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In order to reduce the loss value in the data recovery process and ensure the integrity of the procurement information, a data recovery method for online procurement of power grid materials based on local weighted reconstruction is proposed. According to the principle of clustering interval set division of online procurement data of power grid materials, the missing data of online procurement of power grid materials is mined by using the density peak clustering method; the characteristics of procurement missing data are extracted by the combination model formed by the generative model and the discriminant model in the generative adversarial network through the dual semantic perception of the characteristics of the missing data in the procurement, the reconstruction of the missing data in the procurement is realized; after the reconstructed online procurement lost data is detected by the autocorrelation fusion filter detection method, the lost data is optimized and restored by combining the local weight reconstruction, And by using the local weighted learning method to realize the optimal control of lost data recovery. The experimental results show that the method can effectively cluster the missing data; the Rand coefficient is adjusted to keep above 0.5; when the reconstructed data is reconstructed 90 times, the position of the reconstructed data is basically the same as the original data; the restored data is more complete and no loss value is generated. It can effectively mine the lost data of procurement, and realize the recovery of lost data of online procurement of power grid materials.
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