Research on Risk Assessment Method of Long-Tube Trailer Road Transportation ()

Caiyan Dai^{1}, Wenkun Wang^{1}, Ming Xu^{1*}, Chenglong Ma^{1}, Lianqing Yang^{1}, Hong Zhao^{2}, Yuan He^{2}

^{1}School of Engineering & Technology, China University of Geosciences (Beijing), Beijing, China.

^{2}Guizhou Academy of Labor Protection Science and Technology, Zunyi, China.

**DOI: **10.4236/jsea.2023.168021
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Road transport safety has always been paid attention to by the safety production managers of enterprises. In this study, cloud model and analytic hierarchy process were applied to the safety of long-tube trailer transport. The opinions of 30 experts were analyzed, from which 29 key parameters were selected. The study addressed the relevance of the parameters and the possibility of automatic collection and transmission to obtain 12 core risk factors. The macro-safety risk indicator system for long-tube trailers was established based on the identified risk indicators. Finally, a risk assessment model for road transport of long tube trailers consisting of 3 dimensions of likelihood, severity and sensitivity was constructed. This model provides a technical method for strengthening the risk control of road transport of long-tube trailers.

Keywords

Cloud Model, Analytic Hierarchy Process, Long-Tube Trailer, Risk Factors, Risk Assessment Model

Share and Cite:

Dai, C. , Wang, W. , Xu, M. , Ma, C. , Yang, L. , Zhao, H. and He, Y. (2023) Research on Risk Assessment Method of Long-Tube Trailer Road Transportation. *Journal of Software Engineering and Applications*, **16**, 420-441. doi: 10.4236/jsea.2023.168021.

1. Introduction

Long tube trailers are mainly used for inter-regional short and medium distance industrial gas transportation, and are typical mobile pressurized special equipment [1] . With the widespread use of new energy sources such as natural gas, the number of long tube trailers, which are the main means of transportation, is gradually rising. Compared to traditional gas cylinders, long-tube trailer has the advantages of high efficiency, convenience and wide coverage [2] . According to statistics, the number of long tube trailers in China has increased from more than 2000 in 2005 to about 15,000 in 2020. However, long tube trailers are mainly used for storing and transporting flammable, explosive, toxic or corrosive gases such as natural gas and hydrogen [3] . Gas cylinder pressure is generally more than 20 MPa belongs to the high-pressure environment. In this environment, in case of fire, overfilling and other accidents, the pressure inside the cylinder will rise rapidly, which will easily lead to overpressure explosion [4] . The technical conditions for long tube trailers are already relatively mature. From filling to transport, they are equipped with suitable safety devices and standardized operating practices. The danger of storing and transporting long tube trailers has been effectively reduced. But the vehicle driving process is vulnerable to road conditions, traffic accidents, driving operations and other factors, if the use of improper management may occur serious special equipment accidents or special equipment-related accidents, will cause serious harm to people’s lives and property [5] .

The main research object of this study is long tube trailer. Aiming at the problem of lack of dynamic data and insufficient monitoring means during the operation of long tube trailers, cloud modeling, hierarchical analysis and other methods are adopted. Firstly, the safety status parameters are determined and screened by combining qualitative and quantitative methods. Then the macro safety risk indicator system of long-tube trailers is established and the weights of the indicators are determined. Finally, the macro safety risk early warning model of long tube trailer is constructed, with a view to improving the effectiveness of safety supervision of pressurized equipment.

2. Research Methods

Cloud model [6] is an uncertainty conversion model between a certain qualitative concept represented by natural language values. And its quantitative representation proposed by Academician Deyi Li to reflect the uncertainty of concepts in natural language, especially the vagueness and randomness, and to achieve a natural conversion between qualitative language values and quantitative values [7] .

Cloud model [8] is mainly implemented by the forward cloud generator and the backward cloud generator.

1) Forward cloud generator

The forward cloud generator is a mapping from qualitative to quantitative with the input of three numerical characteristics of the cloud—expectation *Ex*, entropy *En* and superentropy *He*, and the number of cloud droplets *N*. The output is the quantitative position of *N* cloud droplets in the number field space and the degree of certainty of the concept represented by each cloud drop. Since normal clouds have universal applicability, the screening is mainly based on the application of normal clouds. The specific algorithm for the one-dimensional forward cloud generator is:

Input: Digital features (*Ex*, *En*, *He*) reflecting the qualitative concept of weight and the number of cloud drops *N*.

Output: *N* cloud drops *X _{i}* and affiliation of each cloud drop to the concept.

a) In the first step, a normal random number Eni’ is generated with *En* as the expected value and *He* as the standard deviation.

b) In the second step, generate a normal random number *X* with *Ex* as the expected value and Eni’ as the standard deviation.

c) In the third step, calculate the affiliation of *X _{i}* to the concept:

${\mu}_{i}=\mathrm{exp}\left[-\frac{{\left(x-Ex\right)}^{2}}{2E{n}_{i}^{2}}\right]$ (1)

In the fourth step, repeat a)-c) until *N* cloud droplets were generated.

2) Backward cloud generator

The function of the reverse cloud generator is to find the three digital eigenvalues (*Ex*, *En*, *He*) of the forward cloud generator from some given cloud drops. The specific algorithm of the reverse cloud generator is as follows:

Input: Take the weight value given by the expert as the sample value, *X *= *X _{i}*, among them,
$i=1,2,\cdots ,n$ .

Output: Digital features reflecting the qualitative concept of parameter weights (*Ex*, *En*, *He*).

Calculation of sample means from *X _{i}*:

$Ex=\stackrel{\xaf}{X}=\frac{1}{N}{\displaystyle {\sum}_{i=1}^{n}{X}_{i}}$ (2)

$En=\sqrt{\frac{\pi}{2}}\times \frac{1}{N}{\displaystyle {\sum}_{i=1}^{N}\left|{X}_{i}-Ex\right|}$ (3)

$He=\sqrt{{S}^{2}-E{n}^{2}}$ , among them, ${S}^{2}=\frac{1}{N-1}{\displaystyle {\sum}_{i=1}^{N}{\left({X}_{i}-\stackrel{\xaf}{X}\right)}^{2}}$ (4)

The Analytic Hierarchy Process [9] (AHP for short) is a multi-level, multi-objective, multi-program comprehensive comparison method proposed by Professor Say in the early 1980s.

The basic principle of Analytic Hierarchy Process can be described as follows: first, find out the various factors affecting the decision of the problem, and arrange these factors into several levels from high to low according to their membership, this process is the construction of a recursive hierarchy. Then ask the authority to compare the importance of the factors in each level in pairs, and then use the mathematical method to find out the weight of the factors in each layer and sort them. Each factor is scored in the actual problem, and the final score is obtained according to the calculated weights. Finally, the results are analyzed to assist in the decision-making [10] .

3. Determination of the Risk Assessment Indicators

3.1. Determine the Initial Safety State Parameters

In order to ensure the scientific, comprehensive and effectiveness of the safety state parameters, the initial parameters are selected first. The initial parameters include three sources: first, to analyze 32 national and local standards, norms and regulations concerning equipment safety, such as the Measures for the Safe Management of Road Transportation of Dangerous Goods, TSGR005-2011 Safety Technical Supervision Regulations for Mobile Pressure Vessels, GB7258-2017 Safety Technical Conditions for Motor Vehicle Operation, etc. second, to collect and sort out the relevant accident data occurring in China [11] . Third, the experience of many experts. Finally, 56 initial parameters of the safety risk of mobile pressure equipment were determined, among which the regulatory sources accounted for 75.0%, the accident statistics sources accounted for 23.3%, and the expert experience sources accounted for 1.8%. The 56 initial parameters were divided into vehicle and tank factors, road factors, human factors and environmental factors [12] . The various initial parameters are shown in Table 1.

Table 1. Initial screening results of macro safety risk parameters of mobile pressure equipment.

In the next step, 56 initial parameters will be screened by the cloud modeling method based on the preliminary selection.

3.2. Safety Status Parameter Screening

1) First of all, 30 experts familiar with and understand the connotation of the parameters will evaluate the importance of the parameters. The language scale of the evaluation is divided into 9 levels, namely: {extremely unimportant, very unimportant, not important, not very important, generally, a bit important, important, very important, extremely important} [13] . The linguistic scales of the expert evaluations were transformed into the corresponding number of intervals, and the number of intervals corresponding to the nine levels were: {[1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7], [6, 8], [7, 9], [8, 9]}. The number of intervals of the 9 uncertain language evaluation scale on the theoretical domain [1, 9] is transformed into 9 one-dimensional normal clouds, specifically as follows (set He_{0} = 0.01):

a) Transforms the number of central intervals $\left[{a}_{0},{b}_{0}\right]=\left[4,6\right]$ into an approximately one-dimensional normal cloud, we get from formula: $a=\mu -k\sigma $ , $b=\mu +k\sigma $ ( $x~N\left(\mu ,{\sigma}^{2}\right)$ , $k=2$ )

${\mu}_{0}=\frac{{a}_{0}+{b}_{0}}{2}=5$

${\sigma}_{0}=\frac{{b}_{0}-{a}_{0}}{4}=0.5$

Therefore, there is: $E{x}_{0}=5$ , $E{n}_{0}=0.5$ , $H{e}_{0}=0.01$ , the one-dimensional forward cloud corresponding to the interval number [4, 6] is ${C}_{0}\left(5,0.5,0.01\right)$ .

b) Similarly, there is $E{x}_{+1}=6$ , $E{n}_{+1}=0.5$ , by the golden split rate approximate method:

$H{e}_{+1}=\frac{H{e}_{0}}{0.618}=0.016$

That is, the one-dimensional forward cloud corresponding to the interval number [5, 7] is ${C}_{+1}\left(6,0.5,0.016\right)$ .

c) It is calculated that the nine one-dimensional forward clouds are

${C}_{-4}\left(E{x}_{-4},E{n}_{-4},H{e}_{-4}\right)={C}_{-4}\left(1.5,0.25,0.068\right)$

${C}_{-3}\left(E{x}_{-3},E{n}_{-3},H{e}_{-3}\right)={C}_{-3}\left(2,0.5,0.042\right)$

${C}_{-2}\left(E{x}_{-2},E{n}_{-2},H{e}_{-2}\right)={C}_{-2}(3,0.5,0.026)$

${C}_{-1}\left(E{x}_{-1},E{n}_{-1},H{e}_{-1}\right)={C}_{-1}\left(4,0.5,0.016\right)$

${C}_{0}\left(E{x}_{0},E{n}_{0},H{e}_{0}\right)={C}_{0}\left(5,0.5,0.01\right)$

${C}_{+1}\left(E{x}_{+1},E{n}_{+1},H{e}_{+1}\right)={C}_{+1}\left(6,0.5,0.016\right)$

${C}_{+2}\left(E{x}_{+2},E{n}_{+2},H{e}_{+2}\right)={C}_{+2}\left(7,0.5,0.026\right)$

${C}_{+3}\left(E{x}_{+3},E{n}_{+3},H{e}_{+3}\right)={C}_{+3}\left(8,0.5,0.042\right)$

${C}_{+4}\left(E{x}_{+4},E{n}_{+4},H{e}_{+4}\right)={C}_{+4}\left(8.5,0.25,0.068\right)$

Nine one-dimensional, normal clouds, the cloud map as shown in Figure 1.

2) The set of 30 experts’ ratings of the importance of parameter *u*_{1} is
$\left\{{V}_{1},{V}_{2},\cdots ,{V}_{10}\right\}$ . The score set of parameters is used as the evaluation sample, and the backward cloud generator generates the weight factor evaluation set
${{C}^{\prime}}_{1}\left(Ex,En,He\right)$ . Then, the forward cloud generator is used to obtain the evaluation cloud map of this parameter [12] .

3) Repeat the steps (2) to obtain the evaluation cloud model of all the parameters. The cloud characteristics of the 56 safe state parameters are shown in Table 2.

4) The nine one-dimensional normal clouds generated by the evaluation language scale are used as the evaluation standard, and the evaluation clouds of the parameters are compared with them one by one. Since the evaluation cloud model reflects the importance of the parameters for the evaluation objectives and there may be differences in experts’ perceptions of the importance of the parameters, it may lead to poor cohesion of the cloud map and show a fog distribution. Therefore, *Ex *≥ 5 is taken in the order of importance from highest to lowest, and the appropriate parameters are selected as safety state parameters in the evaluation cloud model of all factors by combining the cohesive distribution of the cloud diagram. After the calculation to obtain the cloud diagram of each parameter, and observe the cloud diagram and cloud droplet form of each parameter, if the cloud droplet dispersion is small, the cloud diagram as a whole shows a line shape, as shown in Figure 2, which indicates that the experts have a more unified understanding of the importance of the parameter; if the cloud droplet dispersion is larger, the cloud diagram as a whole shows a foggy shape,

Figure 1. 9 one-dimensional normal clouds cloud map.

Table 2. Cloud characteristics of 56 safety state parameters of mobile pressure equipment.

as shown in Figure 3, which indicates that the experts have not yet formed a unified understanding of the importance of the parameter.

5) Finally, 29 safety status parameters were selected, as shown in Table 3.

Figure 2. Cloud diagram of vehicle speed parameters.

Figure 3. Cloud diagram of driving age parameters.

Table 3. 29 safety status parameters of mobile pressure-bearing equipment.

4. Research on the Macro-Safety Risk Early Warning Index System of Long-Tube Trailer

4.1. Handling of Macro-Safety Risk Early-Warning Indicators

First of all, the principle that should be followed for the construction of the indicator system are clarified SMART principles [14] , namely S-Specific, M-Measurable, A-Attainable, R-Relevant, T-Trackable.

Secondly, according to the principles that should be followed, the safety state parameters are transformed into measurable and meaningful indicators, and the selected 29 safety state parameters are analyzed, and appropriately modified or combined to form the real-time risk warning index of mobile pressure equipment [15] .

Then, the importance of the indicators was further tested. Use the risk importance evaluation method to test the index and design the expert opinion scoring table of the index to evaluate the importance of the index [16] , and assigning values to the importance according to Table 4.

70 expert scoring forms were distributed, and the survey experts included the Special Inspection Institute, equipment operation units, Lanke High-Tech, Shougang Group, and scientific research institutes such as the Academy of Safety Sciences and universities, a total of 66 valid questionnaires were recovered. Process the recovered data and calculate the importance coefficient according to

Table 4. Importance of risk warning indicators.

the following formula:

$R{F}_{i}=\frac{{\displaystyle {\sum}_{j=1}^{m}{a}_{ij}}}{m}\left(i=1,2,\cdots ,n;j=1,2,\cdots ,m\right)$ (5)

${\sigma}_{i}=\sqrt{\frac{{\displaystyle {\sum}_{j=1}^{m}{\left({a}_{ij}-R{F}_{i}\right)}^{2}}}{m-1}}$ (6)

$C{V}_{i}=\frac{{\sigma}_{i}}{R{F}_{I}}\left(i=1,2,\cdots ,n\right)$ (7)

*m* Among them, for the total number of survey experts;

${a}_{ij}$ is the evaluation score of the *j*-th expert for the *i*-th indicator;

$R{F}_{i}$ is the importance coefficient of the *i*-th index;

$C{V}_{i}$ is the coefficient of variation of the *i*-th index;

${\sigma}_{i}$ is the sample standard deviation for the *i*-th indicator.

The importance coefficient of each indicator (4.5 is the 50% grade value of the 9-point scoring scale) is specified and the coefficient of variation $C{V}_{i}\le 25\%$ , and the indicators that do not meet the requirements are removed [17] . The index processing process and results are shown in Table 5, and are finally merged into 12 indicators.

4.2. Establish the Macro-Safety Risk Index System of Long-Tube Trailer

Macro safety risk refers to the systematic, comprehensive and social safety risks measured from a large time scale and spatial scope [18] , can be expressed as a combination of likelihood, severity, and sensitivity.

1) Likelihood: the possibility of unsafe events or accidents;

2) Severity: the severity of the possible consequences of unsafe events or accidents;

3) Spatiotemporal sensitivity: the time, space, or system sensitivity of unsafe events or accidents.

Based on the concept of the three dimensions of macro safety risk to determine the pressure-bearing equipment macro safety risk index system is shown in Figure 4.

4.3. Risk Warning Indicator Grading Standard

Some indicators grading description:

1) Equipment inspection status: In accordance with *Pressure Vessels Periodical Inspection Regulation *(TSG R7001-2013), the vehicle should be inspected

Table 5. Processing results and index screening and optimization process of mobile pressure-bearing equipment.

Figure 4. Pressure-bearing equipment macro-safety risk index system.

regularly. In line with the provisions and in the inspection period, according to the distance to the overdue time is divided into three levels, to the overdue time of 3 months and more assigned a value of 1 point; to the overdue time of 2 to 3 months assigned a value of 2 points; to the overdue time of 1 to 2 months assigned a value of 3 points; late inspection is assigned a value of 4 points.

2) Current road type: Considering the actual situation, the system divides the road types into ordinary roads and expressways in the data processing process. Because the speed is slow on the ordinary road, the risk value is low; driving on the highway, high speed, high risk value. The curve road is classified as a special road, with a value of 4 points.

3) Sensitive date: Multiple sensitive dates are saved in the system database, and the current date is compared to the sensitive date in the database during the vehicle driving. If it overlaps, it is high risk; otherwise, it is low risk.

4) Surrounding natural environment: In the process of vehicle driving, the natural environment is more complex than the social environment, with plains, mountains, rivers, which is not easy to classify in detail. The system compares the current position coordinates of the vehicle with the coordinates of rivers and lakes in the database. If the coordinates overlap, the vehicle travels to the sensitive place, the risk value is high, and 4 points are assigned; while the risk value is low, and the risk value is assigned 1 point.

The grading criteria for the 12 indicators are shown in Table 6.

Table 6. Guidelines for grading risk warning indicators for pressure-bearing equipment.

5. Construction of Macro-Safety Risk Early Warning Model for Long-Tube Trailer

5.1. Determining Indicator Weights

1) Build a judgment matrix

Based on the relationship between risk and likelihood, severity, and sensitivity, constructed a risk model
$R=P\times L\times S$ , among them, *R* as risk, *P* as likelihood, *L* as severity, and *S* as spatiotemporal sensitivity. To determine the weights of the secondary indicators using hierarchical analysis, there are seven secondary indicators that have an impact on the likelihood, and to determine their impact ratios, the pairwise comparison method is chosen, two indicators (set as *C _{i}*

$A=\left[\begin{array}{cccc}{a}_{11}& {a}_{12}& \cdots & {a}_{1n}\\ {a}_{21}& {a}_{22}& \cdots & {a}_{2n}\\ \vdots & \vdots & \ddots & \vdots \\ {a}_{n1}& {a}_{n2}& \cdots & {a}_{nn}\end{array}\right]$ (8)

${a}_{ii}=1,{a}_{ji}=\frac{1}{{a}_{ij}}\left(i,j=1,2,\cdots ,n\right)$ , So *A* is an inverse matrix of order *n*.

The value of *RF* is obtained according to the importance evaluation method of risk indicators [20] , Calculate the
$\Delta RF=R{F}_{i}-R{F}_{j}$ * *(
$-8\le \Delta RF\le 8$ ), compare the value of Δ*RF* to the 9-quantile scale method (as shown in Table 7) to determine the value of each element in the matrix.

The following three judgment matrices can be obtained by comparing the importance data of “likelihood” and “sensitivity” and all secondary indicators at the lower level pairwise by the above method [21] , Among them, matrix *A*_{1} is the judgment matrix of “likelihood” indicators, matrix *A*_{2} is the judgment matrix of “severity” indicators, and matrix *A*_{3} is the judgment matrix of “sensitivity” indicators.

${A}_{1}=\left[\begin{array}{ccccccc}1& 2& 1/2& 1/2& 2& 2& 2\\ & 1& 1/3& 1/3& 1/2& 1/2& 1/2\\ & & 1& 2& 2& 2& 3\\ & & & 1& 2& 2& 3\\ & & & & 1& 1/2& 2\\ & & & & & 1& 2\\ & & & & & & 1\end{array}\right]$

Table 7. The Δ*RF* and the 9-quantile scale correspondence table.

${A}_{2}=\left[\begin{array}{cc}1& 3\\ & 1\end{array}\right]$

${A}_{3}=\left[\begin{array}{ccc}1& 1/3& 2\\ & 1& 3\\ & & 1\end{array}\right]$

2) Determine the weight coefficient

The steps to determine the index weight coefficient by using the sum-product method are as follows [22] :

a) The elements of the judgment matrix *A* are normalized by columns to obtain the matrix
$B={\left({b}_{ij}\right)}_{n\times n}$

${b}_{ij}=\frac{{a}_{ij}}{{\displaystyle {\sum}_{i=1}^{n}{a}_{ij}}},\left(i,j=1,2,\cdots ,n\right)$ (9)

b) Summing the elements of matrix *B* by rows gives the vector
$Z={\left({z}_{1},{z}_{2},\cdots ,{z}_{n}\right)}^{\text{T}}$ , among them,

${z}_{i}={\displaystyle {\sum}_{j=1}^{n}{b}_{ij}},\left(i,j=1,2,\cdots ,n\right)$ (10)

c) The vector *Z* is normalized to obtain the feature vector
$W={\left({w}_{1},{w}_{2},\cdots ,{w}_{n}\right)}^{\text{T}}$ , among them,

${w}_{i}=\frac{{z}_{i}}{{\displaystyle {\sum}_{k=1}^{n}{z}_{k}}},\left(i=1,2,\cdots ,n\right)$ (11)

To calculate *A*_{1}, *A*_{2} and *A*_{3} Using the above method, we obtained the weights of “equipment inspection status, Inherent equipment reliability, vessel operating parameters, continuous driving time, speed, local weather, current road type “to the ‘likelihood’ indicators are: 0.1527, 0.0605, 0.2441, 0.2037, 0.1030, 0.1571, 0.0788. The weights of ‘the property of the filling medium, filling capacity’” to the “severity” indicators are: 0.7500 and 0.2500. The weights of the “sensitive date, surrounding social environment and surrounding natural environment” to the “sensitivity” indicators are: 0.2519, 0.5889, and 0.1592.

3) Consistency check

For the consistency test of the judgment matrix, the maximum eigenvalue of the judgment matrix is calculated first, and then calculated the consistency index, *n* denotes the order of the judgment matrix.

$CI=\frac{{\lambda}_{\mathrm{max}}-n}{n-1}$ (12)

If *CI* = 0, it indicates that the judgment matrix has full consistency; if *CI* ≠ 0, the calculation of stochastic consistency ratio is required [23]

$CR=\frac{CI}{RI}$ (13)

where, *RI* is the average stochastic consistency index of the judgment matrix, and its value is related to the order of the matrix. If, *CR** *< 0.1, then the consistency of the judgment matrix and the single-level ranking results is considered acceptable. The *RI* corresponding to the matrices of order 1 to 10 is shown in Table 8.

After calculation, the judgment matrix *A*_{1} corresponding to
$CR=0.0811<0.1$ , the judgment matrix *A*_{2} corresponding to
$CR=0.0000<0.1$ , the judgement matrix *A*_{3} corresponding to
$CR=0.0465<0.1$ . All passed the consistency test.

Finally get:

1) Likelihood impact factor indicator weights ${\omega}_{P}=\left(0.1527,0.0605,0.2441,0.2037,0.1030,0.1571,0.0788\right)$ .

2) Severity impact factorindicator weights ${\omega}_{L}=\left(0.7500,0.2500\right)$ .

3) Sensitivity impact factor index indicator weights ${\omega}_{S}=\left(0.2519,0.5889,0.1592\right)$ .

5.2. Building a Risk Warning Model

Based on the equipment risk early warning theory model, index system and weights [24] , the equipment risk early warning grading model can be obtained as shown below:

$\begin{array}{c}R={\displaystyle \underset{i=1}{\overset{n}{\sum}}{d}_{i}{\omega}_{i}}\cdot {\displaystyle \underset{j=1}{\overset{m}{\sum}}{d}_{j}{\omega}_{j}}\cdot {\displaystyle \underset{k=1}{\overset{l}{\sum}}{d}_{k}{\omega}_{k}}\\ =\left[{d}_{P1},{d}_{P2},{d}_{P3},{d}_{P4},{d}_{P5},{d}_{P6},{d}_{P7}\right]\left[\begin{array}{c}0.1527\\ 0.0605\\ 0.2441\\ 0.2037\\ 0.1030\\ 0.1571\\ 0.0788\end{array}\right]\cdot \left[{d}_{L1},{d}_{L2}\right]\left[\begin{array}{c}0.7500\\ 0.2500\end{array}\right]\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\cdot \left[{d}_{S1},{d}_{S2},{d}_{S3}\right]\left[\begin{array}{c}0.2519\\ 0.5889\\ 0.1592\end{array}\right]\end{array}$

where: *R*—risk score value;

*d*—index score;

*ω*—indicators correspond to the weights.

After the risk value R is obtained through the risk model, the risk classification standard (Table 9) is determined according to the “80/20 rule”, and the risk level of the equipment can be obtained [25] .

6. Project Demonstration

The study used a long tube trailer as a case study for engineering justification

Table 8. The *RI* corresponding to the matrix of order 1 to 10.

Table 9. Real-time risk warning classification standard for long-tube trailer.

Figure 5. Real-time risk level diagram of a long tube trailer.

and obtained the real-time risk level of this long tube trailer as shown in Figure 5.

From the risk statistics chart we can see that the risk level of this long tube trailer increases at point A when it passes through the school area and decreases after leaving, so the risk level of the long-tube trailer during driving changes with the state of the long tube trailer itself, the driver’s driving hours, the road conditions and the environment and so on.

7. Conclusions

1) Macro-safety risk index system was established from three dimensions: probability, severity and sensitivity by using cloud model methods, including 12 indicators: equipment inspection status, Inherent equipment reliability, vessel operating parameters, continuous driving time, speed, local weather, current road type, the property of the filling medium, filling capacity, sensitive date, surrounding social environment and surrounding natural environment.

2) The weight of the macro safety risk index of the long-tube trailer is determined, the macro safety risk calculation model is established, and a more comprehensive risk evaluation model and grading criteria with 12 evaluation indicators and 4 risk levels were obtained. It provides the theoretical basis and method guidance for giving the safety risk management and response plan of pressure equipment from the perspective of supervision and realizing the precise and dynamic supervision of special equipment.

3) Automation and visualization are inevitable requirements for achieving precise supervision, and future research can incorporate safety state parameters such as driver fatigue that are not easy to obtain automatically into the model to improve the scientific of risk assessment.

Acknowledgements

The authors also are indebted to all those who provided earnest assistance and editorial guidance.

Declarations

Ethics Approval and Consent to Participate

This article does not contain any studies with human or animal subjects performed by any of the authors. All authors consent to participate.

Consent for Publication

All authors consent for publication.

Availability of Data and Materials

Thanks to China Special Equipment Testing and Research Institute for providing the data and materials.

Authors’ Contributions

Wenkun Wang: Literature review, Data collection and collation, Experiment, Indicator system and early warning model construction. Ming Xu: Data curation, Formal analysis, Guidance and modification. CaiYan Dai, ChengLong Ma, LianQing Yang: Data collection and collation, Experiment. All authors reviewed the manuscript.

Funding

Primarily, the authors acknowledge financial support of The National Key Research and Development Program of China (2016YFC0801906) and Guizhou Province Science and Technology Plan Project (62912021002).

Funding information: Guizhou Provincial Science and Technology Plan Project “Research and Demonstration of Key Technologies for Rescue and Deduction Based on the Scenario of Rainstorm Collapse Debris Flow Disaster Chain” (Qian Kehe Support [2021] is generally 513).

Competing Interests

There is no competing interest in this article.

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- 10. Zhang, C., Cheng, H.Z., Xun, X.I., Xia, Y., Shen, X.L., Zeng-Hui, X.I. (2006) A Study of Distribution Network Feeding Modes Selection Based on Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation. Power System Technology, 30, 5. https://doi.org/10.3321/j.issn:1000-3673.2006.22.011
- 11. Wang, X., Weiyao, L. and Hailian, L. (2015) Intelligent Monitoring and Early Warning System for Dangerous Goods Transport Vehicles Based on 3S Technology. Computer & Digital Engineering, 31, 175-178, 203.
- 12. Tang, Y.X. (2016) Risks and Countermeasures Chemical Enterprise Special Equipment Safety Management. Chemical Engineering Design Communications, 42, 2. https://doi.org/10.3969/j.issn.1003-6490.2016.02.074
- 13. Yang, W., Dong, Y. and Xie, Q. (2018) Study on Slope Risk Assessment Method Based on Cloud Model and Its Application. Journal of Huazhong University of Science and Technology (Natural Science Edition), 46, 5.https://doi.org/10.13245/j.hust.180406
- 14. Dong, Z., Wang, C., Zhang, W. and Feng, J. (2018) Research on Evaluation Index System of Coal Mine Safety Training Course Based on SMART Principle and SEM Model. Industrial Safety and Environmental Protection, 44, 4.https://doi.org/10.3969/j.issn.1001-425X.2018.03.012
- 15. Luo, Y. (2012) Preliminary Study on Axioms and Theorems of Public Safety Science. China Public Security (Academy Edition), No. 3, 32-33.https://doi.org/10.3969/j.issn.1672-2396.2012.03.005
- 16. Madala, B. (2000) A Simulation Study for Hazardous Materials Transportation Risk Assessment. Master’s Thesis, Concordia University, Montreal.
- 17. Zhang, J.H., Zheng, X.P., and Peng, J.W. (2007) Research on Emergency Capacity Evaluation Based on Fuzzy Analytic Hierarchy Process. Safety and Environmental Engineering, No. 3, 80-82.
- 18. Wang, G., Luo, S., Luo, Y., Pei, J. and Ming, X.U. (2016) Assessment Model of Macro Safety Risk for Special Equipment Based on Efficacy Coefficient Method. Journal of Safety Science and Technology, 12, 146-151. https://doi.org/10.11731/j.issn.1673-193x.2016.09.026
- 19. Saaty, T.L. (1994) Highlights and Critical Point in the Theory and Application of the Analytic Hierarchy Process. European Journal of Operational Research, 74, 426-447. https://doi.org/10.1016/0377-2217(94)90222-4
- 20. Jiang, F., Zhang, H. and Ma, Q. (2021) The Optimize of Tug Configuration Based on Fuzzy Comprehensive Evaluation and Entropy Weight Analysis. IOP Conference Series: Earth and Environmental Science, 809, Article ID: 012018. https://doi.org/10.1088/1755-1315/809/1/012018
- 21. Liu, Q., Dong, D., Han, T. and Amp, A.E. (2016) Research on Risk Assessment Index System for Distribution Network Based on Analytic Hierarchy Process. Electrical Engineering, No. 9, 39-42.
- 22. Zhao, X. and Wen, R. (2019) Research on Evaluation Index System of University Students’ Credit Risk Based on Analytic Hierarchy Process. West China Finance, No. 3, 50-56.
- 23. Zhuo, C., Lei, Z., Hao, Y. and Xiao, L. (2015) Study on Mission Evaluation of the Special Equipment Supervision and Inspection Based on AHP. China Special Equipment Safety, 31, 53-55, 63. https://doi.org/10.3969/j.issn.1673-257X.2015.05.011
- 24. Yang, Z.L. and Liu, J.L. (2008) Study of Special Equipment’s Risk Evaluation Based on AHP. Pressure Vessel Technology, 25, 28-33. https://doi.org/10.3969/j.issn.1001-4837.2008.09.007
- 25. Li, C.G. (2009) Study on Safety Planning for Chemical Industrial Park Base on Gross Risk Quantity. China Safety Science Journal, 19, 116-121. https://doi.org/10.16265/j.cnki.issn1003-3033.2009.06.027

Conflicts of Interest

There is no competing interest in this article.

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[2] | Luo, Q.G., Cui, D.L. and Xie, F. (2022) Research on Residual Deformation of Large-Volume Long-Tube Trailer Gas Cylinder Hydrotest Based on ANSYS. Chemical Engineering & Equipment, 8, 35-37. |

[3] | Bo, K., Cheng, L., Li, Z., Deng, G. and Gu, C. (2017) Safety Relief Device for Tube Trailer and Its Function in Fire Accident. China Special Equipment Safety, 33, 24-28. |

[4] | Li, H.J. (2022) A CNG Long-Hose Trailer Fire Fighting War Case Review Focuses on the Analysis of Problems. Petrochemical Technology, 29, 257-259. |

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[6] |
Gao, Z.F., Yang, Q. and Peng, D.H. (2013) Research on TOPSIS Decision-Making Method Based on Cloud Model. Value Engineering, 32, 8-10. https://doi.org/10.3969/j.issn.1006-4311.2013.29.003 |

[7] |
Xu, X.U., Xie, X., Guo, Y., Huang, Z. and Qin, X.U. (2020) Research on Reliability Evaluation of Ventilation System Based on Cloud Model Theory. Industrial Minerals & Processing, 9, 7-11, 19. https://doi.org/10.16283/j.cnki.hgkwyjg.2020.01.002 |

[8] |
Wang, J.C. and Zhang, F. (2017) Study on Evaluation of Gas Explosion Based on Entropy Right and Two-Dimensional Cloud Model in Coal Mine. Coal Mine Machinery, 38, 166-168. https://doi.org/10.13436/j.mkjx.201709063 |

[9] |
Liu, G.C., Li, G.J. and Yang, L. (2012) Risk Assessments of Debris Flow Based on Improved Analytic Hierarchy Process and Efficacy Coefficient Method. Global Geology, 15, 231-236. https://doi.org/10.3969/j.issn.1673-9736.2012.03.07 |

[10] |
Zhang, C., Cheng, H.Z., Xun, X.I., Xia, Y., Shen, X.L., Zeng-Hui, X.I. (2006) A Study of Distribution Network Feeding Modes Selection Based on Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation. Power System Technology, 30, 5. https://doi.org/10.3321/j.issn:1000-3673.2006.22.011 |

[11] | Wang, X., Weiyao, L. and Hailian, L. (2015) Intelligent Monitoring and Early Warning System for Dangerous Goods Transport Vehicles Based on 3S Technology. Computer & Digital Engineering, 31, 175-178, 203. |

[12] |
Tang, Y.X. (2016) Risks and Countermeasures Chemical Enterprise Special Equipment Safety Management. Chemical Engineering Design Communications, 42, 2. https://doi.org/10.3969/j.issn.1003-6490.2016.02.074 |

[13] |
Yang, W., Dong, Y. and Xie, Q. (2018) Study on Slope Risk Assessment Method Based on Cloud Model and Its Application. Journal of Huazhong University of Science and Technology (Natural Science Edition), 46, 5. https://doi.org/10.13245/j.hust.180406 |

[14] |
Dong, Z., Wang, C., Zhang, W. and Feng, J. (2018) Research on Evaluation Index System of Coal Mine Safety Training Course Based on SMART Principle and SEM Model. Industrial Safety and Environmental Protection, 44, 4. https://doi.org/10.3969/j.issn.1001-425X.2018.03.012 |

[15] |
Luo, Y. (2012) Preliminary Study on Axioms and Theorems of Public Safety Science. China Public Security (Academy Edition), No. 3, 32-33. https://doi.org/10.3969/j.issn.1672-2396.2012.03.005 |

[16] | Madala, B. (2000) A Simulation Study for Hazardous Materials Transportation Risk Assessment. Master’s Thesis, Concordia University, Montreal. |

[17] | Zhang, J.H., Zheng, X.P., and Peng, J.W. (2007) Research on Emergency Capacity Evaluation Based on Fuzzy Analytic Hierarchy Process. Safety and Environmental Engineering, No. 3, 80-82. |

[18] |
Wang, G., Luo, S., Luo, Y., Pei, J. and Ming, X.U. (2016) Assessment Model of Macro Safety Risk for Special Equipment Based on Efficacy Coefficient Method. Journal of Safety Science and Technology, 12, 146-151. https://doi.org/10.11731/j.issn.1673-193x.2016.09.026 |

[19] |
Saaty, T.L. (1994) Highlights and Critical Point in the Theory and Application of the Analytic Hierarchy Process. European Journal of Operational Research, 74, 426-447. https://doi.org/10.1016/0377-2217(94)90222-4 |

[20] |
Jiang, F., Zhang, H. and Ma, Q. (2021) The Optimize of Tug Configuration Based on Fuzzy Comprehensive Evaluation and Entropy Weight Analysis. IOP Conference Series: Earth and Environmental Science, 809, Article ID: 012018. https://doi.org/10.1088/1755-1315/809/1/012018 |

[21] | Liu, Q., Dong, D., Han, T. and Amp, A.E. (2016) Research on Risk Assessment Index System for Distribution Network Based on Analytic Hierarchy Process. Electrical Engineering, No. 9, 39-42. |

[22] | Zhao, X. and Wen, R. (2019) Research on Evaluation Index System of University Students’ Credit Risk Based on Analytic Hierarchy Process. West China Finance, No. 3, 50-56. |

[23] |
Zhuo, C., Lei, Z., Hao, Y. and Xiao, L. (2015) Study on Mission Evaluation of the Special Equipment Supervision and Inspection Based on AHP. China Special Equipment Safety, 31, 53-55, 63. https://doi.org/10.3969/j.issn.1673-257X.2015.05.011 |

[24] |
Yang, Z.L. and Liu, J.L. (2008) Study of Special Equipment’s Risk Evaluation Based on AHP. Pressure Vessel Technology, 25, 28-33. https://doi.org/10.3969/j.issn.1001-4837.2008.09.007 |

[25] |
Li, C.G. (2009) Study on Safety Planning for Chemical Industrial Park Base on Gross Risk Quantity. China Safety Science Journal, 19, 116-121. https://doi.org/10.16265/j.cnki.issn1003-3033.2009.06.027 |

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