The world economy is developing rapidly. Behind the rapid development, the management of economic regions is very important. In the process of economic management, the government plays the role of macrocontrol and is responsible for the management of various economic affairs and social and economic services. While undertaking infrastructure construction, it creates a good environment for economic development. However, with the deepening of economic development and the more and more complex economic data, the current economic management has gradually exposed a range of issues that arise during the process of economic development, and these problems need to be solved urgently. At this time, the application scope of artificial intelligence in the economic field is getting wider and wider, and it has a great positive effect on economic development. Therefore, in order to solve the problem of economic management in the process of economic development, this paper proposes a development path that integrates AI and economic management and provides intelligent technology support for the development of economic management to help the smooth operation of economic development. In addition, this paper shows through experiments that the path of integration of AI and economic management can promote the development and smooth operation of the economy, and AI has a positive impact on economic management.

The implementation of the rural revitalization strategy is an important strategic plan to solve the social “three rural” problems on the new road of building a socialist modernized country in an all-round way. It is also the current society to promote the comprehensive rule of law and to create and improve a modern rural social governance system under the leadership of the party committee, the government is responsible, the society is coordinated, and the country is governed by law. However, due to the wide distribution of rural areas and the large population base of farmers, there are many risks in the implementation of the rural revitalization strategy. In the comprehensive use of economic, administrative, and legal means, legal means has become the key. For this reason, this article conducted in-depth research on the legal path of rural revitalization under the risk prevention of Internet of Things algorithm decision-making. The research results showed that the risk prevention of Internet of Things algorithm decision-making was introduced into the research on the legal path of rural revitalization, and a sound rural governance system that combined autonomy, rule of law, and morality is an important part of the revitalization of seven villages. It can improve the 2.67% effectiveness of risk prevention in decision-making and can also play a key role in ruling the country according to law, showing the correct direction of agricultural legal system construction and at the same time high-level rural construction and development under the framework of the rule of law. It will vigorously promote the comprehensive modernization of agriculture, the comprehensive progress of rural areas, and the revitalization and development of rural areas.

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As an important transmission component of industrial robots, the harmonic reducer determines the positioning accuracy, bearing capacity and service life of the robot end-effector. Predicting the performance can grasp the working status in advance and avoid major losses caused by uncertain factors such as component damage. The current paper focuses on a harmonic reducer performance prediction algorithm based on Multivariate State Estimation Technique (MSET) and LargeVis dimensionality reduction. Firstly, an accelerated life test platform is designed to collect multi-dimensional parameters that can characterize the operating state of the harmonic reducer throughout the life cycle. Afterwards, as far as the MSET method is concerned, the fault warning threshold is set according to the residual between the constructed memory matrix of the health state data and the actual observed value. Finally, utilizing LargeVis to reduce the dimensionality of multi-dimensional features, combining with Mahalanobis distance to construct a health index degradation model, and then selecting Long Short-Term Memory (LSTM) network to predict the downward trend of the harmonic reducer. The analysis of the accelerated life test data of the harmonic reducer demonstrates that the proposed method can send out the fault warning signal 18 minutes in advance in the sample with a life of 5.7 hours, and has a strong ability to predict the degradation trend of the harmonic reducer.

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