Methods and devices for processing ride-hailing order data

Abstract

This disclosure provides an order allocation method and apparatus. The method allocates target orders to the user terminal based on the vehicle resource utilization efficiency of each pending order, which can improve the effective working time of vehicle resources and also improve the order execution efficiency. In this embodiment, orders are first pre-allocated and an acceptable waiting time, namely a first future preset time, is set. If it is predicted that a pending order with higher vehicle resource utilization efficiency will be generated within the first future preset time, the pre-allocated order is not directly allocated to the user terminal. Instead, the user terminal waits for the first future preset time and allocates the pending order with higher vehicle resource utilization efficiency generated during the waiting period to the user terminal. This further improves the effective utilization rate of vehicle resources and better optimizes order allocation.

Description

[0001] This application is a divisional application of Chinese invention patent application No. 202111474960.8, filed on December 3, 2021, entitled "An Order Allocation Method and Apparatus". Technical Field

[0002] This disclosure relates to the field of computer technology, and more specifically, to a method and apparatus for processing ride-hailing order data. Background Technology

[0003] With the rapid development of technology, ride-hailing has become one of the mainstream modes of transportation. Passengers can send ride requests to ride-hailing platforms, which can generate orders based on passenger needs and assign these orders to drivers on the platform. After receiving the order, the driver goes to the pick-up location to pick up the passenger and take them to the destination of the order.

[0004] Typically, ride-hailing platforms allocate orders randomly based on passenger and driver locations. However, due to the limited availability of vehicles on ride-hailing platforms, this allocation method affects the efficiency of vehicle resource utilization, meaning that vehicle resources cannot be used efficiently. Summary of the Invention

[0005] This disclosure provides at least one order allocation method and apparatus.

[0006] In a first aspect, embodiments of this disclosure provide an order allocation method, including:

[0007] In response to an order allocation instruction for a user terminal, at least one order to be allocated is identified within the target location area where the user terminal is located;

[0008] From the pending orders, the order with the highest vehicle resource utilization efficiency is selected for the user as the pre-allocated order, and it is predicted whether a pending order with higher vehicle resource utilization efficiency will be generated in the target location area within a first future preset time period; the vehicle resource utilization efficiency is used to characterize the relationship between the consumed vehicle resources and the expected return.

[0009] If it is predicted that no more efficient vehicle resource utilization orders will be generated, then the pre-allocated orders will be assigned to the user as target orders.

[0010] If it is predicted that a more efficient vehicle resource utilization order will be generated, then within the first preset future time period, the generated more efficient vehicle resource utilization order will be allocated as the target order to the user terminal.

[0011] In one optional implementation, the vehicle resource utilization efficiency of the orders to be assigned is determined through the following steps:

[0012] Obtain the current location information of the user terminal, the pick-up location information and destination location information corresponding to the order to be assigned, and determine the expected return information of the order to be assigned;

[0013] Based on the current location information, the pick-up location information, the destination location information, and the expected return information, the vehicle resource utilization efficiency of the order to be assigned is determined.

[0014] In one optional implementation, the vehicle resource utilization efficiency of the order to be assigned is determined based on the current location information, the pick-up location information, the destination location information, and the expected return information, including:

[0015] Based on the pick-up location information and the current location information of the user terminal, determine the pick-up time required for the user terminal to reach the pick-up location of the order to be assigned; and, based on the pick-up location information and the destination location information, determine the estimated execution time of the order to be assigned.

[0016] Based on the destination location information, the pick-up time, and the estimated execution time, the waiting time for the user to be assigned a new order after completing the pending order is predicted.

[0017] Based on the pick-up time, the estimated execution time, the order waiting time, and the expected return information, the vehicle resource utilization efficiency of the orders to be assigned is determined.

[0018] In one optional implementation, predicting whether, within a first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated within the target location area includes:

[0019] Acquire information on multiple first historical orders completed within the target location area, first traffic status information of the target location area that matches the generation time of the first historical order information, and second traffic status information of the target location area that matches the first future preset duration;

[0020] Based on the first historical order information, the first traffic status information, and the second traffic status information, it is predicted whether, within the first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated in the target location area.

[0021] In one optional implementation, determining at least one order to be assigned within the target location area of ​​the user terminal includes:

[0022] Orders whose pick-up location is within the target location area and whose destination location is within the target order allocation location area are the orders to be allocated.

[0023] In one optional implementation, the target order location area is determined according to the following steps:

[0024] Obtain information on multiple second historical orders generated within the parent location region that includes the target location region;

[0025] Based on the historical pick-up location information in the second historical order information, the second historical order information is matched with multiple sub-location areas within the target location area;

[0026] Based on the number of second historical order information matched by each of the sub-level location regions, at least one target sub-level location region is selected from the multiple sub-level location regions;

[0027] Based on the target sub-level location region, the target order location region is determined.

[0028] In one optional implementation, the method further includes:

[0029] If there is no order to be assigned within the target location area where the user terminal is located, the target order to be assigned to the user terminal is selected from the order to be assigned in the candidate location areas surrounding the target location area.

[0030] In an optional implementation, if there is no order to be assigned within the target location area where the user terminal is located, the target order to be assigned to the user terminal is selected from the candidate location areas surrounding the target location area, including:

[0031] Predict whether, within a second future preset time period, there will be any pending orders in the target location area with a vehicle resource utilization efficiency higher than the preset efficiency.

[0032] If it is predicted that no pending orders will be generated with a vehicle resource utilization efficiency higher than the preset efficiency, then the target orders to be allocated to the user terminal will be selected from the pending orders in the candidate location area.

[0033] In one optional implementation, the method further includes:

[0034] If it is predicted that there will be pending orders with higher vehicle resource utilization efficiency than the preset efficiency, then within the second future preset time period, the pending orders with higher vehicle resource utilization efficiency than the target efficiency will be allocated to the user as the target orders.

[0035] In one optional implementation, the method further includes:

[0036] If there are no orders to be assigned in the candidate location area, then the probability value of generating an order to be assigned in the candidate location area within a third future preset time period is predicted;

[0037] If the probability value is higher than a preset threshold, a movement command to move to the candidate location area is sent to the user terminal.

[0038] In one optional implementation, the method further includes:

[0039] After the target order is completed on the user's end, the billing information corresponding to the target order is determined based on the pick-up time and pick-up distance, drop-off time and drop-off distance, and waiting time at the destination of the target order.

[0040] Secondly, embodiments of this disclosure also provide an order allocation device, comprising:

[0041] The determination module is used to determine at least one order to be assigned within the target location area where the user terminal is located in response to an order assignment instruction for the user terminal;

[0042] The prediction module is used to select the order with the highest vehicle resource utilization efficiency from the pending orders as the pre-allocated order for the user terminal, and to predict whether a pending order with higher vehicle resource utilization efficiency will be generated in the target location area within a first future preset time period; the vehicle resource utilization efficiency is used to characterize the relationship between the consumed vehicle resources and the expected return.

[0043] The allocation module is used to allocate the pre-allocated order as a target order to the user terminal when it is predicted that no allocation order with higher vehicle resource utilization efficiency will be generated; and to allocate the generated allocation order with higher vehicle resource utilization efficiency as the target order to the user terminal within a first future preset time period when it is predicted that an allocation order with higher vehicle resource utilization efficiency will be generated.

[0044] In an optional implementation, the determining module is further configured to:

[0045] Obtain the current location information of the user terminal, the pick-up location information and destination location information corresponding to the order to be assigned, and determine the expected return information of the order to be assigned;

[0046] Based on the current location information, the pick-up location information, the destination location information, and the expected return information, the vehicle resource utilization efficiency of the order to be assigned is determined.

[0047] In one optional implementation, when determining the vehicle resource utilization efficiency of the order to be assigned based on the current location information, the pick-up location information, the destination location information, and the expected return information, the determining module is used to:

[0048] Based on the pick-up location information and the current location information of the user terminal, determine the pick-up time required for the user terminal to reach the pick-up location of the order to be assigned; and, based on the pick-up location information and the destination location information, determine the estimated execution time of the order to be assigned.

[0049] Based on the destination location information, the pick-up time, and the estimated execution time, the waiting time for the user to be assigned a new order after completing the pending order is predicted.

[0050] Based on the pick-up time, the estimated execution time, the order waiting time, and the expected return information, the vehicle resource utilization efficiency of the orders to be assigned is determined.

[0051] In an optional implementation, when the prediction module predicts whether, within a first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated within the target location area, it is used to:

[0052] Acquire information on multiple first historical orders completed within the target location area, first traffic status information of the target location area that matches the generation time of the first historical order information, and second traffic status information of the target location area that matches the first future preset duration;

[0053] Based on the first historical order information, the first traffic status information, and the second traffic status information, it is predicted whether, within the first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated in the target location area.

[0054] In one optional implementation, when the determining module determines at least one order to be assigned within the target location area where the user terminal is located, it is used to:

[0055] Orders whose pick-up location is within the target location area and whose destination location is within the target order allocation location area are the orders to be allocated.

[0056] In an optional implementation, the determining module is further configured to:

[0057] Obtain information on multiple second historical orders generated within the parent location region that includes the target location region;

[0058] Based on the historical pick-up location information in the second historical order information, the second historical order information is matched with multiple sub-location areas within the target location area;

[0059] Based on the number of second historical order information matched by each of the sub-level location regions, at least one target sub-level location region is selected from the multiple sub-level location regions;

[0060] Based on the target sub-level location region, the target order location region is determined.

[0061] In an optional implementation, the allocation module is further configured to:

[0062] If there is no order to be assigned within the target location area where the user terminal is located, the target order to be assigned to the user terminal is selected from the order to be assigned in the candidate location areas surrounding the target location area.

[0063] In an optional implementation, when the allocation module selects a target order to be allocated to the user terminal from the candidate location areas surrounding the target location area if no such order exists within the target location area of ​​the user terminal, it is configured to:

[0064] Predict whether, within a second future preset time period, there will be any pending orders in the target location area with a vehicle resource utilization efficiency higher than the preset efficiency.

[0065] If it is predicted that no pending orders will be generated with a vehicle resource utilization efficiency higher than the preset efficiency, then the target orders to be allocated to the user terminal will be selected from the pending orders in the candidate location area.

[0066] In an optional implementation, the allocation module is further configured to:

[0067] If it is predicted that there will be pending orders with higher vehicle resource utilization efficiency than the preset efficiency, then within the second future preset time period, the pending orders with higher vehicle resource utilization efficiency than the target efficiency will be allocated to the user as the target orders.

[0068] In an optional implementation, the allocation module is further configured to:

[0069] If there are no orders to be assigned in the candidate location area, then the probability value of generating an order to be assigned in the candidate location area within a third future preset time period is predicted;

[0070] If the probability value is higher than a preset threshold, a movement command to move to the candidate location area is sent to the user terminal.

[0071] In one optional implementation, the apparatus further includes a billing module for:

[0072] After the target order is completed on the user's end, the billing information corresponding to the target order is determined based on the pick-up time and pick-up distance, drop-off time and drop-off distance, and waiting time at the destination of the target order.

[0073] Thirdly, embodiments of this disclosure also provide an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the steps of the first aspect above, or any possible implementation of the first aspect, are performed.

[0074] Fourthly, embodiments of this disclosure also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the first aspect or any possible implementation of the first aspect.

[0075] The order allocation method and apparatus provided in this disclosure first respond to an order allocation instruction for a user terminal by determining at least one pending order within a target location area where the user terminal is located. Then, from the pending orders, the user terminal selects the order with the highest vehicle resource utilization efficiency as a pre-allocated order, and predicts whether a pending order with higher vehicle resource utilization efficiency will be generated within a first future preset time period in the target location area. The vehicle resource utilization efficiency characterizes the relationship between consumed vehicle resources and expected returns. Finally, if it is predicted that no pending order with higher vehicle resource utilization efficiency will be generated, the pre-allocated order is allocated to the user terminal as a target order. If it is predicted that a pending order with higher vehicle resource utilization efficiency will be generated, the generated pending order with higher vehicle resource utilization efficiency is allocated to the user terminal as the target order within the first future preset time period.

[0076] This embodiment of the disclosure uses the vehicle resource utilization efficiency of each pending order as a reference to allocate target orders to the user terminal, which can improve the effective working time of vehicle resources and also improve the order execution efficiency. This embodiment of the disclosure first pre-allocates orders and sets an acceptable waiting time, namely a first future preset time. If it is predicted that a pending order with higher vehicle resource utilization efficiency will be generated within the first future preset time, the pre-allocated order will not be directly allocated to the user terminal. Instead, it will wait for the first future preset time and allocate the pending order with higher vehicle resource utilization efficiency generated during the waiting period to the user terminal, which further improves the effective utilization rate of vehicle resources and better optimizes order allocation.

[0077] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0078] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0079] Figure 1 A flowchart of an order allocation method provided by an embodiment of this disclosure is shown;

[0080] Figure 2 One of the schematic diagrams of the user terminal provided in the embodiments of this disclosure is shown;

[0081] Figure 3 A second schematic diagram of the user terminal provided in an embodiment of this disclosure is shown;

[0082] Figure 4 A third schematic diagram of the user terminal provided in this embodiment of the present disclosure is shown;

[0083] Figure 5 A schematic diagram of the billing mode provided in the embodiments of this disclosure is shown;

[0084] Figure 6 A flowchart of another order allocation method provided by an embodiment of this disclosure is shown;

[0085] Figure 7 A schematic diagram of an order allocation device provided in an embodiment of this disclosure is shown;

[0086] Figure 8 A schematic diagram of an electronic device provided in an embodiment of the present disclosure is shown. Detailed Implementation

[0087] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. The components of the embodiments of this disclosure described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this disclosure provided in the accompanying drawings is not intended to limit the scope of the claimed disclosure, but merely represents selected embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure.

[0088] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0089] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0090] Research has revealed that after a ride-hailing vehicle completes an order, it remains in the same location area as the previous order's destination. The server then redistributes orders within that area to the corresponding user. However, some orders have remote destinations with limited availability in their respective areas. Vehicles often need to be relocated to other areas to secure orders, resulting in longer travel times and extended periods of idle time for these vehicles. Consequently, the expected return on such orders is typically lower.

[0091] Based on the above research, this disclosure provides an order allocation method that uses the vehicle resource utilization efficiency of each pending order as a reference to allocate target orders to the user terminal. This can improve the effective working time of vehicle resources and also improve the order execution efficiency. In this embodiment, orders are first pre-allocated, and an acceptable waiting time, namely a first future preset time, is set. If it is predicted that a pending order with higher vehicle resource utilization efficiency will be generated within the first future preset time, the pre-allocated order will not be directly allocated to the user terminal. Instead, the user terminal will wait for the first future preset time and allocate the pending order with higher vehicle resource utilization efficiency generated during the waiting period to the user terminal. This further improves the effective utilization rate of vehicle resources and better optimizes order allocation.

[0092] To facilitate understanding of this embodiment, a detailed description of the order allocation method disclosed in this disclosure will be provided first. The execution entity of the order allocation method provided in this disclosure is generally a computer device with a certain computing power, such as an in-vehicle processor, a terminal device, a server, or other processing devices. In some possible implementations, the order allocation method can be implemented by the processor calling computer-readable instructions stored in memory.

[0093] See Figure 1 The diagram shows a flowchart of an order allocation method provided in an embodiment of this disclosure. The method includes steps S101 to S104, wherein:

[0094] S101. In response to an order allocation instruction for a user terminal, determine at least one order to be allocated within the target location area where the user terminal is located.

[0095] In this step, the user terminal can be an in-vehicle terminal deployed on the vehicle or a terminal device used by the vehicle driver, and the vehicle resource can be a vehicle used for passenger delivery, which may include, but is not limited to, four-wheeled vehicles, motor vehicles, and driverless vehicles.

[0096] The server used to allocate orders can send instructions to the user client, and the user client can also report its own status, current location and other information to the server.

[0097] After receiving an order allocation request from a user, the server can generate an order allocation instruction and respond to the instruction to start allocating orders to the user. Alternatively, it can periodically check the status of the user at preset time intervals and generate an order allocation instruction for that user when it detects a user that is idle or waiting for orders.

[0098] After detecting the order allocation instruction, the system can identify the orders that can be allocated to the user. To prevent the pick-up distance of vehicle resources from being too long, only orders within the target location area where the user is currently located can be identified as orders to be allocated.

[0099] Furthermore, to ensure efficient utilization of vehicle resources and reduce the number of vehicles entering remote areas without orders after fulfilling existing orders, orders whose destinations are outside the target allocation area can be removed. Only orders whose pick-up locations and destinations are both within the target allocation area can be designated as pending orders. The target allocation area refers to the location area where order volume meets the target requirements.

[0100] The target order placement area can be determined based on past order data. Example steps are as follows:

[0101] Obtain multiple second historical order information generated within the parent location area containing the target location area; based on the historical pick-up location information in the second historical order information, match the second historical order information with multiple sub-location areas within the target location area; based on the number of second historical order information matched by each sub-location area, filter out at least one target sub-location area from the multiple sub-location areas; based on the target sub-location area, determine the target order matching location area.

[0102] The parent location region can include multiple child location regions, which do not overlap and collectively fill the parent location region. The parent location region is generally large, typically covering an entire city, while the size of the child location regions is controlled to allow vehicles to quickly travel from one side of the region to the other. The order popularity can be represented by the number of second historical order information matched to each of the child location regions.

[0103] In this step, based on the obtained historical pick-up location information, the sub-level location area to which each second historical order belongs can be determined. If the historical pick-up location information in a second historical order falls within a sub-level location area, then the historical order information and its corresponding sub-level location area can be considered successfully matched. After matching, the sub-level location areas can be filtered, eliminating those with fewer than the target number of matched second historical orders, resulting in at least one target sub-level location area. These target sub-level location areas are then combined to form a target order placement location area. This way, the resulting target order placement location area is more likely to generate orders.

[0104] See Figure 2 The diagram shown is one of the schematic diagrams of a user terminal provided in an embodiment of this disclosure. Figure 2This is an illustration showing the target order location area to the user. For example... Figure 2 As shown, taking YY City as an example, the parent location area of ​​the target order location area covers areas A1, A2, and A3, while area A4 is not covered by the target order location area.

[0105] S102. From the orders to be allocated, select the order with the highest vehicle resource utilization efficiency for the user terminal as the pre-allocated order, and predict whether, within a first future preset time period, an order with higher vehicle resource utilization efficiency than the pre-allocated order will be generated in the target location area; the vehicle resource utilization efficiency is used to characterize the relationship between the consumed vehicle resources and the expected return.

[0106] In this step, the vehicle resource utilization rate can be determined based on the consumed vehicle resources and the expected return. Generally, the lower the consumed vehicle resources and the higher the expected return, the higher the vehicle resource utilization rate. With a higher vehicle resource utilization rate, more orders can be processed in the same amount of time. Therefore, the orders with the highest vehicle resource utilization rate can be selected from the pending orders as pre-allocated orders.

[0107] Since order generation is random, it is possible that an order with the highest vehicle resource utilization rate was just assigned to a vehicle resource, but then an order with an even higher vehicle resource utilization rate appears. In order to further improve the effective utilization rate of vehicle resources and reduce the idle time of vehicle resources, it is possible to predict whether an order with a higher vehicle resource utilization rate than the pre-assigned order will be generated in the target location area within a first future preset time period. The order will then be allocated based on the prediction results, thereby finding a better order allocation scheme.

[0108] Vehicle resource utilization efficiency can be determined through the following steps:

[0109] Obtain the current location information of the user terminal, the pick-up location information and destination location information corresponding to the order to be assigned, and determine the expected return information of the order to be assigned; determine the vehicle resource utilization efficiency of the order to be assigned based on the current location information, the pick-up location information, the destination location information and the expected return information.

[0110] The pick-up location information refers to the location where the passenger is picked up. Vehicles must travel from their current location to the location corresponding to this pick-up location in order to fulfill the order. During the pick-up process, the vehicle is idle, which constitutes a consumption of vehicle resources. Therefore, the consumption of vehicle resources during the pick-up process needs to be determined based on the user's current location and the pick-up location information. Furthermore, the time spent waiting for new orders after completing an order also constitutes a consumption of vehicle resources, and the consumption of vehicle resources waiting for new orders needs to be determined using destination information.

[0111] In this way, by measuring the vehicle resource consumption on the way to the order, the vehicle resource consumption when executing the order, and the vehicle resource consumption while waiting for a new order, we can obtain the total vehicle resource consumption required to complete an order. Then, by determining the ratio of the expected return information of the order to the total vehicle resource consumption, we can obtain the vehicle resource utilization efficiency of the orders to be assigned.

[0112] For example, the system can first determine the pick-up time required for the user to reach the pick-up location of the order to be assigned, based on the pick-up location information and the user's current location information; then, based on the pick-up location information and the destination location information, the system can determine the estimated execution time of the order to be assigned; next, based on the destination location information, the pick-up time, and the estimated execution time, the system can predict the waiting time for the user to be assigned a new order after completing the order to be assigned; finally, based on the pick-up time, the estimated execution time, the waiting time, and the expected return information, the system can determine the vehicle resource utilization efficiency of the order to be assigned.

[0113] The waiting time can be predicted based on past order data. For example, the starting point for waiting for orders can be determined first based on the pick-up time and the expected execution time, and then the waiting time required to receive new orders at that time point can be predicted based on historical data.

[0114] In the aforementioned step of predicting whether, within a first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated in the target location area, prediction can be made using historical order information. Specifically, multiple first historical order information completed within the target location area, first traffic status information of the target location area matching the generation time of the first historical order information, and second traffic status information of the target location area matching the first future preset time period can be obtained. Then, using the first historical order information, the first traffic status information, and the second traffic status information, it is possible to predict whether, within the first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated in the target location area.

[0115] For example, a neural network model can be used to learn the relationship between the first historical order information, the order generation time, and the first traffic status information to obtain a trained neural network model. Then, the second traffic status and the time node corresponding to the first future preset duration are input into the trained neural network model to determine whether an unassigned order with higher resource utilization efficiency than the pre-assigned order will be generated within the first future preset duration.

[0116] S103. If it is predicted that no more vehicle resource utilization efficiency will be generated, the pre-allocated order will be allocated to the user terminal as the target order.

[0117] In this step, if it is predicted that no orders with higher vehicle resource utilization efficiency will be generated, the pre-allocated orders can be determined as the optimal solution. The pre-allocated orders are directly assigned to the user as target orders so that the user can go to the pick-up location of the target order and start executing the order.

[0118] S104. If it is predicted that a more efficient vehicle resource utilization order will be generated, then within the first future preset time period, the generated more efficient vehicle resource utilization order will be allocated to the user terminal as the target order.

[0119] In this step, if it is predicted that there will be orders to be allocated with higher vehicle resource utilization efficiency, then the orders can be not allocated to the user first. Instead, the orders to be allocated with higher vehicle resource utilization efficiency will be generated and then allocated to the user.

[0120] During this process, pre-assigned orders can be allocated to other users or retained until the first future preset time expires, or until a more efficient vehicle resource utilization order is generated.

[0121] Here, a minimum difference in vehicle resource utilization efficiency can be set. If the difference in vehicle resource utilization efficiency between the pending orders and the pre-allocated orders generated in the prediction is higher than or equal to the minimum difference, the prediction result will be generated. If it is lower than the minimum difference, the prediction result will be not generated.

[0122] In this way, users assigned to orders do not need to travel to other locations, and at the same time receive the best order in their current location area, resulting in high vehicle resource utilization efficiency.

[0123] If no more efficient vehicle resource utilization orders are available after the first future preset time period ends, the previously reserved pre-allocated orders can be assigned to the user as target orders, or pre-allocated orders and target orders can be reassigned to the vehicle.

[0124] Furthermore, if there are no orders to be assigned within the target location area where the user is located, target orders to be assigned to the user can be filtered from the candidate location areas surrounding the target location area. The steps for filtering target orders in the candidate location areas can be similar to the steps for filtering target orders in the target location area.

[0125] For example, it can be predicted whether, within a second future preset time period, there will be any pending orders in the target location area with a vehicle resource utilization efficiency higher than the preset efficiency. If it is predicted that such orders will occur, there is no need to allocate orders from the candidate location area to the user's corresponding vehicle resources. Instead, an instruction to wait in the target location area can be sent to the user until the second future preset time period ends or a pending order with a vehicle resource utilization efficiency higher than the target efficiency is allocated to the user as the target order, thereby saving the resources consumed by the user when traveling to the candidate location area.

[0126] See Figure 3 The image shown is a second schematic diagram of a user terminal provided in an embodiment of this disclosure. Figure 3 As shown, the server predicts that within a second future preset time period, there will be unassigned orders in the target location area with a vehicle resource utilization efficiency higher than the preset efficiency. The user terminal receives the instruction from the server to wait in the target location area, and the user interface indicates to rest and wait in the target location area.

[0127] If the prediction does not generate any pending orders with a vehicle resource utilization efficiency higher than the preset efficiency, it means that it is difficult to obtain new pending orders even if the user stays in the target location area. In this case, the target orders to be assigned to the user can be selected from the pending orders in the candidate location areas around the target location area.

[0128] Furthermore, if there are no orders to be assigned in the candidate location area, the probability of an order to be assigned in the candidate location area can be predicted within a preset time period in the third future. If the probability value is higher than a preset threshold, a movement instruction to move to the candidate location area can be sent to the user.

[0129] Here, if there are multiple candidate location areas, the candidate location with the highest probability of generating an order to be assigned, which is higher than a preset threshold, can be used as the target of the movement instruction.

[0130] See Figure 4 The image shown is a third schematic diagram of a user terminal provided in an embodiment of this disclosure. Figure 4 As shown, the server predicts that within a second future preset time period, no pending orders with vehicle resource utilization efficiency higher than the preset efficiency will be generated in the target location area. The user terminal receives the instruction sent by the server to move to the candidate location area, and the user interface indicates to move to the candidate location area.

[0131] Traditional order billing methods only consider the delivery process and not the pick-up time and waiting time at the user's location. Drivers may refuse orders with excessively long pick-up times or be less willing to move to candidate locations. Therefore, to increase the probability of users cooperating with order allocation, after the user completes the target order, the billing information for the target order can be determined based on the pick-up time and distance, the delivery time and distance, and the waiting time at the target order's destination.

[0132] For example, time fees can be calculated based on the duration of drop-off, waiting time, and pick-up time, while mileage fees can be calculated based on the pick-up and drop-off distances.

[0133] In this way, even during the process of picking up drivers and waiting for orders, the corresponding drivers on the user's end can receive corresponding compensation, which increases the probability of the user's end receiving order allocation and dispatch, thereby improving the efficiency of vehicle resource utilization and the overall delivery efficiency of orders.

[0134] See Figure 5 The diagram shown is a schematic representation of the billing model provided in an embodiment of this disclosure. Figure 5 As shown, Figure 5 The top section shows the traditional billing model, which only calculates mileage and time fees for the driver drop-off process. The bottom section shows the new billing model provided by this implementation, which calculates time fees for the duration of driver drop-off, waiting time, and pick-up time, and calculates mileage fees for the pick-up and drop-off distances.

[0135] This embodiment of the disclosure uses the vehicle resource utilization efficiency of each pending order as a benchmark to allocate target orders to the user terminal, so as to optimize the vehicle resource utilization efficiency, thereby increasing the effective working time of vehicle resources in executing orders, reducing the waiting time of vehicle resources for order allocation, and improving the order processing efficiency. Furthermore, when it is predicted that a pending order with higher vehicle resource utilization efficiency will be generated within a first future preset time period, the newly generated order with higher vehicle resource utilization efficiency will be allocated to the user terminal, further improving the effective utilization rate of vehicle resources and better optimizing order allocation.

[0136] See Figure 6 The image shows another order allocation method provided in an embodiment of this disclosure. Figure 6As shown, after detecting an order allocation instruction for a user, the method first determines whether there are any pending orders in the target location area. If so, it filters pre-allocated orders from the pending orders in the target location area based on vehicle resource utilization efficiency, and makes a prediction. It predicts whether, within a first future preset time period, a pending order with higher vehicle resource utilization efficiency than the pre-allocated order will be generated in the target location area. Then, it makes a judgment: if the prediction result is that no such order will be generated, the pre-allocated order is directly allocated to the user as the target order; if the prediction result is that such an order will be generated, a standby instruction is sent to the user, and within the first future preset time period, the pending order with higher vehicle resource utilization efficiency is allocated to the user as the target order. If no such order exists in the target location area... For pending orders, it predicts whether pending orders with higher vehicle resource utilization efficiency than the preset efficiency will be generated in the target location area within a second future preset time period. If it predicts that pending orders with higher vehicle resource utilization efficiency than the preset probability will be generated, a standby command is sent to the user terminal. If it predicts that no pending orders with higher vehicle resource utilization efficiency than the preset probability will be generated, the target order is filtered from the candidate location area. When filtering orders from the candidate location area, if there are no pending orders in the candidate location area, the probability value of pending orders being generated in the candidate location area within a third future preset time period can be predicted. If the probability value is higher than or equal to a preset threshold, a movement command to move to the candidate location area can be sent to the user terminal. If it is lower than the preset threshold, a standby command can be sent to the user terminal.

[0137] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0138] Based on the same inventive concept, this disclosure also provides an order allocation device corresponding to the order allocation method. Since the principle of the device in this disclosure for solving the problem is similar to the order allocation method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0139] Reference Figure 7 The diagram shown is a schematic representation of an order allocation device provided in an embodiment of this disclosure. The device includes:

[0140] The determining module 710 is configured to determine at least one order to be assigned within the target location area where the user terminal is located in response to an order assignment instruction for the user terminal.

[0141] The prediction module 720 is used to select the order with the highest vehicle resource utilization efficiency from the pending orders for the user terminal as the pre-allocated order, and predict whether a pending order with higher vehicle resource utilization efficiency than the pre-allocated order will be generated in the target location area within a first future preset time period; the vehicle resource utilization efficiency is used to characterize the relationship between the consumed vehicle resources and the expected return.

[0142] The allocation module 730 is used to allocate the pre-allocated order as a target order to the user terminal when it is predicted that no allocation order with higher vehicle resource utilization efficiency will be generated; and to allocate the generated allocation order with higher vehicle resource utilization efficiency as the target order to the user terminal within a first future preset time period when it is predicted that an allocation order with higher vehicle resource utilization efficiency will be generated.

[0143] In an optional implementation, the determining module 710 is further configured to:

[0144] Obtain the current location information of the user terminal, the pick-up location information and destination location information corresponding to the order to be assigned, and determine the expected return information of the order to be assigned;

[0145] Based on the current location information, the pick-up location information, the destination location information, and the expected return information, the vehicle resource utilization efficiency of the order to be assigned is determined.

[0146] In an optional implementation, when determining the vehicle resource utilization efficiency of the order to be assigned based on the current location information, the pick-up location information, the destination location information, and the expected return information, the determining module 710 is used to:

[0147] Based on the pick-up location information and the current location information of the user terminal, determine the pick-up time required for the user terminal to reach the pick-up location of the order to be assigned; and, based on the pick-up location information and the destination location information, determine the estimated execution time of the order to be assigned.

[0148] Based on the destination location information, the pick-up time, and the estimated execution time, the waiting time for the user to be assigned a new order after completing the pending order is predicted.

[0149] Based on the pick-up time, the estimated execution time, the order waiting time, and the expected return information, the vehicle resource utilization efficiency of the orders to be assigned is determined.

[0150] In an optional implementation, when the prediction module 720 predicts whether, within a first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated within the target location area, it is used to:

[0151] Acquire information on multiple first historical orders completed within the target location area, first traffic status information of the target location area that matches the generation time of the first historical order information, and second traffic status information of the target location area that matches the first future preset duration;

[0152] Based on the first historical order information, the first traffic status information, and the second traffic status information, it is predicted whether, within the first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated in the target location area.

[0153] In an optional implementation, when the determining module 710 determines at least one order to be assigned within the target location area where the user terminal is located, it is configured to:

[0154] Orders whose pick-up location is within the target location area and whose destination location is within the target order allocation location area are the orders to be allocated.

[0155] In an optional implementation, the determining module 710 is further configured to:

[0156] Obtain information on multiple second historical orders generated within the parent location region that includes the target location region;

[0157] Based on the historical pick-up location information in the second historical order information, the second historical order information is matched with multiple sub-location areas within the target location area;

[0158] Based on the number of second historical order information matched by each of the sub-level location regions, at least one target sub-level location region is selected from the multiple sub-level location regions;

[0159] Based on the target sub-level location region, the target order location region is determined.

[0160] In an optional implementation, the allocation module 730 is further configured to:

[0161] If there is no order to be assigned within the target location area where the user terminal is located, the target order to be assigned to the user terminal is selected from the order to be assigned in the candidate location areas surrounding the target location area.

[0162] In an optional implementation, when the allocation module 730 selects target orders to be allocated to the user terminal from candidate location areas surrounding the target location area if no such order exists within the target location area of ​​the user terminal, it is configured to:

[0163] Predict whether, within a second future preset time period, there will be any pending orders in the target location area with a vehicle resource utilization efficiency higher than the preset efficiency.

[0164] If it is predicted that no pending orders will be generated with a vehicle resource utilization efficiency higher than the preset efficiency, then the target orders to be allocated to the user terminal will be selected from the pending orders in the candidate location area.

[0165] In an optional implementation, the allocation module 730 is further configured to:

[0166] If it is predicted that there will be pending orders with higher vehicle resource utilization efficiency than the preset efficiency, then within the second future preset time period, the pending orders with higher vehicle resource utilization efficiency than the target efficiency will be allocated to the user as the target orders.

[0167] In an optional implementation, the allocation module 730 is further configured to:

[0168] If there are no orders to be assigned in the candidate location area, then the probability value of generating an order to be assigned in the candidate location area within a third future preset time period is predicted;

[0169] If the probability value is higher than a preset threshold, a movement command to move to the candidate location area is sent to the user terminal.

[0170] In one optional implementation, the apparatus further includes a billing module for:

[0171] After the target order is completed on the user's end, the billing information corresponding to the target order is determined based on the pick-up time and pick-up distance, drop-off time and drop-off distance, and waiting time at the destination of the target order.

[0172] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.

[0173] Corresponding to Figure 1 In addition to the order allocation method in this disclosure, this embodiment also provides an electronic device 800, such as... Figure 8 The diagram shown is a structural schematic of an electronic device 800 provided in an embodiment of this disclosure, including:

[0174] The system includes a processor 81, a memory 82, and a bus 83. The memory 82 stores execution instructions and includes main memory 821 and external memory 822. The main memory 821, also called internal memory, temporarily stores the computational data in the processor 81, as well as data exchanged with external memory such as a hard disk. The processor 81 exchanges data with the external memory 822 through the main memory 821. When the electronic device 800 is running, the processor 81 communicates with the memory 82 through the bus 83, causing the processor 81 to execute the following instructions:

[0175] In response to an order allocation instruction for a user terminal, at least one order to be allocated is identified within the target location area where the user terminal is located;

[0176] From the pending orders, the order with the highest vehicle resource utilization efficiency is selected for the user as the pre-allocated order, and it is predicted whether a pending order with higher vehicle resource utilization efficiency will be generated in the target location area within a first future preset time period; the vehicle resource utilization efficiency is used to characterize the relationship between the consumed vehicle resources and the expected return.

[0177] If it is predicted that no more efficient vehicle resource utilization orders will be generated, then the pre-allocated orders will be assigned to the user as target orders.

[0178] If it is predicted that a more efficient vehicle resource utilization order will be generated, then within the first preset future time period, the generated more efficient vehicle resource utilization order will be allocated as the target order to the user terminal.

[0179] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the order allocation method described in the above method embodiments. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0180] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the order allocation method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0181] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0182] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0183] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0185] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0186] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for processing ride-hailing order data, characterized in that, The method includes: In response to an order allocation instruction from a user terminal, with the goal of improving vehicle resource utilization efficiency, an order is allocated to the user terminal or a vehicle dispatch instruction is sent. The vehicle resource utilization efficiency characterizes the relationship between consumed vehicle resources and expected return. The vehicle resource utilization efficiency is inversely proportional to the consumed vehicle resources and directly proportional to the expected return. The consumed vehicle resources include vehicle resource consumption on the route to the order pickup point, vehicle resource consumption while executing the order, and vehicle resource consumption while waiting for a new order. The billing duration is determined based on the waiting time, pick-up time, and drop-off time for multiple consecutive assigned orders; and Billing shall be based at least on the stated billing duration.

2. The method according to claim 1, characterized in that, The sending of vehicle dispatch instructions includes sending instructions to the user to wait in the target location area or to move to the candidate location area, wherein the target location area is the location area where the user terminal is located.

3. The method according to claim 1, characterized in that, The process of allocating orders to the user terminal includes: The client is assigned orders within the target location area, or orders within the candidate location area surrounding the target location area.

4. The method according to claim 1, characterized in that, The billing based at least on the billing duration specifically means: Billing is based on the billing duration and the mileage of the assigned order.

5. The method according to claim 1, characterized in that, Assigning orders or sending vehicle dispatch instructions to the user terminal includes: Identify at least one order to be assigned within the target location area where the user terminal is located; From the pending orders, select the order with the highest vehicle resource utilization efficiency for the user as the pre-allocated order, and predict whether a pending order with higher vehicle resource utilization efficiency will be generated in the target location area within a first future preset time period. If it is predicted that no more efficient vehicle resource utilization orders will be generated, then the pre-allocated orders will be assigned to the user as target orders. If it is predicted that a more efficient vehicle resource utilization order will be generated, then within the first preset future time period, the generated more efficient vehicle resource utilization order will be allocated as the target order to the user terminal.

6. The method according to claim 5, characterized in that, The vehicle resource utilization efficiency of the orders to be assigned is determined by the following steps: Obtain the current location information of the user terminal, the pick-up location information and destination location information corresponding to the order to be assigned, and determine the expected return information of the order to be assigned; Based on the current location information, the pick-up location information, the destination location information, and the expected return information, the vehicle resource utilization efficiency of the order to be assigned is determined.

7. The method according to claim 5, characterized in that, Predicting whether, within a first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated within the target location area, including: Acquire information on multiple first historical orders completed within the target location area, first traffic status information of the target location area that matches the generation time of the first historical order information, and second traffic status information of the target location area that matches the first future preset duration; Based on the first historical order information, the first traffic status information, and the second traffic status information, it is predicted whether, within the first future preset time period, a pending order with higher resource utilization efficiency than the pre-allocated order will be generated in the target location area.

8. The method according to claim 5, characterized in that, Determining at least one pending order within the target location area of ​​the user terminal includes: Orders whose pick-up location is within the target location area and whose destination location is within the target order location area are the orders to be assigned, wherein the target order location area is at least one target sub-location area pre-determined based on second historical order information.

9. The method according to claim 5, characterized in that, Assigning orders or sending vehicle dispatch instructions to the user terminal also includes: If it is predicted that there will be unassigned orders with higher vehicle resource utilization efficiency, an instruction will be sent to the user to wait in the target location area.

10. The method according to claim 5, characterized in that, Assigning orders or sending vehicle dispatch instructions to the user terminal also includes: If there is no order to be assigned within the target location area where the user terminal is located, the target order to be assigned to the user terminal is selected from the order to be assigned in the candidate location areas surrounding the target location area.

11. The method according to claim 10, characterized in that, Among the pending orders in the candidate location areas surrounding the target location area, the target orders to be assigned to the user are selected, including: Predict whether, within a second future preset time period, there will be any pending orders in the target location area with a vehicle resource utilization efficiency higher than the preset efficiency. If it is predicted that no pending orders will be generated with a vehicle resource utilization efficiency higher than the preset efficiency, then the target orders to be allocated to the user terminal will be selected from the pending orders in the candidate location area.

12. The method according to claim 11, characterized in that, Assigning orders or sending vehicle dispatch instructions to the user terminal also includes: If it is predicted that there will be pending orders with higher vehicle resource utilization efficiency than the preset efficiency, then within the second future preset time period, the pending orders with higher vehicle resource utilization efficiency than the target efficiency will be allocated to the user as the target orders.

13. The method according to claim 10, characterized in that, Assigning orders or sending vehicle dispatch instructions to the user terminal also includes: If there are no orders to be assigned in the candidate location area, then the probability value of generating an order to be assigned in the candidate location area within a third future preset time period is predicted; If the probability value is higher than a preset threshold, a movement command to move to the candidate location area is sent to the user terminal.

14. A ride-hailing order data processing device, characterized in that, The device includes: The first unit is configured to, in response to an order allocation instruction for a user terminal, allocate orders or send vehicle dispatch instructions to the user terminal with the goal of improving vehicle resource utilization efficiency. The vehicle resource utilization efficiency characterizes the relationship between consumed vehicle resources and expected returns. The vehicle resource utilization efficiency is inversely proportional to the consumed vehicle resources and directly proportional to the expected returns. The consumed vehicle resources include vehicle resource consumption on the route to the order, vehicle resource consumption while executing the order, and vehicle resource consumption while waiting for new orders. The second unit is configured to determine the billing duration based on the waiting time, pick-up time, and drop-off time of multiple consecutive assigned orders; and The third unit is configured to charge at least based on the billing duration.

15. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the memory via the bus, and the machine-readable instructions, when executed by the processor, perform the method steps as described in any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the method steps as described in any one of claims 1 to 13.

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