A city-granularity-based vehicle transfer path calculation method, system and terminal machine

Abstract

The application provides a kind of city granularity-based vehicle transfer path calculation method, system and terminal machine, belongs to the field of vehicle path planning, constructs adjacent city data network;Vehicle path data network is constructed;According to the input condition, the shortest path between nodes is calculated using Dijkstra algorithm, and the strategy of greedy algorithm is adopted, the adjacent nodes of the vertex closest to the starting point and not visited are traversed each time, until the end point is expanded, to obtain the hottest route between any two nodes or multiple nodes.The method is based on the actual vehicle operating conditions, from the perspective of city transfer, analyzes the transfer trend and heat between cities, and provides an effective method for finding popular routes, with strong practical value.

Description

Technical Field

[0001] This invention belongs to the field of vehicle route planning, specifically relating to a method, system, and terminal for calculating vehicle transfer routes based on urban granularity. Background Technology

[0002] With social and economic development, the car ownership rate in cities is increasing, and commercial vehicles are being widely used. Commercial vehicles can be used to transport people and goods. Between cities, commercial vehicles serve as commuters and can also be used for tourism. Commercial vehicles used for cargo transport facilitate the transfer of goods and materials, realizing logistics and playing an important role in both production and daily life.

[0003] Commercial vehicles typically travel long distances between cities, frequently traversing multiple intercity routes to transport goods. Generally, their routes utilize intercity highways or national roads to facilitate intercity travel. To analyze the movement of vehicles between cities and reflect information such as city influence and road usage, it's necessary to analyze intercity transfer trends and provide users with relevant data. Therefore, analyzing intercity transfer trends and trends, and providing popular routes for any two or more cities, is a pressing technical challenge. Summary of the Invention

[0004] This invention proposes a method for calculating vehicle transfer routes based on city granularity. The method can provide popular route references for any two or more cities by using intercity transfer heat information.

[0005] The methods include:

[0006] S101. Construct a data network between neighboring cities;

[0007] S102. Construct a vehicle routing data network;

[0008] S103. Based on the input, use Dijkstra's algorithm to calculate the shortest path between nodes. Adopt a greedy algorithm strategy, traverse the adjacent nodes of the vertex closest to the starting point that has not been visited each time, until the destination is reached, to obtain the most popular route between any two or more nodes.

[0009] It should be further noted that step S101 also includes:

[0010] The city is divided into fragments using formula (1):

[0011] fragement i =(Max(lat), Max(lon), Min(lat), Min(lon)) (1)

[0012] In formula (1), fragmentation i The tuple representing fragmented information of city i (lat) max lon max lat min lon min The data in the tuple represent the maximum latitude, maximum longitude, minimum latitude, and minimum longitude of the fragment, respectively.

[0013] Max(lat), Max(lon), Min(lat), and Min(lon) represent the maximum latitude, maximum longitude, minimum latitude, and minimum longitude of the city boundary data, respectively.

[0014] It should be further explained that,

[0015] Finding neighboring cities in fragments based on formula (2):

[0016]

[0017] In formula (2), neighbor i Let pos represent the neighboring cities of city i. j This represents the data portion of city j within the fragmented data of city i, where bound is the boundary. i This represents the boundary data of city i, distance(pos) j ,bound i ) min This represents the minimum boundary distance between city j and city i;

[0018] The threshold is the boundary threshold between adjacent cities; cities with boundaries less than this threshold are considered adjacent.

[0019] Build a data network between neighboring cities.

[0020] Configure the list of neighboring cities of city i [a, b, c, ...], and add node city i and edge [(i, a), (i, b), (i, c), ...] to the city data network;

[0021] Traverse all cities, form a topology, and build a data network of adjacent cities.

[0022] It should be further noted that step S102 also includes:

[0023] (2.1) Establish the vehicle routing data network structure;

[0024] (2.2) Update edge weights based on formula (3):

[0025] new_weight = 2-old_weight (3)

[0026] In formula (3), new_weight is the weight of the edge after the update, and old_weight is the weight before the update;

[0027] After updating the weights according to formula (3), the edge with the higher heat value has a lower weight, which means it is considered to be the shorter distance between the two data points.

[0028] It should be further noted that in step (2.1), the adjacent city data network is initially assigned a weight of 0 to any edge.

[0029] Load the vehicle route transfer data and iterate through it. If there is an edge of a vehicle route in the adjacent city data network, then increment the weight of that edge by 1.

[0030] Once the vehicle path traversal is complete, delete the edges with a weight of 0 in the adjacent city data network, and the vehicle path data network is now constructed.

[0031] The present invention also provides a vehicle transfer path calculation system based on city granularity, the system comprising: an adjacent city network construction module, a vehicle path construction module, and a path calculation module;

[0032] The neighboring city network construction module is used to build a neighboring city data network.

[0033] Among them, urban fragments are divided using formula (1):

[0034] fragement i =(Max(lat), Max(lon), Min(lat), Min(lon)) (1);

[0035] The vehicle routing construction module is used to build a vehicle routing data network;

[0036] Among them, the vehicle path data network structure is constructed; the edge weights are updated based on formula (3):

[0037] new_weight = 2 -old_weight (3)

[0038] After updating the weights according to formula (3), the edge with the higher heat value has the lower weight, which means it is regarded as the shorter the distance between the two data points.

[0039] The path calculation module is used to calculate the shortest path between nodes using Dijkstra's algorithm based on the input. It adopts a greedy algorithm strategy, traversing the adjacent nodes of the vertex closest to the starting point that has not been visited, until it extends to the destination, to obtain the most popular route between any two or more nodes.

[0040] The present invention also provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a vehicle transfer path calculation method based on city granularity.

[0041] As can be seen from the above technical solutions, the present invention has the following advantages:

[0042] The vehicle transfer path calculation method based on city granularity provided by this invention is based on actual vehicle operation status, and takes the perspective of urban transfer to analyze the transfer trends and popularity between cities. At the same time, this invention provides a practical and effective method for finding popular routes and has strong practical value.

[0043] This invention can summarize and analyze intercity transfer trends and popularity, facilitating access for analysts and monitors and effectively improving the efficiency of intercity route finding. It can also efficiently collect, store, and process actual vehicle operation data, enabling process monitoring through intercity transfer popularity information and using multi-dimensional space to describe vehicle transfer paths. This improves the competitiveness of vehicle transfer path calculation, promptly identifying popular routes offered by any two or more cities, thereby enhancing intercity route management and traffic efficiency. Ultimately, it achieves timely and scientific supervision, management, and control of the entire vehicle transfer path calculation process. Attached Figure Description

[0044] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 The flowchart shows the method for calculating vehicle transfer paths based on city granularity.

[0046] Figure 2 A diagram showing the structure of the adjacent city data network;

[0047] Figure 3 A schematic diagram of the vehicle heat path network before weighting;

[0048] Figure 4 This is a schematic diagram of the vehicle heat path network after the weights have been updated;

[0049] Figure 5 This is an example of a hottest path. Detailed Implementation

[0050] like Figures 1 to 5The illustrations provided in the method for calculating vehicle transfer paths based on city granularity provided by this invention are only schematic representations of the basic concept of this invention. Therefore, the illustrations only show the modules relevant to this invention and not the actual number and function of the modules in actual implementation. In actual implementation, the function, quantity, and role of each module can be arbitrarily changed, and the function and purpose of the modules may also be more complex.

[0051] The city-level vehicle transfer path calculation method can acquire and process related data using artificial intelligence technology. Specifically, the vehicle transfer path calculation method of this invention utilizes digital computers or digitally controlled machines to simulate, extend, and expand human intelligence, perceiving the environment, acquiring knowledge, and using that knowledge to obtain optimal results. This includes theories, methods, technologies, and application devices.

[0052] Of course, the vehicle transfer path calculation method of the present invention also has machine learning functions. The machine learning and deep learning in the method of the present invention usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formulaic learning.

[0053] The vehicle transfer route calculation method utilizes data information from neighboring cities. By establishing a city fragmentation model, a neighboring city search model, and an edge weight model, and by using intercity state transitions, intercity transfer heat information, and combining data processing and data transmission technologies, it can realize vehicle transfer route calculation based on city granularity, and thus provide popular route references for any two or more cities.

[0054] The city-level vehicle transfer path calculation method is applied to one or more terminals. The terminal is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0055] A terminal can be any electronic product that allows human-computer interaction, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), or interactive network television (IPTV).

[0056] The terminal may also include network devices and / or user equipment. The network devices include, but are not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0057] The network in which the terminal is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0058] The following will combine Figures 1 to 5 The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0059] Step 1: Construct a data network connecting neighboring cities;

[0060] The city is divided into fragments using formula (1):

[0061] fragement i =(Max(lat), Max(lon), Min(lat), Min(lon)) (1)

[0062] In formula (1), fragmentation i The tuple representing fragmented information of city i (lat) max lon max lat min lon min The tuple represents the maximum latitude, maximum longitude, minimum latitude, and minimum longitude of the fragment, respectively. Max(lat), Max(lon), Min(lat), and Min(lon) represent the maximum latitude, maximum longitude, minimum latitude, and minimum longitude of the city boundary data, respectively.

[0063] Finding neighboring cities in fragments based on formula (2):

[0064]

[0065] In formula (2), neighbor i Let pos represent the neighboring cities of city i. j This represents the data portion of city j within the fragmented data of city i, where bound is the boundary. i This represents the boundary data of city i, distance(pos)j ,bound i ) min This represents the minimum boundary distance between city j and city i. `threshold` is the boundary threshold for adjacent cities; distances less than this threshold are considered adjacent. Because the boundaries between cities within provinces (e.g., Jining in Shandong and Xuzhou in Jiangsu) are not perfectly aligned, gaps may exist between city boundaries. Therefore, `threshold` is introduced to control these gaps.

[0066] Building a data network of neighboring cities

[0067] Using the steps described above, obtain a list of neighboring cities [a, b, c, ...] for city i, and add nodes for city i and edges [(i, a), (i, b), (i, c), ...] to the graph. Traverse all cities, gradually improve the topology, and construct a neighboring city data network.

[0068] Step 2: Construct a vehicle routing data network;

[0069] (2.1) Establishing a vehicle routing data network structure

[0070] The constructed neighboring city data network is initially assigned a weight of 0 to any edge. Vehicle path transfer data is loaded and traversed; if an edge representing a vehicle path exists in the neighboring city data network, its weight is incremented by 1. After the vehicle path traversal is complete, edges with a weight of 0 in the neighboring city data network are deleted, and the vehicle path data network is thus constructed.

[0071] (2.2) Update edge weights based on formula (3):

[0072] new_weight = 2 -old_weight (3)

[0073] In formula (3), new_weight is the weight of the edge after the update, and old_weight is the weight before the update. After updating the weight according to formula (3), the edge with the higher popularity value (number of cars) has a lower weight, which means it is regarded as the shorter distance between the two data points.

[0074] Step 3: Based on the input, calculate the most popular route between any two or more nodes.

[0075] In the vehicle routing data network, the weight of an edge between two cities represents the number of vehicles passing through it, indirectly reflecting the popularity of the route. Dijkstra's algorithm is used to calculate the shortest path between nodes, employing a greedy algorithm strategy: each time, the adjacent nodes of the nearest unvisited vertex to the starting point are traversed until the path extends to the destination.

[0076] This invention can summarize and analyze intercity transfer trends and popularity, facilitating access for analysts and monitors and effectively improving the efficiency of intercity route finding. It can also efficiently collect, store, and process actual vehicle operation data, enabling process monitoring through intercity transfer popularity information and using multi-dimensional space to describe vehicle transfer paths. This improves the competitiveness of vehicle transfer path calculation, promptly identifying popular routes offered by any two or more cities, thereby enhancing intercity route management and traffic efficiency. Ultimately, it achieves timely and scientific supervision, management, and control of the entire vehicle transfer path calculation process.

[0077] Furthermore, as a refinement and extension of the specific implementation methods described above, and to fully illustrate the specific implementation process in this embodiment, the vehicle transfer path calculation method constructs a hypothetical dataset to further illustrate the specific implementation methods of the present invention. Assume that there is currently driving data for 5 vehicles, which contains the vehicle's path information.

[0078] Building a data network between neighboring cities:

[0079] Divide the city into fragments. Use formula (1) to obtain the regional fragments of each city. For example, the boundary data of the city where city A is located is: [(34, 23), (29, 36), (22, 25), (26, 20), (30, 27)], then the regional fragment of city A is (34, 36, 22, 20).

[0080] Find neighboring cities in the fragment. Use formula (2) to calculate the boundary distance between other cities in the fragment and the core city. For example: In the fragment of city A, there are some boundary points of city B [(33, 23), (32, 25)] and boundary point of city C [(25, 36)]. The threshold is 2. The distances between the relevant cities in this fragment and the core city A are shown in Table 1.

[0081] Table 1: Distances between other cities in fragment A and city A

[0082]

[0083] The shortest distance between city B and city A The shortest distance between cities C and A Therefore, AB is considered a neighboring city, while AC is considered a non-neighboring city. Thus, nodes A and B are added to the neighboring city data network, along with an edge (A, B).

[0084] Repeat the above steps in other fragments to gradually improve the data network of adjacent cities.

[0085] Assuming the final adjacent city data network structure is as follows Figure 2 As shown.

[0086] Building a vehicle routing data network:

[0087] Assume the current travel routes of the 5 vehicles are as shown in Table 2:

[0088] Table 2: Vehicle Driving Route Table

[0089]

[0090] Traverse the vehicle paths and add weight information to the edges of the adjacent city data network to initially construct the vehicle path data network. For example, the path of vehicle 1 can be decomposed as [(A,B), (B,D), (D,F)], then the weight of its edges in the network is increased by 1; the path of vehicle 2 can be decomposed as [(B,A), (A,F), (F,C), (C,A)], but in the adjacent city network, there is no direct connection between city A and city C, so this path is treated as abnormal data, and the (C,A) path is ignored. Traverse all vehicles and assign corresponding weights to the edges to complete the construction of the vehicle path data network, such as... Figure 3 As shown.

[0091] The weights of the vehicle routing data network are updated according to formula (3), and the final network structure is as follows: Figure 4 As shown.

[0092] Based on the input, calculate the most popular route between any two or more nodes:

[0093] If we want to query the most popular route from city A to city C in the vehicle routing data network constructed in the previous step, we can use Dijkstra's algorithm in the graph to calculate the shortest route between two nodes, and obtain the shortest route <A, B, C>, which is the most popular route from city A to city C.

[0094] For multiple nodes (with waypoints), a segmented calculation and aggregation strategy can be used to find the most popular route. For example, to query the most popular route starting from city A, passing through city D, and ending in city C, we can first calculate the most popular path from A to D (<A, B, D>), then calculate the most popular path from D to C (<D, F, C>), and the aggregation result is the path <A, B, D, F, C>, which is the most popular route starting from A, passing through D, and ending in C. The route result is as follows: Figure 5 As shown.

[0095] Analysis of the above process shows that this invention effectively analyzes the trends and popularity of inter-city migration, and provides a practical and effective method for finding popular routes with high flexibility, thus possessing strong practical value.

[0096] The following are embodiments of the vehicle transfer path calculation system based on city granularity provided in this disclosure. This method belongs to the same inventive concept as the vehicle transfer path calculation methods based on city granularity in the above embodiments. For details not described in detail in the embodiments of the vehicle transfer path calculation system based on city granularity, please refer to the embodiments of the vehicle transfer path calculation methods based on city granularity described above.

[0097] The system includes: a neighboring city network construction module, a vehicle route construction module, and a route calculation module;

[0098] The neighboring city network construction module is used to build a neighboring city data network.

[0099] Among them, urban fragments are divided using formula (1):

[0100] fragement i =(Max(lat), Max(lon), Min(lat), Min(lon)) (1):

[0101] The vehicle routing construction module is used to build a vehicle routing data network;

[0102] Among them, the vehicle path data network structure is constructed; the edge weights are updated based on formula (3):

[0103] new_weight = 2 -old_weight (3)

[0104] After updating the weights according to formula (3), the edge with the higher heat value has the lower weight, which means it is regarded as the shorter the distance between the two data points.

[0105] The path calculation module is used to calculate the shortest path between nodes using Dijkstra's algorithm based on the input. It adopts a greedy algorithm strategy, traversing the adjacent nodes of the vertex closest to the starting point that has not been visited, until it extends to the destination, to obtain the most popular route between any two or more nodes.

[0106] The units and algorithm steps of the various examples described in the embodiments of the city-level vehicle transfer path calculation method and system provided by this invention can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0107] The flowcharts and block diagrams in the accompanying drawings of the vehicle transfer path calculation method illustrate the architecture, functionality, and operation of possible implementations of the apparatus, method, and computer program product according to various embodiments of this disclosure. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] In the vehicle transfer route calculation method, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include, but are not limited to, object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or power server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (exemplarily using an Internet service provider for Internet connection).

[0109] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating vehicle transfer paths based on city-level granularity, characterized in that, The methods include: S101. Construct a data network between neighboring cities; S102. Construct a vehicle routing data network; S103. Based on the input, use Dijkstra's algorithm to calculate the shortest path between nodes. The greedy algorithm strategy is adopted. Each time, the adjacent nodes of the vertex closest to the starting point and not visited are traversed until the destination is reached, so as to obtain the most popular route between any two or more nodes. Step S102 includes: (2.1) Establish the vehicle routing data network structure; (2.2) Update edge weights based on formula (3): （3） In formula (3), It is the updated weight. The weights are the weights before the update; After updating the weights according to formula (3), the edge with the higher heat value has a lower weight, which means it is considered to be the shorter distance between the two data points. In step (2.1), the adjacent city data network is constructed, and the weight of any edge is initially assigned to 0. Load the vehicle route transfer data and iterate through it. If there is an edge for a vehicle route in the adjacent city data network, then increment the weight of that edge by 1. Once the vehicle path traversal is complete, delete the edges with a weight of 0 in the adjacent city data network, and the vehicle path data network is now constructed.

2. The method for calculating vehicle transfer paths based on city granularity according to claim 1, characterized in that, Step S101 includes: The city is divided into fragments using formula (1): （1） In formula (1), Represents city fragmented information tuples The data in the tuple represent the maximum latitude, maximum longitude, minimum latitude, and minimum longitude of the fragment, respectively. , , , These represent the maximum latitude, maximum longitude, minimum latitude, and minimum longitude of the city boundary data, respectively.

3. The method for calculating vehicle transfer paths based on city granularity according to claim 2, characterized in that, Finding neighboring cities in fragments based on formula (2): （2） In formula (2), Represents city Neighboring cities, Indicates in the city Fragmented data of cities The data section, Represents city Boundary data, Represents city With the city The minimum boundary distance; This is the threshold for adjacent city boundaries; cities with boundaries less than this threshold are considered adjacent. Build a data network between neighboring cities.

4. The method for calculating vehicle transfer paths based on city-level granularity according to claim 3, characterized in that, Configure city List of neighboring cities Adding node cities to the city data network and edge ; Traverse all cities, form a topology, and build a data network of adjacent cities.

5. A vehicle transfer path calculation system based on city-level granularity, characterized in that, The system employs the vehicle transfer path calculation method based on urban granularity as described in any one of claims 1 to 4; The system includes: a neighboring city network construction module, a vehicle route construction module, and a route calculation module; The neighboring city network construction module is used to build a neighboring city data network. Among them, urban fragments are divided using formula (1): （1）； The vehicle routing construction module is used to build a vehicle routing data network; Among them, the vehicle path data network structure is constructed; the edge weights are updated based on formula (3): （3） After updating the weights according to formula (3), the edge with the higher heat value has a lower weight, which means it is considered to be the shorter distance between the two data points. The path calculation module is used to calculate the shortest path between nodes using Dijkstra's algorithm based on the input. It adopts a greedy algorithm strategy, traversing the adjacent nodes of the vertex closest to the starting point that has not been visited each time, until it extends to the destination, to obtain the most popular route between any two or more nodes. The construction of the vehicle routing data network structure includes the construction of a neighboring city data network, with the weight of any edge initially assigned to 0; Load the vehicle route transfer data and iterate through it. If there is an edge for a vehicle route in the adjacent city data network, then increment the weight of that edge by 1. Once the vehicle path traversal is complete, delete the edges with a weight of 0 in the adjacent city data network, and the vehicle path data network is now constructed.

6. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the vehicle transfer path calculation method based on city granularity as described in any one of claims 1 to 4.

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