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
INFORMAZIONI SU QUESTO ARTICOLO
Pubblicato online: 21 mar 2025
Ricevuto: 20 nov 2024
Accettato: 18 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0560
Parole chiave
© 2025 Lihua Zhang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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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% |