Scale-controllable effective path set generation method, device and equipment

Abstract

The invention discloses a scale-controllable effective path set generation method, device and equipment, and relates to the field of path planning. The method comprises the following steps: creating a Dial rule family for screening effective paths; selecting a rule from the Dial rule family as a compression rule, screening road sections meeting the compression rule in the traffic network, and forming a sub-network; using a maximum flow algorithm in the graph theory to determine the minimum edge cut from the starting point to the end point in the sub-network; determining a preposed effective path set meeting a rule of being farther and farther away from the starting point from the starting point to a preposed node of the minimum edge cut; determining a post effective path set satisfying a rule of being closer to the end point between the effective path sets from the post node of the minimum edge cut to the end point; and combining the front effective path in the front effective path set, the minimum edge cut and the rear effective path in the rear effective path set to obtain an effective path set from the starting point to the ending point. The number of effective paths can be controlled.

Description

Technical Field

[0001] The present application relates to the field of path planning, and in particular to a method, device and equipment for generating an effective path set with controllable scale. Background Art

[0002] In traffic network analysis and traffic flow allocation, the identification of effective path sets is crucial to understanding and optimizing traffic flow allocation. Effective path sets usually refer to those paths between the starting point and the end point that are "reasonable" or "efficient" for drivers. These paths are not only the basis of traffic planning and optimization, but also have wide applications in traffic engineering, urban planning and intelligent transportation system design.

[0003] In the past, the number of valid path sets cannot be controlled, which has always been a challenge. Therefore, how to find a way to control the number of valid paths is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present application is to provide a method, device and equipment for generating a set of effective paths with controllable scale, which can control the number of effective paths.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for generating a set of effective paths with controllable scale, including: creating a Dial rule family for screening effective paths; the Dial rule family includes at least: a rule of getting farther away from a starting point and a rule of getting closer to an end point; selecting a rule from the Dial rule family as a compression rule, and screening road sections that satisfy the compression rule in a traffic network; forming a subnetwork with the screened road sections that satisfy the compression rule; using a maximum flow algorithm in graph theory to determine the minimum edge cut from a starting point to an end point in the subnetwork; determining a set of preceding effective paths between preceding nodes from the starting point to the minimum edge cut that satisfy the rule of getting farther away from the starting point; determining a set of succeeding effective paths between a set of effective paths from a succeeding node of the minimum edge cut to the end point that satisfy the rule of getting closer to the end point; combining the preceding effective paths in the preceding effective path set, the minimum edge cut, and the succeeding effective paths in the succeeding effective path set to obtain a set of effective paths from the starting point to the end point.

[0007] In a second aspect, the present application provides a scalable effective path set generation device, comprising:

[0008] A rule family creation module, used to create a Dial rule family for screening valid paths; the Dial rule family at least includes: a rule of getting farther and farther from the starting point and a rule of getting closer and closer to the end point;

[0009] A road segment screening module, used to select a rule from the Dial rule family as a compression rule, and screen road segments satisfying the compression rule in the traffic network;

[0010] A sub-network construction module, used for forming a sub-network from the selected road sections that meet the compression rule;

[0011] A minimum edge cut determination module, used to determine the minimum edge cut from the starting point to the end point in the subnetwork using the maximum flow algorithm in graph theory;

[0012] A preceding path determination module is used to determine a set of preceding valid paths from the starting point to the preceding node with the minimum edge cut that meet the rule of being farther and farther from the starting point;

[0013] A post-path determination module, used to determine a post-valid path set from a post-node of a minimum edge cut to a valid path set of a terminal point that satisfies a rule of getting closer and closer to the terminal point;

[0014] The effective path combination module is used to combine the preceding effective path in the preceding effective path set, the minimum edge cut and the following effective path in the following effective path set to obtain a set of effective paths from the starting point to the end point.

[0015] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-described methods for generating a scalable effective path set.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects:

[0017] The present application provides a method, device and equipment for generating a set of effective paths with controllable scale, which screens road sections that meet the compression rules in the traffic network, and then forms a subnetwork with the screened road sections that meet the compression rules. In this method, effective paths are determined in the subnetwork, which ensures that the number of effective paths has an upper limit, controls the output scale of the effective path set from the starting point to the end point, and realizes the control of the number of effective paths. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1A flowchart of a method for generating a scalable effective path set provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a traffic network provided in another embodiment of the present application;

[0021] Figure 3 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0024] At present, the Dial rule used in the control of the number of valid path sets has guiding significance in theory, but it has limitations in practical applications, especially in terms of computational efficiency and the reality of path allocation in large-scale networks. The Dial rule determines the validity of a path by comparing the travel costs between nodes, but its strict conditions sometimes lead to unrealistic path allocation, such as no flow allocation on some paths, which may actually carry many vehicles. To solve this problem, subsequent studies have proposed more relaxed rules to define valid paths. These rules consider not only the starting point and end point of the path, but also the travel time and length of the path to more realistically reflect the behavior of drivers. For example, Tong relaxed the valid path to "far from the starting point or close to the end point". However, as the conditions are relaxed, the number of valid paths sometimes becomes difficult to control. For example, the maximum number of valid paths for the OD pairs of the c1 condition in Barcelona and Chicago-Sketch reached 69,988 and 72,618, respectively, which is impossible in reality because even in large-scale networks, it is impossible for an ordinary traveler to consider so many "valid paths".

[0025] In view of this, in order to find a method to control the number of valid paths in the process of relaxing the conditions, or find a special relaxation rule so that it has upper and lower limits. In an exemplary embodiment, Figure 1 As shown, a method for generating a scale-controllable effective path set is provided, comprising the following steps 101 to 107. Among them:

[0026] Step 101: Create a Dial rule family for screening valid paths; the Dial rule family includes at least a rule of getting farther from the starting point and getting closer to the end point.

[0027] Step 102: Select a rule from the Dial rule family as a compression rule, and select road sections satisfying the compression rule in the traffic network.

[0028] Step 103: The selected road segments satisfying the compression rule are formed into a sub-network.

[0029] Step 104: Use the maximum flow algorithm in graph theory to determine the minimum edge cut from the starting point to the end point in the subnetwork.

[0030] Step 105: Determine a set of preceding valid paths from the starting point to the preceding node of the minimum edge cut that meet the rule of being farther and farther from the starting point.

[0031] Step 106: Determine a set of subsequent valid paths from the subsequent node of the minimum edge cut to the end point that meets the rule of getting closer to the end point.

[0032] Step 107: Combine the preceding valid path in the preceding valid path set, the minimum edge cut, and the succeeding valid path in the succeeding valid path set to obtain a valid path set from the starting point to the end point.

[0033] By implementing the above steps 101 to 107, road sections that meet the compression rules are screened in the traffic network, and then the screened road sections that meet the compression rules form a subnetwork. In this method, valid paths are determined in the subnetwork, ensuring that the number of valid paths has an upper limit, controlling the output scale of the valid path set from the starting point to the end point, and realizing the control of the number of valid paths.

[0034] In another exemplary embodiment of the present application, the following parameter meanings are given:

[0035] c1 condition: getting farther and farther away from the starting point.

[0036] c2 condition: getting closer to the end point.

[0037] Dial condition: Both "getting farther away from the starting point" and "getting closer to the end point" are satisfied, that is, c1&c2.

[0038] Same condition: satisfying "getting farther and farther away from the starting point" or satisfying "getting closer and closer to the end point", that is, c1||c2.

[0039] Dial rule family: A collection of valid path rules. The valid paths derived from the valid path rules in this collection include the valid paths derived from the Dial condition. The Dial rule family includes: Dial condition, Tong condition, c1 and c2. The complete definition of the Dial rule family is as follows: where i and j are od pairs, P r ij represents the valid path set derived by rule r. G(V,E) is a traffic network, V and E are vertex set and edge set respectively. The edge set corresponds to the set of all road segments in the traffic network. The vertex set is the set of all road segment intersections in the traffic network. R represents the Dial rule family, Represents the set of all valid paths between vertex i and vertex j calculated using the Dial condition.

[0040]

[0041] st cut: G = (V, E) is a transportation network. Let S and T form a partition of V, such that s belongs to S and t belongs to T. Then the set of links from S to T is called the st cut of the graph G. The set of cut edges is denoted as (S, T). In the real context, the s-tcut represents the bottleneck area of ​​the entire transportation network.

[0042] join operator: The join operator is a Cartesian join operator for a set of paths.

[0043] In another exemplary embodiment of the present application, four types of valid path rules are added to the set R to obtain the Dial rule family. The specific rules in R are as follows:

[0044] 1. The path must get farther and farther away from the starting point. This rule ensures that the path starts from the starting point and expands outward, avoiding the path wandering near the starting point.

[0045] 2. The path must get closer and closer to the end point. This rule ensures that the path moves towards the end point and avoids the path moving away from the end point.

[0046] 3. The path is getting farther away from the starting point or closer to the end point. This rule allows the path to expand without going directly to the end point, and does not restrict the exploration of the path near the starting point.

[0047] 4. The path is getting farther away from the starting point and closer to the end point at the same time. This rule is used to identify those paths that are both far away from the starting point and close to the end point. These paths are usually candidates for the optimal path.

[0048] In another exemplary embodiment of the present application, a rule r (e.g., rule 3 in step 1) is selected from the four types of rules in R as a compression rule. This rule r is used to reduce the complexity of the traffic network by excluding those sections that do not satisfy rule r. The number of valid path sets that can be calculated by rule r is the upper limit of the valid path calculation method proposed in this article. In practical applications, the upper limit, i.e., rule r, can be selected according to actual conditions. Then the above step 102 of screening the sections that satisfy the compression rule in the traffic network can be replaced by the following steps 201 to 204:

[0049] Step 201: traverse each road segment in the traffic network.

[0050] Step 202: Check whether each road segment satisfies the compression rule.

[0051] Step 203: If the road segment does not satisfy the compression rule, it is excluded from the traffic network.

[0052] Step 204: If the road segment meets the compression rule, it is retained.

[0053] In another exemplary embodiment of the present application, a new sub-network is constructed using the remaining valid road segments, and the sub-network only includes those road segments that meet the compression rule. Then the above step 103 can be replaced by the following steps 301 to 303:

[0054] Step 301: Copy the traffic network to obtain a new traffic network.

[0055] Step 302: Delete the road sections that do not meet the compression rule in the new traffic network.

[0056] The traffic network is defined as G(V, E), and the new traffic network is defined as G_new(V, E). Then, the non-valid road sections that do not meet the compression rule in step 203 can be deleted from G_new(V, E).

[0057] Step 303: Update the vertex and edge information in the traffic network after deleting the road section to obtain a sub-network.

[0058] In another exemplary embodiment of the present application, a maximum flow algorithm in graph theory, such as the Ford-Fulkerson algorithm, is used to determine the minimum edge cut. Then the above step 104 can be replaced by the following steps 401 to 405:

[0059] Step 401: Using the Ford-Fulkerson algorithm, the maximum flow from the starting point to the end point in the subnetwork is solved.

[0060] Step 402: Allocate the flow value of the maximum flow to each road segment in the subnetwork.

[0061] Using Ford-Fulkerson's flow-up results for all augmented paths, the flow value of the maximum flow is decomposed into each road segment in G_new.

[0062] Step 403: construct a residual network using the sub-network.

[0063] Step 404: Perform a breadth-first search in the remaining network from the starting point, and mark the visited nodes as visited nodes.

[0064] Step 405: After the breadth-first search is completed, a set of edges from visited nodes to unvisited nodes in the remaining network is determined as the minimum edge cut.

[0065] In another exemplary embodiment of the present application, the above step 403 may be replaced by the following steps 501 to 503:

[0066] Step 501: Copy all vertices in the sub-network to the remaining network.

[0067] Step 502: Set each edge from the starting point to the end point in the sub-network to an edge with the same flow from the end point to the starting point in the remaining network.

[0068] Step 503: If the flow value from the starting point to the end point is less than the preset capacity, then add another edge with a flow value of cf from the starting point to the end point in the surviving network to obtain the final surviving network; where c is the preset capacity and f is the flow value from the starting point to the end point.

[0069] In another exemplary embodiment of the present application, the above step 105 uses the rule of "getting farther and farther away from the starting point" to calculate all valid paths from the starting point to the predecessor node of the minimum edge cut. The minimum edge cut is the edge that connects the visited nodes with the unvisited nodes. The vertex set of the visited nodes in each edge of the minimum edge cut constitutes the predecessor node set. Identify the path that can reach the predecessor node of the minimum edge cut from the starting point. The specific operations include:

[0070] ① Use the breadth-first algorithm to search for paths. The input parameters of the algorithm are the starting point s and the set of predecessor nodes of the minimum edge cut. Each path stops when the predecessor node in the minimum edge cut is found. The output of the algorithm is all the paths from the starting point s to the predecessor node.

[0071] ② Among these paths, check whether the rule of "getting farther and farther away from the starting point" is met.

[0072] ③ Collect all paths that meet the requirement of “getting farther and farther away from the starting point” to form a set of valid paths.

[0073] In another exemplary embodiment of the present application, the above step 106 uses the rule of "getting closer to the end point" to calculate all valid paths from the post-node of the minimum edge cut to the end point t. The vertex set of unvisited nodes in each edge of the minimum edge cut constitutes the post-node set. Identify the path from the post-node of the minimum edge cut to the end point. The specific operations include:

[0074] 1) Use the breadth-first algorithm to search for paths. The input parameters of the algorithm are the set of post-nodes with the smallest edge cut and the end point t. Each path stops when the end point is found. The output of the algorithm is all the paths from the post-node to the end point t.

[0075] 2) Among these paths, check whether the rule of "getting closer to the end point" is satisfied.

[0076] 3) Collect all paths that meet the requirement of “getting closer to the end point” to form a set of valid paths.

[0077] In another exemplary embodiment of the present application, the above step 107 may be replaced by the following steps 601 to 604:

[0078] Step 601: define a join operator for connecting the preceding valid path, the minimum edge cut and the succeeding valid path.

[0079] Step 602: For each pair of a preceding valid path in the preceding valid path set and a succeeding valid path in the succeeding valid path set, check whether each pair of the preceding valid path and the succeeding valid path can be connected by a minimum edge cut.

[0080] Step 603: If they can be connected by the minimum edge cut, the minimum edge cut and a pair of preceding valid paths and succeeding valid paths are combined into a complete valid path from the start point to the end point using the join operator.

[0081] Step 604: Collect all complete valid paths from the starting point to the end point to form a valid path set from the starting point to the end point.

[0082] For example, if the predecessor path set is {1-2-3,1-5-6}, the minimum edge cut is {3-7,6-8}, and the successor path set is {7-9-10,8-11-10}, the connection result is {1-2-3-7-9-10,1-5-6-8-11-10}.

[0083] In another exemplary embodiment of the present application, step 105 and step 106 have no logical dependencies and can support parallel computing.

[0084] In another exemplary embodiment of the present application, after obtaining a set of valid paths from a starting point to an end point, a driver may select a valid path in the set of valid paths for navigation.

[0085] In actual traffic networks, the effective path set generation method proposed in this application can generate a moderate number of path sets, which allows humans to control the output scale of the path set. Figure 2 For example, the starting point is 0 and the end point is 8.

[0086] The number on each edge is the travel time of the road section. The valid path set derived by the Dial rule is {0-3-4-5-8, 0-3-6-7-8}, and the valid path set derived by the c1 condition is {0-1-4-5-8, 0-1-4-7-8, 0-3-4-5-8, 0-3-4-7-8, 0-3-6-7-8}. The valid path set derived using the method proposed in this application is {0-1-4-5-8, 0-3-4-5-8, 0-3-6-7-8}. It can be found that the number of path sets generated by the method of this application is in the middle. In actual scenarios, the method of this application can avoid the uncontrolled size of the effective path set.

[0087] In theory, the valid path set derived by the method proposed in this application can be mathematically proven to have upper and lower bounds. Steps 102-103 ensure that the number of path sets derived by the method of this application has an upper bound. Steps 104-107 can prove that the lower bound of the path set is the valid path set of the Dial rule. The proof process is as follows, where the cut edge set D = (S, T).

[0088]

[0089] in, Using the Dial rule, calculate the valid path from the starting point o to the end point d. Under the premise that the starting point is o and the end point is d, the set of all subpaths from the starting point o to an internal node u included in the valid path set calculated using the Dial rule. (u, v): The edge connecting u and v in the minimum edge cut. On the premise that the starting point is o and the end point is d, the valid path set calculated using the Dial rule contains all sub-path sets from a certain internal node v to the end point d. The set of valid paths from vertex o to vertex u calculated using the c1 condition. The set of valid paths from vertex v to vertex d calculated using the c2 condition. The set of valid paths from vertex o to vertex d calculated using the SR rule. The definition of SR is: use the maximum flow algorithm to find the minimum edge cut of the graph, use "getting farther from the starting point" to calculate the valid path from the starting point to the node preceding the minimum edge cut, use "getting closer to the end point" to calculate the valid path from the node following the minimum edge cut to the end point, and combine the two parts of the path with the minimum edge cut to get the valid path set. This method of generating valid paths is called a special rule, abbreviated as SR.

[0090] The method of the present application mainly includes: using the existing valid path rules to delete the invalid sections in the road network to obtain a sub-network. Calculate a minimum edge cut between the starting point and the end point in the sub-network. Around the minimum edge cut, calculate the valid paths from the starting point to the preceding node and the following node to the end point respectively. Combine the paths of these three parts to obtain a set of valid paths from the starting point to the end point.

[0091] Based on the same inventive concept, the embodiment of the present application also provides a scalable effective path set generation device for implementing the scalable effective path set generation method involved in the above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the embodiments of one or more scalable effective path set generation devices provided below can refer to the limitations of the scalable effective path set generation method above, and will not be repeated here.

[0092] In an exemplary embodiment, a scalable effective path set generation device is provided, which includes: a rule family creation module, a segment screening module, a subnetwork construction module, a minimum edge cut determination module, a preceding path determination module, a succeeding path determination module and an effective path combination module.

[0093] The rule family creation module is used to create a Dial rule family for screening effective paths; the Dial rule family at least includes: a rule of getting farther and farther from the starting point and a rule of getting closer and closer to the end point.

[0094] The road segment screening module is used to select a rule from the Dial rule family as a compression rule, and screen the road segments satisfying the compression rule in the traffic network.

[0095] The sub-network construction module is used to construct a sub-network from the screened road segments that meet the compression rule.

[0096] The minimum edge cut determination module is used to determine the minimum edge cut from the starting point to the end point in the sub-network by using the maximum flow algorithm in graph theory.

[0097] The preceding path determination module is used to determine a set of preceding valid paths from the starting point to the preceding node with the minimum edge cut that meet the rule of being farther and farther from the starting point.

[0098] The post-path determination module is used to determine a post-valid path set from the post-node of the minimum edge cut to the end point, which satisfies the rule of getting closer and closer to the end point.

[0099] The effective path combination module is used to combine the preceding effective path in the preceding effective path set, the minimum edge cut and the following effective path in the following effective path set to obtain a set of effective paths from the starting point to the end point.

[0100] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store a set of valid paths from a starting point to an end point. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for generating a set of valid paths with controllable scale is implemented.

[0101] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0102] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating a scalable effective path set, characterized in that: include: Create a Dial rule family for filtering valid paths; The Dial rule family includes at least: a rule of getting farther from the starting point and a rule of getting closer to the end point; Selecting a rule from the Dial rule family as a compression rule, and screening a road section satisfying the compression rule in the traffic network; The selected road sections satisfying the compression rule are formed into a sub-network; Using the maximum flow algorithm in graph theory, determine the minimum edge cut from the starting point to the end point in the subnetwork; Determine the set of valid preceding paths from the starting point to the preceding node with the minimum edge cut that satisfy the rule of getting farther and farther away from the starting point; Determine the set of subsequent valid paths from the subsequent node of the minimum edge cut to the end point that meet the rule of getting closer and closer to the end point; The preceding valid path in the preceding valid path set, the minimum edge cut, and the following valid path in the following valid path set are combined to obtain a valid path set from the starting point to the end point.

2. The method for generating a scalable effective path set according to claim 1, characterized in that: The Dial rule family also includes: Dial conditions and Tong conditions; The Dial condition is to simultaneously satisfy the rules of getting farther away from the starting point and getting closer to the end point; The same condition is to satisfy the rule of getting farther and farther away from the starting point, or to satisfy the rule of getting closer and closer to the end point.

3. The method for generating a scalable effective path set according to claim 1, characterized in that: The road sections satisfying the compression rule are selected in the traffic network, specifically including: Traverse every road segment in the transportation network; Check whether each road segment satisfies the compression rule; If the road segment does not satisfy the compression rule, it is excluded from the traffic network; If the road segment meets the compression rule, it is retained.

4. The method for generating a scalable effective path set according to claim 1, characterized in that: The selected road segments that meet the compression rule are formed into a sub-network, which specifically includes: Copy the traffic network to get a new traffic network; Deleting the road sections that do not meet the compression rule in the new traffic network; Update the vertex and edge information in the traffic network after deleting the road section to obtain the subnetwork.

5. The method for generating a scalable effective path set according to claim 1, characterized in that: Using the maximum flow algorithm in graph theory, determine the minimum edge cut from the starting point to the end point in the subnetwork, specifically including: Using the Ford-Fulkerson algorithm to solve the maximum flow from the starting point to the end point in the subnetwork; Distributing the flow value of the maximum flow to each road segment in the subnetwork; Using the sub-network, constructing a residual network; Performing a breadth-first search in the residual network from a starting point, and marking visited nodes as visited nodes; After the breadth-first search is completed, a set of edges from visited nodes to unvisited nodes in the remaining network is determined as the minimum edge cut.

6. The method for generating a scalable effective path set according to claim 5, characterized in that: Using the sub-network, constructing a residual network specifically includes: Copy all vertices in the sub-network to the remaining network; Set each edge from the starting point to the end point in the subnetwork to an edge with the same flow from the end point to the starting point in the residual network; If the flow value from the starting point to the end point is less than the preset capacity, then add another edge with a flow value of cf from the starting point to the end point in the surviving network to obtain the final surviving network; where c is the preset capacity and f is the flow value from the starting point to the end point.

7. The method for generating a scalable effective path set according to claim 1, characterized in that: The vertex set of visited nodes in each edge of the minimum edge cut constitutes a preceding node set, and the vertex set of unvisited nodes in each edge of the minimum edge cut constitutes a succeeding node set.

8. The method for generating a scalable effective path set according to claim 1, characterized in that: Combine the preceding valid path in the preceding valid path set, the minimum edge cut, and the following valid path in the following valid path set to obtain a valid path set from the starting point to the end point, specifically including: Define a join operator to connect the preceding valid path, the minimum edge cut, and the succeeding valid path; For each pair of a preceding valid path in the preceding valid path set and a subsequent valid path in the subsequent valid path set, check whether each pair of preceding valid paths and subsequent valid paths can be connected by a minimum edge cut; If they can be connected by a minimum edge cut, the minimum edge cut and a pair of preceding valid paths and succeeding valid paths are combined into a complete valid path from the starting point to the end point using the join operator; Collect all complete valid paths from the starting point to the end point to form a set of valid paths from the starting point to the end point.

9. A scalable effective path set generation device, characterized in that: The scale-controllable effective path set generation device comprises: A rule family creation module, used to create a Dial rule family for screening valid paths; the Dial rule family at least includes: a rule of getting farther and farther from the starting point and a rule of getting closer and closer to the end point; A road segment screening module, used to select a rule from the Dial rule family as a compression rule, and screen road segments satisfying the compression rule in the traffic network; A sub-network construction module, used for forming a sub-network from the selected road sections that meet the compression rule; A minimum edge cut determination module, used to determine the minimum edge cut from the starting point to the end point in the subnetwork using the maximum flow algorithm in graph theory; A preceding path determination module is used to determine a set of preceding valid paths from the starting point to the preceding node with the minimum edge cut that meet the rule of being farther and farther from the starting point; A post-path determination module, used to determine a post-valid path set from a post-node of a minimum edge cut to a valid path set of a terminal point that satisfies a rule of getting closer and closer to the terminal point; The effective path combination module is used to combine the preceding effective path in the preceding effective path set, the minimum edge cut and the following effective path in the following effective path set to obtain a set of effective paths from the starting point to the end point.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the scalable effective path set generation method described in any one of claims 1 to 8.

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