A method, device, computer equipment and readable storage medium for dispatching cross-platform car-hailing serial orders

Abstract

The present invention discloses a method, device, computer equipment and readable storage medium for dispatching serial orders for ride-hailing across platforms, including: first obtaining the ride-hailing order and the initiation location of the user of the first ride-hailing platform, and then obtaining the pending driver terminal configuration information within a preset range. When the driver service status is in service, the target driver terminal is determined according to the vehicle location. The navigation information of the target driver on the second platform is obtained, and the predicted navigation information is generated and integrated, and pushed to the first ride-hailing platform for display to the user. With such a design, efficient and accurate dispatching across platforms is achieved, and the user experience and the efficiency of the driver receiving orders are optimized.

Description

Technical Field

[0001] The present invention relates to the field of Internet communications, and in particular to a method, device, computer equipment and readable storage medium for dispatching cross-platform ride-hailing serial orders. Background Art

[0002] With the rapid development of the online car-hailing industry, users have increasing requirements for the efficiency and accuracy of car-hailing services. Existing car-hailing dispatching methods are often limited to a single platform, and are not smart and accurate enough in determining the pick-up driver and planning the navigation route, resulting in long waiting times for users and low efficiency of drivers in accepting orders, which cannot fully meet market demand. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, computer equipment and readable storage medium for dispatching cross-platform ride-hailing serial orders.

[0004] In a first aspect, an embodiment of the present invention provides a method for dispatching a cross-platform ride-hailing serial order, comprising:

[0005] Acquire a ride-hailing order issued by a target user terminal on a first ride-hailing platform, wherein the ride-hailing order includes an order initiation location;

[0006] According to the order initiation location, obtaining configuration information of multiple pending driver terminals within a preset range, wherein the configuration information includes vehicle location information and service status information;

[0007] When the service status information indicates that the service is in progress, a target driver terminal is determined from the plurality of pending driver terminals according to the vehicle location information to receive the ride order;

[0008] Obtaining current navigation information of the target driver terminal on the second ride-hailing platform;

[0009] Calling a preset map service to generate predicted navigation information according to the current navigation information and the order initiation location information;

[0010] The current navigation information and the predicted navigation information are integrated to obtain integrated navigation information, and the integrated navigation information is pushed to the first ride-hailing platform and displayed to the target user terminal.

[0011] In a possible implementation, determining a target driver terminal from the plurality of pending driver terminals to receive the ride order based on the vehicle location information includes:

[0012] Determine the current destination of the current order being served according to the vehicle location information;

[0013] Determine the estimated pick-up distance and estimated pick-up time corresponding to each of the pending driver terminals according to the order initiation location and the current destination;

[0014] The target driver terminal is determined from the multiple pending driver terminals to receive the ride order based on the estimated pick-up distance and the estimated pick-up time.

[0015] In a possible implementation, determining the target driver terminal from the plurality of pending driver terminals to receive the ride order according to the estimated pick-up distance and the estimated pick-up time includes:

[0016] Calculate the ratio of the estimated pick-up distance corresponding to each of the pending driver terminals to the preset maximum pick-up distance to obtain a first order acceptance coefficient corresponding to each of the pending driver terminals;

[0017] Calculate the ratio of the estimated pick-up time corresponding to each of the pending driver terminals to the preset maximum pick-up time to obtain a second order acceptance coefficient corresponding to each of the pending driver terminals;

[0018] Calculate the complementary number of the product of the first order acceptance coefficient and the second order acceptance coefficient corresponding to each of the pending driver terminals, and use the complementary number as the order acceptance matching value, where the complementary number plus the product of the first order acceptance coefficient and the second order acceptance coefficient is equal to 1;

[0019] According to the order matching value corresponding to each pending driver terminal, the terminals are sorted in descending order, and the pending driver terminal with the largest order matching value is selected as the target driver terminal, and the ride-hailing order is accepted.

[0020] In a possible implementation, the method further includes:

[0021] If the target driver terminal reports an abnormal situation, the steps of re-sorting the order matching values ​​corresponding to each of the pending driver terminals from large to small, selecting the pending driver terminal with the largest order matching value as the target driver terminal, and accepting the ride-hailing order are performed.

[0022] In a possible implementation, the configuration information further includes order preference information, and the acquiring, according to the order initiation location, configuration information of multiple pending driver terminals within a preset range includes:

[0023] According to the order initiation location, obtaining configuration information of multiple initial driver terminals within a preset range;

[0024] According to the order acceptance preference information corresponding to each of the initial driver terminals, the multiple initial driver terminals are cleaned to obtain the configuration information of the multiple pending driver terminals.

[0025] In a possible implementation, the method further includes:

[0026] Get the service status information of the current driver terminal;

[0027] When the service status information of the current driver terminal indicates that the service is about to be completed, obtaining the vehicle location information of the current driver terminal;

[0028] According to the vehicle location information, obtaining a car booking order issued by a plurality of pending user terminals on a first car booking platform within a preset range, wherein the car booking order includes an order initiation location;

[0029] Determine the current order initiation location corresponding to the current user terminal from the multiple pending user terminals according to the vehicle location information of the current driver terminal and the order initiation location;

[0030] Obtaining current navigation information of the current driver terminal on the second ride-hailing platform;

[0031] Generate predicted navigation information corresponding to the current user terminal according to the current order initiation location and the current navigation information;

[0032] The current navigation information and the predicted navigation information are integrated to obtain integrated navigation information, and the integrated navigation information is pushed to the first ride-hailing platform and displayed on the current user terminal.

[0033] In a possible implementation manner, the fusing the current navigation information and the predicted navigation information to obtain fused navigation information includes:

[0034] Obtain real-time road congestion and weather conditions;

[0035] The current navigation information, the predicted navigation information, the real-time road congestion situation and the weather factors are integrated to obtain the integrated navigation information.

[0036] In a second aspect, an embodiment of the present invention provides a cross-platform car-hailing serial order dispatching device, comprising:

[0037] An acquisition module, configured to acquire a ride-hailing order issued by a target user terminal on the first ride-hailing platform, wherein the ride-hailing order includes an order initiation location;

[0038] A calculation module is used to obtain configuration information of multiple pending driver terminals within a preset range according to the order initiation location, wherein the configuration information includes vehicle location information and service status information; when the service status information is in service, determine a target driver terminal from the multiple pending driver terminals to receive the ride-hailing order according to the vehicle location information; obtain current navigation information of the target driver terminal on the second ride-hailing platform; and call a preset map service to generate predicted navigation information according to the current navigation information and the order initiation location information;

[0039] The dispatching module is used to fuse the current navigation information and the predicted navigation information to obtain fused navigation information, and push the fused navigation information to the first car-hailing platform and display it to the target user terminal.

[0040] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor and a non-volatile memory storing computer instructions, wherein when the computer instructions are executed by the processor, the computer device executes the method described in at least one possible implementation of the first aspect.

[0041] In a fourth aspect, an embodiment of the present invention provides a readable storage medium, wherein the readable storage medium includes a computer program, and the computer program controls a computer where the readable storage medium is located when the computer program is executed.

[0042] Compared with the prior art, the beneficial effects provided by the present invention include: adopting a cross-platform ride-hailing serial order dispatching method, device, computer equipment and readable storage medium disclosed in the present invention, by obtaining the ride-hailing order and initiation location of the user of the first ride-hailing platform, and then obtaining the pending driver terminal configuration information within a preset range. When the driver service status is in service, the target driver terminal is determined according to the vehicle location. The navigation information of the target driver on the second platform is obtained, and the predicted navigation information is generated and integrated, and pushed to the first ride-hailing platform for display to the user. With this design, efficient and accurate dispatching across platforms is achieved, and the user experience and driver order acceptance efficiency are optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can also be obtained based on these drawings without creative work.

[0044] Figure 1 A schematic diagram of the steps of the method for dispatching a cross-platform ride-hailing serial order provided by an embodiment of the present invention;

[0045] Figure 2 A schematic block diagram of the structure of a cross-platform car-hailing serial order dispatching device provided in an embodiment of the present invention;

[0046] Figure 3 A schematic block diagram of the structure of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0048] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings.

[0049] In order to solve the technical problems in the aforementioned background technology, Figure 1 This is a flow chart of a method for dispatching a serial order for hailing a car across platforms provided in an embodiment of the present disclosure. The method for dispatching a serial order for hailing a car across platforms is introduced in detail below.

[0050] Step S201, obtaining a ride-hailing order issued by a target user terminal on a first ride-hailing platform, wherein the ride-hailing order includes an order initiation location;

[0051] Step S202, according to the order initiation location, obtaining configuration information of multiple pending driver terminals within a preset range, wherein the configuration information includes vehicle location information and service status information;

[0052] Step S203, when the service status information is in service, determining a target driver terminal from the plurality of pending driver terminals to receive the ride order according to the vehicle location information;

[0053] Step S204, obtaining the current navigation information of the target driver terminal on the second ride-hailing platform;

[0054] Step S205, calling a preset map service to generate predicted navigation information according to the current navigation information and the order initiation location information;

[0055] Step S206, the current navigation information and the predicted navigation information are integrated to obtain integrated navigation information, and the integrated navigation information is pushed to the first ride-hailing platform and displayed to the target user terminal.

[0056] In an embodiment of the present invention, for example, suppose that on a busy weekday morning, a user named Xiao Ming initiates a car booking order on the first car booking platform (such as A platform). Xiao Ming's departure place is the community where his home is located, specifically No. XX, XX Road. The server monitors Xiao Ming's car booking request in real time and obtains detailed information of the order, which clearly records the order initiation location. After receiving Xiao Ming's order, the server immediately sets a preset range, such as a circular area with a radius of 5 kilometers, with the order initiation location as the center. Then, the server searches for the configuration information of multiple pending driver terminals within this range. For example, there are driver A, driver B, and driver C within this range. Driver A's vehicle is located near a supermarket 3 kilometers away from the order initiation location, and the service status is in service, and the current passenger is about to be sent to the destination; driver B's vehicle is located at the door of an office building 4 kilometers away from the order initiation location, and the service status is in service, and it will take 10 minutes to reach the current passenger's destination; driver C's vehicle is located at the door of a park 2 kilometers away from the order initiation location, and the service status is idle. For the two drivers, Driver A and Driver B, who are currently serving, the server determines the estimated pick-up distance and estimated pick-up time based on their vehicle location information. Assume that the destination of Driver A's current order is 2 kilometers away from the location where Xiao Ming's order was initiated. According to the current road conditions, the estimated pick-up time is 5 minutes. The destination of Driver B's current order is 3 kilometers away from the location where Xiao Ming's order was initiated, and the estimated pick-up time is 8 minutes. The server calculates the order matching value according to the predetermined algorithm. Assume that the preset maximum pick-up distance is 5 kilometers and the preset maximum pick-up time is 15 minutes. For Driver A, the first order coefficient is 2 / 5=0.4, the second order coefficient is 5 / 15≈0.33, and the order matching value is 1-0.4×0.33=0.87. For Driver B, the first order coefficient is 3 / 5=0.6, the second order coefficient is 8 / 15≈0.53, and the order matching value is 1-0.6×0.53=0.68. Comparing the order matching values ​​of the two drivers, Driver A's order matching value of 0.87 is greater than Driver B's order matching value of 0.68. Therefore, the server determines Driver A as the target driver terminal to accept Xiao Ming's ride-hailing order. Driver A not only uses the first ride-hailing platform (Platform A), but also registers and turns on navigation on the second ride-hailing platform (such as Platform B). The server obtains Driver A's current navigation information on Platform B, including the current driving route, remaining distance, and estimated arrival time of the order he is serving. The server calls the preset map service and combines Driver A's current navigation information on Platform B with the initiation location of Xiao Ming's order for analysis. Based on real-time traffic conditions, road restrictions and other factors, the map service generates predicted navigation information from Driver A's current location to the initiation location of Xiao Ming's order after completing the current service order.For example, the predicted navigation information may prompt driver A that after completing the current order, he needs to turn right at a certain intersection, pass a main road, and then pick up Xiao Ming at the gate of a certain community. The server merges driver A's current navigation information on platform B with the generated predicted navigation information. The fusion process takes into account a variety of factors, such as real-time road congestion (assuming that a traffic accident on a certain road causes congestion) and weather factors (such as rain causing driving speed to slow down). The fused navigation information is more accurate and comprehensive. Then, the server pushes the fused navigation information to the first ride-hailing platform (platform A) and displays it on Xiao Ming's user terminal. Xiao Ming can see driver A's current location, driving route, and estimated time to arrive at his location in real time. Suppose that during this process, driver A suddenly encounters a vehicle failure and cannot continue to accept Xiao Ming's order. After receiving the abnormal situation reported by driver A, the server re-determines the new target driver terminal from other pending driver terminals according to the above process, and regenerates and pushes the fused navigation information to Xiao Ming. For example, in another scenario, at 10 o'clock in the evening, user Xiao Mei initiates a ride-hailing order on the first ride-hailing platform, and the order is initiated at a shopping mall. The server obtains the pending driver terminal information within the preset range according to the same process. It is found that driver Ding and driver Wu meet the conditions. Driver Ding is currently serving, 4 kilometers away from the current order destination, and the estimated pick-up time is 12 minutes; driver Wu is idle and 6 kilometers away from Xiaomei. After calculating the order matching value, driver Ding is determined as the target driver terminal. Obtain its navigation information on the second ride-hailing platform, generate and integrate the predicted navigation information, and push it to Xiaomei. Through the examples of various specific scenarios above, we can clearly see the actual operation process of this cross-platform ride-hailing chain order dispatching method under different circumstances, which can effectively provide users and drivers with efficient and accurate ride-hailing services.

[0057] In the embodiment of the present invention, the aforementioned step S203 can be implemented through the following example execution.

[0058] Determine the current destination of the current order being served according to the vehicle location information;

[0059] Determine the estimated pick-up distance and estimated pick-up time corresponding to each of the pending driver terminals according to the order initiation location and the current destination;

[0060] The target driver terminal is determined from the multiple pending driver terminals to receive the ride order based on the estimated pick-up distance and the estimated pick-up time.

[0061] In an embodiment of the present invention, for example, during the morning rush hour of a weekday, the server receives a ride order issued by user Xiao Li on the first ride-hailing platform. The order initiation location is an office building in the city center. Based on this order initiation location, the server obtains the vehicle location information and service status information of multiple pending driver terminals within a preset range. Among them, driver A is in service, and his vehicle is located near a subway station. The passenger of the current order is about to get off 5 kilometers away from the order initiation location. Driver B is also in service, and his vehicle is located near a school. The end point of the current order is 3 kilometers away from the order initiation location. The server first determines the current end point of the current order being served by driver A and driver B based on the vehicle location information. Then, based on the order initiation location and these current end points, the estimated pick-up distance and estimated pick-up time corresponding to each pending driver terminal are calculated. For driver A, since the current end point is 5 kilometers away from the order initiation location and the current road is congested, the estimated pick-up time is 20 minutes. For driver B, the current end point is 3 kilometers away from the order initiation location, the road is relatively unobstructed, and the estimated pick-up time is 10 minutes. Next, the server determines the target driver terminal based on these estimated pick-up distances and estimated pick-up times. Assume that the preset maximum pick-up distance is 8 kilometers and the preset maximum pick-up time is 30 minutes. Driver A's first order acceptance coefficient is 5 / 8=0.625, the second order acceptance coefficient is 20 / 30≈0.67, and the order matching value is 1-0.625×0.67=0.58. Driver B's first order acceptance coefficient is 3 / 8=0.375, the second order acceptance coefficient is 10 / 30≈0.33, and the order matching value is 1-0.375×0.33=0.88. Comparing the order matching values ​​of the two, driver B's 0.88 is greater than driver A's 0.58, so the server determines that driver B is the target driver terminal to accept the ride-hailing order.

[0062] For another example, in the evening, user Xiao Wang initiates a ride-hailing order on the first ride-hailing platform, and the order initiation location is a residential community. The server obtains the information of driver C and driver D within the preset range. Driver C is in service, and the end point of the current order is 4 kilometers away from the order initiation location. At this time, the road is severely congested, and the estimated pick-up time is 18 minutes. Driver D is also in service, and the end point of the current order is 6 kilometers away from the order initiation location. The road condition is slightly better, and the estimated pick-up time is 15 minutes. The server determines the current end point of the current order that driver C and driver D are serving. For driver C, the preset maximum pick-up distance is 10 kilometers, the preset maximum pick-up time is 25 minutes, its first order coefficient is 4 / 10=0.4, the second order coefficient is 18 / 25=0.72, and the order matching value is 1-0.4×0.72=0.71. For driver D, the first order acceptance coefficient is 6 / 10=0.6, the second order acceptance coefficient is 15 / 25=0.6, and the order acceptance matching value is 1-0.6×0.6=0.64. After comparison, the server finds that driver C's order acceptance matching value of 0.71 is higher than driver D's 0.64, so driver C is determined as the target driver terminal to accept Xiao Wang's ride order.

[0063] For another example, at noon on a holiday, user Xiao Zhang places a ride-hailing order on the first ride-hailing platform near a scenic spot. The server obtains information about nearby drivers E and F. Driver E is currently in service, and the destination of the current order is 7 kilometers away from the location where the order was initiated. Due to the large volume of people on the roads around the scenic spot, the estimated pick-up time is 30 minutes. Driver F is also in service, and the destination of the current order is 5 kilometers away from the location where the order was initiated, and the estimated pick-up time is 20 minutes. The server specifies that drivers E and F are currently serving the order. Assume that the preset maximum pick-up distance is 12 kilometers and the preset maximum pick-up time is 40 minutes. Driver E's first order coefficient is 7 / 12≈0.58, the second order coefficient is 30 / 40=0.75, and the order matching value is 1-0.58×0.75=0.53. Driver F's first order coefficient is 5 / 12≈0.42, the second order coefficient is 20 / 40=0.5, and the order matching value is 1-0.42×0.5=0.79. By comparison, the order matching value of driver F, 0.79, is greater than that of driver E, 0.53. The server selects driver F as the target driver terminal to serve Xiao Zhang's ride order. Through the detailed descriptions of the above different scenarios, we can see how the server can accurately and efficiently determine the target driver terminal to receive ride orders based on information such as vehicle location and order initiation location under various traffic and service conditions.

[0064] In an embodiment of the present invention, determining the target driver terminal from the multiple pending driver terminals to receive the ride-hailing order based on the estimated pick-up distance and the estimated pick-up time can be implemented through the following example.

[0065] Calculate the ratio of the estimated pick-up distance corresponding to each of the pending driver terminals to the preset maximum pick-up distance to obtain a first order acceptance coefficient corresponding to each of the pending driver terminals;

[0066] Calculate the ratio of the estimated pick-up time corresponding to each of the pending driver terminals to the preset maximum pick-up time to obtain a second order acceptance coefficient corresponding to each of the pending driver terminals;

[0067] Calculate the complementary number of the product of the first order acceptance coefficient and the second order acceptance coefficient corresponding to each of the pending driver terminals, and use the complementary number as the order acceptance matching value, where the complementary number plus the product of the first order acceptance coefficient and the second order acceptance coefficient is equal to 1;

[0068] According to the order matching value corresponding to each pending driver terminal, the terminals are sorted in descending order, and the pending driver terminal with the largest order matching value is selected as the target driver terminal, and the ride-hailing order is accepted.

[0069] In an embodiment of the present invention, illustratively, the server receives a ride-hailing order issued by user Xiao Zhao on the first ride-hailing platform, and the order initiation location is a shopping mall. The server obtains three pending driver terminals within the preset range, namely driver A, driver B and driver C. Driver A's estimated pick-up distance is 4 kilometers, and the preset maximum pick-up distance is 6 kilometers. The first order coefficient is calculated to be 4÷6≈0.67. The estimated pick-up time is 15 minutes, the preset maximum pick-up time is 20 minutes, and the second order coefficient is 15÷20=0.75. The product of the first order coefficient and the second order coefficient is 0.67×0.75=0.5025, and the complementary number, that is, the order matching value, is 1-0.5025=0.4975. Driver B's estimated pick-up distance is 3 kilometers, the preset maximum pick-up distance is 6 kilometers, and the first order coefficient is 3÷6=0.5. The estimated pick-up time is 10 minutes, the preset maximum pick-up time is 20 minutes, and the second order coefficient is 10÷20=0.5. The product is 0.5×0.5=0.25, and the order matching value is 1-0.25=0.75. The estimated pick-up distance of driver C is 5 kilometers, the preset maximum pick-up distance is 6 kilometers, and the first order coefficient is 5÷6≈0.83. The estimated pick-up time is 18 minutes, the preset maximum pick-up time is 20 minutes, and the second order coefficient is 18÷20=0.9. The product is about 0.747, and the order matching value is 1-0.747≈0.253. The server sorts the order matching values ​​of the three drivers from large to small as follows: Driver B (0.75)>Driver C (0.253)>Driver A (0.4975). Therefore, driver B is selected as the target driver terminal to accept Xiao Zhao's car order.

[0070] For another example, user Xiao Qian initiates a ride-hailing order on the first ride-hailing platform, and the order is initiated from a movie theater. The server obtains nearby drivers Ding, Wu, and Ji. Driver Ding's estimated pick-up distance is 5 kilometers, the preset maximum pick-up distance is 8 kilometers, and the first order coefficient is 5÷8=0.625. The estimated pick-up time is 12 minutes, the preset maximum pick-up time is 16 minutes, and the second order coefficient is 12÷16=0.75. The product is 0.625×0.75=0.46875, and the order matching value is 1-0.46875=0.53125. Driver Wu's estimated pick-up distance is 6 kilometers, the preset maximum pick-up distance is 8 kilometers, and the first order coefficient is 6÷8=0.75. The estimated pick-up time is 14 minutes, the preset maximum pick-up time is 16 minutes, and the second order coefficient is 14÷16=0.875. The product is 0.65625, and the order matching value is 1-0.65625=0.34375. The estimated pick-up distance of driver Ji is 4 kilometers, the preset maximum pick-up distance is 8 kilometers, and the first order coefficient is 4÷8=0.5. The estimated pick-up time is 10 minutes, the preset maximum pick-up time is 16 minutes, and the second order coefficient is 10÷16=0.625. The product is 0.3125, and the order matching value is 1-0.3125=0.6875. The order matching values ​​are ranked from large to small: driver Ji (0.6875)>driver Ding (0.53125)>driver Wu (0.34375). Therefore, the server selects driver Ji as the target driver terminal to accept Xiao Qian's order.

[0071] For another example, user Xiao Sun places a ride-hailing order on the first ride-hailing platform, and the order is initiated at a subway station. The server obtains the surrounding drivers Geng, Xin, and Ren. Driver Geng's estimated pick-up distance is 3 kilometers, the preset maximum pick-up distance is 5 kilometers, and the first order coefficient is 3÷5=0.6. The estimated pick-up time is 8 minutes, the preset maximum pick-up time is 12 minutes, and the second order coefficient is 8÷12≈0.67. The product is about 0.402, and the order matching value is 1-0.402=0.598. Driver Xin's estimated pick-up distance is 2 kilometers, the preset maximum pick-up distance is 5 kilometers, and the first order coefficient is 2÷5=0.4. The estimated pick-up time is 6 minutes, the preset maximum pick-up time is 12 minutes, and the second order coefficient is 6÷12=0.5. The product is 0.2, and the order matching value is 1-0.2=0.8. The estimated pick-up distance of driver Ren is 4 kilometers, the preset maximum pick-up distance is 5 kilometers, and the first order coefficient is 4÷5=0.8. The estimated pick-up time is 10 minutes, the preset maximum pick-up time is 12 minutes, and the second order coefficient is 10÷12≈0.83. The product is about 0.664, and the order matching value is 1-0.664=0.336. The order matching value is ranked as follows: driver Xin (0.8)>driver Geng (0.598)>driver Ren (0.336). The server finally determines driver Xin as the target driver terminal to pick up Xiao Sun's ride order. Through the detailed calculation and comparison under the above different scenarios, the server can accurately determine the target driver terminal that is most suitable for picking up the ride order based on the order matching value, which improves the rationality and efficiency of dispatching.

[0072] In the embodiments of the present invention, the following implementation modes are also provided.

[0073] If the target driver terminal reports an abnormal situation, the steps of re-sorting the order matching values ​​corresponding to each of the pending driver terminals from large to small, selecting the pending driver terminal with the largest order matching value as the target driver terminal, and accepting the ride-hailing order are performed.

[0074] In an embodiment of the present invention, for example, scenario 1: vehicle failure, on a busy Friday evening, the server selects the target driver terminal for user Xiao Chen, driver Lao Zhang, according to the normal process. Shortly after Lao Zhang accepted the order, his vehicle suddenly broke down and he could not continue to pick up Xiao Chen. Lao Zhang fed back this abnormal situation to the server through the terminal. After receiving Lao Zhang's abnormal feedback, the server immediately re-executed the step of selecting the target driver terminal according to the order matching value. At this time, the server re-acquires the relevant information of other pending driver terminals within the preset range. For example, there are also driver Lao Wang, driver Lao Li and driver Lao Zhao. The server calculates their order matching values ​​respectively according to the previous calculation method. Driver Lao Wang's estimated pick-up distance is 5 kilometers, the preset maximum pick-up distance is 8 kilometers, and the first order coefficient is 5÷8=0.625. The estimated pick-up time is 15 minutes, the preset maximum pick-up time is 20 minutes, and the second order coefficient is 15÷20=0.75. The product of the two is 0.625×0.75=0.46875, and the order matching value is 1-0.46875=0.53125. The estimated pick-up distance for driver Lao Li is 4 kilometers, the preset maximum pick-up distance is 8 kilometers, and the first order coefficient is 4÷8=0.5. The estimated pick-up time is 12 minutes, the preset maximum pick-up time is 20 minutes, and the second order coefficient is 12÷20=0.6. The product is 0.5×0.6=0.3, and the order matching value is 1-0.3=0.7. The estimated pick-up distance for driver Lao Zhao is 6 kilometers, the preset maximum pick-up distance is 8 kilometers, and the first order coefficient is 6÷8=0.75. The estimated pick-up time is 18 minutes, the preset maximum pick-up time is 20 minutes, and the second order coefficient is 18÷20=0.9. The product is 0.75×0.9=0.675, and the order matching value is 1-0.675=0.325. The order matching values ​​are ranked from large to small as follows: driver Lao Li (0.7)>driver Lao Wang (0.53125)>driver Lao Zhao (0.325). The server reselects driver Lao Li as the target driver terminal and notifies him to accept Xiao Chen's ride order.

[0075] Scenario 2: The driver has something to do temporarily. On a weekday morning, the server selects the target driver terminal for user Xiao Zhou - driver Lao Sun. However, Lao Sun suddenly received an emergency call before departure and needed to deal with family affairs, so he could not accept the order. Lao Sun promptly reported the abnormality to the server. After receiving the feedback, the server quickly resumed the work of selecting the target driver terminal. The pending driver terminals participating in the competition this time are driver Lao Wu, driver Lao Zhou, and driver Lao Zheng. The estimated pick-up distance for driver Lao Wu is 3 kilometers, the preset maximum pick-up distance is 6 kilometers, and the first order coefficient is 3÷6=0.5. The estimated pick-up time is 10 minutes, the preset maximum pick-up time is 15 minutes, and the second order coefficient is 10÷15≈0.67. The product is approximately 0.5×0.67=0.335, and the order matching value is 1-0.335=0.665. The estimated pick-up distance for driver Lao Zhou is 4 kilometers, the preset maximum pick-up distance is 6 kilometers, and the first order coefficient is 4÷6≈0.67. The estimated pick-up time is 12 minutes, the preset maximum pick-up time is 15 minutes, and the second order coefficient is 12÷15=0.8. The product is approximately 0.67×0.8=0.536, and the order matching value is 1-0.536=0.464. The estimated pick-up distance for driver Lao Zheng is 5 kilometers, the preset maximum pick-up distance is 6 kilometers, and the first order coefficient is 5÷6≈0.83. The estimated pick-up time is 14 minutes, the preset maximum pick-up time is 15 minutes, and the second order coefficient is 14÷15≈0.93. The product is approximately 0.83×0.93=0.7719, and the order matching value is 1-0.7719=0.2281. The order matching values ​​are ranked from large to small as follows: driver Lao Wu (0.665) > driver Lao Zhou (0.464) > driver Lao Zheng (0.2281). Therefore, the server re-determines driver Lao Wu as the target driver terminal to pick up Xiao Zhou's ride order.

[0076] Scenario 3: A traffic accident blocked the route. At noon on the weekend, the server selected the target driver terminal for user Xiao Wu - driver Lao Chen. However, Lao Chen encountered a traffic accident while driving, and the road was severely blocked. It was estimated that he could not arrive at the pick-up location on time. Lao Chen reported this situation to the server. After receiving the abnormal feedback, the server started the process of reselecting the target driver terminal again. At this time, the pending driver terminals are driver Lao Hu, driver Lao Xu, and driver Lao Yang. The estimated pick-up distance of driver Lao Hu is 4 kilometers, the preset maximum pick-up distance is 7 kilometers, and the first order coefficient is 4÷7≈0.57. The estimated pick-up time is 11 minutes, the preset maximum pick-up time is 18 minutes, and the second order coefficient is 11÷18≈0.61. The product is approximately 0.57×0.61=0.3477, and the order matching value is 1-0.3477=0.6523. The estimated pick-up distance for driver Lao Xu is 3 kilometers, the preset maximum pick-up distance is 7 kilometers, and the first order coefficient is 3÷7≈0.43. The estimated pick-up time is 9 minutes, the preset maximum pick-up time is 18 minutes, and the second order coefficient is 9÷18=0.5. The product is approximately 0.43×0.5=0.215, and the order matching value is 1-0.215=0.785. The estimated pick-up distance for driver Lao Yang is 5 kilometers, the preset maximum pick-up distance is 7 kilometers, and the first order coefficient is 5÷7≈0.71. The estimated pick-up time is 13 minutes, the preset maximum pick-up time is 18 minutes, and the second order coefficient is 13÷18≈0.72. The product is approximately 0.71×0.72=0.5112, and the order matching value is 1-0.5112=0.4888. The order matching values ​​are ranked from large to small as follows: driver Lao Xu (0.785) > driver Lao Hu (0.6523) > driver Lao Yang (0.4888). The server reselects driver Lao Xu as the target driver terminal to pick up Xiao Wu. Through the detailed descriptions of the above different scenarios, we can see that when an abnormality occurs in the target driver terminal, the server can quickly and effectively reselect the appropriate driver terminal to receive the ride order, ensuring that the user's travel needs are met.

[0077] In an embodiment of the present invention, the configuration information also includes order preference information, and the aforementioned step S202 can be implemented through the following example execution.

[0078] According to the order initiation location, obtaining configuration information of multiple initial driver terminals within a preset range;

[0079] According to the order acceptance preference information corresponding to each of the initial driver terminals, the multiple initial driver terminals are cleaned to obtain the configuration information of the multiple pending driver terminals.

[0080] In an embodiment of the present invention, for example, on a busy weekend afternoon, the server receives a ride-hailing order issued by user Xiao Yang on the first ride-hailing platform, and the order initiation location is a bustling commercial district in the city center. The server first obtains the configuration information of multiple initial driver terminals within a preset range according to the order initiation location. For example, the configuration information of driver A, driver B, driver C, driver D, and driver E is obtained. Driver A's order acceptance preference is to only accept orders within 3 kilometers from the current location and with a higher travel amount; driver B's order acceptance preference is not to accept orders to congested areas; driver C's order acceptance preference is to give priority to orders that are on the way and have a longer journey; driver D's order acceptance preference is to only accept orders within a specific time period; driver E has no special order acceptance preference. The server cleans the initial driver terminal according to these order acceptance preference information. Since the current order initiation location is more than 3 kilometers away from driver A and the travel amount does not meet his requirements, driver A is excluded. Driver B is also excluded because the current route to the order initiation location is relatively congested and does not meet his order acceptance preference. Driver D is also excluded because he is not currently in the order acceptance time period set by him. After cleaning, driver C and driver E are left as pending driver terminals. The server obtains their vehicle location information, service status information and other configuration information to carry out the subsequent target driver terminal determination process.

[0081] For another example, in the early morning of a weekday, user Xiao Zhou places a ride-hailing order from a residential area. According to the location where the order was initiated, the server obtains the configuration information of multiple initial driver terminals within a preset range, including driver A, driver B, driver C, and driver D. Driver A's order acceptance preference is to only accept orders to the airport or train station; driver B's order acceptance preference is not to accept orders during the morning rush hour; driver C's order acceptance preference is to give priority to orders within 5 kilometers and with good passenger reviews; driver D's order acceptance preference is not to accept carpooling orders. The server cleans according to these order acceptance preferences. Because the current order is not to the airport or train station, driver A is excluded. Because it is currently during the morning rush hour, driver B does not meet the requirements and is excluded. Driver D is also excluded because this order may be a carpooling order and does not meet his preferences. In the end, only driver C meets the order acceptance preferences and becomes a pending driver terminal. The server obtains its detailed configuration information and prepares to determine whether it is the target driver terminal.

[0082] For another example, at noon on holidays, user Xiao Liu places a ride-hailing order near a tourist attraction. The server obtains the initial driver terminals within the preset range, including driver E, driver F, driver G, and driver H. Driver E's order acceptance preference is to only accept orders with a journey of more than 10 kilometers; driver F's order acceptance preference is not to accept orders near scenic spots; driver G's order acceptance preference is to give priority to orders with children or the elderly; driver H's order acceptance preference is to only accept orders after 2 pm. The server performs cleaning based on these preferences. Driver E is excluded because the current order journey is expected to be less than 10 kilometers. Driver F is excluded because he does not meet the requirements because the current order is located near the scenic spot. Driver H is also excluded because it has not yet reached the set order acceptance time. Finally, only driver G becomes the pending driver terminal, and the server obtains its configuration information for subsequent judgment and selection. Through the detailed processing process under the above different scenarios, the server can accurately screen the pending driver terminals according to the order initiation location and the driver's order acceptance preference information, improve the accuracy and efficiency of dispatching orders, and better meet the needs of users and drivers.

[0083] In the embodiments of the present invention, the following implementation modes are also provided.

[0084] Get the service status information of the current driver terminal;

[0085] When the service status information of the current driver terminal indicates that the service is about to be completed, obtaining the vehicle location information of the current driver terminal;

[0086] According to the vehicle location information, obtaining a car booking order issued by a plurality of pending user terminals on a first car booking platform within a preset range, wherein the car booking order includes an order initiation location;

[0087] Determine the current order initiation location corresponding to the current user terminal from the multiple pending user terminals according to the vehicle location information of the current driver terminal and the order initiation location;

[0088] Obtaining current navigation information of the current driver terminal on the second ride-hailing platform;

[0089] Generate predicted navigation information corresponding to the current user terminal according to the current order initiation location and the current navigation information;

[0090] The current navigation information and the predicted navigation information are integrated to obtain integrated navigation information, and the integrated navigation information is pushed to the first ride-hailing platform and displayed on the current user terminal.

[0091] In an embodiment of the present invention, for example, at a busy intersection in the city center, the driver Mr. Li is about to complete the current service order. The server obtains Mr. Li's service status information in real time and confirms that he is about to complete the service. Then, the server obtains Mr. Li's current vehicle location information, for example, he is now near a shopping mall in the city center. Then, based on Mr. Li's vehicle location information, the server searches for car booking orders issued by multiple pending user terminals on the first car booking platform within a preset range. Assume that the car booking orders of users Ms. Wang, Mr. Zhao and Ms. Sun are obtained, and these orders all include detailed order initiation locations. The server analyzes and calculates based on Mr. Li's vehicle location and these order initiation locations. For example, Ms. Wang's order initiation location is closer to Mr. Li, Mr. Zhao's is slightly farther, and Ms. Sun's is the farthest. After comprehensive evaluation, it is determined that Ms. Wang's order initiation location is the current order initiation location corresponding to the current user terminal. The server obtains Mr. Li's current navigation information on the second car booking platform, and the navigation information shows that he is about to go to the end point of the current order. Then, the server generates the corresponding predicted navigation information of Ms. Wang based on Ms. Wang's current order initiation location and Mr. Li's current navigation information. For example, it is predicted that after Mr. Li completes his current order, he only needs to drive a few blocks and pass a traffic light to reach Ms. Wang's pick-up point. Subsequently, the server merges Mr. Li's current navigation information on the second ride-hailing platform with the predicted navigation information generated for Ms. Wang to obtain fused navigation information. Finally, the server pushes the fused navigation information to the first ride-hailing platform and displays it on Ms. Wang's user terminal, so that Ms. Wang can know Mr. Li's estimated arrival time and route in advance.

[0092] For another example, on a suburban highway, the driver Ms. Chen is about to end her current trip. The server obtains this service status information and obtains the location of her vehicle, such as near a gas station on a highway. Based on the vehicle location, the server obtains pending user orders on the first ride-hailing platform within a preset range. Suppose that the ride-hailing requests of student Xiao Zhang, office worker Xiao Li, and retired old man Liu Daye are obtained. By comparing the vehicle location and the order initiation location, the server determines that Xiao Zhang's location is the most suitable and uses it as the current order initiation location corresponding to the current user terminal. After obtaining Ms. Chen's current navigation information on the second ride-hailing platform, the server generates predicted navigation information for Xiao Zhang. It may be that you continue to drive along the current road for a distance, then turn right and enter a small road to reach the gate of the community where Xiao Zhang is located. After integrating the relevant navigation information, the server pushes it to the first ride-hailing platform and displays it on Xiao Zhang's terminal, so that Xiao Zhang can prepare for the ride.

[0093] For another example, on the exit road of the airport, the driver Mr. Wu's service is about to be completed. The server obtains this situation and the location of his vehicle, such as near the parking lot of the airport. The server obtains the car-hailing orders of multiple users on the first car-hailing platform within the preset range, including Ms. Zhao, a business person who just got off the plane, a family who is traveling, and Mr. Qian who is going to attend a meeting. After comprehensive consideration, the server determines that the order initiation location of Ms. Zhao is the current order initiation location corresponding to the current user terminal. The current navigation information of Mr. Wu on the second car-hailing platform is obtained, and predictive navigation information is generated for Ms. Zhao, such as starting from the parking lot, passing through an exit of the airport highway, and then driving to the hotel where Ms. Zhao is located. After integrating the navigation information, it is pushed to the first car-hailing platform and displayed on Ms. Zhao's terminal, so that Ms. Zhao can grasp the dynamics of the trip. Through the detailed description of the above different scenarios, we can see how the server accurately matches new orders for drivers who are about to complete the service in various actual situations and provides users with accurate navigation information.

[0094] In the embodiment of the present invention, the aforementioned step S206 can be implemented through the following example execution.

[0095] Obtain real-time road congestion and weather conditions;

[0096] The current navigation information, the predicted navigation information, the real-time road congestion situation and the weather factors are integrated to obtain the integrated navigation information.

[0097] In an embodiment of the present invention, exemplarily, during the morning rush hour of a weekday, the server obtains the current navigation information of the driver Mr. Wang on the second car-hailing platform. He is driving along the main road of the city and is about to complete the current service order. At the same time, the server receives multiple new car-hailing orders. The server first obtains the real-time road congestion situation and finds that vehicles on the main roads of the city are driving slowly and multiple intersections are seriously congested. In addition, in terms of weather factors, it was drizzling that day and the road was slippery, which may affect the driving speed. Then, the server generates the predicted navigation information for one of the passengers, Ms. Li. Ms. Li's order was initiated at the gate of a community. The server integrates the current navigation information, the predicted navigation information, the real-time road congestion situation and the weather factors. Due to the current road congestion, the server will consider avoiding the most congested section on the main road when integrating the navigation information. The route originally planned through the main road in the predicted navigation information is adjusted to bypass through some branch roads. Although the journey may be slightly longer, it can avoid serious congestion. At the same time, considering the slippery road and poor visibility caused by rainy weather, the server will appropriately reduce the estimated driving speed in the fused navigation information and remind the driver Mr. Wang to pay attention to driving safety. Ultimately, the fused navigation information obtained will provide the driver Mr. Wang with a more reasonable driving route and estimated arrival time, and also allow the passenger Ms. Li to understand in advance the possible impact on the trip.

[0098] On a weekend afternoon, the driver Ms. Zhao is about to complete the current service, and the server obtains her current navigation information. At this time, the real-time road congestion shows that there are many vehicles on the roads around the commercial street in the city, but the traffic is still relatively smooth. In terms of weather factors, the sun is bright and the climate is pleasant. A passenger Mr. Sun has placed a ride order, and the order was initiated near a park. The server generates fusion navigation information. Considering that the road has a certain amount of traffic but is generally smooth, the fusion navigation information still plans a relatively direct route. However, since there are many pedestrians around the commercial street on weekends, the server will remind the driver Ms. Zhao to pay attention to pedestrians during fusion. The good weather is also reflected in the fusion navigation information, and the driving speed is expected to remain at a normal level. The final generated fusion navigation information allows both the driver Ms. Zhao and the passenger Mr. Sun to have a clear and accurate expectation of the trip. In the evening of the holiday, the driver Mr. Chen is about to end his service. The server obtains his current navigation and new ride orders. The real-time road congestion is extremely serious, and there is a long-distance congestion on the highway out of the city. In terms of weather factors, strong winds are blowing, which may have a certain impact on driving. Passenger Ms. Zhou places a ride-hailing order at a high-speed railway station. When the server integrates the navigation information, it chooses to avoid the highway section in the direction of leaving the city and takes the national and provincial roads instead, due to the congestion on the highway. At the same time, due to the strong wind, the expected driving speed is reduced, and the driver Mr. Chen is reminded to pay attention to the crosswind. The integrated navigation information provides the driver Mr. Chen and passenger Ms. Zhou with the best travel plan for dealing with complex road conditions and weather. Through the detailed descriptions of the above different scenarios, we can clearly see how the server integrates various factors to generate integrated navigation information to provide better services.

[0099] In order to more clearly describe the solution provided by the embodiments of the present application, a complete implementation method is provided below.

[0100] Basic information: The system needs to monitor the driver's location, status (whether the driver is out and in service) and passenger order requests in real time. Drivers can set order preferences in the system, such as whether it is on the way and the type of order. The system integrates map services, positioning technology, database management, real-time communication and other technologies to support efficient order processing and information sharing. Monitor the destination of each order, the estimated completion time, and whether there are more orders that the driver can take on, so that the driver can have more orders to choose from after completing each order with a high probability.

[0101] Finding drivers for orders: The driver-side system device can collect the driver's personal information, order setting information, location information, and service status information in real time, and upload them to the back-end system device. The back-end system device can record the status based on the uploaded driver information, and clean and correct the location coordinates.

[0102] The operation backend system can configure the pick-up distance limit and pick-up time limit for multi-channel serial orders.

[0103] When a passenger places an order, the order enters the dispatching backend system and will include the passenger’s ordered vehicle type, city, channel, location, estimated amount, estimated time, and estimated mileage.

[0104] Based on the passenger's order information, read the configuration in the operation background system. Use GEO to select a group of drivers, and obtain the driver's vehicle model and status based on the driver's information.

[0105] Find a driver in service and calculate the estimated pick-up distance.

[0106] (1) The driver's location information will be uploaded in real time, and a location point will be reported every 3 seconds. The embodiment of the present invention binds the estimated remaining distance + time of navigation to the LBS point.

[0107] (2) Use the driver's navigation to calculate the remaining estimated distance and estimated time

[0108] (3) Use a map provider (Amap, Baidu Map, or Tencent Map) to calculate the estimated distance and time from the driver’s current order destination to the next order’s starting point.

[0109] Use (2) + (3) to calculate the estimated distance and time for the next pickup.

[0110] Key points: The current industry practice is to directly call the map provider, and when there are multiple drivers, use the map provider's batch route calculation function, that is, a driver needs to call the map provider twice. However, the embodiment of the present invention uses the estimated distance and estimated time of the remaining navigation on the driver side (the driver is serving an order) as the first segment, and calls the map provider for the second segment, which will reduce the map provider call and improve efficiency.

[0111] According to the configuration of the operation background system, find the driver who meets the estimated pick-up distance and estimated pick-up time.

[0112] When there are multiple drivers, find the driver whose estimated pick-up time is closest to the estimated pick-up time.

[0113] Algorithm: sqrt(1-(pickup distance / maximum pickup distance)*(pickup time / maximum pickup time))

[0114] Then the navigation from the driver’s current location to the destination of the current order and the starting point of the next order will be pushed in real time to the platform where the passenger places the order. The platform where the passenger places the order will show the passenger’s trajectory and the driver’s real-time location in real time.

[0115] Key point: Because passengers place orders on different platforms, the embodiment of the present invention needs to give the destination of the previous passenger to the platform where the next passenger places the order. If the order is placed on the same platform, this step is not required.

[0116] Drivers looking for orders: Drivers will upload their location points every 3 seconds. This location point will be bound to the order information and navigation information in the driver service. This information will be passed to the backend system.

[0117] When the driver is about to complete the order, draw a circle with the order that the driver is about to complete as the end point and the center.

[0118] Look for a batch of orders in the call.

[0119] Find the order with the closest estimated pick-up distance and estimated pick-up time.

[0120] Algorithm: sqrt(1-(pickup distance / maximum pickup distance)*(pickup time / maximum pickup time))

[0121] Then the navigation from the driver’s current location to the destination of the current order and the starting point of the next order will be pushed in real time to the platform where the passenger places the order. The platform where the passenger places the order will show the passenger’s trajectory and the driver’s real-time location in real time.

[0122] Key point: Because passengers place orders on different platforms, the embodiment of the present invention needs to give the destination of the previous passenger to the platform where the next passenger places the order. If the order is placed on the same platform, this step is not required.

[0123] The current industry practice is to use orders to find drivers. In the embodiment of the present invention, orders find drivers and drivers find orders in parallel. Advantages: (1) Orders find drivers can quickly respond to passengers' requests to ensure the passenger experience. (2) Drivers find orders to meet drivers' demands, ensure that drivers' income is maximized and orders are received more recently, and improve drivers' efficiency.

[0124] Please refer to Figure 2 , Figure 2 A cross-platform car-hailing serial order dispatching device 110 provided in an embodiment of the present invention includes:

[0125] An acquisition module 1101 is used to acquire a ride-hailing order issued by a target user terminal on a first ride-hailing platform, wherein the ride-hailing order includes an order initiation location;

[0126] The calculation module 1102 is used to obtain configuration information of multiple pending driver terminals within a preset range according to the order initiation location, wherein the configuration information includes vehicle location information and service status information; when the service status information is in service, determine a target driver terminal from the multiple pending driver terminals to receive the ride-hailing order according to the vehicle location information; obtain current navigation information of the target driver terminal on the second ride-hailing platform; and call a preset map service to generate predicted navigation information according to the current navigation information and the order initiation location information;

[0127] The dispatching module 1103 is used to fuse the current navigation information and the predicted navigation information to obtain fused navigation information, and push the fused navigation information to the first car-hailing platform and display it to the target user terminal.

[0128] It should be noted that the implementation principle of the aforementioned cross-platform car-hailing serial order dispatching device 110 can refer to the implementation principle of the aforementioned cross-platform car-hailing serial order dispatching method, which will not be repeated here. It should be understood that the division of the various modules of the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or physically separated. And these modules can all be implemented in the form of software called by processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software called by processing elements, and some modules can be implemented in the form of hardware. For example, the cross-platform car-hailing serial order dispatching device 110 can be a separately established processing element, or it can be integrated in a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a processing element of the above-mentioned device. The function of the above cross-platform car-hailing serial order dispatching device 110. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in a processor element or an instruction in software form.

[0129] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more microprocessors (digital signal processors, DSP), or one or more field programmable gate arrays (FPGA), etc. For another example, when a module above is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0130] The embodiment of the present invention provides a computer device 100, which includes a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device 100 executes the aforementioned cross-platform car-hailing serial order dispatching device 110. Figure 3 As shown, Figure 3 The computer device 100 provided in the embodiment of the present invention is a structural block diagram. The computer device 100 includes a cross-platform car-hailing serial order dispatching device 110, a memory 111, a processor 112 and a communication unit 113.

[0131] In order to realize data transmission or interaction, the memory 111, the processor 112 and the communication unit 113 are electrically connected to each other directly or indirectly. For example, the electrical connection between these elements can be realized through one or more communication buses or signal lines. The cross-platform car-hailing serial order dispatching device 110 includes at least one software function module that can be stored in the memory 111 in the form of software or firmware or solidified in the operating system (OS) of the computer device 100. The processor 112 is used to execute the cross-platform car-hailing serial order dispatching device 110 stored in the memory 111, such as the software function modules and computer programs included in the cross-platform car-hailing serial order dispatching device 110.

[0132] An embodiment of the present invention provides a readable storage medium, which includes a computer program. When the computer program is running, it controls the computer device where the readable storage medium is located to execute the aforementioned cross-platform car-hailing chain order dispatching method.

[0133] For illustrative purposes, the foregoing description is made with reference to specific embodiments. However, the above illustrative discussion is not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. Numerous modifications and variations are possible in accordance with the above teachings. These embodiments are selected and described in order to best illustrate the principles of the present disclosure and its practical application, so that those skilled in the art can best utilize the present disclosure and utilize various embodiments with different modifications to suit the intended specific application.

Claims

1. A method for dispatching a cross-platform car-hailing serial order, characterized in that: include: Acquire a ride-hailing order issued by a target user terminal on a first ride-hailing platform, wherein the ride-hailing order includes an order initiation location; According to the order initiation location, obtaining configuration information of multiple pending driver terminals within a preset range, wherein the configuration information includes vehicle location information and service status information; When the service status information indicates that the service is in progress, a target driver terminal is determined from the plurality of pending driver terminals according to the vehicle location information to receive the ride order; Obtaining current navigation information of the target driver terminal on the second ride-hailing platform; Calling a preset map service to generate predicted navigation information according to the current navigation information and the order initiation location information; fusing the current navigation information and the predicted navigation information to obtain fused navigation information, and pushing the fused navigation information to the first ride-hailing platform and displaying it to the target user terminal; The step of determining a target driver terminal from the plurality of pending driver terminals to receive the ride order according to the vehicle location information includes: Determine the current destination of the current order being served according to the vehicle location information; Determine the estimated pick-up distance and estimated pick-up time corresponding to each of the pending driver terminals according to the order initiation location and the current destination; According to the estimated pick-up distance and the estimated pick-up time, determining the target driver terminal from the multiple pending driver terminals to receive the ride order; The step of determining the target driver terminal from the plurality of pending driver terminals to receive the ride order according to the estimated pick-up distance and the estimated pick-up time includes: Calculate the ratio of the estimated pick-up distance corresponding to each of the pending driver terminals to the preset maximum pick-up distance to obtain a first order acceptance coefficient corresponding to each of the pending driver terminals; Calculate the ratio of the estimated pick-up time corresponding to each of the pending driver terminals to the preset maximum pick-up time to obtain a second order acceptance coefficient corresponding to each of the pending driver terminals; Calculate the complementary number of the product of the first order acceptance coefficient and the second order acceptance coefficient corresponding to each of the pending driver terminals, and use the complementary number as the order acceptance matching value, where the complementary number plus the product of the first order acceptance coefficient and the second order acceptance coefficient is equal to 1; According to the order matching value corresponding to each of the pending driver terminals, the pending driver terminal with the largest order matching value is sorted in descending order, and the pending driver terminal with the largest order matching value is selected as the target driver terminal, and the ride order is accepted; The method further comprises: If the target driver terminal reports an abnormal situation, re-execute the step of sorting the order matching values ​​corresponding to each of the pending driver terminals from large to small, select the pending driver terminal with the largest order matching value as the target driver terminal, and accept the ride order; The configuration information also includes order preference information. The configuration information of multiple pending driver terminals within a preset range is obtained according to the order initiation location, including: According to the order initiation location, obtaining configuration information of multiple initial driver terminals within a preset range; Cleaning the multiple initial driver terminals according to the order acceptance preference information corresponding to each of the initial driver terminals, and obtaining configuration information of the multiple pending driver terminals; The method further comprises: Get the service status information of the current driver terminal; When the service status information of the current driver terminal indicates that the service is about to be completed, obtaining the vehicle location information of the current driver terminal; According to the vehicle location information, obtaining a car booking order issued by a plurality of pending user terminals on a first car booking platform within a preset range, wherein the car booking order includes an order initiation location; Determine the current order initiation location corresponding to the current user terminal from the multiple pending user terminals according to the vehicle location information of the current driver terminal and the order initiation location; Obtaining current navigation information of the current driver terminal on the second ride-hailing platform; Generate predicted navigation information corresponding to the current user terminal according to the current order initiation location and the current navigation information; fusing the current navigation information and the predicted navigation information to obtain fused navigation information, and pushing the fused navigation information to the first ride-hailing platform and displaying it on the current user terminal; The fusing the current navigation information and the predicted navigation information to obtain fused navigation information includes: Obtain real-time road congestion and weather conditions; The current navigation information, the predicted navigation information, the real-time road congestion situation and the weather factors are integrated to obtain the integrated navigation information.

2. A cross-platform car-hailing serial order dispatching device, characterized in that: include: An acquisition module, configured to acquire a ride-hailing order issued by a target user terminal on the first ride-hailing platform, wherein the ride-hailing order includes an order initiation location; A calculation module is used to obtain configuration information of multiple pending driver terminals within a preset range according to the order initiation location, wherein the configuration information includes vehicle location information and service status information; when the service status information indicates that the service is in progress, a target driver terminal is determined from the multiple pending driver terminals according to the vehicle location information to receive the ride order; Obtaining current navigation information of the target driver terminal on the second ride-hailing platform; Calling a preset map service to generate predicted navigation information according to the current navigation information and the order initiation location information; A dispatching module, configured to fuse the current navigation information and the predicted navigation information to obtain fused navigation information, and push the fused navigation information to the first car-hailing platform and display it to the target user terminal; The calculation module is specifically used for: Determine the current destination of the current order being served based on the vehicle location information; determine the estimated pick-up distance and estimated pick-up time corresponding to each of the pending driver terminals based on the order initiation location and the current destination; According to the estimated pick-up distance and the estimated pick-up time, determining the target driver terminal from the multiple pending driver terminals to receive the ride order; The calculation module is further specifically used for: Calculate the ratio of the estimated pick-up distance corresponding to each of the pending driver terminals to the preset maximum pick-up distance to obtain a first order acceptance coefficient corresponding to each of the pending driver terminals; calculate the ratio of the estimated pick-up time corresponding to each of the pending driver terminals to the preset maximum pick-up time to obtain a second order acceptance coefficient corresponding to each of the pending driver terminals; Calculate the complementary number of the product of the first order acceptance coefficient and the second order acceptance coefficient corresponding to each of the pending driver terminals, and use the complementary number as the order acceptance matching value, where the complementary number plus the product of the first order acceptance coefficient and the second order acceptance coefficient equals 1; sort the pending driver terminals according to the order acceptance matching values ​​corresponding to each of the pending driver terminals from large to small, select the pending driver terminal with the largest order acceptance matching value as the target driver terminal, and accept the ride-hailing order; The calculation module is also used for: If the target driver terminal reports an abnormal situation, re-execute the step of sorting the order matching values ​​corresponding to each of the pending driver terminals from large to small, select the pending driver terminal with the largest order matching value as the target driver terminal, and accept the ride order; The configuration information also includes order preference information, and the calculation module is specifically used to: According to the order initiation location, the configuration information of multiple initial driver terminals within a preset range is obtained; according to the order acceptance preference information corresponding to each of the initial driver terminals, the multiple initial driver terminals are cleaned to obtain the configuration information of the multiple pending driver terminals; The calculation module is also used for: Obtaining service status information of the current driver terminal; if the service status information of the current driver terminal indicates that the service is about to be completed, obtaining vehicle location information of the current driver terminal; obtaining, based on the vehicle location information, a ride-hailing order issued by a plurality of pending user terminals on a first ride-hailing platform within a preset range, wherein the ride-hailing order includes an order initiation location; Determine the current order initiation location corresponding to the current user terminal from the multiple pending user terminals according to the vehicle location information of the current driver terminal and the order initiation location; Obtaining current navigation information of the current driver terminal on the second ride-hailing platform; Generate predicted navigation information corresponding to the current user terminal according to the current order initiation location and the current navigation information; fusing the current navigation information and the predicted navigation information to obtain fused navigation information, and pushing the fused navigation information to the first ride-hailing platform and displaying it on the current user terminal; The calculation module is specifically used for: Acquire real-time road congestion conditions and weather factors; fuse the current navigation information, the predicted navigation information, the real-time road congestion conditions and the weather factors to obtain the fused navigation information.

3. A computer device, characterized in that: The computer device comprises a processor and a non-volatile memory storing computer instructions. When the computer instructions are executed by the processor, the computer device performs the method of claim 1.

4. A readable storage medium, characterized in that: The readable storage medium includes a computer program, and when the computer program is executed, the computer device where the readable storage medium is located is controlled to execute the method according to claim 1.

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