Efficient distributed association management method of data, model, and knowledge for digital twin railway

Figures & data

Figure 1. Pervasive challenges of current decentralized and independent management strategies. Figure (a) depicts a general analysis process where implicit and incomplete relationships between objects are emphasized. Figure (b) expresses the inconsistent organizational strategy of data, models, and knowledge commonly observed in most railway engineering projects. (a) Insufficient association relationships and (b) Inconsistent organizational patterns.

Figure 2. The overall workflow encapsulating the key steps involved in our proposed association management method for data, models, and knowledge in digital twin railway.

Figure 3. Schematic and an example of the DMK-$\mathrm{Graph}\mathrm{model}$.

Figure 4. UML diagram describing the data structure of vertices in the DMK-$\mathrm{Graph}\mathrm{model}$.

Table 1. Association relationships among data, models and knowledge.

Relationship Type	Subdivided Type	Optional Input Pairings (Bidirectional)	Optional Results
Time	Lifecycle Stage	Data-Model, Data-Knowledge, or Model-Knowledge	contain, containedBy, equal, before, after
	Date Time	Data-Model	contain, containedBy, equal, before, after,overlap
Space	Spatial Scale	Data-Model, Data-Knowledge, or Model-Knowledge	equal, above, below
	Spatial Dimension	Data-Model	equal, above, below
	Spatial Resolution	Data-Model	equal, above, below
	Spatial Scope	Data-Model	contain, containedBy, equal, disjoint, overlap,meet
	Mileage Scope	Data-Model	contain, containedBy, equal, before, after,overlap, meet
Interaction	Coupling	Data-Model, Data-Knowledge, or Model-Knowledge	describe, supplement, input, output, feedback
	Chain Effect		sequential, parallel, iterative

Figure 5. Design of association management architecture for digital twin railway information: an iterative feedback flow pipeline between physical space and information space.

Algorithm 1: Parallel Index Generation for $\mathrm{DMK}-\mathrm{Graph}\mathrm{index}$

Input: $\mathcal{D}$ (Data sets), $\mathcal{M}$ (Model sets), $\mathcal{K}$ (Knowledge sets), CPUs (Number of CPU cores)

Output: G (The $\mathrm{DMK}-\mathrm{Graph}\mathrm{index}$)

1 Initialize an empty graph G;

2 Define $G.V_D,G.V_M,G.V_K$ as empty sets of vertices with types ‘Data’, ‘Model’, and ‘Knowledge’, respectively.

3 foreach element $e\in \{\mathcal{D},\mathcal{M},\mathcal{K}\}$ do

4  Add vertex to the corresponding subset of $G.V$ (i.e. $G.V_D,G.V_M,$ or $G.V_K$) based on type derived from e and attributes derived from e;

5 end

6 foreach pair of vertex sets $(V_{\alpha },V_{\text{β}})\in \left\{\right(G.V_D,G.V_M),(G.V_D,G.V_K),(G.V_M,G.V_K\left)\right\}$ do

7  Partition vertex sets $V_{\alpha }$ and $V_{\text{β}}$ into CPUs subsets: $V_{{\alpha }_i}$ and $V_{{\text{β}}_i}$ respectively, where $i=1,2,\dots ,\mathrm{CPUs}$;

8  foreach pair of vertex subsets $(V_{{\alpha }_i},V_{{\text{β}}_i})$ in parallel using CPUs do

9   foreach vertex $v\in V_{{\alpha }_i}$ do

10    foreach vertex $u\in V_{{\text{β}}_i}$ do

11     $R(v,u)\leftarrow \mathit{CalculateDirectedRelationships}\mathit{(}\mathit{v}\mathit{,}\mathit{u}\mathit{)}$;

12     if $R(v,u)\ne \mathrm{\varnothing }$ then

13      Add directed edges $(v,u,R(v,u\left)\right)$ to $G.E$;

13      Add directed edges $(u,v,\mathit{InverseRelationships}\mathit{(}\mathit{R}\mathit{(}\mathit{v}\mathit{,}\mathit{u}\mathit{)}\mathit{\left)}\mathit{\right)}$ to $G.E$;

15     end

16    end

17   end

18  end

19 end

20   return G;

Figure 6. The mechanism of the global association index and local indexes in association management architecture is described through two key processes: search and update.

Figure 7. Prototype system design and implementation based on the browser/server architecture.

Figure 8. A brief steps summary of high-temperature safety risk analysis in railway tunnel engineering based on the study in this paper.

Figure 9. Design of quantitative tasks and related software deployment environments.

Figure 10. An association retrieval result obtained by exploratory interaction retrieval in the prototype system.

Figure 11. Time consumption records and statistical analysis for association and non-association retrieval processes, emphasizing the method's practicality rather than direct efficiency comparisons.