenation ratio of the device according to the incoming water condition. The MAE of the model on the validation data is 0.731, and the R2 is 0.992, which is a good fitting result, and verified the accuracy of the model with actual device. By integrating the developed functions, two automatic operation modes of the intelligent management control system were designed and verified, which can reduce the energy consumption of the device by about 25.0% in the water quantity mode, and by about 43.3% in the water quantity and water quality mode.