Efficient train path optimization and route control method for urban rail transit

Abstract

The invention provides an efficient train path optimization and route control method for urban rail transit, and the method comprises the steps: obtaining real-time train track map route data in a train driving process; constructing a rail transit network through the concurrent dictionary data structure, and determining a starting node and an ending node for train path optimization; initializing a rail transit network, and determining a shortest path from a starting node to an ending node from the initialized rail transit network; and searching a rail transit network to obtain a train running line segment formed by edges, so as to inquire at least one train route set from the train track diagram, and determine an optimal route set for scheduling the routes of the train. According to the method and the device, the defects that limitation exists when multiple routes are selected, front routes cannot be handled, and when the train needs to automatically plan the optimal driving path, the optimal driving path is difficult to plan in real time in a complex track network are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation systems, and in particular to a method for efficiently optimizing train paths and controlling routes for urban rail transit. Background Art

[0002] Train path optimization and approach control technology (ATS) has been widely used in the field of intelligent transportation. The ATS system uses computer control technology to realize functions such as automatic driving, operation monitoring and fault handling of trains, which greatly improves the operational efficiency and safety of the rail transit system. The system can alleviate the work pressure of train dispatchers to a certain extent and improve the overall operational efficiency. However, with the continuous expansion of the rail transit network, the existing ATS system still has some shortcomings when dealing with complex road networks and high-density traffic flows. Among them, path optimization is one of the main technical bottlenecks faced by train path optimization and approach control technology.

[0003] In the existing technology, it is difficult to calculate the optimal driving path in real time in a complex track network, especially when there are obvious limitations when multiple routes are available and when the route ahead cannot be handled, the train needs to automatically plan the optimal path. Route selection is a crucial part of the train operation process, affecting the train's operating efficiency and safety. Summary of the invention

[0004] The present invention provides a method for efficient train path optimization and route control for urban rail transit, which is used to solve the defects in the prior art that there are obvious limitations in the selection of multiple routes and it is difficult to plan the optimal travel route in real time in a complex rail network when the front route cannot be processed and the train needs to automatically plan the optimal travel route.

[0005] The present invention provides a method for efficient train path optimization and route control for urban rail transit, the method comprising the following steps: During the running of the train, real-time train track map route data is obtained, wherein the train track map route data includes nodes and edges, the nodes represent train stations or track sections, and the edges represent tracks for connecting the train stations or the track sections; The train track map route data is used to construct a rail transit network through a concurrent dictionary data structure.

[0006] and determining a starting node and an ending node for train path optimization from the rail transit network; Initializing the rail transit network, and determining the shortest path between the starting node and the ending node from the initialized rail transit network; According to the shortest path, searching for a train travel segment formed by the edges from the rail transit network; Based on the train travel line segment, at least one train route set is queried from the train track map, and an optimal route set is determined from the train route sets for scheduling the train routes.

[0007] In some embodiments, initializing the rail transit network includes: Taking the starting node in the rail transit network as a processed node, and setting the initial shortest distance of the starting node to 0; The other nodes in the rail transit network except the starting node are regarded as unprocessed nodes, and the initial shortest distance of the unprocessed nodes is set to infinity.

[0008] In some embodiments, determining the shortest path from the start node to the end node from the initialized rail transit network includes: The initial node is used as a predecessor node, and the nodes adjacent to the predecessor node in the initialized rail transit network are used as target unprocessed nodes and added to a list of nodes to be processed; When the list of nodes to be processed is not empty, iteratively processing the target unprocessed nodes in the set of nodes to be processed based on the predecessor node, and removing the processed target unprocessed nodes from the set of nodes to be processed as processed nodes; When the processed node is the end node, the iterative processing ends; Starting from the end node, the predecessor nodes obtained in the iterative process are traversed in reverse order to obtain the shortest path from the start node to the end node.

[0009] In some embodiments, the edges between the nodes in the rail transit network are provided with edge weights, and the iterative processing of the target unprocessed nodes in the set of nodes to be processed based on the predecessor nodes includes: During each iteration, the following process is performed: Determine a target unprocessed node with the smallest current distance from the set of nodes to be processed, wherein the current distance is an edge weight of an edge connecting the target unprocessed node and the predecessor node; Determine at least one adjacent node of the target unprocessed node in the rail transit network, and determine a new distance between the predecessor node and the adjacent node; When the new distance is less than the initial shortest distance of the adjacent nodes, the minimum current distance is used as the shortest distance of the target unprocessed node, the adjacent node is added to the set of nodes to be processed, and the predecessor node is updated to the target unprocessed node.

[0010] In some embodiments, starting from the end node and traversing the predecessor nodes in reverse order to obtain the shortest path from the start node to the end node includes: Determine each predecessor node obtained in sequence during the iterative process; Starting from the end node, traverse each of the predecessor nodes in reverse order; When the traversed predecessor node is the initial node, the shortest path from the start node to the end node is determined based on the predecessor node obtained during the traversal process.

[0011] In some embodiments, searching for a train travel segment formed by the edges from the rail transit network according to the shortest path includes: Query each predecessor node in the shortest path from the rail transit network; Determining an edge connecting the predecessor node from the rail transit network; A train travel segment is determined from the rail transit network according to the edge connecting the predecessor node.

[0012] In some embodiments, the method further comprises: During the running of the train, the train track map route data is updated; Constructing an updated rail transit network for the updated train track map route data, and determining an updated shortest path for train path optimization based on the updated rail transit network; Determine an updated optimal route set based on the updated shortest path, and use it to schedule the train travel path in real time; Wherein, the updating of the train track map route data includes at least one of the following: Adding nodes and edges to the train track diagram route data; Nodes and edges are deleted from the train track graph route data.

[0013] The present invention also provides a device for efficient train path optimization and route control in urban rail transit, the device comprising the following modules: An acquisition module, used to acquire real-time train track map route data during the running of the train, wherein the train track map route data includes nodes and edges, the nodes represent train stations or track sections, and the edges represent tracks for connecting the train stations or the track sections; A construction module is used to construct a rail transit network based on the train track map route data through a concurrent dictionary data structure,

[0014] and determining a starting node and an ending node for train path optimization from the rail transit network; A determination module, used to initialize the rail transit network and determine the shortest path between the starting node and the ending node from the initialized rail transit network; A search module, configured to search for a train travel segment formed by the edges from the rail transit network according to the shortest path; The scheduling module is used to query at least one train route set from the train track map based on the train travel segment, and determine the optimal route set from the train route set for scheduling the train route.

[0015] The present invention provides a method for efficient train path optimization and route control for urban rail transit, which obtains real-time train track map route data during train travel, and constructs a rail transit network for the train track map route data through a concurrent dictionary data structure, and then determines the starting node and the end node for train path optimization and the corresponding shortest path from the rail transit network; determines the train travel segment according to the shortest path, and finally determines the optimal route set for scheduling the train route. The rail transit network is constructed and stored through a concurrent dictionary data structure, and even in a complex rail network, the thread security can be guaranteed when the shortest path is calculated in a multi-threaded environment, and the path calculation and route scheduling are performed in real time during the train travel, thereby improving the operation efficiency of the train in a complex rail network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced one by one below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of a method for efficient train path optimization and route control for urban rail transit provided by the present invention.

[0018] Figure 2It is a schematic diagram of searching for a train travel segment provided by the present invention.

[0019] Figure 3 It is a structural schematic diagram of a highly efficient train path optimization and route control device for urban rail transit provided by the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The method for efficient train path optimization and route control for urban rail transit provided by the present invention can be applied to vehicle-mounted systems, such as manual driving systems or automatic driving systems, etc., which are referred to as systems in the following description.

[0022] Combine the following Figure 1-Figure 2 The invention describes a method for efficient train path optimization and route control for urban rail transit. Figure 1 This is one of the flow charts of the method for efficient train path optimization and route control for urban rail transit provided by the present invention, such as Figure 1 As shown, the method includes the following steps.

[0023] Step 101: During the running of the train, real-time train track map route data is obtained.

[0024] During the train's travel, real-time train track map data is obtained. This data can be extracted from the train track map through an algorithm or queried from the background database. The train track map route data includes nodes and edges. Nodes represent train stations or track sections, and edges represent tracks used to connect train stations or track sections. Each edge is also set with an edge weight, which can represent the travel time or travel distance of this track, and the edge is generally a directed edge, that is, the train has two opposite travel distances on the track. In this way, a huge rail transit network can be formed based on nodes and edges.

[0025] Step 102: construct a rail transit network for the train track map route data through a concurrent dictionary data structure, and determine the starting node and the ending node for train path optimization from the rail transit network.

[0026] Considering that in the field of rail transit scenarios, the number of nodes and edges in the train track map route data is very large, and nodes and edges may be added in real time during the train running process, further expanding the scale of the data. To perform path planning calculations on such a large number and scale of train track map data, the system needs to have strong computing performance. For example, a parallel computing method will be adopted, using multi-core and multi-threading to calculate the large number and scale of train track map data. Therefore, in the embodiment of the present invention, a rail transit network is constructed for the train track map route data through a concurrent dictionary data structure, which can ensure the efficiency and stability of the calculation in the calculation of large-scale data.

[0027] In the rail transit network, the object created for each node is called a node object, which contains node attributes such as node name, edge set, level, shortest distance, path, and query identifier. The object created for each edge is an edge object, which contains edge name, edge weight, edge direction, etc. Then each node object can be stored using a concurrent dictionary data structure.

[0028] Step 103: Initialize the rail transit network, and determine the shortest path between the start node and the end node from the initialized rail transit network.

[0029] The rail transit network can vividly represent the structure of the train track diagram. When optimizing the train route, it is necessary to determine the starting node and the ending node of the train route optimization from the rail transit network. The starting node represents the train station or track section where the train starts to depart, and the ending node is the train station or track section that the train needs to reach.

[0030] For example Figure 2 As shown, Figure 2 It is a schematic diagram of searching for train travel segments provided by the present invention. In the rail transit network, the starting node is point A, the ending node is point F, and nodes B, C, D, and E are intermediate nodes. Each node is connected by a directed edge, and two adjacent nodes can be called a route unit. For example, point A to point B is a route unit, and point A to point C is also a route unit. According to the directed edges between the three nodes A, B, and C, the length (travel distance) and direction of the route unit can be determined according to the direction of the directed edges and the edge weight values.

[0031] When optimizing the path from the start node to the end node, it is necessary to initialize the rail transit network first. Specifically, the level of the start node is set to 0, the query flag is marked as found, and the shortest distance is set to 0. The other nodes in the rail transit network except the start node are treated as unprocessed nodes, and the shortest distance is set to infinity. The level of the unprocessed node is set to -1, and the query flag is marked as not found. In order to facilitate the exploration of unprocessed nodes in the rail transit network, the embodiment of the present invention sets a set of nodes to be processed Q as a window for node processing, and adds the initial node to the set of nodes to be processed.

[0032] The initialization of the rail transit network serves as the initialization of the path optimization algorithm, providing a basis for the subsequent path algorithm planning process. The next step is to determine the shortest path between the start node and the end node from the initialized rail transit network, as described below.

[0033] First, the initial node is taken as the predecessor node, and the nodes adjacent to the predecessor node in the initialized rail transit network are taken as target unprocessed nodes and added to the list of nodes to be processed, for example Figure 2 As shown, the initial node A is taken as the predecessor node, and the nodes B and C adjacent to the predecessor node in the initialized rail transit network are taken as target unprocessed nodes and added to the list of nodes to be processed Q.

[0034] Next, when the list of nodes to be processed is not empty, the target unprocessed nodes in the set of nodes to be processed are iteratively processed based on the predecessor node, and the processed target unprocessed nodes are removed from the set of nodes to be processed as processed nodes. Here, since the list of nodes to be processed Q has added unprocessed nodes, the iterative process is entered when the list of nodes to be processed Q is not empty. In the iterative process, the target unprocessed nodes in the set of nodes to be processed are processed according to the predecessor node, and the process of node processing is also the process of path planning. After the processing is completed, the processed (and path-planned) target unprocessed nodes are removed from the set of nodes to be processed Q as processed nodes.

[0035] In each iteration, the following steps (1) to (3) are performed: (1) Determine the target unprocessed node with the smallest current distance from the set of nodes to be processed. The current distance is the edge weight of the edge connecting the target unprocessed node and the predecessor node.

[0036] Here, for example Figure 2, there is a starting node (predecessor node) A and target unprocessed nodes B and C adjacent to the predecessor node A in the set of nodes to be processed Q, then the target unprocessed node with the smallest current distance can be determined from them, and the current distance is the edge weight of the edge connecting node A and node B or node A and node C, specifically, the AB edge weight d(B) is 1, and the AC edge weight d(C) is 2. If the edge weight d(B) between node A and node B is the smallest, then the target unprocessed node with the smallest current distance is node B.

[0037] (2) Determine at least one adjacent node of the target unprocessed node in the rail transit network and determine the new distance between the predecessor node and the adjacent node.

[0038] Here, if Figure 2 As shown, the target unprocessed node is B, then it is determined that at least one adjacent node of the target unprocessed node B in the rail transit network is C and D, and the new distances from the predecessor node A to the adjacent nodes C and D are determined to be d(B)+d(B, C) and d(B)+d(B, D), d(B, C) represents the edge weight between node B and node C, and d(B, D) represents the edge weight between node B and node D. The new distances from the predecessor node A to the adjacent nodes C and D are calculated to be 2 and 3.

[0039] (3) When the new distance is less than the initial shortest distance between adjacent nodes, the smallest current distance is used as the shortest distance of the target unprocessed node, the adjacent node is added to the set of nodes to be processed, and the predecessor node is updated to the target unprocessed node.

[0040] Here, the new distances are 2 and 3, which are respectively smaller than the initial shortest distances of nodes C and D, which are infinite. Therefore, a shortest distance update is required, and the minimum current distance 1 (AB edge weight d(B)) is used as the shortest distance of the target unprocessed node B. Then, nodes C and D are added to the set of nodes to be processed Q, and the predecessor node A is updated to node B, removing A from the set of nodes to be processed Q.

[0041] Next, repeat the above steps (1) to (3). If there is a predecessor node A and target unprocessed nodes C and D adjacent to the predecessor node B in the set of nodes to be processed Q, the target unprocessed node with the smallest current distance can be determined to be D. Then, it is determined that at least one adjacent node of the target unprocessed node D in the rail transit network is E and F. The new distances from the predecessor node B to the adjacent nodes E and F are determined to be d(D)+w(D,E) and d(D)+w(D,F), where w(D,E) represents the edge weight between node D and node E, and w(D,F) represents the edge weight between node D and node F. The new distances between the predecessor node B and the adjacent nodes E and F are calculated to be smaller than the initial shortest distance between the adjacent nodes E and F, which is infinite. Then, the minimum current distance 1 (BD edge weight d(D)) is taken as the shortest distance of the target unprocessed node D. Then, nodes E and F are added to the set of nodes to be processed Q, and the predecessor node B is updated to node D. B is removed from the set of nodes to be processed Q. Continue to repeat the above steps (1) to (3).

[0042] The iterative process ends when the processed node is the end node. For example, when the processed target unprocessed node is F, and F is the end node, the iterative process ends. During the iteration process, a predecessor node is removed from the set of nodes to be processed Q, for example Figure 2 In the first iteration, the predecessor node A is removed, the predecessor node B is removed in the second iteration, the predecessor node D is removed in the third iteration, and finally the iteration ends after the predecessor node F is removed.

[0043] Therefore, we can start from the end node and traverse the predecessor nodes obtained in the iteration process in reverse order to obtain the shortest path from the start node to the end node. Specifically, we determine each predecessor node obtained in turn during the iteration process. Here, we use Figure 2 The iterative process in , determines that each predecessor node is A, B, D, F. Starting from the end node F, traverse each predecessor node in reverse order to obtain F, D, B, A. When the traversed predecessor node is the initial node A, determine the shortest path from the starting node A to the end node F based on the predecessor nodes obtained during the traversal process.

[0044] Here, according to the shortest path, the shortest distances corresponding to each predecessor node are determined to be the BD edge weight d (D), the DF edge weight d (F), the AB edge weight d (B), and the shortest distance d (A) of the initial node A. The shortest distance of the initial node A is 0. Then, these shortest distances are summed to determine that the shortest distance from the starting node A to the ending node F is 4 (i.e. 1+2+1+0).

[0045] In some embodiments, the shortest path solving algorithm in the embodiment of the present invention can also use a greedy algorithm (the above introduction of the present invention is an embodiment of a greedy algorithm), a Dijkstra algorithm, a Bellman-Ford algorithm, and a Floyd-Warshall algorithm, which can all solve the shortest distance between the initial node A and the end node F in the rail transit network diagram.

[0046] Step 104: According to the shortest path, a train travel segment consisting of edges is searched from the rail transit network.

[0047] Here, the train travel segment composed of edges is searched from the rail transit network according to the shortest path. Specifically, each predecessor node A, B, D, and F in the shortest path can be queried from the rail transit network to find the train station or track section and route information in the train track map data. First, the edges connecting the predecessor nodes are determined from the rail transit network according to the sequence of these nodes. Then, the train travel segment is determined from the rail transit network according to the edges connecting the predecessor nodes.

[0048] In some embodiments, considering that the edges between nodes have directions, the multiple directed edges connecting these nodes may not be consistent, and also considering that these predecessor nodes may not be adjacent nodes, under actual conditions, there may be multiple child nodes, so the train travel line segment finally determined by the edges may be more than one.

[0049] Step 105: Based on the train travel segment, query at least one train route set from the train track map, and determine the optimal route set from the train route set for scheduling the train route.

[0050] After determining the train travel segment, at least one train route set is queried from the train track map based on the train travel segment, and the optimal route set is determined from the train route set for scheduling the train route. Here, the system will query the corresponding route unit from the train track map according to the train travel segment, obtain the train route set (i.e., a set consisting of multiple route units), and output or visualize it through the system. If it is manual driving, it will be highlighted on the train track map for the train driver to plan and refer to, so as to schedule the train route. If it is an automatic driving system, the train travel segment will be automatically scheduled according to the train route set.

[0051] In an embodiment of the present invention, a method for efficient train path optimization and route control in urban rail transit is performed in real time in the system, and during the train travel, the train track map route data can also be updated in real time, wherein the updating of the train track map route data includes at least one of the following: adding nodes and edges to the train track map route data; deleting nodes and edges from the train track map route data.

[0052] Specifically, the train track map route data can be dynamically updated in real time during the train's travel, or even during the travel according to the scheduled route. Specifically, nodes and edges can be added to the train track map route data, that is, train stations or track sections that the train needs to travel on are added, and corresponding track lines are added. Nodes and edges can also be added to the train track map route data, that is, train stations or track sections that the train currently needs to travel on are reduced, and corresponding track lines are reduced. And in some scenarios, nodes and edges can also be added or reduced at the same time, that is, train stations or track sections can be added or reduced at the same time, and corresponding track lines can be added or reduced at the same time.

[0053] After the train track map route data is updated, the system needs to recalculate the shortest path and determine the optimal route set. Since the constructed rail transit network storage is stored in a concurrent dictionary data structure, even for complex and changeable rail transit networks, the shortest path can be efficiently calculated through parallel computing. Specifically, an updated rail transit network is constructed based on the updated train track map route data, and an updated shortest path for train path optimization is determined based on the updated rail transit network; an updated optimal route set is determined based on the updated shortest path, which is used to timely schedule the train travel path in real time or adjust the optimal route set before the update, thereby further improving the effectiveness of path optimization and route scheduling.

[0054] The embodiment of the present invention obtains real-time train track map route data during the train running process, and constructs a rail transit network for the train track map route data through a concurrent dictionary data structure, and then determines the starting node and the end node for train path optimization and the corresponding shortest path from the rail transit network; determines the train running line segment according to the shortest path, and finally determines the optimal route set for scheduling the train route. By constructing and storing the rail transit network through a concurrent dictionary data structure, even in a complex rail network, the thread security can be guaranteed when calculating the shortest path in a multi-threaded environment, and the path calculation and route scheduling can be performed in real time during the train running, thereby improving the operation efficiency of the train in a complex rail network.

[0055] The following is a description of the efficient train path optimization and route control device for urban rail transit provided by the present invention. The efficient train path optimization and route control device for urban rail transit described below and the efficient train path optimization and route control method for urban rail transit described above can be referenced to each other.

[0056] See also Figure 3 , Figure 3 is a schematic diagram of the structure of the device for efficient train path optimization and route control for urban rail transit provided by the present invention, such as Figure 3 As shown, the device for efficient train path optimization and route control in urban rail transit includes the following modules: an acquisition module 301, a construction module 302, a determination module 303, a search module 304, and a scheduling module 305. Among them, the acquisition module is used to obtain real-time train track map route data during the train travel process, wherein the train track map route data includes nodes and edges, the nodes represent train stations or track sections, and the edges represent tracks used to connect the train stations or the track sections; the construction module 302 is used to construct a rail transit network for the train track map route data through a concurrent dictionary data structure, and determine the starting node and the ending node for train path optimization from the rail transit network; the determination module 303 is used to initialize the rail transit network, and determine the shortest path between the starting node and the ending node from the initialized rail transit network; the search module 304 is used to search for the train travel segment composed of the edges from the rail transit network according to the shortest path; the scheduling module 305 is used to query at least one train route set from the train track map based on the train travel segment, and determine the optimal route set from the train route set for scheduling the train route.

[0057] Obviously, the beneficial effects produced by the efficient train path optimization and route control device for urban rail transit provided by the present invention should be the same as the efficient train path optimization and route control method for urban rail transit described above. Therefore, the beneficial effects produced by the efficient train path optimization and route control device for urban rail transit will not be repeated here.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for efficient train path optimization and route control in urban rail transit, characterized in that: The method comprises: During the running of the train, real-time train track map route data is obtained, wherein the train track map route data includes nodes and edges, the nodes represent train stations or track sections, and the edges represent tracks for connecting the train stations or the track sections; The train track map route data is used to construct a rail transit network through a concurrent dictionary data structure. and determining a starting node and an ending node for train path optimization from the rail transit network; Initializing the rail transit network, and determining the shortest path between the starting node and the ending node from the initialized rail transit network; According to the shortest path, searching for a train travel segment formed by the edges from the rail transit network; Based on the train travel line segment, at least one train route set is queried from the train track map, and an optimal route set is determined from the train route sets for scheduling the train routes.

2. The method for efficient train path optimization and route control for urban rail transit according to claim 1 is characterized in that: The initializing the rail transit network includes: Taking the starting node in the rail transit network as a processed node, and setting the initial shortest distance of the starting node to 0; The other nodes in the rail transit network except the starting node are regarded as unprocessed nodes, and the initial shortest distance of the unprocessed nodes is set to infinity.

3. The method for efficient train path optimization and route control for urban rail transit according to claim 1 is characterized in that: Determining the shortest path between the starting node and the ending node from the initialized rail transit network includes: The initial node is used as a predecessor node, and the nodes adjacent to the predecessor node in the initialized rail transit network are used as target unprocessed nodes and added to a list of nodes to be processed; When the list of nodes to be processed is not empty, iteratively processing the target unprocessed nodes in the set of nodes to be processed based on the predecessor node, and removing the processed target unprocessed nodes from the set of nodes to be processed as processed nodes; When the processed node is the end node, the iterative processing ends; Starting from the end node, the predecessor nodes obtained in the iterative process are traversed in reverse order to obtain the shortest path from the start node to the end node.

4. The method for efficient train path optimization and route control for urban rail transit according to claim 3 is characterized in that: The edges between the nodes in the rail transit network are provided with edge weights, and the iterative processing of the target unprocessed nodes in the set of nodes to be processed based on the predecessor nodes includes: During each iteration, the following process is performed: Determine a target unprocessed node with the smallest current distance from the set of nodes to be processed, wherein the current distance is an edge weight of an edge connecting the target unprocessed node and the predecessor node; Determine at least one adjacent node of the target unprocessed node in the rail transit network, and determine a new distance between the predecessor node and the adjacent node; When the new distance is less than the initial shortest distance of the adjacent nodes, the minimum current distance is used as the shortest distance of the target unprocessed node, the adjacent node is added to the set of nodes to be processed, and the predecessor node is updated to the target unprocessed node.

5. The method for efficient train path optimization and route control for urban rail transit according to claim 3 is characterized in that: Starting from the end node, traversing the predecessor node in reverse order to obtain the shortest path from the start node to the end node includes: Determine each predecessor node obtained in sequence during the iterative process; Starting from the end node, traverse each of the predecessor nodes in reverse order; When the traversed predecessor node is the initial node, the shortest path from the start node to the end node is determined based on the predecessor node obtained during the traversal process.

6. The method for efficient train path optimization and route control for urban rail transit according to claim 1 is characterized in that: The step of searching for a train travel segment formed by the edges from the rail transit network according to the shortest path includes: Query each predecessor node in the shortest path from the rail transit network; Determining an edge connecting the predecessor node from the rail transit network; A train travel segment is determined from the rail transit network according to the edge connecting the predecessor node.

7. The method for efficient train path optimization and route control for urban rail transit according to claim 1 is characterized in that: The method further comprises: During the running of the train, the train track map route data is updated; Constructing an updated rail transit network for the updated train track map route data, and determining an updated shortest path for train path optimization based on the updated rail transit network; Determine an updated optimal route set based on the updated shortest path, and use it to schedule the train travel path in real time; Wherein, the updating of the train track map route data includes at least one of the following: Adding nodes and edges to the train track diagram route data; Nodes and edges are deleted from the train track graph route data.

8. A device for efficient train path optimization and route control in urban rail transit, characterized in that: The device comprises: An acquisition module, used to acquire real-time train track map route data during the train running process, wherein the train track map route data includes nodes and edges, the nodes represent train stations or track sections, and the edges represent tracks used to connect the train stations or the track sections; A construction module, used to construct a rail transit network for the train track map route data through a concurrent dictionary data structure, and determine a start node and an end node for train path optimization from the rail transit network; A determination module, used to initialize the rail transit network and determine the shortest path between the starting node and the ending node from the initialized rail transit network; A search module, configured to search for a train travel segment formed by the edges from the rail transit network according to the shortest path; The scheduling module is used to query at least one train route set from the train track diagram based on the train travel segment, and determine the optimal route set from the train route set for scheduling the train route.

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