APPARATUS AND METHOD FOR DETECTING IMPACT FACTOR FOR AN OPERATING ENVIRONMENT

• US 20200134480A1
• Filed: 11/29/2018
• Published: 04/30/2020
• Est. Priority Date: 10/26/2018
• Status: Active Application

• Current Assignee: Institute For Information Industry
• Original Assignee: Institute For Information Industry
• Inventors: PENG, Huan-Chi, SU, Yu-Xuan, TIEN, Yin-Jing, WU, Yi-Hsin, YU, Cheng-Juei
• Application Number:
  US16/205,218
• Publication Number:
  US 20200134480A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24558 Binary matching operations
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

An apparatus and method for detecting impact factors for an operating environment. The apparatus generates a detection result for each of the first factors of a plurality of first historical records by analyzing a dissimilarity degree of the plurality of first data corresponding to each first factor. Each detection result is a continuous data type or a discrete data type. The apparatus trains a data type recognition model according to the first historical records and the detection results. The apparatus establishes a basic prediction model by a training set of a plurality of second historical records, generates a comparison set by rearranging the second data corresponding to a specific factor in the training set, establishes a comparison prediction model by the comparison set, and determines a degree of importance of the specific factor by comparing the accuracies of the basic prediction model and the comparison prediction model.

Claims

1. An apparatus for detecting impact factors for an operating environment, comprising:

a storage, being configured to store a plurality of first historical records and store a plurality of second historical records of the operating environment, each of the first historical records comprising a plurality of first data corresponding to a plurality of first factors one-to-one, and each of the second historical records comprising a plurality of second data corresponding to a plurality of second factors one-to-one; and

a processor electrically connected to the storage, being configured to generate a first detection result for each of the first factors by analyzing a first dissimilarity degree of the first data corresponding to each of the first factors, each of the first detection results being one of a continuous data type and a discrete data type,

wherein the processor further trains a data type recognition model according to the first historical records and the first detection results, determines a data type of each of the second factors by using the data type recognition model to analyze the second data corresponding to each of the second factors, establishes a basic prediction model by a first subset of the second historical records and the data types, generates a first comparison set by rearranging the second data corresponding to a first specific factor in the first subset, establishes a first comparison prediction model by the first comparison set and the data types, obtains a basic accuracy by using a second subset of the second historical records to test the basic prediction model, obtains a first accuracy by using the second subset to test the first comparison prediction model, and determines a first degree of importance of the first specific factor by comparing the basic accuracy with the first accuracy.

2. The apparatus of claim 1, wherein the processor generates the first detection result corresponding to each of the first factors by performing the following operations on each of the first factors:

generating a first comparison result by comparing a mode count of the first data corresponding to the first factor with a first threshold,

generating a second comparison result by comparing a distinct count of the first data corresponding to the first factor with a second threshold, and

deciding the first detection result according to the first comparison result and the second comparison result.

3. The apparatus of claim 1, wherein the processor further generates a second detection result for each of the first factors by comparing the first data corresponding to each of the first factor with a normal distribution model, each of the second detection results is one of the continuous data type and the discrete data type,

wherein the processor trains the data type recognition model according to the first historical records, the first detection results, and the second detection results.

4. The apparatus of claim 1, wherein the processor further generates a third detection result for each of the first factors by analyzing a discontinuity of the first data corresponding to each of the first factors by a LabelEncoder, each of the third detection results is one of the continuous data type and the discrete data type,

wherein the processor trains the data type recognition model according to the first historical records, the first detection results, and the third detection results.

5. The apparatus of claim 1, wherein the processor further generates a fourth detection result for each of the first factors by performing the following operations on each of the first factors:

dividing the first data corresponding to the first factor into a plurality of data groups,

calculating a measure of central tendency of each of the data groups,

calculating a second dissimilarity degree among the measures of central tendency, and

deciding the fourth detection result according to the second dissimilarity degree, wherein the fourth detection result is one of the continuous data type and the discrete data type,

wherein the processor trains the data type recognition model according to the first historical records, the first detection results, and the fourth detection results.

6. The apparatus of claim 1, wherein the data type recognition model has a threshold, and the processor determines the data type of each of the second factors by performing the following operations on each of the second factors:

calculating a data type recognition value by the data type recognition model and the second data corresponding to the second factor, and

determining the data type by comparing the data type recognition value with the threshold.

7. The apparatus of claim 6, wherein the processor further calculates a data type accuracy of each of the second factors according to the data type recognition value of each of the second factors and the threshold.

8. The apparatus of claim 1, wherein the processor further generates a second comparison set by rearranging the second data corresponding to a second specific factor in the first subset, establishes a second comparison prediction model by the second comparison set and the data types, obtains a second accuracy by using the second subset to test the second comparison prediction model, and determines a second degree of importance of the second specific factor by comparing the basic accuracy with the second accuracy.

9. The apparatus of claim 8, wherein the processor further calculates a first absolute difference between the basic accuracy and the first accuracy, calculates a second absolute difference between the basic accuracy and the second accuracy, determines that the first absolute difference is greater than the second absolute difference, and determines that the first degree of importance is higher than the second degree of importance according to the determination result that the first absolute difference is greater than the second absolute difference.

10. The apparatus of claim 1, further comprising:

a display electrically connected to the processor, being configured to display the second data corresponding to each of the second factors in a display mode corresponding to the data types of the second factors.

11. A method for detecting impact factors for an operating environment, being executed by an electronic apparatus, the electronic apparatus storing a plurality of first historical records and storing a plurality of second historical records of the operating environment, each of the first historical records comprising a plurality of first data corresponding to a plurality of first factors one-to-one, each of the second historical records comprising a plurality of second data corresponding to a plurality of second factors one-to-one, and the method comprising:

(a) generating a first detection result for each of the first factors by analyzing a first dissimilarity degree of the first data corresponding to each of the first factors, wherein each of the first detection results is one of a continuous data type and a discrete data type;

(b) training a data type recognition model according to the first historical records and the first detection results;

(c) determining a data type of each of the second factors by using the data type recognition model to analyze the second data corresponding to each of the second factors;

(d) establishing a basic prediction model by a first subset of the second historical records and the data types;

(e) generating a first comparison set by rearranging the second data corresponding to a first specific factor in the first subset;

(f) establishing a first comparison prediction model by the first comparison set and the data types;

(g) obtaining a basic accuracy by using a second subset of the second historical records to test the basic prediction model;

(h) obtaining a first accuracy by using the second subset to test the first comparison prediction model; and

(i) determining a first degree of importance of the first specific factor by comparing the basic accuracy with the first accuracy.

12. The method of claim 11, wherein the step (a) generates the first detection result corresponding to each of the first factors by performing the following operations on each of the first factors:

generating a first comparison result by comparing a mode count of the first data corresponding to the first factor with a first threshold;

generating a second comparison result by comparing a distinct count of the first data corresponding to the first factor with a second threshold; and

deciding the first detection result according to the first comparison result and the second comparison result.

13. The method of claim 11, further comprising:

generating a second detection result for each of the first factors by comparing the first data corresponding to each of the first factor with a normal distribution model, wherein each of the second detection results is one of the continuous data type and the discrete data type,

wherein the step (b) trains the data type recognition model according to the first historical records, the first detection results, and the second detection results.

14. The method of claim 11, further comprising:

generating a third detection result for each of the first factors by analyzing a discontinuity of the first data corresponding to each of the first factors by a LabelEncoder, wherein each of the third detection results is one of the continuous data type and the discrete data type,

wherein the step (b) trains the data type recognition model according to the first historical records, the first detection results, and the third detection results.

15. The method of claim 11, further comprising:

generating a fourth detection result for each of the first factors by performing the following steps on each of the first factors;

dividing the first data corresponding to the first factor into a plurality of data groups,

calculating a measure of central tendency of each of the data groups;

calculating a second dissimilarity degree among the measures of central tendency; and

deciding the fourth detection result according to the second dissimilarity degree, wherein the fourth detection result is one of the continuous data type and the discrete data type,

wherein the step (b) trains the data type recognition model according to the first historical records, the first detection results, and the fourth detection results.

16. The method of claim 11, wherein the step (c) determines the data type of each of the second factors by performing the following steps on each of the second factors:

calculating a data type recognition value by the data type recognition model and the second data corresponding to the second factor; and

determining the data type by comparing the data type recognition value with a threshold of the data type recognition model.

17. The method of claim 16, further comprising:

calculating a data type accuracy of each of the second factors according to the data type recognition value of each of the second factors and the threshold.

18. The method of claim 11, further comprising:

generating a second comparison set by rearranging the second data corresponding to a second specific factor in the first subset;

establishing a second comparison prediction model by the second comparison set and the data types;

obtaining a second accuracy by using the second subset to test the second comparison prediction model; and

determining a second degree of importance of the second specific factor by comparing the basic accuracy with the second accuracy.

19. The method of claim 18, further comprising:

calculating a first absolute difference between the basic accuracy and the first accuracy;

calculating a second absolute difference between the basic accuracy and the second accuracy;

determining that the first absolute difference is greater than the second absolute difference; and

determining that the first degree of importance is higher than the second degree of importance according to the determination result that the first absolute difference is greater than the second absolute difference.

20. The method of claim 11, further comprising:

displaying the second data corresponding to each of the second factors in a display mode corresponding to the data types of the second factors.