In the management of power grid infrastructure construction projects, the management of construction sites plays a very important role. At present, the company's construction site management business lacks supporting management tools and software, and it is difficult to achieve timely adjustment of the current construction with traditional evaluation methods. To this end, this paper studies the sensing information collection and virtualization construction of smart grid infrastructure scenarios. This paper establishes an objective and intelligent evaluation index system, pure evaluation standards and evaluation methods, and introduces and evaluates the impact on construction, which can improve the efficiency of construction and has very important practicability. Experiments show that the BiLSTM+CRF model using the word embedding vector as input can effectively capture long-distance semantic dependencies, which can not only improve the accuracy of named entity recognition, but also greatly reduce the training time of the neural network model. It shows better performance in mining tasks.
Electric energy is one of the important boosting forces for the progress of social and economic development and the improvement of people's quality of life. As the basic carrier of electric energy, the transmission and transformation project can be completed and put into operation on schedule, which is of great significance for ensuring the safe and effective supply of electric energy. Therefore, based on the analysis of construction schedule management problems and influencing factors of power transmission and transformation projects, this paper constructs a support vector machine-based construction schedule early warning method model, and verifies the validity of the model based on actual engineering cases. This model can provide a reference for improving the management level of the construction schedule of power transmission and transformation projects.
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