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With the increasing scarcity of conventional energy and environmental degradation, countries around the world are increasing their investment in renewable energy development. In order to make a scientific investment evaluation of renewable energy projects, this paper examines the analysis and control of their financial data. The intelligent analysis system of financial data is constructed based on OLAP. Logistic regression model and decision tree algorithm model are selected as the operation algorithm of the system to complete the intelligent analysis of data. Combining random forest algorithm and autoregressive moving average model, under the guidance of Bagging idea, the financial status of renewable energy projects after investment is judged in order to achieve the purpose of dynamic control. According to the results of analysis and control of financial data of renewable energy projects, it is known that the correct probability of intelligent analysis of financial data reached 94.5%, 83.1%, and 92.7% for different sample sizes of data sets, respectively. There were significant improvements in the efficiency of capital usage and asset quality, with an increase in capital concentration of 30.42%, an increase in inventory turnover from 10.68% to 13.04%, and an increase in the recovery rate of overdue accounts receivable from 60.31% to 67.83%. It has been proven that the method can help investors to better utilize uncertainty to improve the investment value of project, providing investors with a new way of thinking about decision-making.

In today’s rapid development of the economic environment, the company’s financial crisis can significantly reflect the enterprise crisis, and it has a significant impact on the development of the enterprise. Therefore, the prediction of the company’s financial crisis and the establishment of an early warning system have important practical significance. Based on the method of pattern recognition and artificial intelligence, this paper establishes a new forecasting model of corporate financial crisis, predicts its financial index data and realises the dynamic financial crisis warning. The model developed in this paper employs pattern recognition and artificial intelligence, and is thus endowed with high classification accuracy, good anti-noise ability and the ability to be sorted according to the importance of its index features.

ObjectiveTo investigate and analyze the epidemiological characteristics of a wild mushroom poisoning event in Qinhuangdao City, identify the species of poisonous mushroom, summarize the experience of investigation and treatment, and put forward prevention and control measures, and provide reference for the treatment and prevention of wild mushroom poisoning patients.MethodsThe poisoning events were analyzed by using the method of epidemiological investigation, morphological identification and mushroom toxin detection.ResultsFour villagers in Qinhuangdao city had nausea, abdominal pain, diarrhea, sweating and shivering after eating wild mushrooms. Patients were treated with vomiting, gastric lavage, atropine and other symptomatic treatment in the hospital, and the prognosis of the patients was good. After morphological identification and toxin detection, it was determined that food poisoning caused by mushroom of inocybe.ConclusionThis is the first reported poisoning event caused by mushroom of inocybe in Hebei Province. The reason of poisoning was misguided by the Internet and poor self-awareness of food safety. Therefore, publicity and education should be strengthened, enhancing the awareness to prevent the poisoning of wild mushroom.