KR102328566B1 - Method, apparatus and program for acquiring failure prediction model in industrial internet of things (iiot) environment - Google Patents

Abstract

A failure prediction model acquisition system in an industrial Internet of Things (IIoT) environment is provided. The control method of the system includes, by the server, obtaining data from a plurality of sensors, the server pre-processing the obtained data, the server obtaining the importance of a feature based on the pre-processed data step, the server selecting a feature based on the obtained importance level, generating a plurality of models based on the selected feature, and the server selecting at least one model from among the plurality of generated models including the steps of

Description

Method, Apparatus and Program for Acquisition of Failure Prediction Model in Industrial Internet of Things (IIoT) Environment

The present invention relates to a method, apparatus, and program for acquiring a failure prediction model in an industrial Internet of Things (IIoT) environment.

An IoT system, including the Industrial Internet of Things (IIoT), allows a large number of devices to interact to achieve the desired purpose of the system. Since such an IIoT system includes a plurality of devices, if any one of the plurality of devices fails, the entire system may fail. In particular, recently, the Industrial Internet of Things (IIoT) is a key technology for advanced manufacturing, such as connectivity and Because it provides analysis functions, it is widely adopted by various companies, and IIoT systems are also using a vast amount of sensors.

In order to predict the failure of the IIoT system, a method of constructing a failure prediction model by collecting a large amount of data from a plurality of sensors included in the IIoT system has been proposed. Specifically, in order to predict failure, it is necessary to build a failure prediction model that determines whether a failure occurs, and most existing studies use machine learning technology to build a failure prediction model.

However, existing studies use all data in the data set when building a failure prediction model. Therefore, existing studies have shown that in an actual IIoT environment where a vast number of sensors are used, the prediction accuracy may be significantly reduced due to the effect on the data regardless of the failure.

Therefore, there is a need to construct a failure prediction model with high prediction accuracy by selecting data irrelevant to failure.

Registered Patent Publication No. 10-1538709, 2015.07.16

An object of the present invention is to provide a method, apparatus, and program for acquiring a failure prediction model in an industrial Internet of Things (IIoT) environment.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

A control method of a failure prediction model acquisition system in an industrial Internet of Things (IIoT) environment according to an aspect of the present invention for solving the above-described problems includes, by a server, acquiring data from a plurality of sensors; pre-processing, by the server, the obtained data; obtaining, by the server, the importance of the feature based on the pre-processed data; selecting, by the server, a feature based on the obtained importance level, and generating a plurality of models based on the selected feature; and selecting, by the server, at least one model from among the plurality of generated models. includes

In this case, the step of generating the plurality of models may be generated by the following algorithm 1.

[Algorithm 1]

In this case, cnt is the counter value, SF is the selected feature, PA is the prediction accuracy, sf _cnt is the selected feature in cnt, and the importance set of the feature is IMP=[imp ₀ ,...,imp _j ,...imp _{nf- 1} ], the set of features is F=[f ₀ ,...,f _j ,...,f _nf-1 ], imp _j is the importance value of the (j+1)th feature, nf is the The number, modelBuildEvalFunction(SF), may be any function for building and evaluating a predictive model.

In this case, the generating of the plurality of models may include: generating based on SF and SVM (Support Vector Machine) algorithms obtained by Algorithm 1; may include.

In this case, the selecting of the at least one model may include: obtaining max(PA) for _{pa i} having the highest prediction accuracy among _{pa 0} to pa _{cnt obtained by the algorithm 1;} obtaining at least one feature having a prediction accuracy equal to or greater than a preset value by obtaining the index of max(PA); and selecting the at least one model based on the max(PA) and at least one feature having a prediction accuracy equal to or greater than the preset value. may include.

In this case, the pre-processing may include: removing at least one feature from among a plurality of features corresponding to each of the data acquired from the plurality of sensors; normalizing data for a plurality of features from which the at least one feature has been removed; and dividing the normalized data.

In this case, the step of removing the feature may include: acquiring invalid data from among the data acquired from each of the plurality of sensors; removing a feature corresponding to the invalid data when the ratio of the invalid data is greater than a preset validity coefficient in a range of [0,1]; may include.

In this case, the step of removing the feature may include: obtaining respective variance values for data obtained from each of the plurality of sensors; when the variance value is less than or equal to a preset value, removing a feature corresponding to a variance value equal to or less than the preset value; may include.

Other specific details of the invention are included in the detailed description and drawings.

According to the above-described various embodiments of the present invention, there is a new effect of constructing an efficient and accurate failure prediction model in the IIoT environment through the failure prediction model obtained by repeatedly selecting various features.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a system diagram illustrating an IIoT system according to an embodiment of the present invention.
2 is a block diagram illustrating a method for obtaining a failure prediction model according to an embodiment of the present invention.
3 is a flowchart illustrating a method for obtaining a failure prediction model according to an embodiment of the present invention.
4 is a flowchart illustrating a method for selecting a failure prediction model according to an embodiment of the present invention.
5 to 9 are exemplary views for explaining the experimental results according to an embodiment of the present invention.
10 is a block diagram of an apparatus according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, by way of example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe the correlation between a component and other components. A spatially relative term should be understood as a term that includes different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer means all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include a smartphone, a tablet PC, a desktop, a notebook computer, and a user client and an application running on each device, but is not limited thereto.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

1 is a system diagram illustrating an IIoT system according to an embodiment of the present invention.

As shown in FIG. 1 , the IIoT system may include a server 100 and a plurality of sensors 200 - 1 to 200 - 5 .

The server 100 may acquire sensing data from the plurality of sensors 200 - 1 to 200 - 5 . The server 100 may build a failure prediction model for predicting system failure based on the acquired sensing data and the plurality of sensors 200-1 to 200-5 corresponding to the sensing data.

2 is a block diagram illustrating a method for obtaining a failure prediction model according to an embodiment of the present invention.

The server 100 may obtain a failure prediction model based on a feature selected in order to maximize the prediction accuracy of the failure prediction model. To this end, the server 100 may repeatedly perform feature selection, and may acquire a plurality of failure prediction models based on the repeatedly selected feature. Thereafter, the server 100 may obtain a failure prediction model having the highest prediction accuracy among a plurality of failure prediction models.

Specifically, as shown in FIG. 2, in order to obtain a server 100 failure prediction model, 1) a pre-processing step (Processing), 2) an importance measurement step (Importance Measurement), 3) a feature selection step (Feature Selection), 4) Model Building and 5) Model Selection may be performed.

Specifically, in the pre-processing step, the server 100 may perform feature removal, missing data replacement, normalization, and data segmentation steps. Thereafter, the server 100 may acquire the importance of the feature obtained through the preprocessing process. Thereafter, the server 100 may repeatedly perform the feature selection and model building steps. Thereafter, the server 100 may build a failure prediction model through the SVM based on the selected feature in the model building step.

3 is a flowchart illustrating a method for obtaining a failure prediction model according to an embodiment of the present invention.

In step S110, the server 100 may obtain data from a plurality of sensors.

According to one embodiment, in various embodiments of the present invention, the term feature and the term sensor may be interpreted the same.

In step S120, the server 100 may pre-process the obtained data.

Specifically, according to an embodiment of the present invention, the server 100 may remove at least one feature from among a plurality of features corresponding to each of data obtained from a plurality of sensors.

Specifically, the server 100 may acquire invalid data among data acquired from each of a plurality of sensors.

In an embodiment, the server 100 may remove the feature corresponding to the invalid data when the ratio of the invalid data for the specific feature is greater than the validity coefficient preset in the range of [0,1]. .

In this case, the invalid data may mean data having no value among data collected from the feature. In one embodiment, when the feature is a temperature sensor, data in which the temperature is not sensed may be invalid data. Invalid data may be data corresponding to a row having no value for each feature in the collected data set (which may include a training data set and a test data set). In another embodiment, the server 100 may obtain a respective variance value for data obtained from each of a plurality of sensors. Thereafter, when the variance value is equal to or less than the preset value, the server 100 may remove features corresponding to the variance value equal to or less than the preset value.

Specifically, in the pre-processing step, the server 100 may perform feature removal, missing data replacement, normalization, and data segmentation steps. Specifically, the server 100 retrieves the invalid (NA) data of each feature to remove invalid features from the input data set (i.e., data collected from multiple sensors), and calculates the variance of each feature. have.

According to an embodiment, when the ratio of NA data of a specific feature is greater than a preset validity coefficient in the range [0, 1], the server 100 may remove the specific feature from the input data set.

As another embodiment, when the variance of a specific feature is less than or equal to a preset value, the server 100 may remove the corresponding feature from the input data set. Preferably, the closer the variance of a particular feature to zero, the higher the probability that the feature will be removed from the input data set.

On the other hand, when missing data exists (that is, when NA data exists in the features remaining after feature removal), the server 100 may replace the missing data with the average value of the non-missing data of each feature. .

Thereafter, the server 100 may normalize data for a plurality of features from which at least one feature is removed.

Specifically, the server 100 may perform a normalization process according to the data size of each feature. According to an embodiment, the server 100 may perform a normalization process of each feature based on Equation 1 below.

[Equation 1]

xi' = (xi-m) / s

In this case, xi' may be the (i + 1)-th normalized characteristic data, xi may be the (i + 1)-th data of the feature, m may be the average of the features, and s may be the standard deviation of the features.

In this case, the average of features may mean an average of data values collected from each feature (eg, a sensor).

As an embodiment, when the feature is a temperature sensor, the average of the features may mean an average value of data of a plurality of temperatures obtained by the temperature sensor. It is evident that the standard deviation of the features and the variance of the features are also the standard deviation or variance of the data values collected from each feature.

Thereafter, the server 100 may divide the normalized data.

Specifically, the server 100 may classify the normalized data set into a training data set and a test data set. The server 100 may use the training data set to obtain a failure prediction model, and the test data set to evaluate the performance of the predictive model.

In step S130, the server 100 may acquire the importance of the feature based on the pre-processed data.

According to an embodiment, the server 100 may acquire a training data set from among the data acquired through the pre-processing step. Thereafter, the server 100 may acquire the importance of a feature corresponding to the training data based on the random forest technique for the training data.

Specifically, the server 100 may acquire the importance of the feature obtained through the preprocessing process. The server 100 may obtain the importance of each feature by analyzing the training data set based on the random forest technique.

Specifically, the server 100 may analyze the relationship between each feature and Fail by comprehensively considering a plurality of decision trees created through the random forest technique and the created decision trees.

According to an embodiment, in the random forest technique performed by the server 100 , a plurality of subsets of the training data set may be generated in order to obtain a decision tree differently, respectively. Each of the plurality of subsets generated may consist of different data and features.

To this end, the server 100 may randomly select n data (ie, n rows) and mtry features (ie, mtry columns) from the training data set. The server 100 may repeat the random selection until the number of obtained subsets reaches a predefined number of decision trees with ntrees.

Then, each decision tree can be built separately based on one of the obtained subsets. That is, after constructing an ntree decision tree, the importance of each feature is measured through the Mean Decrease Gini (MDG, Gini importance score), which can be an indicator of how much each feature affects accurate prediction results.

More specifically, in each decision tree, the server 100 may calculate the sum of differences in Gini impurities between a parent node using a specific feature and a child node of the parent node. Thereafter, the server 100 may calculate an average of all decision tree results. In this case, the decision tree may be composed of several nodes that make a decision using a threshold value of a specific feature. Each node can also contain Gini impurities to measure the probability of a wrong decision.

Meanwhile, the random forest technique is an ensemble method for learning a plurality of decision trees, and the random forest can be used for various problems such as detection, classification, and regression.

On the other hand, in the random forest according to the present invention, the trees have slightly different characteristics from each other due to randomness, and this characteristic causes the predictions of each tree to be decorrelated, and as a result, generalization ), there is an effect that can improve the performance. Also, randomization can make the forest robust against data including noise. Randomization is performed in the training process of each tree, and bagging and randomized node optimization, an ensemble learning method using a random learning data extraction method, may be used.

In step S140 , the server 100 may select a feature based on the obtained importance level and generate a plurality of models based on the selected feature.

The server 100 may repeatedly perform the feature selection and model building steps. Specifically, Algorithm 1 below shows the overall operation of the feature selection step and the model building step.

On the other hand, in Algorithm 1, the importance set of _{features can be defined as IMP=[imp 0} ,...,imp _j ,...imp _nf-1 ] , and the set of features is F=[f ₀ ,.. .,f _j ,...,f _nf-1 ] can be defined.

In this case, nf may be the number of features, and imp _j may be the importance of the (j + 1)-th feature.

In Algorithm 1, variables are initialized at the start, sf _cnt means a feature selected in the (cnt + 1)-th iteration, and pa _cnt means the failure prediction accuracy of the model built in the (cnt + 1)-th iteration, , cnt may mean a counter value that increases from 0 to 1.

The server 100 may repeat Algorithm 1 until the importance of the feature newly selected in Algorithm 1 is less than the importance threshold. That is, the server 100 may repeat the feature selection step and the model building step until the importance of the newly selected feature is less than the importance threshold.

In this case, the maximum number of iterations (max) of Algorithm 1 may be set equal to the number of features whose importance is greater than the importance threshold. Accordingly, when cnt reaches the maximum number of iterations (max), the server 100 may terminate the iteration of Algorithm 1. While the feature selection step is repeated, the server 100 may select a new feature with the highest importance and add the selected feature to the feature set SF. In conclusion, the number of features included in SF can increase by one as the number of iterations increases.

Then, when the SF is updated, the server 100 may build a failure prediction model through the SVM based on the SF in the model building step.

Thereafter, the server 100 may update the prediction accuracy set PA by evaluating the prediction accuracy of the built failure prediction model. In this case, in Algorithm 1, modelBuildEvalFunction (SF) is a function for building and evaluating a prediction model, and of course, a conventional prediction evaluation function may be used.

For example, if cnt is 2 (the third loop) and the selected feature set SF = [F39, F12, F19], the server 100 uses only the training data set of the three features and creates an SVM-based prediction model. can create _{Thereafter, the server 100 may calculate pa 2} with the test data set for the three features and update the PA set. (i.e., the number of pa in the PA set increases from 2 to 3. PA=[pa ₀ , pa ₁ , pa ₂ ])

Meanwhile, according to an embodiment of the present invention, the SF may be a set of selected features, and may be arranged in order of increasing importance. That is, each time cnt increases by 1 in the Repeat loop of Algorithm 1, a feature having a large importance included may be added one by one.

The server 100 may derive prediction accuracy by taking SF as an input. When the iteration ends, the server 100 may derive the final SF and PA as a result of the operation.

That is, the server 100 may obtain prediction accuracy (PA) using SF as an input value, and as the iteration of Algorithm 1 is terminated, finally derived SF and PA may be calculated as result values.

In step S150 , the server 100 may select at least one model from among a plurality of generated models.

4 is a flowchart illustrating a method for selecting a failure prediction model according to an embodiment of the present invention.

In step S210 , the server 100 may obtain max(PA) for _{pa i} having the highest prediction accuracy among _{pa 0} to pa _{cnt obtained by Algorithm 1 .}

In step S220 , the server 100 may obtain an index of max(PA) to obtain at least one feature having a prediction accuracy equal to or greater than a preset value.

In step S230 , the server 100 may select the at least one model based on the max(PA) and at least one feature having a prediction accuracy equal to or greater than the preset value.

Thereafter, in the model selection step, the server 100 may select a failure prediction model having the highest prediction accuracy with reference to the PA. Specifically, the server 100 selects the highest prediction accuracy through max(PA) and derives the index of max(PA) to calculate the number of iterations with the highest prediction accuracy (ie, the number of selected features). . The server 100 may select a failure prediction model in consideration of the index and SF of max(PA). In this case, of course, max(PA) may mean the largest value among PAs (ie, PA sets) obtained through repetition of Algorithm 1.

In this case, the index of max(PA) may mean in which order the maximum accuracy is included in the PA set. That is, the index is an index indicating the highest accuracy when the value of cnt is (ie, what number of iterations).

The index compares max(PA) with the pa values in the PA set, so that the sequence number (index=cnt) of the same pa as max(PA) can be obtained from the PA set, and the server 100 is the max in the PA set. A predictive model can be constructed by obtaining the index (ie, cnt value) of (PA) and finally selecting features in the iterative trial.

For example, as shown in Figure 8, the index of max(PA) is 7, where the selected feature set is SF = [F60, F349, F41, F289, F427, F65, F66, F154] (i.e., 8 features in order of importance), and an SVM-based prediction model made from data of features included in the SF can be finally selected and used.

5 to 9 are exemplary views for explaining the experimental results according to an embodiment of the present invention.

In the present invention, an experimental implementation was performed to verify the validity of the proposed failure prediction model using the open source R version 3.4.3. For this purpose, the SECOM data set provided by the UCI repository was used. The data set consisted of 1567 data and 591 features, and data were collected from the semiconductor manufacturing process by monitoring sensors and process measurement points. Data for 590 features were measured from multiple sensors, and the data for the rest of the features are results of family tree tests marked as Pass and Fail.

In this experiment, the effectiveness coefficient was set to 0.1 for feature removal. That is, features with NA data of 10% or more and features without variance were removed from the data set. Through the above-described feature removal process, it can be seen that the number of features is reduced from 591 to 393.

The ratio of the training data set and the test data set was set to 7:3. Random forest technique and caret package were used to measure the importance of each feature. n, mtry, and ntree were set to 1000, 19 and 500, respectively. With this setup, 500 decision trees were built using a randomly generated 1000*19 matrix. The significance of each feature was measured through MDG (mean Decrease Gini).

5 shows the importance of the top 30 features. The x-axis and y-axis represent MDG and features, respectively. In the figure, it can be seen that F60 has the highest MDG (1.52) among all features.

Meanwhile, in this experiment, in order to perform an iterative feature selection process, the importance threshold was set to 0.7. Therefore, max was determined to be 24, which means that the number of iterations is 24.

The training data set contains 70 Fails and 1038 Passes. This imbalance in the training data set makes the failure prediction model difficult to predict failure cases, and to solve this problem, sampling was performed before creating the failure prediction model. decreased. As described above, a failure prediction model was built using the SVM through the feature selection results and the sampled training data set, and the failure prediction model was built using the e1071 package in R. The table shown in Fig. 6 shows the obtained SF and PA. In the table of FIG. 6 , it can be seen that max(PA) is 0.72 and the index is 7. In conclusion, eight features (eg, F60, F349, F41, F289, F427, F65, F66, and F154) are selected.

For performance evaluation, the prediction accuracy of the proposed model was compared with that of the existing model. 7 to 9 show ROC (receiver operating characteristic) curves for three failure prediction models using different numbers of features. The ROC curve is a measure of the performance of a predictive model, and represents the relationship between the true positive rate (TPR) and the false positive rate (FPR). The ROC curve uses the area under the curve (AUC) to evaluate the prediction accuracy of the model. Specifically, the larger the AUC, the higher the prediction accuracy. In Fig. 5, it can be seen that the failure prediction model built on the basis of iterative feature selection has the largest AUC compared to other models. This is because the model is iteratively built using a different number of features and one of the models with the highest prediction accuracy is selected.

In other words, since it is difficult to build an accurate predictive model due to irrelevant features, the prediction accuracy can be relatively poor when a fixed number of features are used to build the model.

In other words, if you build a model with more features that are not related to failure, the model's prediction accuracy may decrease. Therefore, when all features of the data set are used as shown in Fig. 5, the AUC decreases significantly. Quantitatively, the proposed model obtains 14.3 and 22.0% higher AUCs compared to the fixed number of features and all feature cases, respectively.

However, the present invention repeatedly builds a failure prediction model using a different number of features, and furthermore, selectively acquires failure-related features to build a failure prediction model, so as shown in FIG. can produce a better effect than

10 is a block diagram of an apparatus according to an embodiment of the present invention.

The processor 102 may include one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus, etc.) for transmitting and receiving signals to and from other components. .

The processor 102 according to one embodiment performs the method described with respect to FIGS. 3 and 4 by executing one or more instructions stored in the memory 104 .

On the other hand, the processor 102 is a RAM (Random Access Memory, not shown) and ROM (Read-Only Memory: ROM) for temporarily and / or permanently storing a signal (or data) processed inside the processor 102. , not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and controlling the processor 102 . Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , Java, assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100 : server
200-1 to 200-5: a plurality of IoT devices

Claims (9)

In the control method of a failure prediction model acquisition system in an industrial Internet of Things (IIoT) environment,
obtaining, by the server, data from a plurality of sensors;
pre-processing, by the server, the obtained data;
obtaining, by the server, the importance of the feature based on the pre-processed data;
selecting, by the server, a feature based on the obtained importance level, and generating a plurality of models based on the selected feature; and
selecting, by the server, at least one model from among the plurality of generated models; including,
The step of generating the plurality of models comprises:
generated by the following algorithm 1,
The step of selecting the at least one model comprises:
obtaining max(PA) for _{PA i} having the highest prediction accuracy among _{pa 0} to PA _cnt obtained by the algorithm 1;
obtaining the index of max(PA) to obtain at least one feature having a prediction accuracy equal to or greater than a preset value; and
selecting the at least one model based on the max(PA) and at least one feature having a prediction accuracy equal to or greater than the preset value; A control method comprising a.
[Algorithm 1]

In this case, cnt is the counter value, SF is the selected feature, PA is the prediction accuracy, sf _cnt is the selected feature in cnt, and the importance set of the feature is IMP=[imp ₀ ,...,imp _j ,...imp _{nf- 1} ], the set of features is F=[f ₀ ,...,f _j ,...,f _nf-1 ], imp _j is the importance value of the (j+1)th feature, nf is the number, modelBuildEvalFunction(SF) is an arbitrary function for building and evaluating a predictive model. delete According to claim 1,
The step of generating the plurality of models comprises:
generating based on the SF and SVM (Support Vector Machine) algorithms obtained by the algorithm 1; A control method comprising a. delete According to claim 1,
The pre-processing step is
removing at least one feature from among a plurality of features corresponding to each of the data acquired from the plurality of sensors;
normalizing data for a plurality of features from which the at least one feature has been removed; and
segmenting the normalized data; Control method comprising a. 6. The method of claim 5,
The step of removing the feature comprises:
acquiring invalid data from among the data acquired from each of the plurality of sensors;
removing a feature corresponding to the invalid data when the ratio of the invalid data is greater than a preset validity coefficient in a range of [0,1]; A control method comprising a. 6. The method of claim 5,
The step of removing the feature comprises:
obtaining respective variance values for the data obtained from each of the plurality of sensors;
when the variance value is less than or equal to a preset value, removing a feature corresponding to a variance value equal to or less than the preset value; A control method comprising a. a memory storing one or more instructions; and
a processor executing the one or more instructions stored in the memory;
The processor by executing the one or more instructions,
An apparatus for performing the method of claim 1 . A computer program stored in a computer-readable recording medium in combination with a computer, which is hardware, to perform the method of claim 1.

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