Data processing method and device

Abstract

The embodiment of the invention discloses a data processing method and device. A driver starting point, a get-on point set of at least one target passenger, a get-off point set of at least one target passenger and a driver terminal point serve as driving nodes of a target driver, at least one candidate sequence is determined according to the driving nodes, and a candidate passenger combination is determined by restraining passengers at the get-on point and / or the get-off point. A target order and a target station are determined based on each candidate order and each candidate passenger combination. Therefore, the passenger experience and the route efficiency can be balanced, and the service quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a data processing method and device. Background Art

[0002] With the rapid development of the Internet economy, carpooling has become an indispensable part of the urban transportation system. By integrating the travel needs of multiple passengers, carpooling services not only achieve the effective use of transportation resources and cost sharing, but also significantly improve users' travel efficiency and experience.

[0003] In the prior art, carpooling route planning is usually designed based on the shortest path principle. Specifically, after determining the driver's current location and the passenger set, the system will calculate an optimal pick-up and drop-off sequence to minimize the total driving distance.

[0004] However, existing route planning technologies mostly rely on the nearest boarding and alighting stations or pursue optimal efficiency. Focusing only on the nearest boarding and alighting stations fails to fully consider the complexity of the actual road network, which may cause drivers to make extra detours, increase driving time and mileage, and reduce service efficiency and quality. At the same time, excessive focus on route efficiency may ignore user experience, such as requiring passengers to go to stations that are inconvenient to reach or too far away, which ultimately leads to an increase in passenger cancellation rates and a reduction in the scale of service users, affecting the overall service quality. Summary of the invention

[0005] In view of this, an object of an embodiment of the present invention is to provide a data processing method and device that can balance passenger experience and route efficiency and improve service quality.

[0006] In a first aspect, an embodiment of the present invention provides a data processing method, the method comprising:

[0007] Determine the travel nodes of the target driver, wherein the travel nodes include the driver's starting point, a set of at least one target passenger's boarding point, a set of at least one target passenger's alighting point, and the driver's destination;

[0008] Determine at least one candidate order according to the travel nodes, the candidate order being the ranking of the travel nodes;

[0009] Determine a candidate passenger combination, where passengers in the candidate passenger combination are passengers with restricted boarding points and / or alighting points;

[0010] A target sequence and a target stop are determined based on the candidate sequences and candidate passenger combinations.

[0011] In some embodiments, the candidate order satisfies the following conditions:

[0012] The driver’s starting point is the first driving node;

[0013] The driver's destination is the last travel node;

[0014] The same passenger's boarding point collection is before the alighting point collection.

[0015] In some embodiments, the candidate passenger combination is empty or the candidate passenger combination includes one or more target passengers.

[0016] In some embodiments, determining the candidate passenger combination specifically includes:

[0017] 0 to N target passengers are arbitrarily selected from the target passengers to generate the candidate passenger combination, where N is the total number of target passengers.

[0018] In some embodiments, determining the target sequence and the target station based on the candidate sequences and the candidate passenger combinations includes:

[0019] Determine the directed graph corresponding to each candidate order;

[0020] Constraining the stations in the boarding point set and / or the alighting point set in the directed graph according to the candidate passenger combination to obtain a constrained directed graph;

[0021] The target order and target site are determined according to the constrained directed graph.

[0022] In some embodiments, the edge weights of the directed graph are road mileage and / or estimated driving time.

[0023] In some embodiments, constraining the stations in the boarding point set and / or the alighting point set in the directed graph according to the candidate passenger combination to obtain the constrained directed graph includes:

[0024] The optimal station for the target passenger in the candidate passenger combination is selected from the stations in the boarding point set and / or the alighting point set in the directed graph.

[0025] In some embodiments, determining the target order and the target site according to the constrained directed graph comprises:

[0026] Generating candidate paths for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph;

[0027] Determine a target path according to the travel costs of each of the candidate paths;

[0028] The target sequence and the target site are determined according to the target path.

[0029] In some embodiments, generating candidate paths for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph includes:

[0030] Generating a traversable path for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph;

[0031] An optimal navigable path is determined as the candidate path.

[0032] In some embodiments, the travel cost is determined based on one or more of the total mileage, estimated travel time, walking distance of the target passenger, driver cost, and passenger cost.

[0033] In some embodiments, the method further comprises:

[0034] A planned path is generated according to the target sequence and target sites and sent.

[0035] In some embodiments, the method further comprises:

[0036] A carpooling plan is generated and executed according to the target sequence and the target site.

[0037] In a second aspect, an embodiment of the present invention provides a data processing device, the device comprising:

[0038] A node determination unit, used to determine a travel node of a target driver, wherein the travel node includes a driver starting point, a set of at least one target passenger boarding point, a set of at least one target passenger alighting point, and a driver destination;

[0039] An order determination unit, configured to determine at least one candidate order according to the travel nodes, wherein the candidate order is a ranking of the travel nodes;

[0040] A combination determining unit, used to determine a candidate passenger combination, wherein the passengers in the candidate passenger combination are passengers with restricted boarding points and / or alighting points;

[0041] The result determination unit is used to determine the target sequence and the target station based on each candidate sequence and candidate passenger combination.

[0042] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer program product, wherein the computer program product includes a computer program. When the computer program runs on a computer, the computer executes the method described in the first aspect.

[0044] In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described in the first aspect is implemented.

[0045] The technical solution of the embodiment of the present invention takes the driver's starting point, at least one target passenger's boarding point set, at least one target passenger's alighting point set, and the driver's destination as the target driver's travel nodes, determines at least one candidate sequence according to the travel nodes, determines a candidate passenger combination by constraining the passengers at the boarding point and / or the alighting point, and determines a target sequence and a target station based on each candidate sequence and each candidate passenger combination. In this way, it is possible to balance passenger experience and route efficiency and improve service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0047] Figure 1 is a schematic diagram of a data processing system according to an embodiment of the present invention;

[0048] Figure 2 is a flow chart of a data processing method according to a first embodiment of the present invention;

[0049] Figure 3 is a flow chart of determining a travel node according to an embodiment of the present invention;

[0050] Figure 4 is a flow chart of determining a target result according to an embodiment of the present invention;

[0051] Figure 5 is a schematic diagram of a directed graph according to an embodiment of the present invention;

[0052] Figure 6 is a schematic diagram of a directed graph according to another embodiment of the present invention;

[0053] Figure 7 is a schematic diagram of a directed graph of yet another embodiment of the present invention;

[0054] Figure 8 is a schematic diagram of a directed graph of yet another embodiment of the present invention;

[0055] Fig. 9 is a flow chart of determining a target order and a target site according to an embodiment of the present invention;

[0056] Fig.10 is a flow chart of a data processing method according to a second embodiment of the present invention;

[0057] Fig.11 is a schematic diagram of a data processing device according to an embodiment of the present invention;

[0058] Fig.12 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present application is described below based on embodiments, but the present application is not limited to these embodiments. In the detailed description of the present application below, some specific details are described in detail. It is possible for those skilled in the art to fully understand the present application without the description of these details. In order to avoid confusing the essence of the present application, known methods, processes, flows, components and circuits are not described in detail.

[0060] In addition, persons of ordinary skill in the art will appreciate that the drawings provided herein are for illustration purposes and are not necessarily drawn to scale.

[0061] Unless the context clearly requires otherwise, the words "include", "comprising" and similar words throughout the application should be interpreted as including rather than exclusive or exhaustive; that is, the meaning is "including but not limited to".

[0062] In the description of this application, it should be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" is two or more.

[0063] The solutions described in this specification and in the examples, if they involve the processing of personal information, will be processed on the premise of having a legal basis (such as obtaining the consent of the subject of personal information, or being necessary for the performance of a contract, etc.), and will only be processed within the scope of regulations or agreements. If a user refuses to process personal information other than the necessary information for basic functions, it will not affect the user's use of basic functions.

[0064] Figure 1 Schematic diagram of a data processing system according to an embodiment of the present invention. Figure 1 As shown, the data processing system of the embodiment of the present invention includes a server 1, at least one driver terminal 2 and at least one passenger terminal 3. The server 1, the driver terminal 2 and the passenger terminal 3 are connected via the Internet or other communication networks to achieve information exchange and service provision.

[0065] Server 1 is responsible for managing and coordinating data and services, and can also process data tasks from multiple driver terminals and passenger terminals in real time. The server performs core functions including but not limited to matching algorithms, route planning, billing systems, etc. At the same time, the server also supports API interfaces to connect with other third-party service platforms, such as payment gateways, map service providers, etc.

[0066] The driver terminal 2 is used to receive order information from the server and display important information such as navigation instructions and passenger location to the driver. In addition, the driver can also update his status (such as whether he can accept orders), communicate with passengers, and provide feedback after completing the order through the driver terminal. The driver terminal can be implemented through a mobile device or a specially designed vehicle-mounted device, such as a smartphone, tablet computer, or dedicated hardware.

[0067] The passenger terminal 3 refers to a smart device used by passengers to initiate a ride request, such as an application on a smartphone or tablet computer. Passengers can use the passenger terminal to enter their destination, select the type of service (such as ordinary travel, carpooling, etc.), and view the estimated arrival time and fare estimate. Once the order is confirmed, the passenger terminal will also provide information about the pick-up vehicle, such as license plate number, model, color and driver information.

[0068] When a passenger submits a ride request, the passenger terminal will send relevant information to the server; the server screens qualified drivers based on a specific algorithm and pushes the order details to the driver terminal of the selected driver. During the trip, both the driver and the passenger can keep in sync with the server through their respective terminals to ensure that both parties obtain the latest trip information in a timely manner. After the trip, the driver terminal uploads the driving data to the server to complete the order settlement process.

[0069] The embodiment of the present invention is described by taking the carpooling scenario as an example. The passenger selects the "carpooling" service option through the application of the passenger terminal and enters the departure place and destination, etc. After the passenger confirms, the passenger terminal organizes these data into a structured request and sends it to the server. The driver can actively publish the willingness to accept orders in the idle state or a specific time period on the driver terminal, including information such as the number of seats that can be provided and the service area, which will also be sent to the server to better match potential passengers. After receiving the request from the passenger terminal, the server starts the matching algorithm, comprehensively considers factors such as the location, route planning, and estimated arrival time of the current active driver, and evaluates possible carpooling paths to ensure the optimal overall itinerary. Once a suitable driver and potential carpooling partner are found, the server will create a carpooling order and send a notification to the selected driver terminal, and inform the passenger of the carpooling details, including the estimated waiting time and cost estimate. The driver terminal receives the notification from the server and displays the content containing the passenger's location, boarding and alighting locations, and other necessary information. For carpooling orders, the driver can also see the information of all passengers participating in the carpooling. The driver decides whether to accept the order based on his own situation. If you accept, you need to start the trip according to the best pick-up and drop-off order recommended by the system; if you refuse, the server will continue to look for other qualified drivers. When the driver accepts the order and confirms that he is ready, the system automatically updates the status and pushes the latest trip progress information to all relevant parties, indicating that the carpooling order has officially entered the execution stage.

[0070] It should be noted that the above carpooling process is only an example provided in the embodiment of the present invention. The embodiment of the present invention does not limit the process of driver issuing orders and passenger issuing orders, which can be implemented in various ways.

[0071] Figure 2 FIG. 1 is a flow chart of a data processing method according to a first embodiment of the present invention. Figure 2 As shown, the data processing method of the embodiment of the present invention includes the following steps:

[0072] Step S100: Determine the driving node of the target driver.

[0073] In this embodiment, the travel nodes include the driver's starting point, at least one passenger's boarding point set, at least one passenger's alighting point set and the driver's end point. The boarding point set includes one or more candidate boarding points, and the alighting point set includes one or more candidate alighting points. The data processing method of the embodiment of the present invention mainly determines the optimal order of each travel node, determines the optimal boarding point from multiple candidate boarding points, and determines the optimal alighting point from multiple candidate alighting points. One passenger corresponds to one boarding point set and one alighting point set.

[0074] Figure 3 FIG. 1 is a flow chart of determining a driving node according to an embodiment of the present invention. Figure 3 As shown, determining the driving node of the target driver includes the following steps:

[0075] Step S110: Determine a target driver and at least one target passenger.

[0076] In this embodiment, the server determines the target driver and the target passenger in response to a triggering event, wherein the triggering event includes at least one of the following: a passenger to be added to a group order, a passenger who has already added a group order canceling an order, deviation from the route, and abnormal road conditions.

[0077] The embodiment of the present invention divides the triggering events into two order acceptance stages: the addition of passengers to be added to the order is in the driver-passenger matching stage, and the cancellation of the order, deviation from the route, and abnormal road conditions by passengers who have already added the order are in the route planning stage.

[0078] In the driver-passenger matching stage, the server determines the target driver and at least one target passenger by recalling potential carpooling relationships. Specifically, the server first receives carpooling requests from various passenger terminals, which contain the passengers' departure place, destination and other preference information. Based on these data, the server builds a passenger library containing multiple passengers to be carpooled. At the same time, the server also obtains a driver library, which includes multiple drivers who can participate in carpooling. Here, "carpooling drivers" refer to those drivers who currently have no passengers who have accepted orders or have passengers who have accepted orders but still have vacant seats. Among them, "carpooling passengers" refer to those passengers who have completed two-way confirmation with the driver and established a binding relationship. The server matches the passengers to be carpooled in the passenger library and the drivers to be carpooled in the driver library according to predetermined rules to recall the driver-passenger combination that may complete carpooling, namely the so-called "carpooling relationship". These rules can be used alone or in combination based on a variety of factors, such as the number of seats, driving route, driving direction, etc., to ensure the best matching effect. Finally, the target driver and target passenger determined from the recalled carpooling relationship will enter the next step of route planning and service arrangement.

[0079] For example, suppose there are three passengers A1, A2, and A3 waiting to ride together in the passenger database, and there are two drivers B1 and B2 available to ride together in the driver database. B1 currently does not have any passengers who have already ridden together, while B2 already has passenger C1 but still has an available seat. According to the preset matching rules, the server analyzes each passenger's destination, travel time, and preferences, as well as each driver's location, driving direction, and remaining seats, and the results are: Passenger A1 can only ride together with driver B1; Passenger A2 can ride together with drivers B1 and B2; Passenger A3 can only ride together with driver B2. In this case, the ride-to-ride relationships include:

[0080] The possible relationship R1: driver B1, passenger A1;

[0081] The possible relationship R2: driver B1, passenger A2;

[0082] Possible relationship R3: driver B1, passengers A1, A2;

[0083] Possible relationship R4: driver B2, passengers A2, C1;

[0084] Possible relationship R5: driver B2, passengers A3, C1;

[0085] The possible relationship R6: driver B2, passengers A2, A3, C1.

[0086] For each possible relationship, the target driver and the target passenger are the driver and the passenger in the possible relationship.

[0087] During the route planning stage, when triggering events such as passengers canceling the order, deviation from the route, and abnormal road conditions occur, the route needs to be replanned. At this time, the target driver is the driver corresponding to the triggering event, and the target passenger is the passenger who has shared the order with the target driver.

[0088] Step S120: Determine the driver starting point and driver ending point of the target driver.

[0089] In this embodiment, the driver starting point of the target driver can be determined according to the driver's current location or the driver's setting. Specifically, the driver starting point can be the driver's current location, and the driver's current location can be obtained by the positioning function of the vehicle or the driver's terminal. Alternatively, the driver starting point can be pre-set by the driver, for example, for the driver's planned trip, the driver can pre-set the driver starting point.

[0090] The driver's destination of the target driver can be determined by the server or according to the driver's settings. Specifically, if the driver sets the destination of the trip in advance, the destination set by the driver is determined as the driver's destination. For example, in the scenario of carpooling, the driver needs to set the destination. If the driver does not set the destination of the trip, the server determines a virtual terminal for the driver as the driver's destination. For example, in the scenario of real-time carpooling, the driver does not need to set the destination, and the server can set a hotspot area as the driver's destination.

[0091] Step S130: Determine the boarding point set and the alighting point set of the target passenger.

[0092] In this embodiment, the boarding point set includes one or more candidate boarding points, and the alighting point set includes one or more candidate alighting points.

[0093] Specifically, for each target passenger, the boarding point set is generated according to the departure place of the target passenger, and the alighting point set is generated according to the destination of the target passenger. The departure place and the destination are input by the passenger when placing an order, and the server recalls one or more candidate boarding points according to the departure place and generates the boarding point set, and recalls one or more candidate alighting points according to the destination and generates the alighting point set according to a predetermined point recall method.

[0094] Therefore, through the technical solution of the embodiment of the present invention, the passenger's boarding point is dynamically variable before the passenger order is answered (that is, the driver-passenger matching stage), and the optimal boarding point selection may be different when the current passenger is matched with different ride-sharing friends and drivers. The passenger's drop-off point is dynamically variable before it is about to arrive at the destination. If other ride-sharing friends join or cancel the ride, the passenger's optimal drop-off point will follow the change of the itinerary route to match the optimal drop-off point. During the carpooling trip, when the road conditions are congested, or there are abnormal road conditions such as temporary traffic control, construction / road closure on the planned road, the carpooling route will be changed in real time, and the passenger's drop-off point will also follow the change of the itinerary route to match the optimal drop-off point. If the driver deviates during the journey, the carpooling route will be replanned, and the passenger's drop-off point will also follow the change of the itinerary route to match the optimal drop-off point. Thus, flexible and efficient travel services can be provided. Passengers can enjoy shorter waiting time and more convenient boarding and alighting locations, which improves the overall travel experience. At the same time, the drop-off point is dynamically adjusted according to the actual situation during the trip, ensuring that passengers can reach the optimal drop-off point near the destination as quickly as possible. The server can respond quickly based on real-time traffic conditions (such as traffic congestion, road construction, etc.), automatically re-plan routes and adjust boarding and alighting points to ensure the safety and punctuality of the journey.

[0095] Step S200: determining at least one candidate order according to the travel nodes.

[0096] In this embodiment, the candidate order is the order of the travel nodes. Specifically, the candidate order satisfies the following conditions:

[0097] The driver’s starting point is the first driving node;

[0098] The driver's destination is the last travel node;

[0099] The same passenger's boarding point collection is before the alighting point collection.

[0100] For example, assume that the result of determining the driver's travel nodes is: the target driver is B3, the target passengers include A4 and A5, the driver's starting point is P1, the driver's end point is P2, the passenger A4's boarding point set is P3, the passenger A4's alighting point set is P4, the passenger A5's boarding point set is P5, and the passenger A5's alighting point set is P6.

[0101] According to the conditions that the driver's starting point is the first travel node, the driver's destination is the last travel node, and the pickup point set of the same target passenger is before the drop-off point set, the generated candidate order includes:

[0102] P1, P3, P4, P5, P6, P2;

[0103] P1, P3, P5, P4, P6, P2;

[0104] P1, P3, P5, P6, P4, P2;

[0105] P1, P5, P3, P4, P6, P2;

[0106] P1, P5, P3, P6, P4, P2;

[0107] P1, P5, P6, P3, P4, P2.

[0108] Thus, the candidate order can be obtained.

[0109] Step S300: Determine candidate passenger combinations.

[0110] In this embodiment, 0 to N target passengers are randomly selected from the target passengers to generate the candidate passenger combination, where N is the total number of target passengers, and the passengers in the candidate passenger combination are passengers with restricted boarding points and / or alighting points. That is, assuming that the target driver has N target passengers, 0 target passengers are selected to generate the candidate passenger combination, 1 target passenger is selected to generate the candidate passenger combination, and so on, until N target passengers are selected to generate the candidate passenger combination.

[0111] For example, if the target driver has only one target passenger D1, then 0 target passengers are selected to generate the candidate passenger combination, and 1 target passenger is selected to generate the candidate passenger combination. Therefore, the candidate passenger combination generated by selecting 0 target passengers is empty, which can be recorded as {}; the candidate passenger combination generated by selecting 1 target passenger only has the target passenger D1, which can be recorded as {D1}. The candidate passenger combinations finally generated include {}, {D1}.

[0112] For another example, if the target driver has two target passengers D1 and D2, then 0 target passengers are selected to generate the candidate passenger combination, 1 target passenger is selected to generate the candidate passenger combination, and 2 target passengers are selected to generate the candidate passenger combination. Therefore, the candidate passenger combination generated by selecting 0 target passengers is empty, which can be recorded as {}; the candidate passenger combination generated by selecting 1 target passenger may include target passenger D1 or D2, which can be recorded as {D1}, {D2}. The candidate passenger combination generated by selecting 2 target passengers includes target passengers D1 and D2, which can be recorded as {D1, D2}. The candidate passenger combinations finally generated include {}, {D1}, {D2}, {D1, D2}.

[0113] For another example, if the target driver has two target passengers D1, D2 and D3, then 0 target passengers are selected to generate the candidate passenger combination, 1 target passenger is selected to generate the candidate passenger combination, 2 target passengers are selected to generate the candidate passenger combination, and 3 target passengers are selected to generate the candidate passenger combination. Therefore, the candidate passenger combination generated by selecting 0 target passengers is empty, which can be recorded as {}; the candidate passenger combination generated by selecting 1 target passenger may include target passenger D1, D2, or D3, which can be recorded as {D1}, {D2}, {D3}. The candidate passenger combination generated by selecting 2 target passengers includes any two of the target passengers D1, D2, and D3, which can be recorded as {D1, D2}, {D1, D3}, {D2, D3}. The candidate passenger combination generated by selecting 3 target passengers includes target passengers D1, D2, and D3, which can be recorded as {D1, D2, D3}. The candidate passenger combinations finally generated include {}, {D1}, {D2}, {D3}, {D1, D2}, {D1, D3}, {D2, D3}, {D1, D2, D3}.

[0114] The passengers in the candidate passenger combination are passengers with constrained boarding and / or alighting points. Furthermore, the passengers in the candidate passenger combination are passengers whose boarding and / or alighting points are optimized to ensure the best passenger experience. The embodiment of the present invention takes the shortest walking distance as an example for the best passenger experience. The "constrained boarding and / or alighting point" refers to the station selected for each passenger in the candidate passenger combination that can provide the optimal walking distance, which is used as a standard for measuring the best passenger experience.

[0115] As described above, each target passenger's boarding point set may have multiple candidate boarding points, and each target passenger's alighting point set may have multiple candidate alighting points. Therefore, for each target passenger, there may be multiple candidate boarding points and candidate alighting points to choose from. By accurately locating these stations, it can be ensured that the selected station is the station with the shortest walking distance for the passenger. Therefore, when planning a route, by constraining the boarding points and / or alighting points of the passengers in the candidate passenger combination, the final generated route will significantly improve the overall experience of all participating passengers.

[0116] Furthermore, the embodiment of the present invention may only constrain the boarding point, or only constrain the alighting point, or constrain both the boarding point and the alighting point.

[0117] Step S400: Determine a target sequence and a target station based on each candidate sequence and candidate passenger combination.

[0118] In this embodiment, the optimal target sequence and target station are determined by evaluating each candidate sequence and each candidate passenger combination.

[0119] Specifically, Figure 4 FIG. 1 is a flow chart of determining a target result according to an embodiment of the present invention. Figure 4 As shown, determining the target sequence and the target station based on each candidate sequence and candidate passenger combination includes the following steps:

[0120] Step S410: Determine the directed graph corresponding to each candidate order.

[0121] In this embodiment, the directed graph corresponding to each candidate order is determined according to the order of each travel node in the candidate order. In the directed graph, each travel node is arranged in the order of the candidate order, and each station of the upper travel node can be connected to each station of the lower order for every two adjacent travel nodes. For the station, the station of the driver's starting point and the driver's end point of the target driver is itself, and the station of the target passenger's boarding point set and the alighting point set is the candidate boarding point and the candidate alighting point.

[0122] Furthermore, the edge weight of the directed graph is the road mileage and / or the estimated travel time. That is, the edge weight of each two connected nodes (or sites) is the corresponding mileage or estimated arrival time.

[0123] In some embodiments, the edge weight between the driver's destination and his previous travel node is set to 0 or close to 0 or a lower value.

[0124] Figure 5 is a schematic diagram of a directed graph according to an embodiment of the present invention. Figure 5 In the embodiment shown, it is assumed that the result of determining the driver's travel nodes is: the target driver is B3, the target passengers include A4 and A5, the driver's starting point is P1, the driver's end point is P2, the passenger A4's boarding point set is P3, including three candidate boarding points Q11, Q12, and Q13, the passenger A4's alighting point set is P4, including two candidate alighting points R11 and R12, the passenger A5's boarding point set is P5, including two candidate boarding points Q21 and Q22, and the passenger A5's alighting point set is P6, including three candidate alighting points R21, R22, and R23. The candidate order finally generated includes the six types shown in the above step S200. The embodiment of the present invention takes the third candidate order P1, P3, P5, P6, P4, and P2 as an example for explanation. The directed graph is as shown in FIG. Figure 5 As shown, the meaning represented is that the target driver starts from the driver's starting point P1, picks up passenger A4 first, then picks up passenger A5, then sends passenger A5, then sends passenger A4, and finally reaches the driver's end point P2.

[0125] Step S420: constraining the stations in the boarding point set and / or the alighting point set in the directed graph according to the candidate passenger combination to obtain a constrained directed graph.

[0126] In this embodiment, the optimal station for the target passenger in the candidate passenger combination is selected from the stations in the boarding point set and / or the alighting point set in the directed graph to obtain a constrained directed graph. For each candidate sequence, the stations in the boarding point set and / or the alighting point set in the directed graph are constrained according to each candidate passenger combination to obtain a constrained directed graph.

[0127] Among them, constraints are imposed on the stations in the boarding point set and / or the alighting point set in the directed graph: only the candidate boarding points in the boarding point set may be constrained, only the candidate alighting points in the alighting point set may be constrained, or both the candidate boarding points in the boarding point set and the candidate alighting points in the alighting point set may be constrained.

[0128] by Figure 5 For example, the target driver has two target passengers A4 and A5. Assume that the optimal boarding point for target passenger A4 is Q11 and the optimal alighting point is R11. The optimal boarding point for target passenger A5 is Q21 and the optimal alighting point is R21. The candidate passenger combinations obtained by step S300 include {}, {A4}, {A5}, {A4, A5}. The embodiment of the present invention is explained by taking the example of simultaneously constraining the candidate boarding points in the boarding point set and the candidate alighting points in the alighting point set. The stations in the boarding point set and / or the alighting point set in the directed graph are constrained according to the candidate passenger combinations to obtain the constrained directed graph as follows:

[0129] For the candidate passenger combination {}, since there is no passenger in the candidate passenger combination, no constraint is required. The constrained directed graph is the same as the original directed graph ( Figure 5 ) are consistent. At this time, the site matching efficiency calculated through the directed graph is the highest.

[0130] For the candidate passenger combination {A4}, the optimal boarding point for the target passenger A4 is Q11, and the optimal alighting point is R11. The resulting constrained directed graph is as follows: Figure 6 As shown. At this time, the site matching efficiency is high through the directed graph calculation, and the experience of passenger A4 is the best. That is, both route efficiency and passenger A4's experience are taken into account.

[0131] For the candidate passenger combination {A5}, the optimal boarding point for the target passenger A5 is Q21, and the optimal alighting point is R21. The resulting constrained directed graph is as follows: Figure 7 As shown. At this time, the site matching efficiency is high through the directed graph calculation, and the experience of passenger A5 is the best. That is, both route efficiency and passenger A5's experience are taken into account.

[0132] For the candidate passenger combination {A4, A5}, the optimal boarding point for target passenger A4 is Q11, and the optimal alighting point is R11; the optimal boarding point for target passenger A5 is Q21, and the optimal alighting point is R21. The resulting constrained directed graph is as follows: Figure 8 At this time, the stations calculated by the directed graph do not take route efficiency into consideration, but those of passengers A4 and A5 are both better.

[0133] Thus, a directed graph of constraints of each candidate passenger combination corresponding to a candidate order can be obtained. The above steps are performed for each candidate order, and a directed graph of constraints of each candidate passenger combination corresponding to each candidate order can be obtained.

[0134] Step S430: determine the target order and target site according to the constrained directed graph.

[0135] In this embodiment, the target order and target site corresponding to each candidate order are determined based on the constrained directed graph obtained above.

[0136] Fig. 9 FIG. 1 is a flow chart of determining a target sequence and a target site according to an embodiment of the present invention. Fig. 9 As shown, determining the target order and target site according to the directed graph after the constraints includes the following steps:

[0137] Step S431: Generate candidate paths for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph.

[0138] In this embodiment, the candidate path is the optimal path of each candidate passenger combination corresponding to each candidate order. That is, for each candidate order, the optimal path corresponding to each candidate passenger combination is calculated according to the directed graph after constraints corresponding to each candidate passenger combination, and the optimal path is used as the candidate path, and each candidate passenger combination corresponds to a candidate path. Assuming that the target driver has M1 candidate orders and M2 candidate passenger combinations, after calculation in this step, each candidate order can obtain M2 candidate paths. Finally, there are M1*M2 candidate orders for M1 candidate orders.

[0139] For example, suppose the result of determining the driver's travel nodes is: the target driver is B3, the target passengers include A4 and A5, the driver's starting point is P1, the driver's end point is P2, the passenger A4's boarding point set is P3, the passenger A4's alighting point set is P4, the passenger A5's boarding point set is P5, and the passenger A5's alighting point set is P6. According to the conditions that the driver's starting point is the first travel node, the driver's end point is the last travel node, and the boarding point set of the same target passenger is before the alighting point set, the generated candidate orders include: P1, P3, P4, P5, P6, P2; P1, P3, P5, P4, P6, P2; P1, P3, P5, P6, P4, P2; P1, P5, P3, P4, P6, P2; P1, P5, P3, P6, P4, P2; P1, P5, P3, P6, P4, P2; P1, P5, P6, P3, P4, P2. That is, there are six candidate orders in total. For each candidate order, there are four candidate passenger combinations: {}, {A4}, {A5}, {A4, A5}. The optimal path in each candidate passenger combination is calculated and the optimal path is used as the candidate path. Thus, each candidate order can obtain four candidate paths. Finally, the six candidate orders can obtain 6*4=24 candidate paths.

[0140] Furthermore, the traversable paths of each candidate passenger combination corresponding to each candidate order are generated according to the constrained directed graph, and the optimal traversable path is determined as the candidate path. That is, the candidate path is the optimal path among all traversable paths in a candidate order. For example, for the candidate order of P1, P3, P5, P6, P4, P2, and the candidate passenger combination of {A4}, the constrained directed graph is as follows: Figure 6 As shown, in Figure 6 In the embodiment shown, there are 2*3=6 possible paths. They are as follows:

[0141] P1, Q11, Q21, R21, R11, P2;

[0142] P1, Q11, Q21, R22, R11, P2;

[0143] P1, Q11, Q21, R23, R11, P2;

[0144] P1, Q11, Q22, R21, R11, P2;

[0145] P1, Q11, Q22, R22, R11, P2;

[0146] P1, Q11, Q22, R23, R11, P2;

[0147] After all traversable paths are listed, the edge weight sum of each traversable path is determined, and the traversable path with the smallest edge weight sum is determined as the candidate path. That is, for each candidate sequence, the traversable path with the smallest edge weight sum is selected as the candidate path.

[0148] Step S432: determine the target path according to the travel costs of each candidate path.

[0149] In this embodiment, the travel cost is determined based on one or more of the total mileage, estimated travel time, walking distance of the target passenger, driver cost, and passenger cost.

[0150] Among them, the total mileage is the total mileage of the target driver from the driver's starting point to picking up all target passengers and then to the driver's destination.

[0151] The estimated driving time is the estimated time it takes for the target driver to complete the total mileage.

[0152] The walking distance of the target passengers is the walking distance of each target passenger from the departure point to the boarding point, or the walking distance of each target passenger from the alighting point to the destination, or the sum of the walking distance of each target passenger from the departure point to the boarding point and the walking distance from the alighting point to the destination.

[0153] The driver cost is the cost of the target driver during the entire driving process.

[0154] Passenger cost is the taxi cost for the target passenger.

[0155] The embodiment of the present invention calculates the travel cost based on one or more of the total mileage, estimated travel time, walking distance of the target passenger, driver cost, and passenger cost.

[0156] In a specific implementation, the total mileage, estimated travel time, walking distance of the target passenger, driver cost, and passenger cost are quantified respectively, and the quantification standard is proportional to the travel cost, and then the travel cost is obtained by weighted summation. The candidate path with the lowest travel cost is selected as the target path.

[0157] Step S433: determine the target order and target site according to the target path.

[0158] In this embodiment, the candidate sequence corresponding to the target path is determined as the target sequence, and the candidate site corresponding to the target path is determined as the target site. The candidate site is a candidate boarding point in the boarding point set or a candidate alighting point in the alighting point set.

[0159] In the embodiment of the present invention, the driver's starting point, at least one set of boarding points of target passengers, at least one set of alighting points of target passengers, and the driver's destination are used as the travel nodes of the target driver, at least one candidate sequence is determined according to the travel nodes, and a candidate passenger combination is determined by constraining the passengers at the boarding points and / or alighting points, and a target sequence and a target station are determined based on each candidate sequence and each candidate passenger combination. In this way, the passenger experience and route efficiency can be balanced and the service quality can be improved.

[0160] In some embodiments, after the target order and the target site are obtained, subsequent processing may be performed according to the target order and the target site, such as generating and executing a carpooling plan according to the target order and the target site.

[0161] Fig.10 FIG. 1 is a flow chart of a data processing method according to a second embodiment of the present invention. Fig.10 As shown, the data processing method includes the following steps:

[0162] Step S510: Obtain the departure place and destination input by the passenger.

[0163] In this embodiment, the passenger terminal obtains the inputted departure place and destination.

[0164] Step S520: The passenger issues a ticket.

[0165] In this embodiment, after the passenger inputs the departure place and destination in the passenger terminal, the passenger selects a carpooling travel mode and places an order.

[0166] Step S530: Recall candidate sites.

[0167] In this embodiment, the server recalls the candidate stations. Specifically, the embodiment of the present invention divides passengers into passengers who have already placed a group order and passengers to be placed on a group order, wherein passengers to be placed on a group order are passengers who have placed orders through the above steps S510 and S520, and the order allocation has not been completed at this time. Passengers who have already placed a group order are passengers who have been allocated to the driver.

[0168] For passengers who are waiting to place a ride, the server will recall candidate boarding points and candidate alighting points based on the departure point and destination. For passengers who have already placed a ride, the server will obtain the stations that have been recalled for the passenger during the grouping stage as candidate stations. Candidate stations include candidate boarding points and candidate alighting points.

[0169] Step S540: Recall the possible association relationships.

[0170] In this embodiment, the server recalls the possible carpooling relationships. The server matches the passengers to be carpooled in the passenger database with the possible carpooling drivers in the driver database according to predetermined rules to recall the driver-passenger combinations that may complete the carpooling, i.e., the so-called "possible carpooling relationships". These rules can be used individually or in combination based on a variety of factors, such as the number of seats, driving route, driving direction, etc., to ensure the best matching effect. Finally, the target driver and target passenger determined from the recalled possible carpooling relationships will enter the next step of route planning and service arrangement.

[0171] For example, assuming that after passenger A7 places an order, after matching the drivers in the driver database, driver B4, driver B5, driver B6 can be matched with passenger A7, then the possible relationships include: (A7, B4), (A7, B5), (A7, B6).

[0172] Step S550: Obtain the optimal splice relationship, optimal sequence and optimal site.

[0173] In this embodiment, for each possible association relationship, the corresponding target sequence and target site are obtained through the above steps S100-S400. That is, each possible association relationship can obtain the corresponding target sequence and target site.

[0174] Furthermore, the travel cost corresponding to the spliceable relationship is determined according to the target order and target site corresponding to each spliceable relationship.

[0175] According to the travel cost corresponding to each possible connection relationship, the optimal possible connection relationship is selected, and the target sequence and target station corresponding to the optimal possible connection relationship are determined as the optimal sequence and optimal station.

[0176] Step S560, execute the group order process.

[0177] In this embodiment, the order-sharing process is executed according to the optimal order-sharing relationship. For example, the server assigns the passenger in the optimal order-sharing relationship to the driver to complete the order-sharing; or, the server sends a push message to the passenger and the driver according to the passenger and the driver in the optimal order-sharing relationship, and completes the order-sharing after waiting for the driver and the passenger to confirm.

[0178] Furthermore, after the group ride is completed, a pick-up route is generated based on the optimal order and optimal stops and sent to the passengers and drivers.

[0179] In the embodiment of the present invention, the driver's starting point, at least one set of boarding points of target passengers, at least one set of alighting points of target passengers, and the driver's destination are used as the travel nodes of the target driver, at least one candidate sequence is determined according to the travel nodes, and a candidate passenger combination is determined by constraining the passengers at the boarding points and / or alighting points, and a target sequence and a target station are determined based on each candidate sequence and each candidate passenger combination. In this way, the passenger experience and route efficiency can be balanced and the service quality can be improved.

[0180] In some embodiments, after obtaining the target sequence and the target site, subsequent processing may be performed according to the target sequence and the target site, such as generating and sending a planned path according to the target sequence and the target site. Specifically, in the path planning stage, a planned path is generated according to the target sequence and the target site and sent to the passenger and the driver.

[0181] Fig.11 Schematic diagram of a data processing device according to an embodiment of the present invention. Fig.11 As shown, the data processing device of the embodiment of the present invention includes a node determination unit 111, an order determination unit 112, a combination determination unit 113 and a result acquisition unit 114. The node determination unit 111 is used to determine the travel nodes of the target driver, and the travel nodes include the driver's starting point, at least one set of boarding points of the target passenger, at least one set of alighting points of the target passenger, and the driver's end point. The order determination unit 112 is used to determine at least one candidate order based on the travel nodes, and the candidate order is the ranking of the travel nodes. The combination determination unit 113 is used to determine candidate passenger combinations, and the passengers in the candidate passenger combinations are passengers with constrained boarding points and / or alighting points. The result determination unit 114 is used to determine the target order and target site based on each candidate order and candidate passenger combination.

[0182] In some embodiments, the candidate order satisfies the following conditions:

[0183] The driver’s starting point is the first driving node;

[0184] The driver's destination is the last travel node;

[0185] The same passenger's boarding point collection is before the alighting point collection.

[0186] In some embodiments, the candidate passenger combination is empty or the candidate passenger combination includes one or more target passengers.

[0187] In some embodiments, the combination determination unit is specifically used to:

[0188] 0 to N target passengers are arbitrarily selected from the target passengers to generate the candidate passenger combination, where N is the total number of target passengers.

[0189] In some embodiments, the result determination unit comprises:

[0190] A directed graph determination subunit is used to determine the directed graph corresponding to each candidate order;

[0191] A constraint subunit, used for constraining the stations in the boarding point set and / or the alighting point set in the directed graph according to the candidate passenger combination, so as to obtain a constrained directed graph;

[0192] The result determination subunit is used to determine the target order and target site according to the directed graph after constraints.

[0193] In some embodiments, the edge weights of the directed graph are road mileage and / or estimated driving time.

[0194] In some embodiments, the constraint subunit is used to:

[0195] The optimal station for the target passenger in the candidate passenger combination is selected from the stations in the boarding point set and / or the alighting point set in the directed graph.

[0196] In some embodiments, the result determination subunit comprises:

[0197] A candidate path determination module, used to generate candidate paths for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph;

[0198] A target path determination module, used to determine the target path according to the travel cost of each candidate path;

[0199] A result determination module is used to determine the target sequence and the target site according to the target path.

[0200] In some embodiments, the candidate path determination module includes:

[0201] A traversable path determination submodule, used to generate traversable paths for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph;

[0202] The candidate path determination submodule is used to determine the optimal navigable path as the candidate path.

[0203] In some embodiments, the travel cost is determined based on one or more of the total mileage, estimated travel time, walking distance of the target passenger, driver cost, and passenger cost.

[0204] In some embodiments, the apparatus further comprises:

[0205] The path planning unit is used to generate and send a planned path according to the target sequence and target sites.

[0206] In some embodiments, the apparatus further comprises:

[0207] The carpooling plan generating unit is used to generate and execute a carpooling plan according to the target sequence and the target station.

[0208] In the embodiment of the present invention, the driver's starting point, at least one set of boarding points of target passengers, at least one set of alighting points of target passengers, and the driver's destination are used as the travel nodes of the target driver, at least one candidate sequence is determined according to the travel nodes, and a candidate passenger combination is determined by constraining the passengers at the boarding points and / or alighting points, and a target sequence and a target station are determined based on each candidate sequence and each candidate passenger combination. In this way, the passenger experience and route efficiency can be balanced and the service quality can be improved.

[0209] Fig.12 Schematic diagram of an electronic device according to an embodiment of the present invention. Fig.12 As shown, Fig.12 The electronic device shown is a general data processing device, which includes a general computer hardware structure, which includes at least a processor 121 and a memory 122. The processor 121 and the memory 122 are connected via a bus 123. The memory 122 is suitable for storing instructions or programs executable by the processor 121. The processor 121 can be an independent microprocessor or a collection of one or more microprocessors. Thus, the processor 121 executes the instructions stored in the memory 122, thereby executing the method flow of the embodiment of the present invention as described above to realize the processing of data and the control of other devices. The bus 123 connects the above-mentioned multiple components together, and at the same time connects the above-mentioned components to the display controller 124 and the display device and the input / output (I / O) device 125. The input / output (I / O) device 125 can be a mouse, a keyboard, a modem, a network interface, a touch input device, a somatosensory input device, a printer, and other devices known in the art. Typically, the input / output device 125 is connected to the system through an input / output (I / O) controller 126.

[0210] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices (equipment) or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may adopt a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0211] The present application is described with reference to flowcharts of methods, apparatuses (devices) and computer program products according to embodiments of the present application. It should be understood that each process in the flowchart can be implemented by computer program instructions.

[0212] These computer program instructions may be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device that implements the process Figure 1 A function specified in a process or multiple processes.

[0213] These computer program instructions may also be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce the instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0214] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, wherein the computer-readable program is used for a computer to execute part or all of the above method embodiments.

[0215] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by specifying relevant hardware through a program, and the program is stored in a storage medium, including several instructions for a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0216] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method, characterized in that: The method comprises: Determine the travel nodes of the target driver, wherein the travel nodes include the driver's starting point, a set of at least one target passenger's boarding point, a set of at least one target passenger's alighting point, and the driver's destination; Determine at least one candidate order according to the travel nodes, the candidate order being the ranking of the travel nodes; Determine a candidate passenger combination, where passengers in the candidate passenger combination are passengers with restricted boarding points and / or alighting points; A target sequence and a target stop are determined based on the candidate sequences and candidate passenger combinations.

2. The method according to claim 1, characterized in that The candidate order meets the following conditions: The driver’s starting point is the first driving node; The driver's destination is the last travel node; The same passenger's boarding point collection is before the alighting point collection.

3. The method according to claim 1, characterized in that: The candidate passenger combination is empty or the candidate passenger combination includes one or more target passengers.

4. The method according to claim 3, characterized in that The determination of the candidate passenger combination is specifically as follows: 0 to N target passengers are arbitrarily selected from the target passengers to generate the candidate passenger combination, where N is the total number of target passengers.

5. The method according to claim 1, characterized in that: Determining the target sequence and the target station based on each candidate sequence and candidate passenger combination includes: Determine the directed graph corresponding to each candidate order; Constraining the stations in the boarding point set and / or the alighting point set in the directed graph according to the candidate passenger combination to obtain a constrained directed graph; The target order and target site are determined according to the constrained directed graph.

6. The method according to claim 5, characterized in that The edge weights of the directed graph are road mileage and / or estimated travel time.

7. The method according to claim 5, characterized in that The constraining the stations in the boarding point set and / or the alighting point set in the directed graph according to the candidate passenger combination to obtain the constrained directed graph includes: The optimal station for the target passenger in the candidate passenger combination is selected from the stations in the boarding point set and / or the alighting point set in the directed graph.

8. The method according to claim 5, characterized in that Determining the target order and the target site according to the directed graph after the constraints includes: Generating candidate paths for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph; Determine a target path according to the travel costs of each of the candidate paths; The target sequence and the target site are determined according to the target path.

9. The method according to claim 8, characterized in that Generating candidate paths for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph includes: Generating a traversable path for each candidate passenger combination corresponding to each candidate order according to the constrained directed graph; The optimal navigable path is determined as the candidate path.

10. The method according to claim 8, characterized in that The travel cost is determined based on one or more of the total mileage, estimated travel time, walking distance of the target passenger, driver cost, and passenger cost.

11. The method according to claim 1, characterized in that: The method further comprises: A planned path is generated according to the target sequence and target sites and sent.

12. The method according to claim 1, characterized in that The method further comprises: A carpooling plan is generated and executed according to the target sequence and the target site.

13. A data processing device, characterized in that: The device comprises: A node determination unit, used to determine a travel node of a target driver, wherein the travel node includes a driver starting point, a set of at least one target passenger boarding point, a set of at least one target passenger alighting point, and a driver destination; An order determination unit, configured to determine at least one candidate order according to the travel nodes, wherein the candidate order is a ranking of the travel nodes; A combination determining unit, used to determine a candidate passenger combination, wherein the passengers in the candidate passenger combination are passengers with restricted boarding points and / or alighting points; The result determination unit is used to determine the target sequence and the target station based on each candidate sequence and candidate passenger combination.

14. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 12.

15. A computer program product, comprising a computer program, characterized in that: When the computer program is executed on a computer, the computer executes the method according to any one of claims 1 to 12.

16. A computer-readable storage medium storing computer program instructions, characterized in that: The computer program instructions implement the method according to any one of claims 1 to 12 when executed by a processor.

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