The precise coagulation add-in in the wastewater process treatment is key for efficient contamination removal. However, the complexity of the coagulant chemical theory and affected by many factors (turbidity, pH, conductivity, flow rate, etc.) that it is difficult to determine the optimal dosage. The traditional method in the production process, such as PID controller had a bad adaptability on the complex systems and high performance required systems due to its inefficient parameter coordination, and it has a large time delay, difficult to achieve precise control. Excessive dosage will lead to waste and cost-waste, insufficient dosage could not guarantee the quality of effluent water. In this research study, we proposed an intelligent precisely dosing prediction algorithm based on LightGBM, using the characteristics of the influent water quality parameters PH, turbidity, electrical conductivity and flow rate to predict the dosage of coagulant. Perform experiments based on the actual data collected from the sewage treatment plant. Compared to experimental results with the optimal dosage solution, it demonstrated that the proposed approach could predict the dosage more accurate, resulting in intelligent and precise dosing add-in in water treatment process.

Innovation has some new changes today. Competition evolves into the competition of the ecosystem between companies, which is very obvious in the digital economic activities. However, many innovation systems have been only analyzed from the perspective of statistics and industrial chain with a single dimension, linear and static structure, which were not suitable for the development of the digital economy. This paper proposes a new approach to construct a three-dimensional innovation ecosystem of regional digital economy, which can self-evolution and includes six sub-ecosystems of innovation firms, human resources, financial capital, technological innovation, intermediary services organizations and policy environment. Disruptive innovation is the most important action. It is useful by approved of a case study. Using the ecosystem, the new business growth points and industrial formation are cultivated automatically.

At present, China’s economy is continuously developing and making progress. The deep integration of society and big data artificial intelligence and other technologies is constantly promoting the construction of new smart cities. The major livelihood service systems have provided great convenience to the people. At the same time, they have accumulated massive livelihood data. Mining the value of data to solve people’s main problems has been widely concerned by the government and the community. However, the data structure of each system is different. The data is noisy and the systems cannot be interconnected, which leads to the one-sided and single results of data analysis, and it is impossible to use these data to obtain effective results and conclusions. This paper proposes a Public Sentiment Index that reflects the intensity of public appeals. A new multi-source data fusion analysis model is proposed by using a multi-source data fusion method. By constructing a public sentiment database and a public sentiment index model, it can truly reflect the changes in people’s demands. This research can provide a reliable and scientific basis for the government to improve decision-making capacity and service quality.

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