Path planning method and device, medium and program product

Abstract

The invention relates to the field of financial science and technology, and discloses a path planning method and device, a medium and a program product. The method comprises the following steps: acquiring each node, and generating a traffic map according to each node; performing bidirectional optimal path calculation on each pair of nodes in the traffic map to obtain a transportation cost weight between each pair of nodes, and generating a network transportation map according to the traffic map and the transportation cost weight between each pair of nodes; and through a minimum spanning tree algorithm, obtaining a minimum transportation cost spanning tree corresponding to the network point transportation map, generating a cash transportation scheme according to the minimum transportation cost spanning tree and the daily cash transportation amount corresponding to each network point, and carrying out cash transportation based on the cash transportation scheme. According to the scheme of the embodiment, automatic planning of the cash transport route can be realized, and the safety and punctuality of cash transport can be improved.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a path planning method, device, medium and program product. Background Art

[0002] Cash transportation is a daily occurrence in the banking system and an essential part of banking operations. Optimizing cash transportation routes, reducing cash transportation costs, and improving transportation security are key issues in improving banking operations.

[0003] Currently, existing cash transport route planning methods rely primarily on manual effort based on experience. This approach is highly subjective and cannot meet the high security requirements of cash transport and the punctuality requirements for daily cash deposits. Summary of the Invention

[0004] The present invention provides a path planning method, device, medium and program product, which can realize automatic planning of cash transportation routes and improve the safety and punctuality of cash transportation.

[0005] According to one aspect of the present invention, a path planning method is provided, comprising:

[0006] Obtaining each node and generating a traffic map based on each node;

[0007] By performing a bidirectional optimal path calculation for each pair of nodes in the traffic map, a transportation cost weight between each pair of nodes is obtained, and a network transportation map is generated based on the traffic map and the transportation cost weight between each pair of nodes;

[0008] The minimum transport cost spanning tree corresponding to the network transport map is obtained through the minimum spanning tree algorithm, and a cash transport plan is generated based on the minimum transport cost spanning tree and the daily cash transport volume corresponding to each network, and cash transportation is carried out based on the cash transport plan.

[0009] According to another aspect of the present invention, a path planning device is provided, comprising:

[0010] A traffic map generation module is used to obtain each node and generate a traffic map based on the nodes;

[0011] a network point transportation map generation module, configured to calculate a bidirectional optimal path for each pair of nodes in the transportation map, obtain a transportation cost weight between each pair of nodes, and generate a network point transportation map based on the transportation map and the transportation cost weight between each pair of nodes;

[0012] The cash transport plan generation module is used to obtain the minimum transport cost spanning tree corresponding to the network transport map through the minimum spanning tree algorithm, and generate a cash transport plan based on the minimum transport cost spanning tree and the daily cash transport volume corresponding to each network, and carry out cash transportation based on the cash transport plan.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the path planning method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is configured to enable a processor to implement the path planning method according to any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the path planning method according to any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention obtains each node and generates a traffic map based on each node; obtains the transportation cost weight between each pair of nodes by performing a two-way optimal path calculation on each pair of nodes in the traffic map, and generates a network transportation map based on the traffic map and the transportation cost weight between each pair of nodes; obtains the minimum transportation cost spanning tree corresponding to the network transportation map through a minimum spanning tree algorithm, generates a cash transportation plan based on the minimum transportation cost spanning tree and the daily cash transportation volume corresponding to each network, and performs cash transportation based on the cash transportation plan; establishes a traffic map and performs a two-way optimal path calculation on each pair of nodes in the traffic map to generate a network transportation map, and then performs a minimum spanning tree operation on the network transportation map, thereby finally obtaining a cash transportation plan, which can realize automatic planning of cash transportation routes and improve the security and punctuality of cash transportation.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is a flow chart of a path planning method provided according to the first embodiment of the present invention;

[0023] Figure 2 This is a flow chart of a path planning method provided according to the second embodiment of the present invention;

[0024] Figure 3 This is a schematic structural diagram of a path planning device provided according to a third embodiment of the present invention;

[0025] Figure 4 It is a structural diagram of an electronic device for implementing the path planning method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only 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 making creative efforts should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," "target," etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1A flow chart of a path planning method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of automatically planning cash transportation routes. The method can be executed by a path planning device, which can be implemented in the form of hardware and / or software. Typically, the path planning device can be configured in an electronic device, such as a computer device or a server. Figure 1 As shown, the method includes:

[0030] S110: Acquire each node, and generate a traffic map based on each node.

[0031] The nodes may be geographical nodes involved in the cash transportation process, for example, geographical nodes may include vaults and bank branches. In this embodiment, edge relationships may be constructed between nodes connected by roads to obtain a traffic map.

[0032] Optionally, generating a traffic map based on the nodes may include: determining whether there is a reachable route between two nodes, and if so, generating an edge between the two nodes; and generating the traffic map based on the nodes and the edges between the nodes.

[0033] Specifically, based on the map information, it can be determined whether there is a road between any two nodes that can be used by the armored car. If it is determined that there is, an edge between the two nodes can be generated. The edge can be a directed edge. After completing the judgment of all nodes, the traffic map can be composed of all nodes and the edges between the nodes. For example, the traffic map can be represented as G trans = {V, E}, where any v∈V represents a geographic node and any directed edge e i,j = <v i ,v j >∈E represents the distance from geographic node v i To geographic node v j There is a reachable route.

[0034] In this embodiment, by determining whether there is a reachable route between nodes and generating edges between nodes, it is possible to accurately establish a traffic map.

[0035] S120. Perform bidirectional optimal path calculation on each pair of nodes in the traffic map to obtain the transportation cost weight between each pair of nodes, and generate a network transportation map based on the traffic map and the transportation cost weight between each pair of nodes.

[0036] Among them, the bidirectional optimal path calculation includes the forward optimal path calculation and the reverse optimal path calculation. In this embodiment, the forward optimal path calculation and the reverse optimal path calculation can be performed for each pair of nodes connected by an edge in the traffic map to obtain the forward minimum path cost value and the forward optimal path, as well as the reverse minimum path cost value and the reverse optimal path. Afterwards, the sum or average of the forward minimum path cost value and the reverse minimum path cost value can be calculated to obtain the transportation cost weight between each pair of nodes. Finally, based on the traffic map, the directed edges can be replaced with undirected edges, and the corresponding transportation cost weight can be added to each undirected edge to obtain the network transportation map.

[0037] It should be noted that the nodes in the network transportation graph only include vaults and bank branches, all nodes are undirected edges, and there is at most one edge between each pair of nodes. For example, the network transportation graph can be represented as G branch ={V,E,W}, any v∈V represents a node, any undirected edge e i,j =(v i ,v j )∈E represents node v i With node v j There is a route between them, W i,j represents an undirected edge e i,j The transportation cost weight.

[0038] S130. Obtain a minimum transport cost spanning tree corresponding to the network transport map through a minimum spanning tree algorithm, generate a cash transport plan based on the minimum transport cost spanning tree and the daily cash transport volume corresponding to each network, and perform cash transport based on the cash transport plan.

[0039] In this embodiment, after the network point transportation map is generated, a preset minimum spanning tree algorithm may be used to perform a minimum weighted spanning tree calculation on the network point transportation map to obtain a minimum transportation cost spanning tree. This embodiment does not specifically limit the type of the minimum spanning tree algorithm.

[0040] Specifically, when calculating the minimum weighted spanning tree, the treasury node can be selected as the starting point, and the edge with the smallest transportation cost weight among all the edges associated with the treasury node can be found, and the treasury node, this edge and the node at the other end of this edge can be included in the minimum weighted spanning tree; then, the obtained minimum weighted spanning tree can be regarded as a node, and the above process can be repeated until all nodes of the network transportation map are included in the minimum weighted spanning tree, and the final minimum transportation cost spanning tree can be obtained.

[0041] For example, for any graph G, T = {V, E, W} is an acyclic subgraph of graph G. The point set V of any subgraph T is equal to the point set of graph G, and the sum of all weights in the edge weight set W is the smallest when the point sets are equal, then T is the minimum weighted spanning tree of graph G.

[0042] In this embodiment, after obtaining the minimum transportation cost spanning tree, the optimal path between nodes can be obtained based on the minimum transportation cost spanning tree. Based on the optimal path, the daily cash transportation volume corresponding to each network point, and the preset transportation capacity planning scheme, each transportation route is planned, along with the corresponding number of transportation vehicles, the load capacity of each transportation vehicle, and the number of accompanying personnel, thereby obtaining a cash transportation plan. Finally, transportation vehicles and personnel can be dispatched according to the cash transportation plan to ultimately achieve cash transportation.

[0043] Optionally, generating a cash transportation plan based on the minimum transportation cost spanning tree and the daily cash transportation volume corresponding to each network point may include:

[0044] Obtaining at least one transportation route based on the minimum transportation cost spanning tree, and obtaining the cash transportation volume corresponding to each transportation route based on the daily cash transportation volume corresponding to each network point;

[0045] According to the cash transport volume corresponding to each transport route, the number of transport vehicles and the number of accompanying personnel corresponding to each transport route are obtained, and according to each transport route and the corresponding number of transport vehicles and the number of accompanying personnel, a cash transport plan is generated.

[0046] In one optional example, multiple transport routes can be planned based on the branching of the minimum transport cost spanning tree and the optimal forward and reverse paths between each node. The daily cash transport volume of each node included in each transport route can then be calculated and summed to obtain the cash transport volume corresponding to each transport route. The cash transport volume corresponding to each transport route can then be divided by the maximum load capacity of each transport vehicle and the quotient rounded up to obtain the number of transport vehicles corresponding to the transport route and the load capacity of each transport vehicle. Furthermore, the number of passengers accompanying each transport vehicle can be determined based on the determined load capacity of each transport vehicle and a preset mapping relationship between load capacity and number of passengers. Finally, the transport routes, the number of transport vehicles, the load capacity of each transport vehicle, and the number of passengers can be combined to form a cash transport plan.

[0047] It is understandable that in actual scenarios, the amount of money transported from the vault to the outlet is usually different from the amount of money transported from the outlet to the vault. In this embodiment, the money transportation plan can be planned separately for the route from the vault to the outlet (forward path) and the route from the outlet to the vault (reverse path) to suit different money transportation scenarios.

[0048] In this embodiment, by generating a tree based on the minimum transportation cost, planning the transportation route, and planning the number of transportation vehicles and the number of accompanying personnel corresponding to the transportation route based on the daily cash transportation volume corresponding to each branch, automatic planning of the cash transportation plan can be achieved, which can improve the rationality of the cash transportation plan.

[0049] The technical solution of the embodiment of the present invention obtains each node and generates a traffic map based on each node; obtains the transportation cost weight between each pair of nodes by performing a two-way optimal path calculation on each pair of nodes in the traffic map, and generates a network transportation map based on the traffic map and the transportation cost weight between each pair of nodes; obtains the minimum transportation cost spanning tree corresponding to the network transportation map through a minimum spanning tree algorithm, generates a cash transportation plan based on the minimum transportation cost spanning tree and the daily cash transportation volume corresponding to each network, and performs cash transportation based on the cash transportation plan; establishes a traffic map and performs a two-way optimal path calculation on each pair of nodes in the traffic map to generate a network transportation map, and then performs a minimum spanning tree operation on the network transportation map, thereby finally obtaining a cash transportation plan, which can realize automatic planning of cash transportation routes and improve the security and punctuality of cash transportation.

[0050] Example 2

[0051] Figure 2 This is a flow chart of a path planning method provided in the second embodiment of the present invention. This embodiment is a further refinement of the above technical solution. The technical solution in this embodiment can be combined with one or more of the above implementation methods. Figure 2 As shown, the method includes:

[0052] S210: Obtain each node and determine whether there is a reachable route between two nodes. If so, generate an edge between the two nodes.

[0053] S220: Generate the traffic map based on the nodes and the edges between the nodes.

[0054] S230. According to the traffic map, obtain each connection node corresponding to the current node, and calculate the forward minimum path cost value and the reverse minimum path cost value between the current node and each connection node.

[0055] It should be noted that during cash transportation, vehicles typically transport cash from the vault to various bank branches in the early morning and then from the bank branches back to the vault in the evening. Therefore, for this specific scenario, this embodiment performs both forward and reverse optimal path calculations for each pair of nodes to ensure more accurate path cost calculations.

[0056] Specifically, for the current node, first, the traffic map is searched for nodes in the next level (divided into levels starting from the vault node) that are connected to it by edges, and these nodes are used as connecting nodes. Then, based on the map information, multiple forward paths (paths from the current node to the connecting node) and reverse paths (paths from the connecting node to the current node) can be determined between the current node and the connecting node, and the forward cost value corresponding to each forward path and the reverse cost value corresponding to each reverse path are calculated. Finally, the forward cost values ​​can be numerically compared, and the minimum value can be obtained as the forward minimum path cost value. At the same time, the reverse cost values ​​can be numerically compared, and the minimum value can be obtained as the reverse minimum path cost value.

[0057] Optionally, calculating the forward minimum path cost between the current node and each connected node may include:

[0058] Obtain at least one forward path between the current node and the currently connected node, and obtain the traffic congestion level, distance value, and safety level corresponding to each forward path;

[0059] According to the traffic congestion level, distance value and safety level corresponding to each forward path, a forward cost value corresponding to each forward path is obtained, and according to the forward cost value corresponding to each forward path, a forward minimum path cost value and a forward optimal path are obtained.

[0060] In one optional example, the treasury node is used as the starting point for the first round of calculations. Connecting nodes with edges to the treasury node are identified, and multiple forward paths from the treasury node to the connecting nodes are obtained. Subsequently, based on historical traffic information, congestion and accident data for each forward path over multiple recent days within a specified time interval (the time interval during which cash transportation occurs) are obtained. The congestion level for each forward path is then assessed based on the congestion data over multiple days, and the safety level for each forward path is assessed based on the accident data over multiple days. Simultaneously, the distance value (road distance) corresponding to each forward path can be calculated based on map information.

[0061] For example, based on the congestion situation over multiple days, the ratio of the number of days with congestion to the total number of days can be calculated, and the final level of traffic congestion can be determined based on this ratio and the mapping relationship between the preset ratio range and the level of traffic congestion. Traffic congestion levels can include no congestion, mild congestion, severe congestion, etc., and each level can correspond to a different ratio range. For another example, based on the accident data over multiple days, the ratio of the total number of accidents to the total number of days can be calculated, and the final level of safety can be determined based on this ratio and the mapping relationship between the preset ratio range and the level of safety. The level of safety can include safe, mild risk, severe risk, etc., and each level of safety can correspond to a different ratio range.

[0062] Furthermore, based on a preset scoring rule, the forward cost value corresponding to each forward path can be evaluated according to the traffic congestion level, distance value, and safety level of each forward path. Finally, the minimum value can be screened out from all forward cost values ​​to be used as the minimum forward path cost value, and the forward path corresponding to this minimum value is determined as the optimal forward path.

[0063] After determining the forward minimum path cost and forward optimal path between the vault node and each connected node, each connected node can be used as the starting point for a new round of calculations. Other connected nodes (excluding the vault node) with edges connected to them can be found and the forward minimum path cost and forward optimal path between these nodes can be recalculated. This process continues until all nodes have been calculated, resulting in the optimal one-way path.

[0064] Similarly, the last calculated node can be used as the starting point of the first round of calculation, and the above calculation process can be repeated to obtain the reverse minimum path cost and reverse optimal path between each pair of nodes.

[0065] In this embodiment, by comprehensively evaluating the path cost value based on the traffic congestion level, the distance value, and the safety level, the calculation accuracy of the minimum path cost value can be improved, and the safety of cash transportation can be improved.

[0066] Optionally, obtaining the forward cost value corresponding to each forward path according to the traffic congestion level, distance value, and safety level corresponding to each forward path may include:

[0067] Obtaining a first cost value corresponding to each forward path according to the traffic congestion degree corresponding to each forward path and a preset mapping relationship between the traffic congestion degree and the cost value;

[0068] Obtaining a second cost value corresponding to each forward path according to the distance value corresponding to each forward path and a mapping relationship between a preset distance range and a cost value;

[0069] Obtaining a third cost value corresponding to each forward path according to the security level corresponding to each forward path and a preset mapping relationship between the security level and the cost value;

[0070] According to the first generation value, the second generation value, and the third generation value, a forward cost value corresponding to each forward path is obtained.

[0071] In an optional example, the mapping relationship between the degree of traffic congestion and the cost value, the mapping relationship between the distance range and the cost value, and the mapping relationship between the safety level and the cost value can be preset. Thus, after obtaining the current degree of traffic congestion, distance value and safety level, the corresponding cost value can be determined by searching for the matching preset mapping relationship. For example, the preset mapping relationship between the degree of traffic congestion and the cost value is that no congestion corresponds to cost value A, light congestion corresponds to cost value B, and heavy congestion corresponds to cost value C. The current degree of traffic congestion is light congestion, and the current first cost value is B. Finally, the first cost value, the second cost value and the third cost value can be directly added or weighted summed to obtain the sum value as the final forward cost value. Similarly, the reverse cost value can also be obtained based on the same calculation process.

[0072] In this embodiment, by evaluating the cost value based on the traffic congestion level, the distance value, and the safety level, and synthesizing the cost values ​​to obtain the final cost value, the evaluation accuracy of the cost value can be improved.

[0073] S240 : Obtain transportation cost weights between the current node and each connected node according to the forward minimum path cost value and the reverse minimum path cost value.

[0074] Specifically, the forward minimum path cost and the reverse minimum path cost between the current node and the connecting node can be added or averaged to obtain the transportation cost weight between the current node and the connecting node. Furthermore, the transportation cost weight between each pair of nodes in the traffic map can be calculated based on the same process as above.

[0075] Optionally, obtaining the transportation cost weights between the current node and each connected node according to the forward minimum path cost value and the reverse minimum path cost value includes:

[0076] An average value of the forward minimum path cost value and the reverse minimum path cost value is calculated and determined as a transportation cost weight.

[0077] In an optional example, the average of the forward minimum path cost value and the reverse minimum path cost value can be used as the corresponding transportation cost weight. Alternatively, the forward minimum path cost value and the reverse minimum path cost value can be weighted and summed to obtain the sum value as the corresponding transportation cost weight.

[0078] In this embodiment, by taking the average of the forward minimum path cost value and the reverse minimum path cost value as the transportation cost weight between nodes, accurate assessment of the transportation cost in the cash transportation scenario can be achieved.

[0079] S250: Generate a network transportation map based on the transportation map and the transportation cost weights between each pair of nodes.

[0080] S260. Obtain a minimum transport cost spanning tree corresponding to the network transport map through a minimum spanning tree algorithm, generate a cash transport plan based on the minimum transport cost spanning tree and the daily cash transport volume corresponding to each network, and perform cash transport based on the cash transport plan.

[0081] In this embodiment, when planning cash transport routes, the optimal path between any two branches is first calculated based on geographic nodes using a traffic map. Based on this calculation, a branch transportation map is constructed. Next, a minimum-weighted spanning tree calculation is performed on the branch transportation map to obtain the minimum cost planning result for cash transport by multiple cash transport vehicles. Finally, based on the obtained minimum transportation cost spanning tree, the number of transport vehicles, load capacity, and accompanying personnel for each transport route are planned. This approach optimizes the daily cash transport routes and resource allocation for multiple cash transport vehicles from the vault to various branches, reducing the financial and labor costs of daily cash transportation and improving the security of cash transportation.

[0082] The technical solution of the embodiment of the present invention obtains each node and determines whether there is a reachable route between the two nodes. If so, an edge between the two nodes is generated; a traffic map is generated based on each node and the edge between each node; based on the traffic map, each connection node corresponding to the current node is obtained, and the forward minimum path cost value and the reverse minimum path cost value between the current node and each connection node are calculated; based on the forward minimum path cost value and the reverse minimum path cost value, the transportation cost weight between the current node and each connection node is obtained; based on the traffic map and the transportation cost weight between each pair of nodes, a network transportation map is generated; through the minimum spanning tree algorithm, the minimum transportation cost spanning tree corresponding to the network transportation map is obtained, and based on the minimum transportation cost spanning tree and the daily cash transportation volume corresponding to each network, a cash transportation plan is generated, and cash transportation is performed based on the cash transportation plan; by determining the corresponding transportation cost weight based on the forward minimum path cost value and the reverse minimum path cost value between each pair of nodes, the calculation accuracy of the transportation cost weight can be improved, and the cost of cash transportation can be reduced.

[0083] Example 3

[0084] Figure 3 This is a schematic diagram of the structure of a path planning device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes: a traffic map generation module 310, a network transportation map generation module 320 and a cash transportation plan generation module 330; wherein,

[0085] Traffic map generation module 310, for acquiring each node and generating a traffic map based on the nodes;

[0086] A network point transportation map generation module 320 is configured to calculate a bidirectional optimal path for each pair of nodes in the transportation map, obtain a transportation cost weight between each pair of nodes, and generate a network point transportation map based on the transportation map and the transportation cost weight between each pair of nodes;

[0087] The cash transport plan generation module 330 is used to obtain the minimum transportation cost spanning tree corresponding to the network transportation map through the minimum spanning tree algorithm, and generate a cash transport plan based on the minimum transportation cost spanning tree and the daily cash transport volume corresponding to each network, and perform cash transportation based on the cash transport plan.

[0088] The technical solution of the embodiment of the present invention obtains each node and generates a traffic map based on each node; obtains the transportation cost weight between each pair of nodes by performing a two-way optimal path calculation on each pair of nodes in the traffic map, and generates a network transportation map based on the traffic map and the transportation cost weight between each pair of nodes; obtains the minimum transportation cost spanning tree corresponding to the network transportation map through a minimum spanning tree algorithm, generates a cash transportation plan based on the minimum transportation cost spanning tree and the daily cash transportation volume corresponding to each network, and performs cash transportation based on the cash transportation plan; establishes a traffic map and performs a two-way optimal path calculation on each pair of nodes in the traffic map to generate a network transportation map, and then performs a minimum spanning tree operation on the network transportation map, thereby finally obtaining a cash transportation plan, which can realize automatic planning of cash transportation routes and improve the security and punctuality of cash transportation.

[0089] Optionally, a traffic map generation module 310 is specifically configured to determine whether there is a reachable route between two nodes, and if so, generate an edge between the two nodes;

[0090] The traffic map is generated based on the nodes and the edges between the nodes.

[0091] Optionally, the network transport map generation module 320 includes:

[0092] a path cost calculation unit, configured to obtain, based on the traffic map, each connected node corresponding to the current node, and calculate a forward minimum path cost and a reverse minimum path cost between the current node and each connected node;

[0093] The transportation cost weight acquisition unit is used to acquire the transportation cost weight between the current node and each connected node according to the forward minimum path cost value and the reverse minimum path cost value.

[0094] Optionally, a path cost calculation unit is specifically configured to obtain at least one forward path between the current node and the current connected node, and obtain the traffic congestion level, distance value, and safety level corresponding to each forward path;

[0095] According to the traffic congestion level, distance value and safety level corresponding to each forward path, a forward cost value corresponding to each forward path is obtained, and according to the forward cost value corresponding to each forward path, a forward minimum path cost value and a forward optimal path are obtained.

[0096] Optionally, a path cost value calculation unit is specifically configured to obtain a first cost value corresponding to each of the forward paths according to the traffic congestion levels corresponding to the forward paths and a preset mapping relationship between the traffic congestion levels and the cost values;

[0097] Obtaining a second cost value corresponding to each forward path according to the distance value corresponding to each forward path and a mapping relationship between a preset distance range and a cost value;

[0098] Obtaining a third cost value corresponding to each forward path according to the security level corresponding to each forward path and a preset mapping relationship between the security level and the cost value;

[0099] According to the first generation value, the second generation value, and the third generation value, a forward cost value corresponding to each forward path is obtained.

[0100] Optionally, the transportation cost weight acquisition unit is specifically configured to calculate an average value of the forward minimum path cost value and the reverse minimum path cost value, and determine the average value as the transportation cost weight.

[0101] Optionally, the cash transportation plan generation module 330 is specifically configured to generate a tree based on the minimum transportation cost to obtain at least one transportation route, and obtain the cash transportation volume corresponding to each transportation route based on the daily cash transportation volume corresponding to each network point;

[0102] According to the cash transport volume corresponding to each transport route, the number of transport vehicles and the number of accompanying personnel corresponding to each transport route are obtained, and according to each transport route and the corresponding number of transport vehicles and the number of accompanying personnel, a cash transport plan is generated.

[0103] The path planning device provided in the embodiment of the present invention can execute the path planning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0104] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0105] Example 4

[0106] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device 40 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 40 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] like Figure 4 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory 42 or the computer program loaded from the storage unit 48 to the random access memory 43. Various programs and data required for the operation of the electronic device 40 can also be stored in the RAM 43. The processor 41, ROM 42 and RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0108] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0109] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit, a graphics processing unit, various specialized artificial intelligence computing chips, various processors running machine learning model algorithms, a digital signal processor, and any other suitable processor, controller, microcontroller, etc. Processor 41 executes the various methods and processes described above, such as the path planning method.

[0110] In some embodiments, the path planning method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the path planning method described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to perform the path planning method in any other suitable manner (e.g., via firmware).

[0111] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays, application specific integrated circuits, application specific standard products, system-on-a-chip systems, on-load programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0113] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the foregoing.

[0114] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 40 having: a display device (e.g., a cathode ray tube or a liquid crystal display) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device 40. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0115] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks, wide area networks, blockchain networks, and the Internet.

[0116] A computing system may include clients and servers. The client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server.

[0117] This embodiment may also include a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the path planning method provided by any embodiment of the present invention.

[0118] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0119] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A path planning method, characterized in that: include: Obtaining each node and generating a traffic map based on each node; By performing a bidirectional optimal path calculation for each pair of nodes in the traffic map, a transportation cost weight between each pair of nodes is obtained, and a network transportation map is generated based on the traffic map and the transportation cost weight between each pair of nodes; The minimum transport cost spanning tree corresponding to the network transport map is obtained through the minimum spanning tree algorithm, and a cash transport plan is generated based on the minimum transport cost spanning tree and the daily cash transport volume corresponding to each network, and cash transportation is carried out based on the cash transport plan.

2. The method according to claim 1, characterized in that Generate a traffic map based on each node, including: Determine whether there is a reachable route between two nodes. If so, generate an edge between the two nodes; The traffic map is generated based on the nodes and the edges between the nodes.

3. The method according to claim 1, characterized in that By performing a bidirectional optimal path calculation for each pair of nodes in the traffic map, the transportation cost weight between each pair of nodes is obtained, including: According to the traffic map, each connection node corresponding to the current node is obtained, and the forward minimum path cost value and the reverse minimum path cost value between the current node and each connection node are calculated; The transportation cost weights between the current node and each connected node are obtained according to the forward minimum path cost value and the reverse minimum path cost value.

4. The method according to claim 3, characterized in that Calculate the forward minimum path cost between the current node and each connected node, including: Obtain at least one forward path between the current node and the currently connected node, and obtain the traffic congestion level, distance value, and safety level corresponding to each forward path; According to the traffic congestion level, distance value and safety level corresponding to each forward path, a forward cost value corresponding to each forward path is obtained, and according to the forward cost value corresponding to each forward path, a forward minimum path cost value and a forward optimal path are obtained.

5. The method according to claim 4, characterized in that Obtaining a forward cost value corresponding to each forward path according to the traffic congestion level, distance value, and safety level corresponding to each forward path, including: Obtaining a first cost value corresponding to each forward path according to the traffic congestion degree corresponding to each forward path and a preset mapping relationship between the traffic congestion degree and the cost value; Obtaining a second cost value corresponding to each forward path according to the distance value corresponding to each forward path and a mapping relationship between a preset distance range and a cost value; Obtaining a third cost value corresponding to each forward path according to the security level corresponding to each forward path and a preset mapping relationship between the security level and the cost value; According to the first generation value, the second generation value, and the third generation value, a forward cost value corresponding to each forward path is obtained.

6. The method according to claim 3, characterized in that Obtaining the transportation cost weights between the current node and each connected node according to the forward minimum path cost value and the reverse minimum path cost value, including: An average value of the forward minimum path cost value and the reverse minimum path cost value is calculated and determined as a transportation cost weight.

7. The method according to claim 1, characterized in that Based on the minimum transportation cost spanning tree and the daily cash transportation volume of each branch, a cash transportation plan is generated, including: Obtaining at least one transportation route based on the minimum transportation cost spanning tree, and obtaining the cash transportation volume corresponding to each transportation route based on the daily cash transportation volume corresponding to each network point; According to the cash transport volume corresponding to each transport route, the number of transport vehicles and the number of accompanying personnel corresponding to each transport route are obtained, and according to each transport route and the corresponding number of transport vehicles and the number of accompanying personnel, a cash transport plan is generated.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor, and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the path planning method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the path planning method according to any one of claims 1 to 7 when executed.

10. A computer program product, characterized in that The method comprises a computer program, which implements the path planning method according to any one of claims 1 to 7 when executed by a processor.

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