Travel vehicle matching method, system, device and equipment and storage medium

Abstract

The invention provides a travel vehicle matching method, system, device and equipment and a storage medium, and the method comprises the steps: selecting a preset travel matching model based on the city grid supply-demand ratio of a current city, and the travel matching model comprises a stable marriage problem (GS) algorithm model and / or a rapid matching algorithm model; the obtained three-party flow data are input into a travel matching model, a target travel matching vehicle is obtained, and the three-party flow data comprise at least one of passenger preference data, driver preference data, real-time order data, vehicle position information, vehicle state information and vehicle system feedback information. Through the method, the effect of effectively fusing the third-party service flow data to realize the process of reasonable travel and vehicle matching can be achieved.

Description

Technical Field

[0001] The present application relates to the field of travel dispatching, and in particular, to a method, system, device, equipment and storage medium for matching travel vehicles. Background Art

[0002] Currently, existing travel service matching systems usually allocate orders based on the principle of optimal distance or fastest response time. Although these systems can simplify the matching process, they often ignore the actual needs of drivers and passengers, resulting in low satisfaction and suboptimal resource utilization.

[0003] In addition, for access to third-party service traffic, such as passenger flow data provided by third-party service maps, the existing system cannot effectively integrate data from different sources to achieve customized services.

[0004] Therefore, how to effectively integrate third-party service traffic data to achieve reasonable travel matching vehicle process is a technical problem that needs to be solved. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method for matching vehicles for travel. Through the technical solution of the embodiments of the present application, third-party service traffic data can be effectively integrated to achieve the effect of a reasonable travel matching vehicle process.

[0006] In a first aspect, an embodiment of the present application provides a method for matching travel vehicles, comprising: selecting a preset travel matching model based on the urban grid supply-demand ratio of the current city, wherein the travel matching model includes: a GS algorithm model and / or a fast matching algorithm model; inputting the acquired three-party flow data into the travel matching model to obtain a target travel matching vehicle, wherein the three-party flow data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

[0007] In the above-mentioned embodiment of the present application, the corresponding vehicle matching model is selected by analyzing the supply-demand ratio after the urban grid division, and different methods can be selected to integrate the third-party traffic data according to different supply-demand ratio requirements, so as to achieve the effect of effectively integrating the third-party service traffic data to realize the process of reasonable travel matching vehicles.

[0008] In some embodiments, before selecting a preset travel matching model based on the urban grid supply-demand ratio of the current city, it also includes: obtaining historical three-party traffic data and combining the GS algorithm and the fast matching algorithm to train a basic matching model to obtain a travel matching model.

[0009] In the above-mentioned embodiments of the present application, the travel matching model is trained by using historical three-party traffic data, so that the model can learn to analyze the three-party traffic data to match vehicles.

[0010] In some embodiments, before selecting a preset travel matching model based on the urban grid supply and demand ratio of the current city, it also includes: dividing the user activity area of ​​the current city into grids to obtain multiple grids; based on the supply and demand data obtained in real time, calculating the supply and demand ratio of each grid in the multiple grids to obtain the urban network supply and demand ratio.

[0011] In the above embodiment of the present application, the supply-demand ratio of urban vehicles in each divided area can be accurately calculated through grid division.

[0012] In some embodiments, based on the urban grid supply and demand ratio of the current city, a preset travel matching model is selected, including: when the urban grid supply and demand ratio is greater than or equal to the first preset supply and demand ratio, the GS algorithm model is selected; when the urban grid supply and demand ratio is less than or equal to the second preset supply and demand ratio, the quick matching algorithm model is selected; when the urban grid supply and demand ratio is less than the first preset supply and demand ratio and greater than the second preset supply and demand ratio, the GS algorithm model and the quick matching algorithm model are selected according to a preset weight ratio.

[0013] In the above embodiments of the present application, the GS algorithm model and / or the fast matching algorithm model can be selected according to different urban grid supply-demand ratios to improve the accuracy of vehicle matching.

[0014] In some embodiments, the acquired three-party traffic data is input into a travel matching model to obtain a target travel matching vehicle, including: inputting passenger preference data and driver preference data into the travel matching model, and performing weighted calculation according to the passenger preference data and the driver preference data through the travel matching model to match the target travel matching vehicle.

[0015] In the above embodiments, the present application can combine passenger preferences and driver preferences to select a dispatched vehicle that meets the needs of both parties, thereby improving the user experience of both parties.

[0016] In some embodiments, based on the urban grid supply-demand ratio of the current city, a preset travel matching model is selected, including: based on the grid supply-demand ratio of each grid in the current city, a travel matching model corresponding to each grid is selected; the acquired three-party traffic data is input into the travel matching model to obtain a target travel matching vehicle, including: inputting the three-party traffic data into the travel matching model, scoring the vehicles recalled for each order, and selecting the vehicle corresponding to the highest score as the target travel matching vehicle.

[0017] In the above embodiment of the present application, each grid area in the city can execute the method of the present application in parallel, match vehicles for orders in each grid area, and select the best matching vehicle by scoring vehicle matching to improve matching accuracy.

[0018] In a second aspect, an embodiment of the present application provides a system for matching vehicles for travel, including:

[0019] User driver preset preference management module, algorithm model decision module and algorithm model dispatching module;

[0020] A user driver preset preference management module, used to set user preference data and driver preference data;

[0021] An algorithm model decision module is used to select a preset travel matching model based on the urban grid supply-demand ratio of the current city, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model;

[0022] The algorithm model dispatching module is used to input the acquired three-party traffic data into the travel matching model to obtain the target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

[0023] In a third aspect, an embodiment of the present application provides a device for matching a vehicle for a trip, including:

[0024] A model selection module is used to select a preset travel matching model based on the urban grid supply-demand ratio of the current city, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model;

[0025] The vehicle matching module is used to input the acquired three-party traffic data into the travel matching model to obtain the target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

[0026] Optionally, the device further comprises:

[0027] The training module is used for the model selection module to obtain historical three-party traffic data and train the basic matching model in combination with the GS algorithm and the fast matching algorithm to obtain the travel matching model before selecting the preset travel matching model based on the urban grid supply-demand ratio of the current city.

[0028] Optionally, the device further comprises:

[0029] A grid division module, used for the model selection module to grid the user activity area of ​​the current city to obtain a plurality of grids before selecting a preset travel matching model based on the city grid supply-demand ratio of the current city;

[0030] Based on the supply and demand data obtained in real time, the supply and demand ratio of each grid in multiple grids is calculated to obtain the urban network supply and demand ratio.

[0031] Optionally, the model selection module is specifically used to:

[0032] If the urban grid supply-demand ratio is greater than or equal to the first preset supply-demand ratio, the GS algorithm model is selected;

[0033] The urban grid supply-demand ratio is less than or equal to the second preset supply-demand ratio, and the fast matching algorithm model is selected;

[0034] The urban grid supply-demand ratio is less than the first preset supply-demand ratio and greater than the second preset supply-demand ratio, and the GS algorithm model and the quick matching algorithm model are selected according to the preset weight ratio.

[0035] Optionally, the vehicle matching module is specifically used for:

[0036] The passenger preference data and the driver preference data are input into the travel matching model, and the travel matching model performs weighted calculation according to the passenger preference data and the driver preference data to match the target travel matching vehicle.

[0037] Optionally, the model selection module is specifically used to:

[0038] Based on the grid supply-demand ratio of each grid in the current city, select the travel matching model corresponding to each grid;

[0039] The vehicle matching module is specifically used for:

[0040] The three-party traffic data is input into the travel matching model, the vehicles recalled for each order are scored, and the vehicle with the highest score is selected as the target travel matching vehicle.

[0041] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method provided in the first aspect are executed.

[0042] In a fifth aspect, an embodiment of the present application provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method provided in the first aspect are executed.

[0043] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flow chart of a method for matching vehicles for travel provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of a system for matching vehicles for travel provided in an embodiment of the present application;

[0047] Figure 3 A schematic block diagram of a device for matching vehicles for travel provided in an embodiment of the present application;

[0048] Figure 4 A schematic diagram of the structure of a device for matching vehicles for travel provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0050] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0051] First, some terms involved in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0052] The Gale-Shapley algorithm, also known as the deferred-acceptance algorithm, is a market mechanism designed by Gale and Shapley to find a stable match. Each individual will make a pairing request to all n objects at most. After each request, if a match fails or is replaced by a new preferred object, the individual will continue to request the next object. In general, each individual will make a maximum of n requests, so for n individuals, the total number of requests is n 2 Therefore, the time complexity of the Gale-Shapley algorithm is: O(n 2 ).

[0053] The k-means clustering algorithm is an iterative clustering analysis algorithm. The steps are: divide the data into K groups in advance, randomly select K objects as the initial cluster centers, and then calculate the distance between each object and each seed cluster center, and assign each object to the cluster center closest to it. The cluster centers and the objects assigned to them represent a cluster.

[0054] DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a representative density-based clustering algorithm. Different from partitioning and hierarchical clustering methods, it defines a cluster as the largest set of density-connected points, can divide areas with sufficiently high density into clusters, and can find clusters of any shape in noisy spatial databases.

[0055] This application is applied to the scenario of travel dispatching, and the specific scenario is to integrate third-party service data and combine relevant travel matching models to match travel vehicles for users.

[0056] At present, the existing travel service matching systems usually allocate orders based on the principle of optimal distance or fastest response time. Although these systems can simplify the matching process, they often ignore the actual needs of drivers and passengers, resulting in low satisfaction and suboptimal resource utilization. In addition, for access to third-party service traffic, such as passenger flow data provided by third-party service maps, the existing system cannot effectively integrate data from different sources to achieve customized services.

[0057] To this end, this application selects a preset travel matching model based on the current city grid supply-demand ratio, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model; the acquired three-party traffic data is input into the travel matching model to obtain a target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information. By analyzing the supply-demand ratio after the city grid is divided, the corresponding vehicle matching model can be selected, and different methods can be selected to integrate third-party traffic data according to different supply-demand ratio requirements, so as to achieve the effect of effectively integrating third-party service traffic data to achieve a reasonable travel matching vehicle process.

[0058] In the embodiment of the present application, the execution entity may be a travel matching vehicle device in a travel matching vehicle system. In actual applications, the travel matching vehicle device may be an electronic device such as a terminal device and a server, which is not limited here.

[0059] Combine the following Figure 1 The method for matching vehicles for travel in an embodiment of the present application is described in detail.

[0060] Please see Figure 1 , Figure 1 A flow chart of a method for matching vehicles for travel provided in an embodiment of the present application, such as Figure 1 The method for matching vehicles to travel includes:

[0061] Step 110: Based on the urban grid supply-demand ratio of the current city, a preset travel matching model is selected.

[0062] Among them, the travel matching model includes: a stable GS algorithm model and / or a fast matching algorithm model. The stable GS algorithm model and the fast matching algorithm model respectively include a GS algorithm and a fast matching algorithm that can process third-party traffic data. The third-party traffic data can be third-party service data, for example, map service data provided by various APPs.

[0063] In some embodiments of the present application, before selecting a preset travel matching model based on the city grid supply-demand ratio of the current city, Figure 1 The method shown also includes: obtaining historical three-party traffic data and training a basic matching model in combination with the GS algorithm and the fast matching algorithm to obtain a travel matching model.

[0064] In the above process, the present application trains the travel matching model through historical three-party traffic data, so that the model can learn to analyze the three-party traffic data for vehicle matching.

[0065] The historical three-party traffic data may be the location data of the map and relevant data such as the distance between vehicles and passengers. The historical three-party traffic data is obtained and combined with the GS algorithm and the fast matching algorithm to train the basic matching model to obtain the travel matching model, including integrating the GS algorithm and the fast matching algorithm into the basic matching model to obtain the initial travel matching model, inputting the historical three-party traffic data into the initial travel matching model to obtain the initial matching result, comparing the initial matching result with the preset standard result, adjusting the initial travel matching model, and obtaining the travel matching model.

[0066] In some embodiments of the present application, before selecting a preset travel matching model based on the city grid supply-demand ratio of the current city, Figure 1 The method shown also includes: dividing the user activity area of ​​the current city into grids to obtain multiple grids; based on the supply and demand data obtained in real time, calculating the supply and demand ratio of each grid in the multiple grids to obtain the city network supply and demand ratio.

[0067] In the above process, the present application can accurately calculate the supply-demand ratio of urban vehicles in each divided area through grid division.

[0068] Among them, the user activity area indicates an area where user activities are relatively dense, and the specific density can be set according to demand, such as the city center, suburbs, commercial areas, residential areas, etc. Supply and demand data include vehicle demand and supply in each grid area. For example, a certain area is divided into seven-level hexagonal grids, and there are 1,000 hexagonal grids in the city. If the cold areas are removed, there are still 800 areas where users often move around. Based on the supply and demand data obtained in real time, the supply and demand ratio of each grid in multiple grids is calculated to obtain the supply and demand ratio of the urban network, including: based on real-time supply and demand data, within a certain time window (such as 1 minute), the supply and demand ratio of each grid is dynamically calculated. For example, if the supply and demand ratio of a certain area is 2:1, the system can determine that the current situation of the area is more suitable for the fast matching algorithm. In areas where the supply and demand ratio is close to 1:1, the GS algorithm can improve the stability and satisfaction of matching.

[0069] Optionally, the grid division can use K-means or DBSCAN algorithm to dynamically adjust the division of regions according to historical travel data and urban geographical characteristics.

[0070] In some embodiments of the present application, a preset travel matching model is selected based on the urban grid supply and demand ratio of the current city, including: when the urban grid supply and demand ratio is greater than or equal to the first preset supply and demand ratio, the GS algorithm model is selected; when the urban grid supply and demand ratio is less than or equal to the second preset supply and demand ratio, the quick matching algorithm model is selected; when the urban grid supply and demand ratio is less than the first preset supply and demand ratio and greater than the second preset supply and demand ratio, the GS algorithm model and the quick matching algorithm model are selected according to the preset weight ratio.

[0071] In the above process, the present application can select the GS algorithm model and / or the fast matching algorithm model according to different urban grid supply and demand ratios to improve the accuracy of vehicle matching.

[0072] Among them, the first preset supply-demand ratio and the second preset supply-demand ratio can be set according to demand.

[0073] For example, if the supply-demand ratio of the city grid is greater than or equal to the demand, the driver's waiting time and pick-up distance can be considered to quickly dispatch orders. If the waiting time and pick-up distance are both less than the preset value or the waiting time and pick-up distance of other vehicles, the current vehicle will be considered. If the supply-demand ratio of the city grid is less than the demand, the GS algorithm will be used to dispatch orders, while considering the driver's pick-up distance, time, and continuous profitability of orders.

[0074] Optionally, the GS algorithm model and the quick matching algorithm model can be selected based on the algorithm selection. For example, during peak hours in the morning and evening (unbalanced supply and demand, with demand exceeding supply): the GS algorithm is used as the main algorithm and quick dispatch is used as the auxiliary algorithm. During off-peak hours, the quick dispatch strategy is implemented. The GS algorithm (taking into account the pick-up distance and duration) comprehensively considers the pick-up distance and duration to maximize the preference matching between passengers and drivers and reduce waiting time.

[0075] Step 120: Input the acquired three-party traffic data into the travel matching model to obtain the target travel matching vehicle.

[0076] Among them, the three-party traffic data includes: at least one of passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and vehicle system feedback information, and may also include driver historical order evaluation and vehicle model, etc. Passenger preference data includes: service type preference, select different service types (such as economy, comfort, luxury, etc.); response time preference: for example, hope that the driver arrives as soon as possible with the shortest waiting time. Comfort preference: choose whether to share a car or need a special car service; driver score preference: prefer drivers with high scores or a specific score range; driver preference data includes: order income preference: the driver will have an expectation of hourly income; response time preference: hope to receive orders as soon as possible to reduce waiting time; service order preference: prefer to receive orders for medium and long distances; passenger score preference: give priority to receiving passenger orders with high scores or good historical records; driving route preference: prefer certain routes to avoid busy or unsafe areas. The matching vehicles can be vehicles of various models, such as trucks, taxis and online ride-hailing cars.

[0077] In some embodiments of the present application, based on the city grid supply-demand ratio of the current city, selecting a preset travel matching model includes: based on the grid supply-demand ratio of each grid in the current city, selecting a travel matching model corresponding to each grid;

[0078] Inputting the acquired three-party traffic data into the travel matching model to obtain the target travel matching vehicle, including: inputting the three-party traffic data into the travel matching model, scoring the vehicles recalled for each order, and selecting the vehicle corresponding to the highest score as the target travel matching vehicle.

[0079] In the above process, each grid area in the city can execute the method of the present application in parallel, match vehicles for orders in each grid area, and select the best matching vehicle by scoring vehicle matching to improve matching accuracy.

[0080] Among them, based on the grid supply-demand ratio of each grid in the current city, the travel matching model corresponding to each grid is selected, which means that each area (such as different districts and grids in a city) can be matched independently, with a fast response speed. The algorithm parameters in the area can be flexibly adjusted according to the supply and demand situation, real-time congestion situation, etc. of different areas.

[0081] In some embodiments of the present application, the acquired three-party traffic data is input into a travel matching model to obtain a target travel matching vehicle, including: inputting passenger preference data and driver preference data into the travel matching model, and performing weighted calculation according to the passenger preference data and the driver preference data through the travel matching model to match the target travel matching vehicle.

[0082] During the above process, the present application can combine passenger preferences and driver preferences to select dispatched vehicles that meet the needs of both parties, thereby improving the user experience of both parties.

[0083] Optionally, the GS algorithm may be used to perform weighted calculations based on the passenger preference data and the driver preference data through a travel matching model, and the matching target travel matching vehicle may be obtained through the following formula:

[0084]

[0085] Among them, the passenger preference list P (p1, p2, ..., pn), the driver preference list D (d1, d2, ..., dm), the pick-up distance d ij and pick-up time ij , w ij represents the preference matching degree between the passenger and the driver, α and β are the influence weights of the pick-up distance and the pick-up time, σ is the element of the matching matrix, n and m are positive integers, Match GS Represents the result of travel matching vehicles.

[0086] Optionally, in areas where supply exceeds demand, minimum pickup distance and waiting time can be prioritized to quickly respond to passenger needs.

[0087] In the above Figure 1In the process shown, the present application selects a preset travel matching model based on the supply-demand ratio of the city grid of the current city, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model; the acquired three-party traffic data is input into the travel matching model to obtain a target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information. By analyzing the supply-demand ratio after the city grid is divided and selecting the corresponding vehicle matching model, different methods can be selected to fuse third-party traffic data according to different supply-demand ratio requirements, so as to achieve the effect of effectively fusing third-party service traffic data to realize the process of reasonable travel matching vehicles.

[0088] Combine the following Figure 2 The system for matching vehicles for travel according to an embodiment of the present application is described in detail.

[0089] Please see Figure 2 , Figure 2 A schematic diagram of a system for matching vehicles for travel provided in an embodiment of the present application is shown in FIG. Figure 2 The system for matching vehicles with travel includes:

[0090] A user driver preset preference management module 210, an algorithm model decision module 220 and an algorithm model dispatching module 230;

[0091] A user driver preset preference management module 210, used to set user preference data and driver preference data;

[0092] The algorithm model decision module 220 is used to select a preset travel matching model based on the city grid supply-demand ratio of the current city, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model;

[0093] The algorithm model dispatching module 230 is used to input the acquired three-party traffic data into the travel matching model to obtain the target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

[0094] also, Figure 2 The specific methods and steps performed by the system shown can be found in Figure 1 The method shown will not be described in detail here.

[0095] Previous article Figure 1 Describes the method of matching vehicles for travel. Figure 3-Figure 4 Describes the device for matching vehicles to trips.

[0096] Please refer to Figure 3 , is a schematic block diagram of a travel matching vehicle device 300 provided in an embodiment of the present application, and the device 300 may be a module, program segment or code on an electronic device. Figure 1 The method embodiment corresponds to and can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the device 300 can be found in the description below. To avoid repetition, the detailed description is appropriately omitted here.

[0097] Optionally, the device 300 includes:

[0098] The model selection module 310 is used to select a preset travel matching model based on the city grid supply-demand ratio of the current city, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model;

[0099] The vehicle matching module 320 is used to input the acquired three-party traffic data into the travel matching model to obtain a target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

[0100] Optionally, the device further comprises:

[0101] The training module is used for the model selection module to obtain historical three-party traffic data and train the basic matching model in combination with the GS algorithm and the fast matching algorithm to obtain the travel matching model before selecting the preset travel matching model based on the urban grid supply-demand ratio of the current city.

[0102] Optionally, the device further comprises:

[0103] The grid division module is used for the model selection module to divide the user activity area of ​​the current city into grids to obtain multiple grids before selecting a preset travel matching model based on the supply and demand ratio of the city grid of the current city; based on the supply and demand data obtained in real time, the supply and demand ratio of each grid in the multiple grids is calculated to obtain the supply and demand ratio of the city network.

[0104] Optionally, the model selection module is specifically used to:

[0105] When the urban grid supply-demand ratio is greater than or equal to the first preset supply-demand ratio, the GS algorithm model is selected; when the urban grid supply-demand ratio is less than or equal to the second preset supply-demand ratio, the quick matching algorithm model is selected; when the urban grid supply-demand ratio is less than the first preset supply-demand ratio and greater than the second preset supply-demand ratio, the GS algorithm model and the quick matching algorithm model are selected according to the preset weight ratio.

[0106] Optionally, the vehicle matching module is specifically used for:

[0107] The passenger preference data and the driver preference data are input into the travel matching model, and the travel matching model performs weighted calculation according to the passenger preference data and the driver preference data to match the target travel matching vehicle.

[0108] Optionally, the model selection module is specifically used to:

[0109] Based on the grid supply-demand ratio of each grid in the current city, select the travel matching model corresponding to each grid;

[0110] The vehicle matching module is specifically used for:

[0111] The three-party traffic data is input into the travel matching model, the vehicles recalled for each order are scored, and the vehicle with the highest score is selected as the target travel matching vehicle.

[0112] Please refer to Figure 4 4 is a schematic block diagram of a structure of a device for matching a vehicle with a travel provided in an embodiment of the present application. The device may include a memory 410 and a processor 420. Optionally, the device may also include: a communication interface 430 and a communication bus 440. The device is similar to the above Figure 1 The method embodiment corresponds to and can be executed Figure 1 The various steps involved in the method embodiment and the specific functions of the device can be found in the description below.

[0113] Specifically, the memory 410 is used to store computer-readable instructions.

[0114] Processor 420 is used to process the readable instructions stored in the memory and can execute Figure 1 The steps in the method.

[0115] The communication interface 430 is used for signaling or data communication with other node devices, for example, for communication with a server or a terminal, or for communication with other device nodes, but the embodiments of the present application are not limited thereto.

[0116] The communication bus 440 is used to realize direct connection and communication among the above components.

[0117] The communication interface 430 of the device in the embodiment of the present application is used to communicate signals or data with other node devices. The memory 410 can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The memory 410 can also be at least one storage device located away from the aforementioned processor. The memory 410 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 420, the electronic device executes the aforementioned Figure 1The method process shown. The processor 420 can be used on the device 300 and is used to perform the functions in the present application. Exemplarily, the above-mentioned processor 420 can be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, and the embodiments of the present application are not limited thereto.

[0118] The embodiment of the present application also provides a readable storage medium, when the computer program is executed by a processor, Figure 1 The method process in the method embodiment shown is executed by the electronic device.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the device described above can refer to the corresponding process in the aforementioned method, and will not be described in detail here.

[0120] In summary, the embodiments of the present application provide a method, system, device, equipment and storage medium for matching travel vehicles. The method includes: based on the urban grid supply-demand ratio of the current city, selecting a preset travel matching model, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model; inputting the acquired three-party traffic data into the travel matching model to obtain a target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information. This method can achieve the effect of effectively integrating third-party service traffic data to achieve a reasonable travel matching vehicle process.

[0121] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0122] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0123] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0124] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application. It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0125] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0126] 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 "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for matching vehicles for travel, It is characterized in that include: Based on the current city grid supply-demand ratio, a preset travel matching model is selected, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model; The acquired three-party traffic data is input into the travel matching model to obtain a target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

2. The method according to claim 1, It is characterized in that Before selecting a preset travel matching model based on the urban grid supply-demand ratio of the current city, the method further includes: The historical three-party traffic data is obtained and combined with the GS algorithm and the fast matching algorithm to train the basic matching model to obtain the travel matching model.

3. The method according to claim 1 or 2, It is characterized in that Before selecting a preset travel matching model based on the urban grid supply-demand ratio of the current city, the method further includes: Dividing the user activity area of ​​the current city into grids to obtain a plurality of grids; Based on the supply and demand data acquired in real time, the supply and demand ratio of each grid in the plurality of grids is calculated to obtain the supply and demand ratio of the urban network.

4. The method according to claim 1 or 2, It is characterized in that The method of selecting a preset travel matching model based on the urban grid supply-demand ratio of the current city includes: The urban grid supply-demand ratio is greater than or equal to a first preset supply-demand ratio, and the GS algorithm model is selected; The urban grid supply-demand ratio is less than or equal to a second preset supply-demand ratio, and the fast matching algorithm model is selected; The urban grid supply-demand ratio is less than the first preset supply-demand ratio and greater than the second preset supply-demand ratio, and the GS algorithm model and the quick matching algorithm model are selected according to a preset weight ratio.

5. The method according to claim 1 or 2, It is characterized in that The step of inputting the acquired three-party traffic data into the travel matching model to obtain a target travel matching vehicle includes: The passenger preference data and the driver preference data are input into the travel matching model, and the travel matching model performs weighted calculation according to the passenger preference data and the driver preference data to match the target travel matching vehicle.

6. The method according to claim 1 or 2, It is characterized in that The method of selecting a preset travel matching model based on the urban grid supply-demand ratio of the current city includes: Based on the grid supply-demand ratio of each grid in the current city, selecting a travel matching model corresponding to each grid; The step of inputting the acquired three-party traffic data into the travel matching model to obtain a target travel matching vehicle includes: The three-party traffic data is input into the travel matching model, a score is given to the vehicle recalled for each order, and the vehicle corresponding to the highest score is selected as the target travel matching vehicle.

7. A system for matching vehicles for travel, It is characterized in that include: User driver preset preference management module, algorithm model decision module and algorithm model dispatching module; The user driver preset preference management module is used to set user preference data and driver preference data; The algorithm model decision module is used to select a preset travel matching model based on the urban grid supply-demand ratio of the current city, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model; The algorithm model dispatching module is used to input the acquired three-party traffic data into the travel matching model to obtain a target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

8. A device for matching vehicles for travel, It is characterized in that include: A model selection module, for selecting a preset travel matching model based on the urban grid supply-demand ratio of the current city, wherein the travel matching model includes: a stable marriage problem GS algorithm model and / or a fast matching algorithm model; The vehicle matching module is used to input the acquired three-party traffic data into the travel matching model to obtain a target travel matching vehicle, wherein the three-party traffic data includes: passenger preference data, driver preference data, real-time order data, vehicle location information, vehicle status information and at least one of vehicle system feedback information.

9. An electronic device, It is characterized in that include: A memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps in the method according to any one of claims 1 to 6 are executed.

10. A computer-readable storage medium, It is characterized in that include: A computer program, when the computer program is run on a computer, causes the computer to execute the method according to any one of claims 1 to 6.

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