Cloud computing is a concept first proposed by Google [9]. It is the development of distributed computing, grid computing and parallel computing. It distributes computing tasks to a large number of computers or virtual machines through the network, and realizes the decomposition of huge computing tasks, thereby obtaining the calculation results of the tasks faster. Therefore, it is very suitable for the analysis and processing of massive concurrent data. Currently the main popular cloud computing technologies are Hadoop and Spark. This paper uses Hadoop architecture to build the model. The main task deployment of Hadoop is divided into three parts: client machine, master node and slave node [10]. The master node is responsible for the supervision of HDFS (Hadoop Distributed File System) and Map Reduce, and the slave node is responsible for computing instructions and storing data [11].

The overall framework of Hadoop is shown in Fig. 1. Among them, HDFS is a distributed file system, MapReduce is a programming model, and HDFS and Map Reduce are two key functional modules of Hadoop. In addition, HBase is a distributed database based on the column storage model, and Pig is a data analysis platform that provides operations and programming interfaces for massive data parallel operations. Hive is a tool that provides SQL queries, Sqoop is a tool for data transfer between Hadoop and traditional databases, ZooKeeper is a collaborative working system, and Avro is a data serialization system [12].

Random forest is an ensemble classification algorithm composed of several decision trees based on statistical learning theory [13,14]. The principle is to extract some samples of the original sample set by using the re-sampling method that can be replaced to train multiple decision trees. The final prediction result of the random forest model is obtained by comprehensively training the prediction results of multiple decision trees.

Suppose a random forest RF={ℜ(X,Ti),i=1,2,3,...,N} contains N decision trees, Ti is an independently distributed random vector, and X is a sample set. As shown in the Fig. 2: the basic idea of random forest classification is: randomly extract a sampling sample set A from the sample set, train a decision tree respectively, and use the majority voting method to vote according to the output results of these decision trees to obtain the final random forest classification result RF(x)=arg⁡max∑i=1NF(Ti,x) , where represents the output result of the random forest on the data x, and A represents the indicative function of the classification result of the i-th decision tree on the data x.

When constructing a random forest, an out-of-bag data set will be generated. The out-of-bag error of the out-of-bag data set can be used to measure the performance of the model. The random forest out-of-bag error formula is as follows:

(1) Oob=Err(X−A)X−A

The function Err is the count of the number of misclassifications.

According to random forest RF={ℜ(X,Ti),i=1,2,3,...,N} , the remaining quantity function can be obtained as:

(2) MG(X)=avk(F(Ti,X)=Y)−MAXavk(F(Ti,X)≠Y)

The larger the margin value, the more reliable the classification prediction.

Since the collected electricity consumption data will have problems such as missing and irregularities, the data needs to be preprocessed. Preprocessing is divided into two steps: missing data completion and data standardization.

Use interpolation method to complete the data, set the original data set as X¯={x1,1,...,x¯1,n,...,x¯m,1,...,x¯m,n} , if the data is missing, then

(3) x¯i,j=x¯i,j−1+x¯i,j+12

If continuous data is missing, for example, x¯i,j,...,x¯i,j+t are all missing, then

(4) x¯i,j=x¯i,j−1+x¯i,j+t+12

(5) x¯i,j+i=x¯i,j+i−1+x¯i,j+t+12, i=1,...,t

Suppose the data set to complete the missing value completion is X′={x1,1′,...,x1,n′,...,xm,1′,...,xm,n′} , and then use the min−max function to standardize it so that the values of all data are in the interval [0,1] .The min−max function is

(6) xi,j=x′i,j−x′minx′max−x′min

The data set after preprocessing is X={x1,1,...,x1,n,...,xm,1,...,xm,n} .

This paper uses the unique out-of-bag error of random forest to optimize the internal parameters of random forest and improve random forest to improve the performance of random forest model algorithm. Assume that the random forest model RF={ℜ(X,Ti),i=1,2,3,...,N} trained by the sample set X contains N decision trees, and the number of features of the sample set X are M.

We combine the drosophila algorithm to optimize the internal parameters of the random forest model [15,16]. In the process of random forest classification and recognition, the parameters that determine the accuracy are generally the number of decision trees TN and the size of the attribute feature subset FT. Where the number of decision trees TN is too large, the model training time may be too long, and too small will cause the accuracy to decrease, and the appropriate attribute features subset size FT can also greatly improve the model performance. Therefore, it is very important to TN and FT optimization, and the fruit fly algorithm can meet this requirement.

Suppose the size of the fruit fly population is ps , the maximum number of iterations of the algorithm is α , and the initial fruit fly population coordinates are (OOBbest,TNori,FTori) . The fruit fly algorithm is divided into two stages, namely the olfactory search stage and the visual positioning stage. In the olfactory search stage, calculate the average odor concentration AOBij of the i-th fruit fly in the j-th generation

(7) AOBij=(∑i=1psOOBij)ps

where OOBij represents the odor concentration of the i-th fruit fly at the j-th generation, that is, the error outside the random forest bag, and Adi represent the odor fitness value of the i-th fruit fly. The formula is as follows:

(8) Adi=1TNi2+FTi2

Then calculate the rate of change of AOBij :

(9) V=AOBj−AOBj−1AOBj−1

According to the change rate, the optimal step weight W=V+ε is obtained, where ε is the adaptive threshold.

Calculate the adaptive optimization route of drosophila:

(10) TNi=TNori+WV,FTi=FTori+WV

then:

(11) OOBbest={OOBbest,(OOBbest−OOBij<θ)OOBij,(OOBbest−OOBij≥θ)

Repeat several times until the maximum number of iterations α are reached,

Get the drosophila BEST=[OOBbest,TNbest,FTbest] with the highest odor concentration on the drosophila cluster. Use BEST to construct an optimal random forest.

On the built Hapood platform, use the MapReduce module to build a random forest model, the steps are as follows:

The random subspace sampling method is used for sampling, multiple sets of characteristic attribute sets A={FT1,FT2...,FTN} are extracted, and the number of each group is fixed.