Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Intelligent Coal Mine Construction Connotations
3.2. Coal Mine Intelligent Construction Evaluation Index System
3.3. FDEMATEL Model
- (1)
- Standardized triangular fuzzy numbers.
- (2)
- Normalized left and right values.
- (3)
- Calculate the total standardized value
- (4)
- Calculate the exact value of expert k triangular fuzzy judgment value
- (5)
- Calculate the standard exact value of the evaluation by p experts
- (6)
- Determine the fuzzy direct relationship matrix D.
3.4. ANP Model
3.5. Mixed Weights
4. Results
4.1. Influence Relationship Determination Based on FDEMATEL
4.2. Index Weight Calculation Based on ANP
4.3. Mixed Weight Calculation
5. Discussion
5.1. Comparative Validation
5.2. Results Analysis
- (1)
- Analysis of index influence relationship. From the calculation results of the FDEMATEL method, it can be seen that the influence degree of the basic platform intelligence (I1) is 3.340, which is the highest in the ranking of the influence degrees in the first-grade index, and the influence degrees in the other first-grade indexes are less than three. This shows that the basic platform intelligence is an important cornerstone of the intelligent construction of coal mines, which can provide comprehensive services for the construction of various business systems at a later stage and ensure reliable data collection, transmission, storage, and application. The influence degrees of mobile internet construction (F14), database construction (F11), and big data support (F12) are ranked in the top three in all second-grade indexes, and these three indexes belong to the basic platform intelligence (I1) in the first-grade indexes. Once again, it confirms the key influence of the intelligent construction of the basic platform in promoting the process of intelligent coal mining. Green development intelligence (I5) is the most influenced index among the first-grade indexes, with an influence degree of 3.629, and the influence degrees of other first-grade indexes are less than three. It shows that coal mining should pay attention to the green service of the whole life cycle, to achieve the essential green of coal mining, in order to reach the green development goal of the intelligent construction of coal mines. The top three influenced second-grade indexes are electrical equipment safety monitoring (F33), emergency rescue control (F35), and geological monitoring (F31), and these three indexes belong to the first-grade indexes of safety monitoring intelligence (I3). It shows that the services and applications of safety monitoring are based on the collection of real-time and historical data related to coal mine production scenarios and industrial scenarios, which are realized by integrating and analyzing the collected data.
- (2)
- Analysis of index attributes. The FDEMATEL method analysis shows that the basic platform intelligence (I1), safety monitoring intelligence (I3), and information management intelligence (I4) are the cause factors, and the production process intelligence (I2) and green development intelligence (I5) are the result factors in the first-grade indexes. This indicates that the most important thing to consider in the intelligent construction of coal mines is the application of information technology and the development of safe production. In the whole intelligent coal mine construction evaluation index system, there are 13 second-grade indexes with positive cause degrees, which are cause factors and easily influence other factors in the system. Among them, safety prevention closed-loop decisions (F25), database construction (F11), and big data support (F12) are strong cause factors, and their cause degree is 2.846, 2.269, and 2.025, respectively, while the cause degree of other cause factors is less than two. It indicates that the strong cause factors have an important influence on promoting the intelligent construction of coal mines and should be focused on in the intelligent construction of coal mines. The cause degrees of the remaining 11 second-grade indexes are negative. These indexes are result factors, which are easily influenced by the above-mentioned cause factors in the system, and focusing on the changes in these factors can clarify the improvement path and development direction of coal mine intelligent construction. Among the result factors, electrical equipment safety monitoring (F33) and emergency rescue control (F35) are strong result factors with a cause degree of −3.193 and −2.115, respectively, while the cause degree of other result factors is greater than −2. It indicates that real-time online monitoring of coal mine hazards, determination of risk types, levels, and corresponding solutions through the application of information technology, such as big data, the internet of things, and artificial intelligence, are effective ways to achieve intelligent coal mine safety production and management.
- (3)
- Analysis of index importance. Mixed weights are the comprehensive influence weights obtained by considering the interaction between the evaluation indexes and combining the ANP method. It reflects the weight and correlation degree of each element, provides the priority order of each element, and its ranking result is the ranking of each evaluation index in the whole coal mine intelligent construction evaluation system. Therefore, the best way to promote the intelligent construction of coal mines is to improve the evaluation indexes that have greater weight and relevance in the whole evaluation system. Improving such indexes will increase the value of other evaluation indexes and can promote the high-quality development of the whole intelligent construction of coal mines. From the mixed weights in Table 10, it can be seen that the relative importance of the first-grade indexes is ranked as I1 > I3 > I5 > I4 > I2, the highest relative importance is the basic platform intelligence (I1), and the lowest is the production process intelligence (I2). This is because the information technology system provides technology and equipment support for the intelligent construction of coal mines, which is an important cornerstone of the intelligent construction of coal mines. Therefore, coal mine intelligent construction enterprise managers need to pay more attention to the intelligent construction of the basic platform. The lowest weight of production process intelligence (I2) does not mean that the construction of production process intelligence is insignificant, but it is the least important compared with other first-grade indexes, and it is still an important goal to promote coal mining enterprises to realize a reduction in field staff and efficient development of resources. In the mixed weight ranking of second-grade indexes, database construction (F11), mobile internet construction (F14), big data support (F12), and model algorithm support (F13) are the top four, and all of them belong to the first-grade index base platform intelligence (I1), so the base platform intelligence will provide the basic guarantee for the construction of various business systems in the coal mine.
5.3. Countermeasures and Suggestions to Promote the Intelligent Construction of Coal Mines
- (1)
- Refine the top-level design of the industry and form a unified construction standard. In promoting the construction of intelligent mines, we should further refine the construction standards and standardize the standards of various intelligent parameters, interfaces, and protocols to lay a good foundation for later unified management. At the same time, promote the technical support and security forces, promote teaching and research forces integration, and strengthen long-term stable cooperation with well-known universities, research institutes, and leading information technology enterprises at home and abroad in related fields, to make up for the lack of their own strength.
- (2)
- Strengthen the integration of advanced technologies and accelerate the process of mine intelligence. Promote the integration of cloud computing, big data, the internet of things, artificial intelligence, mobile internet, and other new generation information technology with intelligent mines in depth, and promote the integration of cross-disciplinary technologies.
- (3)
- Strengthen the cultivation of professional talents and build innovative technical teams. Strengthen academic exchanges within the industry, vigorously promote cooperation between mining enterprises and relevant research institutes, and support the application and transformation of scientific and technological achievements. Continuously improve the ability of mine managers and technicians to use data to analyze problems and solve them, strengthen training and education on the quality of information technology for all staff, and build a composite and innovative core talent team.
5.4. Limitations and Future Research
6. Conclusions
- (1)
- The factors influencing the intelligent construction of coal mines are summarized. Through literature research, expert consultation, and a questionnaire survey, the evaluation index system of coal mine intelligent construction was constructed. It includes 5 first-grade indexes and 24 second-grade indexes.
- (2)
- The FDEMATEL-ANP evaluation model was applied to analyze the comprehensive impact of various influencing factors on the intelligent construction of coal mines. The results show that safety prevention closed-loop decisions, database construction, and big data support have a significant impact on the intelligent construction of coal mines. Database construction, mobile internet construction, big data support, and model algorithm support are the key factors affecting the intelligent construction of coal mines.
- (3)
- By analyzing and constructing the index system of influencing factors on coal mine intelligent construction, this study can effectively guide the intelligent construction of coal mines. The case verification shows that the comprehensive weights of indexes calculated by the FDEMATEL-ANP model are basically consistent with the results calculated by the ANP model and consistent with the results calculated by the SWARA model. This calculation result matches with the actual situation and verifies the scientific nature and applicability of the evaluation model. Based on this, the coal mining industry and related enterprises can improve the corresponding standards and take corresponding measures to promote the high-quality development of intelligent coal mine construction.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
First-Grade Indexes | I1 | I2 | I3 | I4 | I5 |
---|---|---|---|---|---|
I1 | 0 | 4 | 4 | 1 | 3 |
I2 | 5 | 0 | 4 | 3 | 5 |
I3 | 2 | 3 | 0 | 2 | 2 |
I4 | 4 | 2 | 1 | 0 | 1 |
I5 | 5 | 3 | 3 | 2 | 0 |
First-Grade Indexes | I1 | I2 | I3 | I4 | I5 |
---|---|---|---|---|---|
I1 | 0 | 1 | 3 | 1 | 1 |
I2 | 1 | 0 | 2 | 1 | 3 |
I3 | 2 | 2 | 0 | 2 | 2 |
I4 | 3 | 2 | 1 | 0 | 1 |
I5 | 2 | 4 | 2 | 4 | 0 |
First-Grade Indexes | I1 | I2 | I3 | I4 | I5 |
---|---|---|---|---|---|
I1 | 0 | 1 | 3 | 4 | 5 |
I2 | 1 | 0 | 3 | 1 | 4 |
I3 | 3 | 3 | 0 | 2 | 4 |
I4 | 1 | 3 | 4 | 0 | 5 |
I5 | 1 | 4 | 4 | 5 | 0 |
First-Grade Indexes | I1 | I2 | I3 | I4 | I5 |
---|---|---|---|---|---|
I1 | 0 | 1 | 1 | 1 | 4 |
I2 | 1 | 0 | 1 | 2 | 3 |
I3 | 2 | 2 | 0 | 2 | 2 |
I4 | 1 | 1 | 1 | 0 | 1 |
I5 | 1 | 3 | 1 | 4 | 0 |
First-Grade Indexes | I1 | I2 | I3 | I4 | I5 |
---|---|---|---|---|---|
I1 | 0 | 5 | 1 | 5 | 1 |
I2 | 1 | 0 | 1 | 2 | 1 |
I3 | 2 | 2 | 0 | 3 | 2 |
I4 | 1 | 1 | 1 | 0 | 3 |
I5 | 1 | 3 | 1 | 1 | 0 |
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Goal Layer | First-Grade Indexes | Second-Grade Indexes |
---|---|---|
Coal mine intelligent construction evaluation A | Basic platform intelligence I1 | Database construction F11 |
Big data support F12 | ||
Model algorithm support F13 | ||
Mobile internet construction F14 | ||
Information safety system construction F15 | ||
Production process intelligence I2 | Reliability of working surface equipment F21 | |
Intelligent diagnosis technology of equipment fault F22 | ||
Mining collaborative design F23 | ||
Production closed-loop control F24 | ||
Safety prevention closed-loop decision F25 | ||
Safety monitoring intelligence I3 | Geological monitoring F31 | |
Ventilation and fire safety monitoring F32 | ||
Electrical equipment safety monitoring F33 | ||
Personnel safety monitoring F34 | ||
Emergency rescue control F35 | ||
Information management intelligence I4 | Information collection coverage capability F41 | |
Data resource mining capability F42 | ||
Data statistical analysis ability F43 | ||
Information management system integration capability F44 | ||
Green development intelligence I5 | Intelligent dust control F51 | |
Intelligent control of toxic and hazardous substances F52 | ||
Drained water reuse F53 | ||
Clean energy utilization F54 | ||
Ecological restoration management F55 |
Linguistic Variable | Expert Ratings | Triangular Fuzzy Number |
---|---|---|
Very low influence (VL) | 1 | (0, 0, 0.25) |
Low influence (L) | 2 | (0, 0.25, 0.5) |
Medium influence (M) | 3 | (0.25, 0.5, 0.75) |
High influence (H) | 4 | (0.5, 0.75, 1) |
Very high influence (VH) | 5 | (0.75, 1, 1) |
Scale | Meaning Description |
---|---|
1 | Index i and index j have the same importance |
3 | Index i is slightly more important than index j |
5 | Index i is significantly more important than index j |
7 | Index i is extremely more important than index j |
9 | Index i is strongly more important than index j |
2, 4, 6, 8 | Intermediate value of the above adjacent judgment |
Reciprocal | The importance ratio of index i to index j is one of the values above, then the importance ratio of index j to index i is its reciprocal |
n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|
RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
First-Grade Indexes | I1 | I2 | I3 | I4 | I5 | fi | Mi | Ni |
---|---|---|---|---|---|---|---|---|
I1 | 0.350 | 0.771 | 0.739 | 0.549 | 0.931 | 3.340 | 5.075 | 1.605 |
I2 | 0.338 | 0.326 | 0.520 | 0.355 | 0.634 | 2.172 | 4.692 | −0.347 |
I3 | 0.362 | 0.583 | 0.469 | 0.538 | 0.862 | 2.814 | 5.608 | 0.021 |
I4 | 0.301 | 0.401 | 0.493 | 0.000 | 0.707 | 1.901 | 3.722 | 0.081 |
I5 | 0.384 | 0.438 | 0.573 | 0.379 | 0.495 | 2.270 | 5.899 | −1.359 |
ei | 1.735 | 2.520 | 2.794 | 1.821 | 3.629 | - | - | - |
Indexes | fi | ei | Mi | Ni |
---|---|---|---|---|
F11 | 5.812 | 3.534 | 9.355 | 2.269 |
F12 | 5.694 | 3.669 | 9.364 | 2.025 |
F13 | 5.574 | 5.274 | 10.848 | 0.300 |
F14 | 5.964 | 4.471 | 10.435 | 1.494 |
F15 | 5.024 | 3.739 | 8.762 | 1.285 |
F21 | 4.662 | 4.260 | 8.921 | 0.402 |
F22 | 4.392 | 6.013 | 10.405 | −1.622 |
F23 | 4.028 | 4.754 | 8.781 | −0.726 |
F24 | 3.994 | 5.734 | 9.728 | −1.741 |
F25 | 5.512 | 2.666 | 8.179 | 2.846 |
F31 | 4.815 | 6.648 | 11.463 | −1.834 |
F32 | 4.635 | 6.247 | 10.882 | −1.613 |
F33 | 4.478 | 7.672 | 12.150 | −3.193 |
F34 | 5.610 | 3.766 | 9.376 | 1.845 |
F35 | 4.850 | 6.965 | 11.815 | −2.115 |
F41 | 5.219 | 4.481 | 9.699 | 0.738 |
F42 | 5.562 | 5.050 | 10.612 | 0.512 |
F43 | 5.283 | 5.061 | 10.344 | 0.222 |
F44 | 5.277 | 4.772 | 10.048 | 0.505 |
F51 | 5.330 | 5.975 | 11.305 | −0.644 |
F52 | 4.750 | 4.557 | 9.307 | 0.194 |
F53 | 4.929 | 5.302 | 10.230 | −0.373 |
F54 | 4.579 | 5.105 | 9.684 | −0.525 |
F55 | 4.694 | 4.947 | 9.641 | −0.253 |
I2 | I1 | I2 | I3 | I4 | I5 | Weight |
---|---|---|---|---|---|---|
I1 | 1 | 1/2 | 1/3 | 1/2 | 3 | 0.134 |
I2 | 2 | 1 | 1/2 | 3 | 2 | 0.244 |
I3 | 3 | 2 | 1 | 4 | 3 | 0.395 |
I4 | 2 | 1/3 | 1/4 | 1 | 2 | 0.142 |
I5 | 1/3 | 1/2 | 1/3 | 1/2 | 1 | 0.085 |
Consistency judgment | CR = 0.076 < 0.1 |
F14 | F41 | F42 | F43 | F44 | Weight |
---|---|---|---|---|---|
F41 | 1 | 3 | 2 | 1/2 | 0.285 |
F42 | 1/3 | 1 | 3 | 1/3 | 0.166 |
F43 | 1/2 | 1/3 | 1 | 1/4 | 0.097 |
F44 | 2 | 3 | 4 | 1 | 0.452 |
Consistency judgment | CR = 0.082 < 0.1 |
F23 | F51 | F52 | F53 | F54 | F55 | |
F51 | 1 | 3 | 1/2 | 4 | 3 | 0.282 |
F52 | 1/3 | 1 | 1/3 | 3 | 1/3 | 0.106 |
F23 | F51 | F52 | F53 | F54 | F55 | |
F53 | 2 | 3 | 1 | 3 | 4 | 0.387 |
F54 | 1/4 | 1/3 | 1/3 | 1 | 1/3 | 0.065 |
F55 | 1/3 | 3 | 1/4 | 3 | 1 | 0.159 |
Consistency judgment | CR = 0.096 < 0.1 |
First-Grade Indexes | Weight Z | Second-Grade Indexes | Mixed Weight Z | Sort |
---|---|---|---|---|
Basic platform intelligence I1 | 0.247 | Database construction F11 | 0.054 | 1 |
Big data support F12 | 0.049 | 3 | ||
Model algorithm support F13 | 0.048 | 4 | ||
Mobile internet construction F14 | 0.050 | 2 | ||
Information safety system construction F15 | 0.045 | 8 | ||
Production process intelligence I2 | 0.177 | Reliability of working surface equipment F21 | 0.037 | 17 |
Intelligent diagnosis technology of equipment fault F22 | 0.034 | 21 | ||
Mining collaborative design F23 | 0.032 | 23 | ||
Production closed-loop control F24 | 0.031 | 24 | ||
Safety prevention closed-loop decision F25 | 0.043 | 12 | ||
Safety monitoring intelligence I3 | 0.207 | Geological monitoring F31 | 0.044 | 11 |
Ventilation and fire safety monitoring F32 | 0.037 | 16 | ||
Electrical equipment safety monitoring F33 | 0.041 | 15 | ||
Personnel safety monitoring F34 | 0.044 | 10 | ||
Emergency rescue control F35 | 0.041 | 14 | ||
Information management intelligence I4 | 0.184 | Information collection coverage capability F41 | 0.046 | 6 |
Data resource mining capability F42 | 0.048 | 5 | ||
Data statistical analysis ability F43 | 0.044 | 9 | ||
Information management system integration capability F44 | 0.046 | 7 | ||
Green development intelligence I5 | 0.185 | Intelligent dust control F51 | 0.042 | 13 |
Intelligent control of toxic and hazardous substances F52 | 0.036 | 20 | ||
Drained water reuse F53 | 0.037 | 18 | ||
Clean energy utilization F54 | 0.034 | 22 | ||
Ecological restoration management F55 | 0.036 | 19 |
Index Code | Expert 1 | Expert 2 | Expert 3 | Expert 4 | Expert 5 | Average Value |
---|---|---|---|---|---|---|
F11 | 0.235 | 0.174 | 0.111 | 0.059 | 0.302 | 0.176 |
F12 | 0.059 | 0.140 | 0.093 | 0.135 | 0.038 | 0.093 |
F13 | 0.035 | 0.060 | 0.074 | 0.209 | 0.077 | 0.091 |
F14 | 0.139 | 0.038 | 0.139 | 0.345 | 0.195 | 0.171 |
F15 | 0.116 | 0.236 | 0.031 | 0.029 | 0.028 | 0.088 |
Applied Methods | F11 | F12 | F13 | F14 | F15 |
---|---|---|---|---|---|
ANP | 0.084 | 0.058 | 0.058 | 0.055 | 0.061 |
Ranking | (1) | (3) | (4) | (5) | (2) |
FDEMATEL-ANP | 0.054 | 0.049 | 0.048 | 0.050 | 0.045 |
Ranking | (1) | (3) | (4) | (2) | (5) |
SWARA | 0.176 | 0.093 | 0.091 | 0.171 | 0.088 |
Ranking | (1) | (3) | (4) | (2) | (5) |
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He, L.; Yuan, D.; Ren, L.; Huang, M.; Zhang, W.; Tan, J. Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP. Sustainability 2023, 15, 2238. https://doi.org/10.3390/su15032238
He L, Yuan D, Ren L, Huang M, Zhang W, Tan J. Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP. Sustainability. 2023; 15(3):2238. https://doi.org/10.3390/su15032238
Chicago/Turabian StyleHe, Lin, Dongliang Yuan, Lianwei Ren, Ming Huang, Wenyu Zhang, and Jie Tan. 2023. "Evaluation Model Research of Coal Mine Intelligent Construction Based on FDEMATEL-ANP" Sustainability 15, no. 3: 2238. https://doi.org/10.3390/su15032238