Method for predicting benchmark value of unit equipment based on XGBoost algorithm and system thereof

A method for predicting benchmark value of unit equipment based on XGBoost algorithm is characterized by comprising the following steps:

• S1. The historical operation data of unit equipment is obtained, the data is preprocessed, and a data set containing a plurality of samples is constructed, and each sample includes the benchmark value of a plurality of parameters of the equipment corresponding to a plurality of features;
• S2. random forest (RF) out-of-bag estimation is used for feature importance calculation to eliminate the features with low importance;
• S3. The data is standardized to eliminate the dimensional effects among features;
• S4. The data set is input to construct an XGBoost model, and Bayesian super parameter optimization is conducted to obtain the prediction model of benchmark values;
• S5. The real-time data of equipment operation is input, and the benchmark values of various equipment parameters are predicted by the prediction model of benchmark values,
• S6. Adjust the operating parameters of the equipment to the benchmark values obtained through S5;
• wherein step S2 is as follows:
• for each feature of the sample, the random forest (RF) out-of-bag estimation is used to rank the importance of the features and select the features, wherein the average precision decline rate (MDA) is used as an indicator to calculate the importance of the feature, and wherein the formula is as follows:
• MDA
• =
• 1
• n
• ⁢
• ∑
• t
• =
• 1
• n
• ⁢
• (
• e
• ⁢
• r
• ⁢
• r
• ⁢
• O
• ⁢
• O
• ⁢
• B
• t
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• errOOB
• t
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• ,
• wherein, n is the number of base classifiers constructed by random forests, errOOBt is the out-of-bag error of the tth base classifier, and errOOB′t is the out-of-bag error of the tth base classifier after noise is added, wherein the more MDA decreases, the higher the importance of the feature;
• in step S3, the data set contains N samples, each sample has L-type features, and Z-score standardization method is used to standardize each type of features of each sample, as follows:
• x
• nl
• *
• =
• x
• nl
• -
• μ
• l
• σ
• l
• wherein, xnl is the feature data of the type 1 features of the nth sample, and
• x
• nl
• *
• is the feature data of the type 1 features of the nth sample after standardization, μl is the mean value of the feature data of the type 1 features in the Nth sample, and σl is the standard deviation of the feature data of the type 1 features in the Nth sample.