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Research on Intelligent Recognition Method of Risk Levels of Electricity Marketing Field Operation Types Based on Data Mining

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21 mar 2025
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Figure 1.

Data mining process
Data mining process

Figure 2.

Construction of FP tree
Construction of FP tree

Figure 3.

Causative karyotype of power operation accidents
Causative karyotype of power operation accidents

Figure 4.

Calculation method of intersection ratio
Calculation method of intersection ratio

Figure 5.

Evaluation model of electric power operation violations
Evaluation model of electric power operation violations

Figure 6.

Experimental scene
Experimental scene

Figure 7.

Model overall accuracy
Model overall accuracy

Figure 8.

Confusion matrix
Confusion matrix

Figure 9.

The relative height of the human head
The relative height of the human head

Figure 10.

The relative height of the body’s knee
The relative height of the body’s knee

Figure 11.

The cosine of the upper leg of the human body
The cosine of the upper leg of the human body

Figure 12.

The risk contribution of all kinds of factors changing at any time
The risk contribution of all kinds of factors changing at any time

Figure 13.

Risk trend prediction curve
Risk trend prediction curve

Abnormal behavior recognition performance

Influencing factor Accuracy rate% Recall rate%
Weak light 91.4 96.9
Weak light and sparse 95.7 96.4
Strong light 92.4 97
Strong light, sparse 97.3 96.4
Total 94.2 96.5

Test data for the power operation scenario

Test frame number Correct number of action frames Success rate
1086 1023 0.942

Training parameters

Training parameter Parameter value
Training data set 35
Test data set 35
Learning rate 0.3
Iteration number 50

Abnormal behavior identification performance comparison

Method Accuracy rate Processing speed/FPS
Light flow method 90.52 8.82
Motion Instability 92.36 11.69
Support vector machine 80.63 16.8
Markov 83.55 15.06
This algorithm 93.87 18.33

The algorithm actually runs the data

Total frame number Inverted action frame number Successful identification number Success in identifying reverse times Overall accuracy Reverse recognition accuracy
1 342 98 334 95 0.928 0.965
2 262 75 248 78 0.936 0.966
3 428 167 387 155 0.932 0.963

The cause of the power operation accident

Accident category Serial number Precondition Results Confidence
Electrical shock (R1) 1 A13,D13 C22 0.902
2 C22,D32 R1 0.991
3 D22 D32 0.829
4 F12,F13 A13 0.969
5 D12 B21 0.993
6 A13,D12 A33 0.92
7 A33,D12 C13 0.944
8 B21,C13 R1 0.997
9 F14 C21 0.84
10 A32,E13 R1 0.996
11 A13,C21 A32 0.944
Object strike(R2) 12 A33,D41 C11 0.944
13 A13 A33 0.815
14 F12,F13 A13 0.964
15 D14 B22 1.000
16 A33,D14 C11 0.938
17 B22,C11 R2 1.000
High fall(R3) 18 D21,D31 A22 0.959
19 F14 C21 0.927
20 C21,E14 B23 0.998
21 A22,B23 R3 1.000
22 F11 A12 0.901
23 E22 B14 0.92
24 A12 A51 0.983
25 A51 B14 0.841
26 D11,B14 R3 0.996
27 D33,A13 A33 0.936
28 F12,F13 A13 0.964
29 A33,D11 C12 0.82
30 C12,D11 R3 1.000

The accuracy and recall rate of three kinds of actions

Precision Recall
0.Sit down 0.71 0.81
1. Standing 0.94 0.86
2.Inversely 0.69 0.96

The cause of the power operation is a frequent collection

Accident category Frequent set Support
Electrical shock (R1) {A13,C22,D13,D22,D32,F12,F13,R1} 0.185
{A13,A33,B21,C13,D12,F12,F13,R1} 0.196
{A13,A32,C21,E13,F12,F13,F14,R1} 0.221
Object strike(R2) {A13,A33,B22,C11,D41,F12,F13,R2} 0.301
{A13,A33,B22,C11,D14,F12,F13,R2} 0.337
High fall(R3) {A22,B23,C21,D21,D31,E14,F14,R3 0.168
{A12,A51,B14,D11,E22,F11,F12,R3} 0.184
{A13,A33,C12,D11,D33,F12,F13,R3} 0.335

Data training results

Iteration rotation Mean loss Top1 accuracy Top5 accuracy
10 4.6245 11.26% 32.76%
20 4.5156 14.45% 29.43%
30 3.8516 19.57% 42.72%
40 3.457 26.77% 48.67%
50 3.3162 25.66% 50.42%
60 3.562 31.22% 54.10%
Lingua:
Inglese
Frequenza di pubblicazione:
1 volte all'anno
Argomenti della rivista:
Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro