Vehicle scheduling method, device and equipment, computer storage medium and program product
By obtaining and analyzing the number of taxis and traffic index in real time, establishing a topological network, and calculating scheduling paths, the problems of lag and low efficiency in taxi scheduling are solved, and more reasonable vehicle resource allocation and traffic network efficiency are achieved.
Patent Information
- Application Number
- CN202411364573.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-27
- Publication Date
- 2025-01-10
AI Technical Summary
The prior art has problems of lag in scheduling decision-making and low efficiency in taxi scheduling, which makes it difficult for passengers to ride or wait for idle vehicles.
By obtaining the real-time total number of vehicles, number of no-load vehicles and traffic busy index of each preset grid area, establishing a real-time demand value matrix and topological matrix, building a topological network between grid areas, and calculating the scheduling path and target scheduling values , realize dynamic scheduling and allocation of vehicle resources.
It improves the real-time and efficiency of vehicle scheduling, balances the vehicle needs between different regions, avoids waste of resources, and improves the operation efficiency of the transportation network.
Smart Images
Figure CN119296306A_ABST
Abstract
Description
Technical Field
[0001] The present application belongs to the field of traffic management technology, and in particular, relates to a vehicle dispatching method, device, equipment, computer storage medium, and program product. Background Art
[0002] With the acceleration of urbanization, urban traffic problems have become increasingly prominent. In particular, in some large and medium-sized cities, due to dense populations and huge traffic flows, there is often a mismatch between taxi supply and demand. For example, in hot spots such as commercial areas and transportation hubs, a large number of taxis gather and passengers have difficulty getting a ride in time; on the contrary, in residential areas or on the edge of the city, passengers have to wait for a long time for idle taxis. This unbalanced distribution of taxis not only affects the travel experience of passengers, but also reduces the operating efficiency of taxis.
[0003] The existing technology for taxi dispatching needs to rely on the passenger's order data or historical order data and the adjustment of the management personnel. The vehicle is dispatched only after the passenger places an order or the management personnel make adjustments. It cannot provide real-time and reasonable dispatch for the vehicle, the dispatch decision has a lag, and the vehicle dispatch efficiency is low. Summary of the invention
[0004] The embodiments of the present application provide a vehicle dispatching method, device, equipment, computer storage medium, and program product to solve the problems of lag in dispatching decisions and low vehicle dispatching efficiency in the prior art.
[0005] In a first aspect, an embodiment of the present application provides a vehicle dispatching method, the method comprising:
[0006] Obtain the real-time total number of vehicles, real-time number of empty vehicles and real-time traffic congestion index for each preset grid area;
[0007] According to the relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index and the vehicle demand value, the real-time vehicle demand value corresponding to the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area is determined, and a real-time demand value matrix is established, wherein each element in the demand value matrix represents the real-time vehicle demand value of each grid area;
[0008] Determine the real-time topology matrix corresponding to the real-time demand value matrix according to the relationship among the preset distance matrix, demand value matrix and topology matrix, each element of the distance matrix represents the distance between the grid area corresponding to the row element and the column element, and each element of the real-time topology matrix represents the real-time scheduling demand value from the row element to the grid area corresponding to the column element;
[0009] A topological network between preset grid areas is established according to the real-time scheduling demand value, wherein the topological network includes a plurality of nodes and edges connecting the nodes, the nodes are the preset grid areas, and the edges are the real-time scheduling demand values between the preset grid areas;
[0010] Obtain a scheduling path composed of edges whose scheduling values are less than the real-time scheduling requirement value in the topological network and increase the scheduling value of the scheduling path, so that each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling requirement value, obtain a target scheduling value for each edge, the initial value of the scheduling value is zero, and the scheduling path is a path composed of edges connecting a first grid area and a second grid area, the first grid area is a grid area corresponding to a node in the topological network having the largest sum of real-time scheduling requirement values with the second network area, and the second grid area is any grid area except the first grid area;
[0011] The vehicles in the second grid area are dispatched to the first grid area according to the target dispatch value of the edge of the topological network.
[0012] In a second aspect, an embodiment of the present application provides a vehicle dispatching device, the device comprising:
[0013] An acquisition module, used for acquiring the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area;
[0014] A determination module is used to determine the real-time vehicle demand value corresponding to the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area according to the relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index and the vehicle demand value, and establish a real-time demand value matrix, wherein each element in the demand value matrix represents the real-time vehicle demand value of each grid area;
[0015] The determination module is further used to determine the real-time topology matrix corresponding to the real-time demand value matrix according to the relationship between the preset distance matrix, the demand value matrix and the topology matrix, each element of the distance matrix represents the distance between the grid area corresponding to the row element and the column element, and each element of the real-time topology matrix represents the real-time scheduling demand value from the row element to the grid area corresponding to the column element;
[0016] An establishment module is used to establish a topological network between preset grid areas according to the real-time scheduling demand value, the topological network includes a plurality of nodes and edges connecting the nodes, the nodes are the preset grid areas, and the edges are the real-time scheduling demand values between the preset grid areas;
[0017] A calculation module is used to obtain a scheduling path composed of edges whose scheduling values are less than the real-time scheduling requirement value in the topological network and increase the scheduling value of the scheduling path, so that each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling requirement value, and obtain a target scheduling value for each edge. The initial value of the scheduling value is zero. The scheduling path is a path composed of edges connecting a first grid area and a second grid area. The first grid area is a grid area corresponding to a node in the topological network having the largest sum of real-time scheduling requirement values with the second network area, and the second grid area is any grid area other than the first grid area.
[0018] The scheduling module is used to schedule the vehicles in the second grid area to the first grid area according to the target scheduling value of the edge of the topological network.
[0019] In a third aspect, an embodiment of the present application provides a terminal device, comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the vehicle scheduling method of the first aspect is implemented.
[0020] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the vehicle dispatching method of the first aspect is implemented.
[0021] In a fifth aspect, an embodiment of the present application provides a computer program product. When instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the vehicle scheduling method as in the first aspect.
[0022] The embodiment of the present application provides a vehicle scheduling method, device, equipment, computer storage medium, and program product. The method first obtains the real-time total number of vehicles, the real-time number of unloaded vehicles, and the real-time traffic congestion index of each preset grid area, and determines the real-time vehicle demand value according to the relationship with the vehicle demand value, and finally establishes a real-time demand value matrix; quantifies the vehicle demand of each grid area through a mathematical model to ensure that vehicle resources are allocated according to actual demand. Determine the real-time topological matrix according to the relationship between the preset distance matrix, the demand value matrix, and the topological matrix; comprehensively consider the relationship between the distance and demand value between different grid areas by calculating the topological matrix, so that the scheduling decision is more reasonable. Establish a topological network between preset grid areas according to the real-time scheduling demand value; obtain a scheduling path composed of edges whose scheduling values in the topological network are less than the real-time scheduling demand value and increase the scheduling value of the scheduling path to obtain the target scheduling value of each edge; calculate the target scheduling value by the real-time scheduling demand value on the scheduling path, which can avoid the waste of vehicle resources, balance the vehicle demand between different areas, and improve the operation efficiency of the entire traffic network. Dispatch the vehicle to the first grid area according to the target scheduling value. By establishing the topological matrix of the grid area, calculating the target scheduling value and scheduling the vehicle, the real-time and efficiency of vehicle scheduling are improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0024] Figure 1 It is a flowchart of a vehicle dispatching method provided by an embodiment of the present application;
[0025] Figure 2 It is a flowchart of a method for obtaining the maximum flow value between each grid area provided by an embodiment of the present application;
[0026] Figure 3 It is a flowchart of a method for establishing a topology network provided by an embodiment of the present application;
[0027] Figure 4 is a structural schematic diagram of a vehicle dispatching device provided by an embodiment of the present application;
[0028] Figure 5 It is a structural diagram of a terminal device provided by an embodiment of the present application. DETAILED DESCRIPTION
[0029] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.
[0030] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0031] Urban traffic is complex, and taxis often gather in business districts and transportation hubs but cannot wait for passengers. Passengers wait on the roadside for a long time but cannot get a free taxi, resulting in a mismatch between the flow of people and vehicles.
[0032] There are two main solutions at present. One is to hail a taxi through a mobile phone APP before traveling, so that taxis can be online-based. Obtain the passenger's ride request information and the real-time status of the vehicle, and allocate the empty vehicles at regular intervals; dispatch the vehicle according to the ride request information. The second is to plan the roads in advance based on historical orders, so that urban traffic managers can integrate and allocate traffic modes and urban transportation capacity in advance. The first solution requires online orders, and dispatches taxis after obtaining the passenger's order application. It is not convenient for people who are inconvenient to use mobile phones and temporary travelers. The second solution relies on historical order data and management personnel adjustments. It cannot dispatch taxis according to real-time conditions and make reasonable route planning for vehicles. It has a lag of one day or even longer.
[0033] In order to solve the problems of the prior art, the embodiment of the present application provides a vehicle scheduling method, device, equipment, computer storage medium, and program product. The method first obtains the real-time total number of vehicles, the real-time number of unloaded vehicles, and the real-time traffic congestion index of each preset grid area, and determines the real-time vehicle demand value according to the relationship with the vehicle demand value, and finally establishes a real-time demand value matrix; quantifies the vehicle demand of each grid area through a mathematical model to ensure that vehicle resources are allocated according to actual demand. Determine the real-time topological matrix according to the relationship between the preset distance matrix, the demand value matrix, and the topological matrix; comprehensively consider the relationship between the distance and demand value between different grid areas by calculating the topological matrix, so that the scheduling decision is more reasonable. Establish a topological network between preset grid areas according to the real-time scheduling demand value; obtain a scheduling path composed of edges whose scheduling values in the topological network are less than the real-time scheduling demand value and increase the scheduling value of the scheduling path to obtain the target scheduling value of each edge; calculate the target scheduling value by the real-time scheduling demand value on the scheduling path, which can avoid the waste of vehicle resources, balance the vehicle demand between different areas, and improve the operation efficiency of the entire traffic network. Dispatch the vehicle to the first grid area according to the target scheduling value. By establishing the topological matrix of the grid area, calculating the target scheduling value and scheduling the vehicle, the real-time and efficiency of vehicle scheduling are improved.
[0034] The vehicle dispatching method provided in the embodiment of the present application is introduced below with reference to the accompanying drawings.
[0035] Figure 1 FIG. 1 is a flow chart of a vehicle dispatching method provided by an embodiment of the present application. Figure 1 As shown, the method may include the following steps: S101 to S106.
[0036] S101, obtaining the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area.
[0037] In some embodiments, obtaining the real-time total number of vehicles and the real-time number of unloaded vehicles in each preset grid area may include:
[0038] Get the real-time location and passenger status of the vehicle;
[0039] The vehicles are divided into corresponding preset grid areas according to their real-time positions, and the real-time total number of vehicles and the real-time number of unloaded vehicles in each preset grid area are calculated according to the real-time passenger status.
[0040] In some embodiments, each vehicle reports real-time location information and real-time passenger status information once every preset time.
[0041] S102, according to the relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index and the vehicle demand value, determine the real-time vehicle demand value corresponding to the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area and establish a real-time demand value matrix, each element in the demand value matrix represents the real-time vehicle demand value of each grid area.
[0042] The larger the real-time vehicle demand value is, the more vehicles there are in the corresponding grid area and the greater the demand for rides is.
[0043] In some embodiments, the relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index, and the vehicle demand value may include:
[0044] H=x*(xy)*(B-4)
[0045] Among them, H is the vehicle demand value, x is the total number of vehicles, y is the number of empty vehicles, and B is the traffic congestion index.
[0046] By considering the number of unloaded vehicles, the vehicle demand value can be dynamically adjusted to reflect the actual traffic conditions and vehicle utilization efficiency.
[0047] S103, determining the real-time topology matrix corresponding to the real-time demand value matrix according to the relationship between the preset distance matrix, demand value matrix and topology matrix, each element of the distance matrix represents the distance between the grid areas corresponding to the row elements and the column elements, and each element of the real-time topology matrix represents the real-time scheduling demand value from the row elements to the grid areas corresponding to the column elements.
[0048] The larger the real-time dispatch demand value is, the greater the taxi demand and the farther the distance from the row grid point to the column grid point is.
[0049] In some embodiments, the relationship between the preset distance matrix, the vehicle demand value matrix and the topology matrix may include:
[0050]
[0051] Among them, A is the topological matrix, D is the diagonal matrix, P is the distance matrix, and H is the demand value matrix.
[0052] The demand value matrix makes the dispatch decision demand-oriented, ensuring that vehicles are dispatched to areas with higher demand first. The distance matrix is introduced to consider the impact of the distance between different grid areas on vehicle dispatch, which helps to optimize the allocation of vehicles between different areas.
[0053] In some embodiments, when the value of the transfer demand value in the topology matrix is 0, no vehicle is dispatched to the grid area.
[0054] S104, establishing a topological network between preset grid areas according to the real-time scheduling demand value, the topological network including a plurality of nodes and edges connecting the nodes, the nodes being the preset grid areas, and the edges being the real-time scheduling demand values between the preset grid areas.
[0055] By establishing a topological network using the real-time dispatch demand values between preset grid areas as edges, the traffic connections and demands between various areas can be accurately mapped.
[0056] S105, obtaining a scheduling path composed of edges whose scheduling values are less than the real-time scheduling requirement value in the topological network and increasing the scheduling value of the scheduling path, so that each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling requirement value, and obtaining a target scheduling value for each edge. The initial value of the scheduling value is zero, and the scheduling path is a path composed of edges connecting a first grid area and a second grid area. The first grid area is a grid area corresponding to a node in the topological network that has the largest sum of real-time scheduling requirement values with the second network area, and the second grid area is any grid area except the first grid area.
[0057] In some embodiments, a Ford-Fulkerson algorithm is used to calculate the target scheduling value between each grid area. By iteratively searching for augmenting paths, the algorithm can gradually approach the optimal solution to ensure that the target scheduling value is maximized in the network.
[0058] In some embodiments, the scheduling path is a directed path, the direction is from the second grid area to the first grid area, and the edge of the directed path is the real-time scheduling demand value from the second grid area to the first grid area.
[0059] In some embodiments, the first grid area and the second grid area may be connected through one or more grid areas other than the first grid area and the second grid area, and the scheduling path is composed of multiple nodes representing grid areas and connected edges, including at least nodes representing the first grid area and the second grid area.
[0060] S106: Dispatch vehicles in the second grid area to the first grid area according to the target dispatch value of the edge of the topological network.
[0061] Scheduling vehicles according to the target scheduling values of the edges of the topological network helps to achieve supply and demand balance and reduce the waste of vehicle resources.
[0062] The real-time vehicle demand value is determined according to the total number of vehicles, the number of empty vehicles and the traffic congestion index and the relationship with the vehicle demand value, and finally the real-time demand value matrix is established; the vehicle demand of each grid area is quantified through a mathematical model to ensure that vehicle resources are allocated according to actual demand. The real-time topological matrix is determined according to the relationship between the preset distance matrix, the demand value matrix and the topological matrix; the relationship between the distance and demand value between different grid areas is comprehensively considered by calculating the topological matrix, so that the scheduling decision is more reasonable. A topological network between preset grid areas is established according to the real-time scheduling demand value; the scheduling path composed of the edges with scheduling values less than the real-time scheduling demand value in the topological network is obtained and the scheduling value of the scheduling path is increased to obtain the target scheduling value of each edge; the target scheduling value is calculated by the real-time scheduling demand value on the scheduling path, which can avoid the waste of vehicle resources, balance the vehicle demand between different areas, and improve the operation efficiency of the entire traffic network. The vehicle is dispatched to the first grid area according to the target scheduling value. By establishing the topological matrix of the grid area, the target scheduling value is calculated and the vehicle is dispatched, which improves the real-time and efficiency of vehicle scheduling.
[0063] In some embodiments, before obtaining the real-time total number of vehicles, the real-time number of empty vehicles, and the real-time traffic congestion index of each preset grid area, the method may further include:
[0064] The urban area is divided into a plurality of preset grid areas according to the preset area size.
[0065] In some embodiments, when the city area is divided into a plurality of preset grid areas according to a preset area size, the current city can be divided into many 100m*100m grid areas to obtain a grid area matrix L consisting of a total of n*m grid areas.
[0066]
[0067] Among them, l 00 Indicates the 100m*100m grid area in the upper left corner of the city, l nm Indicates the 100m*100m grid area in the lower right corner of the city. 0m Indicates the 100m*100m grid area in the upper right corner of the city. n0 Represents the 100m*100m grid area in the lower left corner of the city.
[0068] In some embodiments, before determining the real-time topology matrix corresponding to the real-time demand value matrix according to the relationship among the preset distance matrix, the demand value matrix and the topology matrix, the method may further include:
[0069] Calculate the distance between the preset grid areas according to the preset grid areas;
[0070] Construct a distance matrix based on the distances between preset grid areas.
[0071] In one example, the distance between the preset grid areas may be the distance between the centers of the grid areas. nm To the grid area p ij The distance between them can be:
[0072]
[0073] The distance matrix P established can be:
[0074]
[0075] Among them, p 00-00 Represents the grid area p 00 To the grid area p 00 The distance between nm -00 indicates the grid area p nm To the grid area p 00 The distance between 00-nm Represents the grid area p 00 To the grid area p nm The distance between nm-nm Represents the grid area p nm To the grid area p nm The distance between.
[0076] In one example, the traffic congestion index can be obtained in real time through the API interfaces of major map manufacturers, and the traffic congestion index classification levels are shown in Table 1.
[0077] Table 1 Classification of traffic busy index
[0078] 0 unknown 1 Smooth 2 amble 3 Congestion 4 Severe congestion
[0079] In some embodiments, Figure 2 As shown, a scheduling path consisting of edges whose scheduling values are less than the real-time scheduling requirement value in the topological network is obtained and the scheduling value of the scheduling path is increased, so that each scheduling path includes at least one edge with a scheduling value equal to the real-time scheduling requirement value, and the target scheduling value of each edge is obtained, which may include: S201 to S204.
[0080] S201, obtaining a scheduling path composed of edges whose scheduling values are less than the real-time scheduling demand value in the topological network;
[0081] S202, taking the minimum remaining real-time scheduling demand value of the edge in the scheduling path as the scheduling value increment of the scheduling path and adding it to the scheduling value on each edge, and the remaining real-time scheduling demand value is the difference between the real-time scheduling demand value and the scheduling value;
[0082] S203, sequentially performing the operation of obtaining a scheduling value increment and adding the scheduling value on each edge for the scheduling path composed of the edges whose remaining scheduling values are less than the real-time scheduling demand value;
[0083] S204: When each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling demand value, the target scheduling value of each edge is used as the target scheduling value.
[0084] By finding a scheduling path composed of edges whose scheduling values are less than the real-time scheduling demand value and updating the scheduling value, this method ensures that the vehicle scheduling resource utilization between grid areas is maximized without exceeding the real-time scheduling demand value, which helps to discover and utilize underutilized paths in the network, thereby optimizing the vehicle scheduling route.
[0085] In some embodiments, before dispatching the vehicle in the second grid area to the first grid area according to the target dispatch value of the edge of the topological network, the method may further include:
[0086] Determine whether the difference between the sum of the real-time scheduling demand values of the first grid area and the sum of the real-time scheduling demand values of the second grid area exceeds a set threshold;
[0087] When the difference exceeds a set threshold, the vehicles in the second grid area are dispatched to the target grid area.
[0088] By avoiding unnecessary vehicle dispatch, resources can be saved and ineffective vehicle movement can be reduced, which helps reduce traffic congestion.
[0089] In some embodiments, Figure 3 As shown, establishing a topological network between the target grid area and the remaining grid areas according to the transfer demand value may include: S301 to S302.
[0090] S301, taking each preset grid area as a central grid area and obtaining adjacent grid areas of the central grid area;
[0091] S302: Establish a topological network between each central grid area and adjacent grid areas.
[0092] By building a topological network with each grid area as the center, the vehicle dispatch between each area can be managed more finely, improving the pertinence and effectiveness of dispatch. Clarifying the transfer requirements between each grid area and its adjacent areas helps improve the accuracy of vehicle dispatch.
[0093] In some embodiments, the topological network may further include each central grid area and grid areas other than the central grid area whose distances from the central grid area are less than a set threshold.
[0094] Figure 4 4 is a schematic diagram of the structure of a vehicle dispatching device 400 provided in an embodiment of the present application. Figure 4 As shown, the device may include an acquisition module 401 , a determination module 402 , an establishment module 403 , a calculation module 404 and a scheduling module 405 .
[0095] An acquisition module 401 is used to acquire the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area;
[0096] A determination module 402 is used to determine the real-time vehicle demand value corresponding to the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area according to the relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index and the vehicle demand value, and establish a real-time demand value matrix, wherein each element in the demand value matrix represents the real-time vehicle demand value of each grid area;
[0097] The determination module 402 is further used to determine the real-time topology matrix corresponding to the real-time demand value matrix according to the relationship between the preset distance matrix, the demand value matrix and the topology matrix, each element of the distance matrix represents the distance between the grid area corresponding to the row element and the column element, and each element of the real-time topology matrix represents the real-time scheduling demand value from the row element to the grid area corresponding to the column element;
[0098] Establishing module 403, used to establish a topological network between preset grid areas according to the real-time scheduling demand value, the topological network includes a plurality of nodes and edges connecting the nodes, the nodes are the preset grid areas, and the edges are the real-time scheduling demand values between the preset grid areas;
[0099] A calculation module 404 is used to obtain a scheduling path composed of edges whose scheduling values are less than the real-time scheduling requirement value in the topological network and increase the scheduling value of the scheduling path, so that each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling requirement value, and obtain a target scheduling value for each edge. The initial value of the scheduling value is zero. The scheduling path is a path composed of edges connecting a first grid area and a second grid area. The first grid area is a grid area corresponding to a node in the topological network having the largest sum of real-time scheduling requirement values with the second network area, and the second grid area is any grid area other than the first grid area.
[0100] The scheduling module 405 is used to schedule the vehicles in the second grid area to the first grid area according to the target scheduling value of the edge of the topological network.
[0101] In some embodiments, when the determination module 402 is used to determine the vehicle demand value of each preset grid area according to the relationship between the number of vehicles, the number of empty vehicles, the traffic congestion index, and the vehicle demand value, the relationship between the number of vehicles, the number of empty vehicles, the traffic congestion index, and the vehicle demand value may include:
[0102] H=x*(xy)*(B-4)
[0103] Among them, H is the vehicle demand value, x is the number of vehicles, y is the number of empty vehicles, and B is the traffic congestion index.
[0104] In some embodiments, when the determination module 402 is used to determine the topological matrix according to the relationship between the preset distance matrix, the demand value matrix and the topological matrix, the relationship between the preset distance matrix, the vehicle demand value matrix and the topological matrix includes:
[0105]
[0106] Among them, A is the topological matrix, D is the diagonal matrix, P is the distance matrix, and H is the demand value matrix.
[0107] In some embodiments, the acquisition module 401 is further used to obtain a scheduling path composed of edges whose scheduling values are less than the real-time scheduling demand value in the topological network;
[0108] The calculation module 404 is further configured to use the minimum remaining real-time scheduling demand value of the edge in the scheduling path as the scheduling value increment of the scheduling path and add it to the scheduling value on each edge, and the remaining real-time scheduling demand value is the difference between the real-time scheduling demand value and the scheduling value;
[0109] The calculation module 404 is further used to sequentially perform the operation of obtaining the scheduling value increment and adding the scheduling value on each edge for the scheduling path composed of the edges whose remaining scheduling value is less than the real-time scheduling demand value;
[0110] The determination module 402 is further configured to use the target scheduling value of each edge as the target scheduling value when each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling demand value.
[0111] In some embodiments, the vehicle dispatching device 400 may further include:
[0112] A judgment module, used for judging whether the difference between the sum of the real-time dispatch demand values of the first grid area and the sum of the real-time dispatch demand values of the second grid area exceeds a set threshold before dispatching the vehicles of the second grid area to the first grid area according to the target dispatch value of the edge of the topological network;
[0113] The dispatch module 405 is further configured to dispatch the vehicles in the second grid area to the target grid area when the difference exceeds a set threshold.
[0114] In some embodiments, the acquisition module 401 is further used to take each preset grid area as the central grid area and acquire adjacent grid areas of the central grid area;
[0115] The establishing module 403 is further used to establish a topological network between each central grid area and adjacent grid areas.
[0116] Figure 4 The various modules in the device shown can be implemented Figure 1 Each step in the above steps can achieve the corresponding technical effect, which will not be repeated here for the sake of brevity.
[0117] Figure 5 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.
[0118] The electronic device may include a processor 501 and a memory 502 storing computer program instructions.
[0119] Specifically, the processor 501 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
[0120] The memory 502 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 502 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 502 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 502 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 502 is a non-volatile solid-state memory.
[0121] In certain embodiments, the memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present application.
[0122] The processor 501 implements any one of the vehicle dispatching methods in the above embodiments by reading and executing computer program instructions stored in the memory 502 .
[0123] In one example, the electronic device may further include a communication interface 503 and a bus 510. Figure 5 As shown, the processor 501, the memory 502, and the communication interface 503 are connected via a bus 510 and communicate with each other.
[0124] The communication interface 503 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.
[0125] Bus 510 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 510 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.
[0126] In addition, in combination with the vehicle dispatching method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the vehicle dispatching methods in the above embodiment is implemented.
[0127] An embodiment of the present application also provides a computer program product, including a computer program, which implements any one of the vehicle scheduling methods in the above embodiments when the computer program is processed and executed.
[0128] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.
[0129] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.
[0130] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.
[0131] The above reference is to the flowchart and / or block diagram of the method, device, equipment, storage medium and computer program product according to the embodiment of the present application. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.
[0132] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.
Claims
1. A vehicle dispatching method, characterized in that: include: Obtain the real-time total number of vehicles, real-time number of empty vehicles and real-time traffic congestion index for each preset grid area; According to the relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index and the vehicle demand value, the real-time vehicle demand value corresponding to the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area is determined, and a real-time demand value matrix is established, wherein each element in the demand value matrix represents the real-time vehicle demand value of each grid area; Determine a real-time topology matrix corresponding to the real-time demand value matrix according to the relationship between the preset distance matrix, the demand value matrix and the topology matrix, wherein each element of the distance matrix represents the distance between the grid areas corresponding to the row elements and the column elements, and each element of the real-time topology matrix represents the real-time scheduling demand value from the row elements to the grid areas corresponding to the column elements; Establishing a topological network between the preset grid areas according to the real-time scheduling demand value, the topological network comprising a plurality of nodes and edges connecting the nodes, the nodes being the preset grid areas, and the edges being the real-time scheduling demand values between the preset grid areas; Obtain a scheduling path composed of edges whose scheduling values are less than the real-time scheduling requirement value in the topological network and increase the scheduling value of the scheduling path, so that each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling requirement value, and obtain a target scheduling value for each edge, wherein the initial value of the scheduling value is zero, and the scheduling path is a path composed of edges connecting a first grid area and a second grid area, wherein the first grid area is a grid area corresponding to a node having the largest sum of real-time scheduling requirement values with the second network area in the topological network, and the second grid area is any grid area other than the first grid area; The vehicles in the second grid area are dispatched to the first grid area according to the target dispatch value of the edge of the topological network.
2. The vehicle dispatching method according to claim 1, characterized in that: The relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index and the vehicle demand value includes: H=x*(xy)*(B-4) Among them, H is the vehicle demand value, x is the total number of vehicles, y is the number of empty vehicles, and B is the traffic congestion index.
3. The vehicle dispatching method according to claim 1, characterized in that: The relationship between the preset distance matrix, demand value matrix and topology matrix includes: Among them, A is the topological matrix, D is the diagonal matrix, P is the distance matrix, and H is the demand value matrix.
4. The vehicle dispatching method according to claim 1, characterized in that: The step of obtaining a scheduling path composed of edges whose scheduling values in the topological network are less than the real-time scheduling requirement value and increasing the scheduling value of the scheduling path so that each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling requirement value, and obtaining a target scheduling value for each edge includes: Obtain the scheduling path composed of edges whose scheduling values in the topological network are less than the real-time scheduling demand value; The minimum remaining real-time scheduling requirement value of the edge in the scheduling path is used as the scheduling value increment of the scheduling path and added to the scheduling value on each edge, and the remaining real-time scheduling requirement value is the difference between the real-time scheduling requirement value and the scheduling value; For the scheduling paths composed of edges whose remaining scheduling values are less than the real-time scheduling demand values, the operation of obtaining the scheduling value increment and adding the scheduling value on each edge is performed in sequence; In the case where each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling demand value, the target scheduling value of each edge is used as the target scheduling value.
5. The vehicle dispatching method according to claim 1, characterized in that: Before dispatching the vehicles in the second grid area to the first grid area according to the target dispatch value of the edge of the topological network, the method further includes: Determine whether the difference between the sum of the real-time scheduling demand values of the first grid area and the sum of the real-time scheduling demand values of the second grid area exceeds a set threshold; When the difference exceeds a set threshold, the vehicles in the second grid area are dispatched to the target grid area.
6. The vehicle dispatching method according to claim 1, characterized in that: The establishing of a topological network between the preset grid areas according to the real-time scheduling requirement value includes: Taking each preset grid area as the central grid area and obtaining the adjacent grid areas of the central grid area; A topological network between each central grid area and adjacent grid areas is established.
7. A vehicle dispatching device, characterized in that: The device comprises: An acquisition module, used for acquiring the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area; A determination module, for determining the real-time vehicle demand value corresponding to the real-time total number of vehicles, the real-time number of empty vehicles and the real-time traffic congestion index of each preset grid area according to the relationship between the total number of vehicles, the number of empty vehicles, the traffic congestion index and the vehicle demand value, and establishing a real-time demand value matrix, wherein each element in the demand value matrix represents the real-time vehicle demand value of each grid area; The determination module is further used to determine the real-time topology matrix corresponding to the real-time demand value matrix according to the relationship between the preset distance matrix, the demand value matrix and the topology matrix, each element of the distance matrix represents the distance between the grid area corresponding to the row element and the column element, and each element of the real-time topology matrix represents the real-time scheduling demand value from the row element to the grid area corresponding to the column element; An establishing module, configured to establish a topological network between the preset grid areas according to the real-time scheduling demand value, wherein the topological network includes a plurality of nodes and edges connecting the nodes, wherein the nodes are preset grid areas, and the edges are real-time scheduling demand values between the preset grid areas; A calculation module, used to obtain a scheduling path composed of edges whose scheduling values are less than the real-time scheduling requirement value in the topological network and increase the scheduling value of the scheduling path, so that each scheduling path includes at least one edge whose scheduling value is equal to the real-time scheduling requirement value, and obtain a target scheduling value for each edge, wherein the initial value of the scheduling value is zero, and the scheduling path is a path composed of edges connecting a first grid area and a second grid area, wherein the first grid area is a grid area corresponding to a node in the topological network having the largest sum of real-time scheduling requirement values with the second network area, and the second grid area is any grid area other than the first grid area; The scheduling module is used to schedule the vehicles in the second grid area to the first grid area according to the target scheduling value of the edge of the topological network.
8. A terminal device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the vehicle dispatching method according to any one of claims 1 to 6 is implemented.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the vehicle dispatching method according to any one of claims 1 to 6 is implemented.
10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the vehicle dispatching method as described in any one of claims 1 to 6.