Research on Navigation Safety Evaluation of Coastal Waters Based on Dynamic Irregular Grid

Abstract

Despite being a minor probability event, marine accidents can cause serious consequences such as casualties, environmental damage, and even massive economic losses. As an important part of the marine traffic safety system, the evaluation of navigation safety in coastal waters is of great significance to ensure the safety of ship navigation. In order to objectively evaluate the safety of navigation, this paper proposes an irregular grid division method that combines influencing factors including seabed topography, ship traffic flow, electronic charts, and marine meteorology. In this work, the navigable boundary was extracted by spatial analysis algorithms of the slope calculation. Based on the alpha-shape algorithm and the Voronoi diagram, the constrained Delaunay triangulation was used to extract the inner and outer boundaries of the ship′s navigation area, and the time-varying factors of marine hydrology and meteorology were integrated to form a dynamic irregular grid. The navigation safety evaluation indicators in coastal waters were divided into five risk levels, namely lower, low, medium, high, and higher. Then, the entropy weight theory was used to calculate the weight of the evaluation index. Finally, a safety evaluation model was constructed to evaluate the risk of navigation safety in coastal waters. Herein, taking the coastal waters of Lianyungang Port as a demonstration area, this paper divided the dynamic irregular grid and conducted the navigation safety analysis and evaluation based on the grid. The experimental results show that our method fully considers the influence of objective factors and the uncertainty of safety evaluation indicators and has favorable adaptability to the evaluation of navigation safety in coastal waters.

1. Introduction

Marine accidents can cause casualties, environmental damage, and even serious economic losses [1]. The characteristics of marine accidents are small probability and serious consequences, which shows that navigation safety evaluation is significantly important to shipping activities [1,2]. Therefore, the navigation safety of coastal ships has always been an important subject in the field of marine and water transportation [3]. Additionally, the complex terrain of coastal waters, frequent transportation, and the influence of severe weather have increased the risk of navigation safety and made the evaluation of navigation safety more complicated [4]. In shipping, safety depends on lots of internal and external factors such as the reliability of ship systems and human operation techniques, as well as marine environments such as wind, waves, and currents [5,6]. As an important part of the marine traffic safety evaluation system, the evaluation and analysis of environmental factors such as traffic conditions, natural conditions of waterways, and hydrometeorological conditions are particularly important [7,8,9,10].

Standardization efforts by the International Maritime Organization (IMO) and the International Regulations for Preventing Collisions at Sea (COLREG) have introduced useful, practical, and reliable methods for ensuring the safety of navigation [11]. In recent years, researchers have made lots of fruitful work on general aviation safety evaluation. They carried out waterway safety evaluation using methods such as risk-cause coupling mechanism simulation, fuzzy rule base and evidence reasoning, entropy weight fuzzy model, set-valued statistics–gray fuzzy, and other methods [12,13,14,15]. Generally, they obtained the overall navigation safety of the channel through quantitative analysis of multiple evaluation factors. In most of the previous studies, the risk status of the coastal area has been evaluated as a whole, rather than by gridding the waters. Nowadays, the development of Geographic Information System (GIS) grid technology provides a scientific method for the refined evaluation of safety [16,17,18,19]. In terms of waterway safety, Tang and Fan et al. analyzed and predicted based on gridding, which provided a good idea for this study [20,21]. They performed marine traffic risk analysis based on cellular units and divided the research waters into a number of geographic units according to certain standards and then organically combined related historical data, mathematical models, and expert experience to quantitatively analyze the geographic distribution of ship collisions and grounding accidents. As another example, Hu et al. established a maritime grid risk early warning model based on analytic hierarchy process and back-propagation neural network to predict the risk level of future periodic grids [22]. In addition, Zhang and Zhong et al. carried out safety risk assessment studies in Huizhou Port and Dongying Port based on grid technology [23,24]. The abovementioned studies provided valuable references for water traffic safety analysis and navigation safety risk assessment; the meshing method, however, should comprehensively consider various factors to be more scientific.

It is remarkable that using unmanned surface vehicles (USVs) to autonomously collect high-quality oceanographic data is a highly effective method to ensure navigation safety, which can be further processed and used for navigation safety evaluation and other applications [25]. USVs are mainly used for missions that are dangerous and unsuitable for manned vessels. Researchers are able to take advantage of USVs’ shallow draft and strong maneuverability to conduct ocean surveying and mapping to provide marine geographic information, data, and basic graphics and to monitor marine meteorological elements, marine water quality elements, and marine biological elements [26]. For example, Mattei et al. described a prototype of a marine drone optimized for very shallow water, which enables bathymetric surveys to be performed in areas that are not feasible for traditional boats [27]. It is certain that the obtained marine data provide an important reference for the construction of navigation safety evaluation.

As previously mentioned, the grids based on geographic grid models or empirical methods have not so far fully integrated the geographical distribution characteristics of environmental influence factors [28]. In this paper, an irregular meshing method that integrates factors such as seabed topography, ship traffic flow, electronic charts, and marine meteorology is proposed. Firstly, the waterway boundary of the coastal waters was obtained through the electronic chart and water depth data. Secondly, the actual navigation boundary of the ships in the coastal waters was extracted by using the AIS data. Then, the overall boundary of the actual navigation of the ship was acquired by fusing the boundaries. Based on the fusion of marine hydrometeorological data, an irregular grid for navigation safety evaluation was finally obtained. Using the time variable of ocean data features, dynamic irregular grids can be generated in units of hours. Overall, our method constructed a spatiotemporal grid model of coastal waters by using the dynamic change characteristics of navigation-influencing factors, comprehensively used systematic GIS theory and fuzzy mathematical theory to analyze the risk factors affecting navigation safety, and then carried out risk metric calculation. In this paper, a risk evaluation index system and an evaluation sample model are established, the quantitative evaluation of the risk degree in the grid of the waterway area is carried out, and the visualization results of the waterway safety analysis and evaluation are formed.

The organization of this paper is as follows: Section 2 introduces materials and methods, which includes the experimental area, experimental data sources, and processing methods. Section 3 analyses the results for the irregular grid division method and the safety evaluation analysis method. We summarize the results of this study and provide future research directions in Section 4.

2. Materials and Methods

In this study, the maritime space with latitude and longitude of 119.17~120.41° and 34.40~35.47° was selected as the research region. As shown in

Table 1. Classification of data elements.

Data Category	Data Items	Year	Resolution/Scale	Data Organization Mode
seabed topography	Gebco global water depth data Bathymetric data	2020	15″ 1:1000	NetCDF formatted table
electronic chart	The channel anchorage Restricted area AIDS to navigation	2020	1:1000	shp file format
ship traffic flow	AIS data	2020	/	Format Table
marine hydrometeorology	Data of wind, wave, and trend	2020	0.04° 0.03° 0.03°	NC file format

, the data of seabed topography, electronic chart, ship traffic flow, and marine hydrometeorology in this area were collected. Because of semantic independence and heterogeneity among data resources and the difference in the temporal and spatial dimensions and storage methods of data, the XML-based distributed spatial data storage method was adopted to form a spatiotemporal data set, which provided the data basis for the follow-up research of this paper.

The seabed topography data intuitively reflect the characteristic elements. Relevant studies have shown that finding the spatial distribution characteristics of terrain and extracting characteristic factors can prevent ships from running aground, hitting rocks, and colliding, greatly reducing navigation risks. In this study, the publicly available global ocean GEBCO data (see Figure 1a) and the data of regular dredging measurements of the waterway (see Figure 1b) were used to construct a digital elevation model (DEM) of sea water depth [29]. Slope analysis plays an important role for ships to avoid obstructions and leave the fairway area. By calculating the angle between the tangent plane and the horizontal plane at the midpoint of the terrain, the slope of the seabed terrain can be analyzed (see Figure 1c). The calculation formula of the inclination of the seabed surface can be obtained by

$$\mathrm{slope}\_\mathrm{degrees}=\frac{360}{2\pi }*\arctan \sqrt{{\left(\frac{dz}{dx}\right)}^2+{\left(\frac{dz}{dy}\right)}^2}$$

(1)

Electronic charts (see Figure 1d) can provide comprehensive navigation-related information for ship navigation safety and effectively prevent navigation hazards. In this paper, according to the data of the latest released chart number (CN337001, CN341001, CN441451, CN441101, CN441221, CN541112, CN541211, CN541212, and CN541213), element information such as channel, anchorage, navigation aids, and restricted areas was obtained after vectorization.

In the process of ships entering and leaving the port, a large number of data resources are accumulated that record the information of ship traffic flow (see Figure 1e). In the past, these massive data were not reused after being used for the first time. In fact, the in-depth mining of the massive data can provide scientific value for the extraction of the navigation safety boundary of waterway ships. Herein, 2020 ship AIS data within the study area were collected. The data preprocessing method was as follows. Firstly, data with obvious errors such as longitude and latitude values and MMSI number values were eliminated through data preprocessing. Then, redundant data were merged to reduce resource consumption in the process of data analysis and operation, and finally, 9 categories were formed such as container ships, cargo ships, and fishing ships. There are a total of 39,595,240 pieces of ship dynamic data.

The comprehensive effect of multiple factors such as ocean hydrology and meteorological conditions has a great impact on the safe navigation of ships. For example, waves affect the direction, speed, and position of the ship, tidal currents cause changes in drift, and wind affects the speed and draft of ships. Herein, the numerical forecast data of wind, wave, and current in 2020 were used for the analysis and research.

After the data collection was completed according to the above method, the division method of the irregular grid was as follows. First, the method of extracting the channel boundary based on the combination of constrained Delaunay triangle (CDT) and alpha shape was researched. Next, the method of extracting the inner and outer boundaries of ship navigation based on the combination of CDT and Voronoi diagram was integrated. Finally, the regular marine hydrometeorological data were reclassified and superimposed with the navigable boundary to form an irregular grid, and the time variable was integrated into the grid to form a dynamic irregular grid.

3. Results and Discussion

3.1. Irregular Grid Division Method

Figure 2 shows the flow of dynamic irregular grid division, including basic geographic data, waterway boundary data, and marine environment data. In terms of fundamental geographic data, based on the natural conditions of the waterway, the seabed topography data and bathymetric data formed a unified coastal water topography through data fusion. Then, a slope analysis was performed to find out the risk boundary with high terrain slope and influence on navigation safety. Combined with the element information such as channel, anchorage, restricted area, etc., extracted from the electronic chart, the fundamental geographic information data grid graph was formed. Regarding navigational boundary data, based on channel depth data and the massive AIS ship data, the ship navigation trajectory map was constructed through the steps of ship point anomaly analysis and processing and data redundancy processing. Then the outer and inner boundaries of the ship′s navigation were extracted by the combination of constrained Delaunay triangle, alpha shape, and Voronoi diagram, respectively, and the grid graphics of the inner and outer boundaries of the channel were formed. In terms of marine environmental data, real-time forecast data such as wind, waves, and currents and the cell grid of the original data were used for data analysis, reclassification, and merging, and the marine environment grid graph was formed according to the time span. On the basis of the grid formed by the three types of data, the irregular grid was divided by spatial superposition analysis, and finally, the dynamic irregular grid of coastal water navigation was constructed.

3.1.1. CDT–Alpha-Shape Boundary Extraction of Waterway

CDT has been widely used in road boundary extraction of crowd-sourced trajectories [30]. Constructing the waterway triangulation, the triangles are formed according to the spatial proximity analysis and do not intersect the constraint line segments. Figure 3a shows the constructed constraint triangle. Liu et al. eliminated redundant triangle sides according to the statistical characteristics of triangle side lengths [31]. The calculation formula is

$$lenValue\left(CDT\right)=lenMean\left(CDT\right)+a*lenVar\left(CDT\right)$$

(2)

where lenMean(CDT) is the average side length of the constrained triangulation, lenVar(CDT) is the standard deviation of the side length of the constrained triangulation, and a is the constraint parameter. By adjusting the value of a, the distance threshold lenValue(CDT) of the side length can be obtained. If the len(CDT) is greater than the threshold, the edge is culled. As shown in Figure 4, the alpha-shape algorithm can abstract its contour shape from a discrete set of spatial points by controlling a rolling circle of radius α [32]. Each point is a boundary point as the radius α of the rolling circle is small enough. A rolling is performed on the boundary point, and the rolling trajectory is the channel boundary as α is appropriately increased. How to determine the relationship between the radius of the circle and the channel survey scale is an important research topic; herein,

Table 2. Reference range of circle radius a.

Scale Bar	Error Limits in Points (mm)	Point Field Distance Limit (m)	Reference Range for a (m)
1:500	1.5~2.0	7.5~10.0	7.5~20.0
1:1000	1.5	15.0	15.0~30.0
1:2000	1.5	30.0	30.0~60.0
1:5000	1.0	50.0	50.0~100.0
1:10,000	1.0	100.0	100.0~200.0

gives the reference range of α value corresponding to different survey scales [33]. Figure 3b shows the final outer boundary contour of the channel obtained by the optimized CDT–alpha-shape algorithm.

3.1.2. CDT–Voronoi for Boundary Extraction of Ship Navigation Track

Compared with channel survey data, ship trajectory data have the characteristics of uneven distribution in coastal waters. As shown in Figure 5a,b, taking the AIS data of container ships as an example, CDT and distance threshold adjustment can be used to eliminate redundant edges and extract the outer boundary. However, it is difficult to extract the inner boundary using the alpha-shape algorithm alone. The experimental data are container ship data. Some researchers have used KMeans, DBSCAN, and other clustering algorithms to extract ship behavior pattern features [34]. For example, Xu et al. identified square regions based on the SpatialNiBlack algorithm and proposed a del-alpha-shape algorithm, which provided a better way of thinking for the extraction of channel boundaries from large-scale ship trajectory data [35]. Cluster analysis can discover the latent patterns of large-scale ship trajectory data distribution. As an important branch of computational geometry, Voronoi diagrams can easily express the spatial relationship of a large number of discrete data with their nearest neighbor feature. Therefore, this paper used CDT–Voronoi to extract the inner and outer boundaries of the ship trajectory.

For the point P_i of the ship point set $P=\left\{p_0,p_1,\cdots ,p_n\right\}$, its Voronoi region R_i is defined as

$$R_i=\left\{x\in X|d\left(x,p_i\right)<d\left(x,p_j\right),j=\left\{0,1,2,......,n\right\},j\ne i\right\}$$

(3)

Voronoi diagram and CDT are duals of each other. Figure 5c shows the Voronoi polygon formed by ship trajectory points, where the circumcenter of each triangle in the Delaunay triangulation is generated, and then the circumcenter of adjacent triangles is connected. The density of the points can be determined by the graphic density value. The larger the graphic density value, the denser the trajectory, and vice versa. The density ρ of ship points is obtained by calculating the area of the polygon and taking its inverse. Based on the extracted outer boundary, the spatial correlation analysis is carried out with reference to the obstruction elements such as electronic charts. When the ship track point intersects with it, the ρ value is set to 0. The inner boundary is extracted by optimizing the range of the density threshold. Figure 5d shows the navigable area range extracted after taking into account terrain, electronic charts, and ship factors.

3.1.3. Construction of Ocean Data Fusion Grid

Marine hydrometeorological information has multi-temporal data characteristics. The wind, wave, and current forecast data used in this study were vector data of regular cell grids. Taking the wind field data as an example, the direction and wind speed were calculated according to the UV components of the grid points, and the geospatial vector map of the wind field was produced.

$$\mathrm{direction}=\frac{180}{\pi }*\arctan \left(\frac{u}{v}\right),\mathrm{speed}=\sqrt{u^2+v^2}$$

(4)

In this step, the wind speed value was mainly used for the division of irregular grids. The values of the 24 h wind field in a day were reclassified, and the classification method was divided into 10 categories at equal intervals, where 1 represents the low value and 10 represents the high value. Figure 6a shows the reclassification results of the wind field data. In the same way, the reclassification map of waves and currents was formed according to the above method. Figure 6b shows the binning of pixel values according to the cell index value. Figure 6c shows the classification diagram of the combined wind, wave, and current data. Based on the attribute values of the merged data, features with the same combination of values were fused into a single feature, while the fused value field was written to the output feature class, and continuous lines were merged at common endpoints to generate multipart features. Figure 6d shows the irregular grid after incorporating wind, wave, and current marine environmental elements. Compared with a single environmental element, the marine environmental factors that affect the safe navigation of ships can be comprehensively analyzed and information extracted through spatial overlay analysis, and the safety impact indicators of the region can be fully displayed.

Figure 7 shows the irregular grid constructed by fusion of marine environmental data in one day (24 h) in the time series. Compared with the characteristics of a single moment, the fused irregular grid can fully show the process of changes in marine environmental elements in a specific period and allow the analysis of the future state of coastal waters based on marine environmental forecast data. The dynamic adjustment of irregular grids with time changes has more practical guiding significance for the safety of ship navigation.

On this basis, spatial overlay analysis was used to segment the irregular grids formed by influencing factors such as terrain, ships, and oceans to form an irregular grid for navigable coastal waters. Further, we exploited the time variable of ocean data features to generate a dynamic irregular grid in hours. The division of the grid in this study provides a space unit diagram for the subsequent navigation safety evaluation and analysis.

3.2. Safety Evaluation Analysis Method

3.2.1. Safety Risk Evaluation Index

Taking into account the natural conditions of the waterway, traffic conditions, hydrometeorological conditions, and other environmental factors, we first determined the key influencing factors and then constructed a factor data set C = (c1, c2, ..., cn). Next, the evaluation set E = (e1, e2, …, en) of each factor was determined for the factor data set, which was used for the risk calculation relationship. The risk level was divided into five categories: lower, low, medium, high, and higher. The evaluation index system is shown in

Table 3. Security risk evaluation index system.

Evaluation Area	Evaluation Factors	Evaluation Parameters	Evaluation Indicators
Navigation safety assessment in coastal waters C	Channel natural conditions c1	Seafloor topography c11	Terrain risk assessment e11
		Anchor ground c12	Anchorage risk assessment e12
		Channel c13	Waterway risk assessment e13
		Restricted area c14	Restricted area risk assessment e14
		Water depth c15	Water depth risk assessment e15
	Traffic conditions c2	Traffic flow c21	Traffic flow risk assessment e21
		Ship density c22	Vessel density risk assessment e22
		Ship speed c23	Vessel speed risk assessment e23
		Navigation aids c24	Risk assessment of navigation aids e24
	Hydrometeorological conditions c3	Wind c31	Wind risk assessment e31
		Wave c32	Evaluation of wave hazard e32
		Trend c33	Trend risk assessment e33
		Tidal c34	Tidal hazard evaluation e34

.

3.2.2. Safety Risk Assessment Model

The construction of the evaluation model is significantly important for the safety risk analysis of irregular grids in coastal waters. In this paper, a fuzzy relationship matrix was established to evaluate the influencing factors e_i of a single dynamic acquisition. The membership degree m_ij of the influencing factors to the elements of the evaluation set e_j (j = 1, 2, …, m) was determined, and the evaluation result for the i element is a single-factor fuzzy evaluation set M_i = (m_i1, m_i2, …, m_im).

The natural channel conditions were evaluated based on the analysis results of Section 3.1.1 and Section 3.1.2. A characteristic function was defined to study the irregular grid of the whole area. A represents the data set suitable for navigation in the area. When the airworthy area within the boundary of the waterway is assigned a value of 0, and the dangerous goods anchorage and restricted area are assigned a value of 1, the characteristic function is ${\mathsf{\mu }}_{\mathrm{A}}\left(\mathrm{x}\right)$, which is defined as follows.

$${\mathsf{\mu }}_{\mathrm{A}}\left(\mathrm{x}\right)=\{\begin{array}{l}0,x\in A\\ 1,x\notin A\end{array}$$

(5)

Based on the divided irregular grid, the traffic conditions were analyzed according to the Gaussian membership function, the hydrometeorological conditions were analyzed according to the S-type membership, and each evaluation factor was calculated.

In the Gaussian membership evaluation, taking the ship traffic as an example, the characteristic function $f\left(x,\sigma ,c\right)$ is defined as follows.

$$f\left(x,\sigma ,c\right)=\{\begin{array}{l}0,x\le a\\ e^{-\frac{{(x-c)}^2}{2{\sigma }^2}},a<x\le b\\ 1,x>b\end{array}$$

(6)

In the formula, σ represents the standard deviation of the normal distribution, c represents the mean of the normal distribution, x represents the number of ships accumulated in the one-hour cell as the independent variable, $a$ is 10, and $b$ is 130.

In the S-type membership evaluation, the wind field data are taken as an example for analysis, and the characteristic function $f\left(x,a,b\right)$ is defined as follows:

$$f(x,a,b)=\{\begin{array}{l}0,x\le a\\ 2{(\frac{x-a}{b-a})}^2,a<x\le \frac{a+b}{2}\\ 1-2{(\frac{x-b}{b-a})}^2,\frac{a+b}{2}<x\le b\\ 1,x>b\end{array}$$

(7)

In the formula, x represents the wind speed. When the wind power is below grade 3, it has no effect on navigation. When the wind power is above grade 9, the risk is high. The value of $a$ is 5.4, and the value of $b$ is 20.8.

According to the safety evaluation membership function, the real-time data of the influencing factors were evaluated. The fuzzy relationship matrix M is formed by the membership degree of each single factor fuzzy evaluation set.

$$M=\left[\begin{array}{l}{M}_1\\ M_2\\ \dots \\ M_n\end{array}\right]=\left(\begin{array}{ccc}{m}_{11}& \dots & m_{1m}\\ ⋮& \ddots & ⋮\\ m_{n1}& \cdots & m_{nm}\end{array}\right)$$

(8)

In the evaluation work, the importance of each factor is different, and thus it is necessary to further determine the weight vector W of the influencing factors, W = (w1, w2, ⋯, wn). Herein, the entropy weight method was used to obtain the information entropy, and the weight of the evaluation index was calculated to reduce the influence of subjective judgment on the weight of the index.

$$W=-{\displaystyle \sum _{i=1}^m{p}_i}{\log }_2{p}_i$$

(9)

After determining the single-factor evaluation matrix M and the factor weight vector W, the vector B can be obtained.

$$B=\overrightarrow{W}·M=[b_1,b_2,b_3,.....b_n]$$

(10)

According to the importance of navigation safety in coastal waters, the maximum value of the calculation result was taken according to the principle of maximum membership to determine the safety level, which constitutes a safety evaluation model.

3.2.3. Safety Risk Assessment Results

Determining the hazard level and performing quantitative analysis through numerical values are the core of the safety evaluation model. In order to obtain the final conclusion, it is necessary to measure the risk through the calculation formula. The risk quantification indexes of each influencing factor were analyzed in turn, and then the safety risk of 62 irregular grids divided into coastal research areas was calculated by constructing and quantitatively analyzing the navigation safety evaluation model of coastal waters. The security risk level was divided into five levels: lower, low, medium, high, and higher (0.2, 0.4, 0.6, 0.8, and 1.0). According to the calculation results, there are 2 high-level grids such as dangerous goods anchorages and restricted navigable areas, 6 high-risk grids, 12 medium-risk grids, and the rest are low-risk and lower-risk grids. In this study, different grades of irregular grids were set with different colors and assigned values to visually display the risk map of coastal waters and provide technical support and security for navigation safety.

In this study, the navigation information extracted from the electronic chart was used. Then, the sounding data were used to extract the boundary of the entrance and exit channels, and the large-scale ship scatter data were extracted to obtain the boundary of the channel. The general navigable boundary of coastal waters was then formed by the method of spatial fusion. On this basis, the spatial overlay analysis of marine hydrometeorological data was carried out to form an irregular grid.

4. Conclusions

In general, this study proposed a dynamic irregular meshing method for coastal waters. The method comprehensively considers natural, traffic, and hydrometeorological factors such as seabed topography, ship traffic flow, electronic charts, marine meteorology, etc., and eliminates the subjective influence of division based solely on geographic grid models or empirical methods. Based on the water depth and topographic data, the inclination of the seabed surface was obtained through the slope analysis, and the safety risk boundary of the navigation channel was found. At the same time, the element information was extracted from the electronic chart to form a preliminary grid. On this basis, combined with large-scale ship trajectory data, CDT, alpha shape, and Voronoi were used to extract the inner and outer boundaries of ship navigation. Finally, the marine hydrometeorological data with time variables were substituted, and a dynamic irregular grid was formed through spatial overlay analysis. By studying the risk assessment theory of navigation safety in coastal waters, the navigation safety factors were analyzed and a safety evaluation index system was constructed. In this study, the risk levels were divided into five categories, namely lower, low, medium, high, and higher. On this basis, this paper evaluated the membership function of each type of risk factor. According to the membership degree of the single factor fuzzy evaluation set, the fuzzy relation matrix was formed, and then the security evaluation analysis was realized by using the entropy weight method, the weight calculation, and the fuzzy relation matrix. This method takes into account the uncertainty of the evaluation factors and the fuzziness of the evaluation criteria and can quantitatively evaluate the divided dynamic irregular grids, which can further obtain more accurate and objective analysis results. In the future, the influence of related factors will be further studied and integrated into the division of irregular grids. At the same time, more evaluation factors will be considered in the safety risk assessment to achieve more accurate goals and provide visual technical support for general navigation.