CN111104298A - LSTM-based power grid server running state prediction device - Google Patents
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Abstract
A power grid server running state prediction device based on LSTM relates to the field of deep learning big data and aims to solve the problem that an existing real-time monitoring system cannot complete server evaluation and early warning. The server data monitoring module is used for acquiring server parameter indexes, extracting monitoring data and constructing a training set; the monitoring data refers to the utilization rate data of the CPU, the memory bank and the hard disk; the server data preprocessing module is used for carrying out normalization processing on the monitoring data acquired by the server data monitoring module and then carrying out feature selection; and the server running state prediction module is used for constructing an LSTM model to carry out big data deep learning processing analysis on the monitoring data after the characteristic selection so as to complete prediction evaluation and early warning of the running state of the power grid server. The method has the advantages that the problem that the prediction accuracy is reduced or prediction is difficult due to the fact that monitoring data information exceeds the computing capacity due to the fact that a single model is used in the existing method is solved.
Description
Technical Field
The invention relates to the field of deep learning big data.
Background
In the smart grid system, big data are generated in each link of the whole system; especially, the monitoring center can generate mass data information every day; the data volume maintained by data centers of many companies and enterprises in China is very large, professional operation and maintenance personnel are lacked, and non-professionals do not know and understand the operation and maintenance technology in place; many times, after some problems occur in the server, the server is usually not careful, and a long time is needed from the occurrence of the problems to the correct positioning of the problems.
Currently, in the related art, real-time monitoring cannot find problems in advance, and only can passively cope with the problems.
Disclosure of Invention
The invention aims to solve the problem that the existing real-time monitoring system cannot complete the evaluation and early warning of a server, and provides an LSTM-based power grid server operation state prediction device.
The LSTM-based power grid server running state prediction device comprises a server data monitoring module, a server data preprocessing module and a server running state prediction module;
the server data monitoring module is used for acquiring server parameter indexes, extracting monitoring data and constructing a training set;
the monitoring data refers to the utilization rate data of a CPU, a memory bank and a hard disk;
the server data preprocessing module is used for carrying out normalization processing on the monitoring data acquired by the server data monitoring module and then carrying out feature selection;
and the server running state prediction module is used for constructing an LSTM model to carry out big data deep learning processing analysis on the monitoring data after the characteristic selection so as to complete prediction evaluation and early warning of the running state of the power grid server.
The method has the advantages that the server parameter indexes are obtained through the server data monitoring module, the monitoring data are extracted, and a training set is constructed; after normalization processing is carried out by a server data preprocessing module, feature selection is carried out; finally, an LSTM model is built through a server running state prediction module to carry out big data deep learning processing analysis on the monitoring data after feature selection, and accurate prediction on the monitoring data of the power grid server is achieved; the method solves the problems that the prediction precision is reduced or difficult to predict because the monitoring data information exceeds the computing power due to the use of a single model in the prior art, and has the effects of dynamic and self-adaptive transformation; and the threshold can be flexibly selected, so that the calculation complexity is simplified, the implementation cost of a prediction algorithm is reduced, the manual investment is reduced, and the operation is more flexible and simpler to change.
Drawings
Fig. 1 is a block diagram of an LSTM-based power grid server operation state prediction apparatus according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the structure of LSTM cells according to the second embodiment.
Detailed Description
The first embodiment is as follows: the present embodiment is described with reference to fig. 1, and the LSTM-based power grid server operation state prediction apparatus in the present embodiment includes a server data monitoring module 1, a server data preprocessing module 2, and a server operation state prediction module 3;
the server data monitoring module 1 is used for acquiring server parameter indexes, extracting monitoring data and constructing a training set;
the monitoring data refers to the utilization rate data of a CPU, a memory bank and a hard disk;
the server data preprocessing module 2 is used for performing normalization processing on the monitoring data acquired by the server data monitoring module 1 and then performing feature selection;
and the server running state prediction module 3 is used for constructing an LSTM model to perform big data deep learning processing analysis on the monitoring data after the characteristic selection so as to complete prediction evaluation and early warning of the running state of the power grid server.
In this embodiment, LSTM is a long-short term memory recurrent neural network; acquiring server parameter indexes through a server data monitoring module 1, extracting monitoring data and constructing a training set; after normalization processing is carried out by the server data preprocessing module 2, feature selection is carried out; finally, an LSTM model is built through a server running state prediction module 3 to carry out big data deep learning processing analysis on the monitoring data after feature selection, and accurate prediction on the monitoring data of the power grid server is achieved; (ii) a The problem that the prediction precision is reduced or is difficult to predict due to the fact that monitoring data information exceeds the computing capacity due to the fact that a single model is used in the existing method is solved, and the power grid server operation state prediction device has the effects of dynamic and self-adaptive transformation; and the threshold can be flexibly selected, so that the calculation complexity is simplified, the implementation cost of a prediction algorithm is reduced, the manual investment is reduced, and the operation is more flexible and simpler to change.
The second embodiment is as follows: the present embodiment is described with reference to fig. 2, and is further limited to the LSTM-based power grid server operation state prediction apparatus according to the first embodiment, in the present embodiment, the constructed LSTM model has an output dimension of 64, an input activation function of tan h, an output activation function of tan h, and tan h is a hyperbolic tangent function; the gate activation function is sigma, and sigma is a sigmoid function; the LSTM model structure is a neural network model with 1 layer of LSTM +1 layer of DENSE.
In this embodiment, the output dimension of the LSTM layer, i.e., the number of cells included in the LSTM layer, increases the number of cells, and increases the internal parameters.
In the embodiment, the DENSE layer is a hidden layer, the inherent rule in the time sequence is deeply excavated through the unique unit structure of the LSTM, the LSTM layer and the DENSE layer are combined to realize the running state prediction of the CPU data of the power grid server, and finally, the time sequence analysis prediction of the power grid server is realized.
The third concrete implementation mode: in the second embodiment, the LSTM model performs a big data deep learning processing analysis on the monitoring data after feature selection by using a BPTT algorithm;
the BPTT algorithm is realized by the following steps;
step one, calculating an output value of the LSTM cell according to a forward calculation method formula; wherein, the forward calculation method has the formula from formula (1) to formula (5);
it＝σ(Wxixt+Whiht-1+Wcict-1+bi) (1)
ft＝σ(Wxfxt+Whfht-1+Wcfct-1+bf) (2)
ct＝ftct-1+ittanh(Wxcxt+Whcht-1+bc) (3)
Ot＝σ(Wxoxt+Whoht-1+Wcoct+bo) (4)
ht＝ottanh(ct) (5)
in the formula: i is an input gate, f is a forgetting gate, c is a cell state, and O is an output gate; w is a corresponding weight coefficient matrix; such as WxfRepresenting input layers toA weight coefficient matrix of the forgetting gate; b is the corresponding bias term; sigma is sigmoid function; tan h is a hyperbolic tangent activation function; x is a given sequence of an input layer, namely the utilization rate of a CPU, a memory bank and a hard disk under a normal condition; h is a hidden layer sequence, namely a prediction evaluation and early warning result of the operation state of the power grid server; t is the current moment, and t-1 is the last moment;
the relationship between x and h is as follows: h ist＝tanh(Wxhxt+Whhht-1+bh)；
Step two, reversely calculating an error term of each LSTM cell, wherein the error term comprises 2 reverse propagation directions according to time and network levels;
step three, calculating the gradient of each weight according to the corresponding error term;
and step four, updating the weight by applying an optimization algorithm based on gradient.
Claims (3)
1. The LSTM-based power grid server operation state prediction device is characterized by comprising a server data monitoring module (1), a server data preprocessing module (2) and a server operation state prediction module (3);
the server data monitoring module (1) is used for acquiring server parameter indexes, extracting monitoring data and constructing a training set;
the monitoring data refers to the utilization rate data of a CPU, a memory bank and a hard disk;
the server data preprocessing module (2) is used for carrying out normalization processing on the monitoring data acquired by the server data monitoring module (1) and then carrying out feature selection;
and the server running state prediction module (3) is used for constructing an LSTM model to carry out big data deep learning processing analysis on the monitoring data after the characteristic selection so as to complete prediction evaluation and early warning of the running state of the power grid server.
2. The LSTM-based power grid server operation state prediction device of claim 1, wherein the constructed LSTM model has an output dimension of 64, an input activation function of tanh, an output activation function of tanh, and a hyperbolic tangent function of tanh; the gate activation function is sigma, and sigma is a sigmoid function; the LSTM model structure is a neural network model with 1 layer of LSTM +1 layer of DENSE.
3. The LSTM-based power grid server operation state prediction device according to claim 2, wherein the LSTM model performs big data deep learning processing analysis on the monitoring data after feature selection by adopting a BPTT algorithm;
the BPTT algorithm is realized by the following steps;
step one, calculating an output value of the LSTM cell according to a forward calculation method formula; wherein, the forward calculation method has the formula from formula (1) to formula (5);
it＝σ(Wxixt+Whiht-1+Wcict-1+bi) (1)
ft＝σ(Wxfxt+Whfht-1+Wcfct-1+bf) (2)
ct＝ftct-1+ittanh(Wxcxt+Whcht-1+bc) (3)
Ot＝σ(Wxoxt+Whoht-1+Wcoct+bo) (4)
ht＝ottanh(ct) (5)
in the formula: i is an input gate, f is a forgetting gate, c is a cell state, and O is an output gate; w is a corresponding weight coefficient matrix; such as WxfA weight coefficient matrix representing the input layer to the forgetting gate; b is the corresponding bias term; sigma is sigmoid function; tan h is a hyperbolic tangent activation function; x is a given sequence of an input layer, namely the utilization rate of a CPU, a memory bank and a hard disk under a normal condition; h is a hidden layer sequence, namely a prediction evaluation and early warning result of the operation state of the power grid server; t is the current moment, and t-1 is the last moment;
the relationship between x and h is as follows:ht＝tanh(Wxhxt+Whhht-1+bh)；
step two, reversely calculating an error term of each LSTM cell, wherein the error term comprises 2 reverse propagation directions according to time and network levels;
step three, calculating the gradient of each weight according to the corresponding error term;
and step four, updating the weight by applying an optimization algorithm based on gradient.
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