Order allocation method and device, equipment and storage medium

Abstract

The application discloses an order allocation method and device, equipment and a storage medium, and belongs to the computer technical field. The method comprises the following steps: determining a first allocation result based on the cost parameters of one-to-one combinations of a plurality of orders and a plurality of candidate transport capacities, wherein the first allocation result comprises a plurality of one-to-one combinations, one candidate transport capacity in the plurality of one-to-one combinations is allocated one order, and the total cost parameter of the first allocation result is the lowest; determining the cost parameters of one-to-one combinations of a target order and the plurality of candidate transport capacities, wherein the target order comprises at least one of a first order which is not allocated a candidate transport capacity in the plurality of orders and a second order whose total cost parameter is reduced after the candidate transport capacity is reallocated; determining a second allocation result based on the cost parameters of one-to-one combinations of the target order and the plurality of candidate transport capacities; and allocating the candidate transport capacity to the plurality of orders based on the first allocation result and the second allocation result. The method can improve the accuracy of order allocation.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to an order allocation method, apparatus, device and storage medium. Background Technology

[0002] With the development of computer technology, services such as food delivery, online shopping, and ride-sharing have emerged, and business orders are constantly increasing. As a result, allocating transportation capacity to these orders has become increasingly important.

[0003] Currently, for multiple orders awaiting allocation, the process involves first identifying multiple candidate transport capacities corresponding to each order, then determining the cost parameters between these candidate capacities and the orders. The lowest cost parameter is then found among these cost parameters, and the candidate transport capacity corresponding to this lowest cost parameter is allocated to the order corresponding to that lowest cost parameter. Since there is overlap in suitable candidate transport capacities for different orders, allocating candidate transport capacity to one order will affect the cost parameters between other orders and that candidate transport capacity. Therefore, based on the current allocation results, the cost parameters between the remaining orders and the candidate transport capacity need to be updated. The lowest cost parameter is then found among the updated cost parameters, and the candidate transport capacity corresponding to this lowest cost parameter is allocated to the order corresponding to that lowest cost parameter. This process of updating cost parameters and allocating orders based on the lowest cost parameter in the updated cost parameters is repeated until candidate transport capacity has been allocated to all orders.

[0004] Therefore, the above scheme only focuses on whether the optimal candidate capacity is assigned to the current order, without considering the impact of allocating the optimal candidate capacity to the current order on other orders. This results in a situation where, after assigning the optimal candidate capacity to some orders, the cost parameters between other orders and the candidate capacity increase significantly. Consequently, only the candidate capacity with higher cost parameters can be assigned to the remaining orders, leading to a higher total cost parameter for multiple orders. Therefore, the accuracy of this order allocation method is low. Summary of the Invention

[0005] This application provides an order allocation method, apparatus, device, and storage medium, which can improve the accuracy of order allocation. The technical solution is as follows:

[0006] On the one hand, an order allocation method is provided, the method comprising:

[0007] Based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities, a first allocation result is determined. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity is assigned to one order, and the total cost parameter of the first allocation result is the lowest.

[0008] Determine the cost parameters of a one-to-one combination of a target order and the plurality of candidate capacity, wherein the target order includes at least one of a first order that was not assigned a candidate capacity and a second order whose total cost parameter is reduced after the candidate capacity is reallocated;

[0009] Based on the cost parameters of the one-to-one combination of the target order and the multiple candidate transport capacities, a second allocation result is determined. The second allocation result includes the candidate transport capacities allocated to the target order. In the second allocation result, one candidate transport capacity is allocated to one target order, and the total cost parameter of the second allocation result is the lowest.

[0010] Based on the first allocation result and the second allocation result, candidate transportation capacity is allocated to the multiple orders.

[0011] On the one hand, an order allocation device is provided, the device comprising:

[0012] The result determination module is used to determine a first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity in the multiple one-to-one combinations is assigned to one order, and the total cost parameter of the first allocation result is the lowest.

[0013] The parameter determination module is used to determine the cost parameters of a one-to-one combination of a target order and the plurality of candidate transport capacities. The target order includes at least one of a first order that was not assigned a candidate transport capacity and a second order whose total cost parameter is reduced after the candidate transport capacity is reallocated.

[0014] The result determination module is used to determine a second allocation result based on the cost parameters of a one-to-one combination of the target order and the plurality of candidate transport capacities. The second allocation result includes candidate transport capacities allocated to the target order. In the second allocation result, one candidate transport capacity is allocated to one target order, and the total cost parameter of the second allocation result is the lowest.

[0015] The allocation module is used to allocate candidate capacity to the multiple orders based on the first allocation result and the second allocation result.

[0016] In one possible implementation, the device further includes:

[0017] The order confirmation module is used to, based on the first allocation result, treat orders that were not allocated the optimal candidate capacity as second orders; or,

[0018] The order determination module is used to determine, based on the first allocation result, orders that have not been allocated the optimal candidate capacity, and select, from the determined orders, orders that meet the first screening condition as the second order. The first screening condition is used to screen orders that have not been allocated the optimal candidate capacity.

[0019] In one possible implementation, the order confirmation module is configured to perform at least one of the following:

[0020] The first screening condition indicates that the optimal candidate capacity corresponding to the second order has the ability to accept the order; from the determined orders, the order with the corresponding optimal candidate capacity having the ability to accept the order is selected as the second order;

[0021] The first filtering condition indicates that the number of the second orders does not exceed a first threshold; based on the determined orders, the cost parameter difference between the candidate capacity allocated to the order and the corresponding optimal candidate capacity is determined; based on the determined cost parameter difference, orders with a number not exceeding the first threshold are selected from the determined orders as the second orders, and the cost parameter difference of the selected orders is greater than the cost parameter difference of the unselected orders in the determined orders.

[0022] In one possible implementation, the order determination module is further configured to: determine, based on the first allocation result, orders allocated with optimal candidate capacity if the number of the second orders does not exceed the first threshold; determine the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the determined orders; and select, based on the determined cost parameter difference, orders not exceeding a target number from the determined orders as the second orders, wherein the cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders among the determined orders, and the target number is the difference between the first threshold and the number of second orders determined from the orders not allocated with optimal candidate capacity.

[0023] In one possible implementation, the order determination module is used to determine, based on the first allocation result, the orders allocated with the optimal candidate capacity; and from the determined orders, to select the orders that meet the second screening condition as the second order, wherein the second screening condition is used to screen the orders allocated with the optimal candidate capacity.

[0024] In one possible implementation, the order confirmation module is configured to perform at least one of the following:

[0025] The second filtering condition indicates that the optimal candidate capacity for the second order is the optimal candidate capacity for other orders to be assigned; from the determined orders, the orders whose optimal candidate capacity is the optimal candidate capacity for other orders to be assigned are selected as the second order;

[0026] The second screening condition indicates that the number of the second orders does not exceed the first threshold; based on the determined orders, the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the orders is determined; based on the determined cost parameter difference, orders with a number not exceeding the first threshold are selected from the determined orders as the second orders, and the cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders among the determined orders.

[0027] In one possible implementation, the result determination module includes:

[0028] An aggregation unit is used to aggregate the multiple orders based on their start and destination points to obtain multiple order groups, wherein each order group includes at least one order.

[0029] A parameter determination unit is used to determine the cost parameters of a one-to-one combination of the plurality of order groups and the plurality of candidate transport capacities;

[0030] The result determination unit is used to determine the first allocation result based on the cost parameters of the one-to-one combination of the multiple order groups and the multiple candidate transport capacities. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity in the multiple one-to-one combinations is assigned to an order group.

[0031] In one possible implementation, the allocation module is configured to allocate candidate capacity to the plurality of orders based on the first allocation result and the second allocation result, provided that the iteration termination condition is met.

[0032] The allocation module is used to repeatedly execute the process of re-determining the target order, determining the one-to-one combination parameters of the target order and the multiple candidate transport capacity if the iteration termination condition is not met, determining the next allocation result based on the one-to-one combination parameters of the target order and the multiple candidate transport capacity, until the iteration termination condition is met, and allocating candidate transport capacity to the multiple orders based on the determined allocation result.

[0033] In one possible implementation, the iteration termination condition includes at least one of the following:

[0034] The target number of iterations has been reached.

[0035] The total cost parameter of the multiple orders is the same as the total cost parameter of the multiple orders after the previous iteration;

[0036] The determined allocation result is that all candidate transport capacities allocated to the multiple orders are the optimal candidate transport capacities.

[0037] In one possible implementation, the result determination module is configured to obtain a third allocation result for multiple reference orders, the third allocation result including the capacity allocated to the reference orders; for the reference orders among the multiple reference orders that have been allocated the target type capacity, the step of determining the first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate capacity is executed.

[0038] On one hand, a computer device is provided, the computer device including one or more processors and one or more memories, the one or more memories storing at least one piece of program code, the at least one piece of program code being loaded and executed by the one or more processors to perform the operations performed by the order allocation method as described in any of the above possible implementations.

[0039] On one hand, a computer-readable storage medium is provided, which stores at least one piece of program code that is loaded and executed by a processor to perform the operations performed by the order allocation method as described in any of the above possible implementations.

[0040] On one hand, a computer program or computer program product is provided, the computer program or computer program product comprising: computer program code, which, when executed by a computer, causes the computer to perform the operations performed by the order allocation method as described in any of the above possible implementations.

[0041] The order allocation method, apparatus, device, and storage medium provided in this application embodiment determine a first allocation result with the lowest total cost parameter based on a one-to-one combination of orders and candidate capacity. Then, it finds at least one of the orders that were not allocated candidate capacity and the target orders whose total cost parameter is reduced after reallocation of candidate capacity, and determines a second allocation result with the lowest total cost parameter for the target orders. Since the first allocation result and the second allocation result are the allocation results with the lowest total cost parameter, it can be seen that this application embodiment no longer focuses on only one order each time, but considers the lowest total cost parameter. Therefore, the obtained allocation result can reduce the total cost parameter of multiple orders and improve the accuracy of order allocation. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application;

[0044] Figure 2 This is a flowchart of an order allocation method provided in an embodiment of this application;

[0045] Figure 3 This is a schematic diagram illustrating an order and candidate capacity combination provided in an embodiment of this application;

[0046] Figure 4 This is a flowchart of an order allocation method provided in an embodiment of this application;

[0047] Figure 5 This is a schematic diagram illustrating an order and candidate capacity combination provided in an embodiment of this application;

[0048] Figure 6 This is a schematic diagram illustrating another combination of order and candidate capacity provided in an embodiment of this application;

[0049] Figure 7 This is a schematic diagram illustrating another combination of order and candidate capacity provided in an embodiment of this application;

[0050] Figure 8 This is a schematic diagram illustrating another combination of order and candidate capacity provided in an embodiment of this application;

[0051] Figure 9 This is a schematic diagram of an order allocation device provided in an embodiment of this application;

[0052] Figure 10 This is a schematic diagram of another order allocation device structure provided in an embodiment of this application;

[0053] Figure 11 This is a schematic diagram of the terminal structure provided in the embodiments of this application;

[0054] Figure 12 This is a schematic diagram of the server structure provided in an embodiment of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0056] It is understood that the terms "first," "second," etc., used in this application may be used to describe various concepts herein, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of this application, a first order may be referred to as a second order, and a second order may be referred to as a first order.

[0057] As used in this application, the terms "at least one", "multiple", "each", and "any" mean that at least one includes one, two, or more than two; multiple includes two or more; each refers to each of the corresponding multiple; and any means refers to any one of the multiple. For example, multiple orders include three orders, where each refers to each of the three orders, and any means refers to any one of the three orders, which could be the first, the second, or the third.

[0058] It should be noted that all information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals involved in this application are authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the orders and cost parameters involved in this application were obtained with full authorization. Furthermore, the aforementioned information and data, after processing, are used in big data application scenarios and cannot be identified to any natural person or have a specific association with them.

[0059] The order allocation method provided in this application can be applied to multiple scenarios that require allocating transportation capacity for orders, such as food delivery, online shopping, and ride-sharing. This application only uses the food delivery scenario as an example for illustrative purposes.

[0060] During peak dining hours, a large number of orders are generated in a short period of time. The order allocation system can identify multiple candidate transport capacities for these orders and select appropriate capacity from these candidates to allocate to the orders. Using the order allocation method provided in this application's embodiments can reduce the total cost parameter of multiple orders and improve the accuracy of order allocation.

[0061] The order allocation method provided in this application is executed by a computer device. In one possible implementation, the computer device is a terminal, such as a desktop computer, tablet computer, or mobile phone. In another possible implementation, the computer device is a server. For example, the server can be a single server, a server cluster consisting of several servers, or a cloud computing service center. In yet another possible implementation, the computer device includes both a terminal and a server.

[0062] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application, such as... Figure 1As shown, the implementation environment includes a user terminal 101, a transport terminal 102, and a server 103. The user terminal 101 and the server 103 are connected via a wireless or wired network. The transport terminal 102 and the server 103 are also connected via a wireless or wired network. The user terminal 101 and the transport terminal 102 can communicate directly or through the server 103; this embodiment does not limit the scope of the communication.

[0063] Among them, user terminal 101 is the terminal used by the user placing the order, and capacity terminal 102 is the terminal used by the transportation capacity. Optionally, user terminal 101 and capacity terminal 102 can be any type of terminal such as a smartphone, tablet, laptop, or desktop computer. Server 103 is a single server, a server cluster consisting of several servers, or a cloud computing service center.

[0064] A first target application, provided by server 103, is installed on user terminal 101. Optionally, the first target application is an application in the operating system of user terminal 101, or an application provided by a third party. For example, the first target application is an e-commerce application with shopping functions. Of course, the e-commerce application may also have other functions, such as review functions, sharing functions, etc.

[0065] A second target application, provided by server 103, is installed on the capacity terminal 102. Optionally, this second target application is an application within the operating system of the capacity terminal 101, or an application provided by a third party. For example, the second target application is an order-receiving application, which has order-receiving functions. Of course, the order-receiving application may also have other functions, such as order transfer functions.

[0066] Optionally, the first target application and the second target application are two different applications. Optionally, the first target application and the second target application are the same application. Optionally, the first target application and the second target application are different versions of the same application, wherein the first target application is the user version of the application, and the second target application is the capacity version of the application.

[0067] In some embodiments, a user places an order through a first target application in a user terminal 101. The server 103 receives the order from the user terminal 101 and pushes the order to the transportation terminal 102. The transportation terminal accepts the order through a second target application in the transportation terminal 102, or the transportation terminal completes the delivery of the order through the second target application in the transportation terminal 102.

[0068] Figure 2 This is a flowchart illustrating an order allocation method provided in an embodiment of this application. This embodiment uses a computer device as an example for illustrative purposes, and includes:

[0069] 201. The computer equipment determines a first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity is assigned to one order, and the total cost parameter of the first allocation result is the lowest.

[0070] In this context, multiple orders are orders awaiting allocation of transportation capacity. In some embodiments, multiple orders are orders generated within the same time period. In some embodiments, multiple orders are orders generated within the same region. This application does not limit the number of orders.

[0071] Multiple candidate delivery capacities are delivery capacities that match at least one of a plurality of orders. In some embodiments, the plurality of orders are orders generated within a target area, and the multiple candidate delivery capacities are delivery capacities that can complete delivery tasks within the target area. In some embodiments, the multiple candidate delivery capacities are delivery capacities that are close to the origin point of at least one of the plurality of orders and have the ability to accept orders. This application embodiment does not limit the multiple candidate delivery capacities.

[0072] In this embodiment, any one of the multiple orders can be combined with any one of the multiple candidate transport capacities. A combination of one order and one candidate transport capacity is a one-to-one combination. Multiple one-to-one combinations of multiple orders and multiple candidate transport capacities include multiple one-to-one combinations, where one order corresponds to one candidate transport capacity, and one candidate transport capacity corresponds to only one order. For example, if there are 5 orders and 8 candidate transport capacities, the first allocation result includes 5 one-to-one combinations. As another example, if there are 7 orders and 4 candidate transport capacities, the first allocation result includes 4 one-to-one combinations.

[0073] There are multiple ways to combine multiple orders with multiple candidate transport capacities. For example, multiple orders include Order 1, Order 2, and Order 3, and multiple candidate transport capacities include Candidate Capacity 1 and Candidate Capacity 2. The one-to-one combination of these multiple orders and candidate transport capacities could be: Order 1 with Candidate Capacity 1, and Order 2 with Candidate Capacity 2; or Order 1 with Candidate Capacity 2, and Order 2 with Candidate Capacity 1; or Order 1 with Candidate Capacity 1, and Order 3 with Candidate Capacity 2, and so on. Therefore, there are various ways to combine multiple orders with multiple candidate transport capacities.

[0074] In this embodiment of the application, the terminal determines the first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacity. That is, the terminal selects the one-to-one combination method corresponding to the lowest total cost parameter as the first allocation result based on the total cost parameters corresponding to the various one-to-one combination methods of multiple orders and multiple candidate transport capacity.

[0075] The cost parameter represents the cost of the allocation target, which refers to the objective to be achieved by the order allocation. For example, if the allocation target is capacity efficiency, the corresponding cost parameter represents the efficiency cost; the higher the cost parameter, the lower the capacity efficiency. Similarly, if the allocation target is user experience, the corresponding cost parameter represents the experience cost; the higher the cost parameter, the worse the user experience. It should be noted that this application embodiment does not limit the allocation target and cost parameter.

[0076] 202. The computer equipment determines the cost parameters of a one-to-one combination of a target order and multiple candidate capacity, wherein the target order includes at least one of a first order that is not assigned a candidate capacity and a second order whose total cost parameters are reduced after the candidate capacity is reallocated.

[0077] As can be seen from the description in step 201 above, since in the one-to-one combination of multiple orders and multiple candidate capacity, one order can only correspond to one candidate capacity, and one candidate capacity can only correspond to one order, when the number of multiple orders exceeds the number of multiple candidate capacity, there will be a first order that is not assigned a candidate capacity.

[0078] Furthermore, in a one-to-one combination of multiple orders and multiple candidate capacities, each order is assigned a different candidate capacity than the others. However, in some cases, an order can be assigned to the same candidate capacity as another order to optimize the overall order allocation. Therefore, in a one-to-one combination of multiple orders and multiple candidate capacities, there may also be a second order whose total cost parameter is reduced after the candidate capacity is reallocated.

[0079] For example, such as Figure 3As shown, multiple orders include Order 1, Order 2, and Order 3, and multiple candidate transport capacities include Candidate Capacity 1, Candidate Capacity 2, and Candidate Capacity 3. The cost parameter between Order 1 and Candidate Capacity 1 is 50, the cost parameter between Order 1 and Candidate Capacity 2 is 3, and Order 1 and Candidate Capacity 3 are mismatched. Order 2 and Candidate Capacity 1 are mismatched, the cost parameter between Order 2 and Candidate Capacity 2 is 6, the cost parameter between Order 2 and Candidate Capacity 3 is 7, and Order 3 is mismatched with both Candidate Capacities 1 and 2, with a cost parameter of 5 between Order 3 and Candidate Capacity 3. When performing one-to-one combinations, mismatched orders and candidate capacity combinations need to be excluded. Using the method in step 201 above, a first allocation result is obtained, which includes combinations of Order 1 and Candidate Capacity 1, combinations of Order 2 and Candidate Capacity 2, and combinations of Order 3 and Candidate Capacity 3. Among them, the cost parameter between order 1 and candidate capacity 1 is very high. Reassigning a candidate capacity (e.g., candidate capacity 2) to order 1 can reduce the cost parameter between order 1 and candidate capacity, and also reduce the total order parameter of multiple orders and multiple candidate capacity.

[0080] In this embodiment of the application, after the target order is determined, the method provided in step 201 is still used to allocate candidate capacity to the target order.

[0081] 203. The computer equipment determines a second allocation result based on the cost parameters of a one-to-one combination of a target order and multiple candidate capacity, the second allocation result including candidate capacity allocated to the target order, one candidate capacity in the second allocation result being allocated to a target order, and the total cost parameter of the second allocation result being the lowest.

[0082] 204. The computer equipment allocates candidate capacity for multiple orders based on the first allocation result and the second allocation result.

[0083] In some embodiments, the target order includes a second order whose total cost parameter is reduced after the candidate capacity is reallocated. Both the first allocation result and the second allocation result include candidate capacity allocated to the second order. When allocating candidate capacity to the second order, the second allocation result prevails. Optionally, the computer device allocates candidate capacity to multiple orders based on the first allocation result and the second allocation result, including: the computer device updates the first allocation result based on the second allocation result to obtain an updated first allocation result, and allocates candidate capacity to multiple orders based on the first allocation result.

[0084] The order allocation method provided in this application provides a first allocation result with the lowest total cost parameter based on a one-to-one combination of orders and candidate capacity. Then, it finds at least one of the following: orders that have not been allocated candidate capacity and target orders whose total cost parameter is reduced after reallocation of candidate capacity. A second allocation result with the lowest total cost parameter is then determined for the target orders. Since the first and second allocation results are the allocation results with the lowest total cost parameter, it can be seen that this application does not focus on only one order each time, but considers the lowest total cost parameter. Therefore, the obtained allocation result can reduce the total cost parameter of multiple orders and improve the accuracy of order allocation.

[0085] Figure 4 This is a flowchart illustrating an order allocation method provided in an embodiment of this application. This embodiment uses a computer device as an example for illustrative purposes, and includes:

[0086] 401. Computer equipment identifies multiple orders and multiple candidate transport capacities.

[0087] In some embodiments, the multiple orders can be any number of orders to be assigned; this application embodiment does not limit the number of orders. The multiple candidate capacities are candidate capacities that match the multiple orders; this application embodiment does not limit the number of candidate capacities.

[0088] In other embodiments, considering that the order allocation method provided in this application greatly improves the accuracy of allocation, but the computational load increases exponentially with the number of orders, multiple orders can be relatively small in number to ensure the timeliness of order allocation.

[0089] In some embodiments, the order allocation method provided in this application can also be applied to related technologies after order allocation has been completed, and based on the determined order allocation results, some orders that need to improve their allocation results are selected and the order allocation method provided in this application is executed to re-allocate candidate capacity to these orders.

[0090] Optionally, orders requiring improved allocation results may be those with low capacity efficiency. For example, based on the determined allocation results, orders with capacity efficiency below a fourth threshold are identified. For these identified orders, steps 401 to 407 are executed. The fourth threshold can be any value, such as an empirical value.

[0091] For example, based on the determined allocation results, orders with user experience parameters below the fifth threshold are identified. For these identified orders, steps 401 to 407 are executed. The fifth threshold can be any value; for example, it can be an empirical value.

[0092] For example, based on the determined allocation results, orders allocated to the target type of capacity are identified, and steps 401 to 407 are executed for the identified orders. In some embodiments, the computer device obtains a third allocation result of multiple reference orders, the third allocation result including the capacity allocated to the reference orders; for the reference orders among the multiple reference orders that have been allocated the target type of capacity, a step is executed to determine a first allocation result based on cost parameters of a one-to-one combination of multiple orders and multiple candidate capacity.

[0093] The target type of capacity can be any type of capacity. For example, the target type of capacity is the capacity that needs to be improved the most among various types of capacity. The embodiments of this application do not limit the division of capacity types or the target type of capacity.

[0094] In some embodiments, if multiple orders are orders assigned to target type capacity, then when reallocating capacity to multiple orders, target type capacity can also be reallocated to those multiple orders. Optionally, the target type capacity involved in determining the third allocation result can be used as multiple candidate capacities.

[0095] For example, using a target algorithm, based on multiple reference orders and multiple reference capacities, a third allocation result of multiple reference orders is determined. Based on the third allocation result, reference orders allocated to target type capacity are determined. The determined reference orders are used as multiple orders in step 401, and the target type capacity among the multiple reference capacities is determined as multiple candidate capacity in step 401.

[0096] 402. The computer equipment determines a first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity is assigned to one order, and the total cost parameter of the first allocation result is the lowest.

[0097] In some embodiments, the computer device determines a first allocation result based on cost parameters and an objective function that is used to determine the allocation result corresponding to the lowest total cost parameter, based on a one-to-one combination of multiple orders and multiple candidate capacities.

[0098] For example, the objective function is: Where, min is a function that takes the minimum value; W is an order group, which includes multiple orders from step 402 above, and i represents the i-th order in the order group; R is a capacity group, which includes multiple candidate capacities from step 402 above, and j represents the j-th candidate capacity, c ij χ represents the cost parameter between the i-th order and the j-th candidate capacity; ij Indicates whether to allocate the j-th candidate capacity to the i-th order, χ ijThe value is either 0 or 1, where 0 indicates that the j-th candidate capacity will not be allocated to the i-th order, and 1 indicates that the j-th candidate capacity will be allocated to the i-th order. This represents the total cost parameter for multiple orders and multiple candidate shipping capacities.

[0099] In some embodiments, the computer device iterates through the objective function to find multiple combinations of one-to-one combinations of multiple orders and multiple candidate capacity, finds multiple combinations corresponding to the lowest total cost parameter, and determines the multiple combinations as the first allocation result.

[0100] Furthermore, considering that some orders may have high similarity (e.g., multiple orders with the same start and destination points, or multiple orders with close start and destination points), the optimal candidate capacity for highly similar orders is often the same. Therefore, multiple highly similar orders can be assigned to the optimal candidate capacity simultaneously. However, if the order allocation method provided in this application is used, since one candidate capacity can only correspond to one order, after one allocation, only one order among the highly similar orders can be assigned the optimal candidate capacity, while the other orders can only be assigned the second-best candidate capacity. To solve this problem, this application provides an allocation method with order groups as the smallest unit of allocation.

[0101] In some embodiments, a computer device determines a first allocation result based on cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities, including: aggregating multiple orders based on their origin and destination points to obtain multiple order groups, each order group including at least one order; determining cost parameters of a one-to-one combination of multiple order groups and multiple candidate transport capacities; and determining a first allocation result based on the cost parameters of the one-to-one combination of the multiple order groups and multiple candidate transport capacities, the first allocation result including multiple one-to-one combinations, wherein one candidate transport capacity in each of the multiple one-to-one combinations is assigned to an order group.

[0102] By grouping multiple orders with high similarity into one order group, these orders can be simultaneously assigned to the optimal candidate capacity, improving the accuracy and efficiency of order allocation while reducing the computational scale of order allocation.

[0103] 403. The computer equipment determines a target order based on the first allocation result, the target order including at least one of a first order that was not allocated candidate capacity among multiple orders and a second order whose total cost parameter is reduced after reallocation of candidate capacity.

[0104] It should be noted that if in step 402 above, multiple orders are aggregated into multiple order groups to determine the first allocation result, then in step 403 above, a target order group is determined based on the first allocation result. This target order group includes the first order group among the multiple order groups that were not allocated candidate capacity and the second order group whose total cost parameter is reduced after reallocating candidate capacity. Similarly, the orders in steps 404 to 407 are all order groups.

[0105] The process of determining the first order in the embodiments of this application is described below as an example.

[0106] In this embodiment of the application, the first allocation result includes multiple one-to-one combinations. Based on these multiple one-to-one combinations, it can be determined which orders among the multiple orders have been allocated candidate capacity and which orders have not been allocated candidate capacity. Therefore, the computer device can determine the first order among the multiple orders that has not been allocated candidate capacity based on the first allocation result.

[0107] The process of determining the second order in the embodiments of this application is described below as an example.

[0108] Based on the initial allocation results, the orders can be roughly divided into three categories. The first category consists of orders allocated the optimal candidate capacity, the second category consists of orders not allocated the optimal candidate capacity, and the third category consists of orders not allocated any candidate capacity. The third category of orders is the first order in this embodiment of the application.

[0109] In one possible implementation, the computer device seeks second-category orders from which the total cost parameter is reduced after the reallocation of candidate capacity. Optionally, all second-category orders are identified as second orders. For example, the process of the computer device identifying second orders includes: based on the first allocation result, the computer device identifies orders that have not been allocated optimal candidate capacity as second orders. Optionally, orders that meet certain conditions are selected from the second-category orders to be identified as second orders. For example, the process of the computer device identifying second orders includes: based on the first allocation result, the computer device identifies orders that have not been allocated optimal candidate capacity, and from the identified orders, selects orders that meet a first screening condition as second orders. This first screening condition is the screening condition corresponding to the orders that have not been allocated optimal candidate capacity, used to screen orders that have not been allocated optimal candidate capacity.

[0110] The second type of order is the order that has not been assigned the optimal candidate capacity. If the cost parameter difference between the candidate capacity assigned to the order and the optimal candidate capacity of the order is low, then in the next allocation, even if the optimal candidate capacity is assigned to the order, the impact on the total cost parameter will not be significant. If the cost parameter difference between the candidate capacity assigned to the order and the optimal candidate capacity of the order is high, then in the next allocation, assigning the optimal candidate capacity to the order will reduce the cost parameter between the order and the candidate capacity, thereby reducing the total cost parameter.

[0111] Therefore, the first screening condition can be expressed as the difference in cost parameters between the candidate capacity allocated to the second order and the optimal candidate capacity of the second order exceeding a second threshold. It should be noted that the second order here refers to the second order determined from the second type of orders. If the second order is determined from other types of orders, it is not necessary to guarantee that the difference in cost parameters between the candidate capacity allocated to the second order and the optimal candidate capacity of the second order exceeds the second threshold.

[0112] Optionally, the computer device selects orders that meet the filtering criteria from the determined orders as the second orders, including: the first filtering criterion indicates that the cost parameter difference between the candidate capacity allocated to the second order and the optimal candidate capacity of the second order exceeds a second threshold; the computer device determines the cost parameter difference between the candidate capacity allocated to the order and the corresponding optimal candidate capacity based on the determined orders; and selects orders from the determined orders whose cost parameter difference exceeds the second threshold as the second orders.

[0113] The second threshold can be any value, and this application embodiment does not limit the second threshold. Optionally, the second threshold is an empirical value.

[0114] Furthermore, considering that if an order is not assigned to the optimal candidate capacity, but the optimal candidate capacity for that order is already unavailable, then in the next allocation, the order will still not be assigned to the optimal candidate capacity. In this case, the order may not proceed to the next allocation round. Therefore, the first screening condition indicates that the optimal candidate capacity corresponding to the second order has the capacity to accept orders. Similarly, the second order here refers to the second order determined from the second type of orders.

[0115] Optionally, the computer equipment selects orders that meet the filtering criteria from the determined orders as the second order, including: the first filtering criteria indicating that the optimal candidate capacity corresponding to the second order has the ability to accept orders; and selecting orders from the determined orders that have the corresponding optimal candidate capacity to accept orders, and determining them as the second order.

[0116] To improve the timeliness of order allocation, the number of orders entering the next round of allocation can be controlled. Therefore, the first screening condition can indicate that the number of second orders does not exceed a first threshold. Optionally, the computer device selects orders that meet the screening condition from the determined orders as second orders, including: the first screening condition indicating that the number of target orders does not exceed the first threshold; the computer device determining the cost parameter difference between the candidate capacity to be allocated to the orders and the corresponding optimal candidate capacity based on the determined orders; and selecting orders whose number does not exceed the first threshold from the determined orders as second orders based on the determined cost parameter difference, wherein the cost parameter difference of the selected orders is greater than the cost parameter difference of the unselected orders in the determined orders.

[0117] The first threshold can be any value, and this application embodiment does not limit the first threshold. Optionally, the first threshold is an empirical value. Optionally, the first threshold is determined based on the quantity of multiple orders. For example, the first threshold is P percent of the quantity of multiple orders. P can be any value between 0 and 100. Optionally, P is an empirical value, a value set by technicians, etc., and this application embodiment does not limit P. Optionally, this application embodiment limits the quantity of target orders and preferentially determines the first order as the target order. Therefore, the first threshold can be determined by the quantity of target orders and the quantity of the first order. For example, computer equipment limits the quantity of target orders to no more than P percent of the quantity of multiple orders, and the first threshold is the difference between the quantity of target orders and the quantity of the first order.

[0118] Optionally, the computer device selects orders from the determined orders whose quantity does not exceed a first threshold as second orders based on the determined cost parameter difference, including: sorting the determined orders according to the cost parameter difference, and sequentially selecting the determined orders as second orders in descending order of cost parameter difference, until the number of target orders reaches the first threshold.

[0119] The orders can be arranged in descending order of cost parameter difference or in ascending order of cost parameter difference; this application does not limit this arrangement.

[0120] Optionally, the first screening condition may be expressed as at least one of the following: the cost parameter difference between the candidate capacity allocated to the second order and the optimal candidate capacity of the second order exceeds a second threshold; the optimal candidate capacity corresponding to the second order has the ability to accept orders; or the number of the second order does not exceed a first threshold. In this embodiment of the application, the first screening condition is not limited.

[0121] In some embodiments, for the second type of order, the cost parameter difference between the candidate capacity allocated to the order and the optimal candidate capacity in the second type of order is calculated. The multiple orders in the second type of order are sorted in descending order of cost parameter difference, and the orders with higher cost parameter difference are added to the second order first. Orders that the optimal candidate capacity does not have the capacity to accept orders are not added to the second order. After the number of second orders reaches a first threshold, no more orders in the second type of order are added to the second order.

[0122] In another possible implementation, the computer device seeks a second order from the first type of orders where the total cost parameter is reduced after the candidate capacity is reallocated. In some embodiments, the process by which the computer device determines the second order includes: determining the order allocated the optimal candidate capacity based on the first allocation result; and selecting, from the determined orders, orders that meet a second screening condition as the second order, wherein the second screening condition is the screening condition corresponding to the order allocated the optimal candidate capacity, used to screen the orders allocated the optimal candidate capacity.

[0123] The first type of order is one that has been assigned the optimal candidate capacity. If the cost parameter difference between the optimal and second-best candidate capacity for this order is small, then assigning the order to either the optimal or second-best candidate capacity will have little impact on the total cost parameter. However, the freed-up optimal candidate capacity may bring higher revenue to other orders, thus reducing the total cost parameter. In other words, a small sacrifice of one order is used to obtain a larger overall benefit.

[0124] Optionally, the second screening condition indicates that the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the second order does not exceed a third threshold. Here, the second order refers to the second order selected from the first type of orders. In some embodiments, the computer device selects orders that meet the second screening condition from the determined orders as the second order, including: the second screening condition indicates that the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the second order does not exceed a third threshold; based on the determined orders, determining the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the order; and selecting orders from the determined orders whose cost parameter difference does not exceed the third threshold as the second order.

[0125] The third threshold can be any value, and this application embodiment does not limit the third threshold. Optionally, the third threshold is an empirical value.

[0126] To minimize the total cost parameter, it's also important to consider whether the optimal candidate capacity waived by an order can be fully utilized. If the optimal candidate capacity waived by an order happens to be the same as the optimal candidate capacity for other orders, then in the next allocation round, this optimal candidate capacity might be allocated to that other order, reducing the cost parameters of those other orders and thus the total cost parameter. If the optimal candidate capacity waived by an order is not the same as the optimal candidate capacity for other orders, then in the next allocation round, this optimal candidate capacity might still be allocated to that order, or the benefit of allocating it to other orders might be relatively small. Therefore, if the optimal candidate capacity waived by an order is not the same as the optimal candidate capacity for other orders, then that order can be excluded from the next allocation round.

[0127] Optionally, from the determined orders, select the orders that meet the second screening condition as the second order, including: the second screening condition indicates that the optimal candidate capacity of the second order is the optimal candidate capacity of other orders to be assigned; from the determined orders, select the orders whose corresponding optimal candidate capacity is the optimal candidate capacity of other orders to be assigned as the second order.

[0128] To ensure the timeliness of order allocation, the quantity of second orders can be controlled. Therefore, the second filtering condition can also indicate that the quantity of second orders does not exceed a first threshold. Optionally, the computer device selects orders that meet the second filtering condition from the determined orders as second orders, including: the second filtering condition indicating that the quantity of second orders does not exceed a first threshold; based on the determined orders, determining the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity for the orders; based on the determined cost parameter difference, selecting orders whose quantity does not exceed the first threshold from the determined orders as second orders, wherein the cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders among the determined orders.

[0129] In some embodiments, for a first type of order, the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity for multiple orders in the first type of order is calculated. The orders in the first type of order are then sorted in ascending order of cost parameter difference, with orders having smaller cost parameter differences being added to the second order list first. Orders whose optimal candidate capacity is not the optimal candidate capacity for other orders to be allocated are not added to the second order list. Furthermore, when the number of second orders reaches a first threshold, no more orders are selected from the first type of order list to be added to the second order list.

[0130] In another possible implementation, the computer device selects orders from both the second type of orders and the first type of orders as the second order, prioritizing the selection from the second type of orders. This is because the benefits of improving the second type of orders are greater.

[0131] In some embodiments, after the computer device selects orders from a determined set of orders with a quantity not exceeding a first threshold as second orders based on a determined cost parameter difference, the method further includes: if the quantity of the second orders does not exceed the first threshold, determining the orders allocated with the optimal candidate capacity based on a first allocation result; determining the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the determined orders; and selecting orders from the determined set of orders with a quantity not exceeding a target quantity as second orders based on the determined cost parameter difference, wherein the cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders in the determined set of orders, and the target quantity is the difference between the first threshold and the quantity of second orders determined from the orders that were not allocated the optimal candidate capacity.

[0132] For example, if the first threshold is 50, and the number of second orders identified from orders that have not been assigned the optimal candidate capacity is 30, then the target number is 20.

[0133] It should be noted that if the second order identified from the orders that have never been assigned the best candidate capacity reaches the first threshold, then there is no need to select an order from the first type of orders as the second order.

[0134] In some embodiments, the number of target orders does not exceed P percent of the total number of multiple orders (assuming the number of target orders does not exceed 100). Orders that have not been allocated candidate capacity are preferentially identified as target orders, and the number of target orders is 30. Then, according to the first screening condition, orders are selected from the second type of orders to be identified as target orders. If the number of target orders reaches 100, the selection of target orders stops. If the number of target orders still does not reach 100 after all orders are selected from the second type of orders, the selection of orders from the first type of orders continues according to the second screening condition until the number of target orders reaches 100, or all orders are selected from the first type of orders.

[0135] 404. Computer equipment determines the cost parameters for a one-to-one combination of a target order and multiple candidate transport capacities.

[0136] Step 404 is similar to step 402, and will not be described in detail here. It should be noted that the multiple candidate capacities in step 404 are the same as the multiple candidate capacities in step 402.

[0137] 405. The computer equipment determines a second allocation result based on cost parameters of a one-to-one combination of a target order and multiple candidate capacity, the second allocation result including candidate capacity allocated to the target order, one candidate capacity in the second allocation result being allocated to a target order, and the total cost parameter of the second allocation result being the lowest.

[0138] Step 404 is similar to step 402, and will not be described in detail here.

[0139] 406. When the iteration termination condition is met, the computer equipment allocates candidate capacity to multiple orders based on the first allocation result and the second allocation result.

[0140] In this embodiment, after determining the allocation result, a target order can be identified, and an allocation result can be determined for the target order to update the determined allocation result. The steps of identifying the target order, determining the allocation result for the target order, and updating the determined allocation result can be performed iteratively. The reason for iteratively performing this step in this embodiment is to reduce the total cost parameter of multiple orders. Therefore, an iteration termination condition can be set, and if the iteration termination condition is met, iteration will cease.

[0141] In some embodiments, the iteration termination condition includes at least one of the following: (1) the number of iterations reaches the target number; (2) the total cost parameter of the multiple orders is the same as the total cost parameter of the multiple orders after the previous iteration; (3) the determined allocation result is that the candidate capacity allocated to the multiple orders is the optimal candidate capacity.

[0142] The target number can be any number of times. To ensure the timeliness of order allocation, the target number should not be too large. Optionally, the target number can be an empirical value, which can effectively reduce the total cost parameter while ensuring the timeliness of order allocation.

[0143] It should be noted that the iteration can terminate when any of the iteration termination conditions are met.

[0144] 407. If the iteration termination condition is not met, the computer equipment repeatedly executes the process of re-determining the target order, determining the one-to-one combination parameters of the target order and the multiple candidate transport capacities, determining the next allocation result based on the one-to-one combination parameters of the target order and the multiple candidate transport capacities, until the iteration termination condition is met, and allocating candidate transport capacities to the multiple orders based on the determined allocation result.

[0145] In step 407 above, the "determined allocation result" refers to multiple allocation results determined during the iteration process.

[0146] Experiments have shown that the method provided in this application embodiment effectively reduces the total cost parameter compared with the methods in related technologies. The experimental values ​​are shown in Table 1.

[0147]

[0148] Table 1

[0149] Method 1 is a method provided by related technologies, and Method 2 is a method provided by the embodiments of this application.

[0150] like Figure 5 As shown, there are multiple orders, including Order 1, Order 2, and Order 3, and multiple candidate capacities, including Candidate Capacity 1, Candidate Capacity 2, and Candidate Capacity 3. The cost parameter between Order 1 and Candidate Capacity 1 is 5, the cost parameter between Order 1 and Candidate Capacity 2 is 3, and Order 1 and Candidate Capacity 3 do not match. Order 2 does not match Candidate Capacity 1, the cost parameter between Order 2 and Candidate Capacity 2 is 6, the cost parameter between Order 2 and Candidate Capacity 3 is 7, and Order 3 does not match either Candidate Capacity 1 or Candidate Capacity 2. The cost parameter between Order 3 and Candidate Capacity 3 is 5. If Method 1 is used (i.e., first determine the multiple candidate capacities corresponding to the multiple orders, then determine the cost parameters between the multiple candidate capacities and the multiple orders, find the lowest cost parameter from the multiple cost parameters, allocate the candidate capacities corresponding to the lowest cost parameter to the orders corresponding to the lowest cost parameter, update the cost parameters between the remaining orders and the candidate capacities based on the current allocation result, find the lowest cost parameter from the updated multiple cost parameters, allocate the candidate capacities corresponding to the lowest cost parameter to the orders corresponding to the lowest cost parameter, and repeat the process of updating cost parameters and allocating orders based on the lowest cost parameter in the updated cost parameters until candidate capacities are allocated to all orders), the allocation result is: Order 1 is assigned to candidate capacities 2 with a cost parameter of 3; Order 3 is assigned to candidate capacities 3 with a cost parameter of 5; Order 2 is assigned to candidate capacities 3 with a cost parameter of 10. Therefore, the total cost parameter is 18. It should be noted that the dashed lines between the orders and candidate capacities in the diagram represent the re-determined cost parameters between them.

[0151] If the first allocation result is determined based on cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities, then as follows: Figure 6 As shown, the allocation result is that order 1 is assigned to candidate capacity 1 with a cost parameter of 5; order 2 is assigned to candidate capacity 2 with a cost parameter of 6; and order 3 is assigned to candidate capacity 3 with a cost parameter of 5. Therefore, the total cost parameter is 16. This demonstrates that the method provided in this application can reduce the total cost parameter.

[0152] like Figure 7As shown, multiple orders include Order 1, Order 2, and Order 3, and multiple candidate transport capacities include Candidate Capacity 1, Candidate Capacity 2, and Candidate Capacity 3. The cost parameter between Order 1 and Candidate Capacity 1 is 50; the cost parameter between Order 1 and Candidate Capacity 2 is 3; Order 1 and Candidate Capacity 3 do not match. Order 2 does not match Candidate Capacity 1, the cost parameter between Order 2 and Candidate Capacity 2 is 6; the cost parameter between Order 2 and Candidate Capacity 3 is 7; Order 3 does not match either Candidate Capacity 1 or Candidate Capacity 2, and the cost parameter between Order 3 and Candidate Capacity 3 is 5. Based on the cost parameters of the one-to-one combination of multiple orders and multiple candidate transport capacities, the determined first allocation result is: Order 1 is assigned to Candidate Capacity 1 with a cost parameter of 50; Order 2 is assigned to Candidate Capacity 2 with a cost parameter of 6; and Order 3 is assigned to Candidate Capacity 3 with a cost parameter of 5. Therefore, the total cost parameter of the first allocation result is 61.

[0153] As shown in Figure 8, if Order 1 and Order 2 are identified as target orders, delivery capacity is reassigned to the target orders, resulting in a second allocation result: Order 1 is assigned to candidate capacity 2 with a cost parameter of 3; Order 2 is assigned to candidate capacity 3 with a cost parameter of 10. Therefore, the total cost parameter is 18.

[0154] Therefore, by identifying the target order and reallocating candidate capacity for it, the embodiments of this application can effectively reduce the total cost parameter.

[0155] The order allocation method provided in this application provides a first allocation result with the lowest total cost parameter based on a one-to-one combination of orders and candidate capacity. Then, it finds at least one of the following: orders that have not been allocated candidate capacity and target orders whose total cost parameter is reduced after reallocation of candidate capacity. A second allocation result with the lowest total cost parameter is then determined for the target orders. Since the first and second allocation results are the allocation results with the lowest total cost parameter, it can be seen that this application does not focus on only one order each time, but considers the lowest total cost parameter. Therefore, the obtained allocation result can reduce the total cost parameter of multiple orders and improve the accuracy of order allocation.

[0156] This application's embodiments divide the many-to-one problem of orders and candidate capacity into multiple one-to-one problems for solution. By solving the problem through multiple allocations, candidate capacity is allocated to orders that have not been allocated, and the orders that have been allocated candidate capacity are optimized, thereby reducing the total cost parameter of multiple orders and improving the accuracy of order allocation.

[0157] Figure 9 This is a schematic diagram of an order allocation device provided in an embodiment of this application. See also... Figure 9 The device includes:

[0158] The result determination module 901 is used to determine a first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity is assigned to one order, and the total cost parameter of the first allocation result is the lowest.

[0159] The parameter determination module 902 is used to determine the cost parameters of a one-to-one combination of a target order and the plurality of candidate transport capacities. The target order includes at least one of the following: a first order that was not assigned a candidate transport capacity and a second order whose total cost parameter is reduced after the candidate transport capacity is reallocated.

[0160] The result determination module 901 is used to determine a second allocation result based on the cost parameters of the one-to-one combination of the target order and the plurality of candidate transport capacities. The second allocation result includes candidate transport capacities allocated to the target order. In the second allocation result, one candidate transport capacity is allocated to one target order, and the total cost parameter of the second allocation result is the lowest.

[0161] The allocation module 903 is used to allocate candidate capacity to the multiple orders based on the first allocation result and the second allocation result.

[0162] like Figure 10 As shown, in one possible implementation, the device further includes:

[0163] The order confirmation module 904 is used to, based on the first allocation result, treat orders that were not allocated the optimal candidate capacity as second orders; or,

[0164] The order determination module 904 is used to determine, based on the first allocation result, orders that have not been allocated the optimal candidate capacity, and select, from the determined orders, orders that meet the first screening condition as the second order. The first screening condition is used to screen orders that have not been allocated the optimal candidate capacity.

[0165] In one possible implementation, the order confirmation module 904 is configured to perform at least one of the following:

[0166] The first screening condition indicates that the optimal candidate capacity corresponding to the second order has the ability to accept the order; from the determined orders, the order with the corresponding optimal candidate capacity having the ability to accept the order is selected as the second order;

[0167] The first filtering condition indicates that the number of the second orders does not exceed a first threshold; based on the determined orders, the cost parameter difference between the candidate capacity allocated to the order and the corresponding optimal candidate capacity is determined; based on the determined cost parameter difference, orders with a number not exceeding the first threshold are selected from the determined orders as the second orders, and the cost parameter difference of the selected orders is greater than the cost parameter difference of the unselected orders in the determined orders.

[0168] In one possible implementation, the order determination module 904 is further configured to: determine, based on the first allocation result, orders allocated with optimal candidate capacity if the number of the second orders does not exceed the first threshold; determine the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the determined orders; and select, based on the determined cost parameter difference, orders not exceeding a target number from the determined orders as the second orders, wherein the cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders among the determined orders, and the target number is the difference between the first threshold and the number of second orders determined from the orders not allocated with optimal candidate capacity.

[0169] In one possible implementation, the order determination module 904 is used to determine, based on the first allocation result, the orders allocated with the optimal candidate capacity; and select, from the determined orders, the orders that meet the second screening condition as the second order, wherein the second screening condition is used to screen the orders allocated with the optimal candidate capacity.

[0170] In one possible implementation, the order confirmation module 904 is configured to perform at least one of the following:

[0171] The second filtering condition indicates that the optimal candidate capacity for the second order is the optimal candidate capacity for other orders to be assigned; from the determined orders, the orders whose optimal candidate capacity is the optimal candidate capacity for other orders to be assigned are selected as the second order;

[0172] The second screening condition indicates that the number of the second orders does not exceed the first threshold; based on the determined orders, the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity of the orders is determined; based on the determined cost parameter difference, orders with a number not exceeding the first threshold are selected from the determined orders as the second orders, and the cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders among the determined orders.

[0173] In one possible implementation, the result determination module 901 includes:

[0174] Aggregation unit 9011 is used to aggregate the multiple orders based on the start point and destination point of the multiple orders to obtain multiple order groups, wherein the order group includes at least one order;

[0175] The parameter determination unit 9012 is used to determine the cost parameters of a one-to-one combination of the plurality of order groups and the plurality of candidate transport capacities;

[0176] The result determination unit 9013 is used to determine the first allocation result based on the cost parameters of the one-to-one combination of the multiple order groups and the multiple candidate transport capacities. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity in the multiple one-to-one combinations is assigned to an order group.

[0177] In one possible implementation, the allocation module 903 is configured to allocate candidate capacity to the plurality of orders based on the first allocation result and the second allocation result, provided that the iteration termination condition is met.

[0178] The allocation module 903 is used to repeatedly execute the process of re-determining the target order, determining the one-to-one combination parameters of the target order and the multiple candidate transport capacity if the iteration termination condition is not met, determining the next allocation result based on the one-to-one combination parameters of the target order and the multiple candidate transport capacity, until the iteration termination condition is met, and allocating candidate transport capacity to the multiple orders based on the determined allocation result.

[0179] In one possible implementation, the iteration termination condition includes at least one of the following:

[0180] The target number of iterations has been reached.

[0181] The total cost parameter of the multiple orders is the same as the total cost parameter of the multiple orders after the previous iteration;

[0182] The determined allocation result is that all candidate transport capacities allocated to the multiple orders are the optimal candidate transport capacities.

[0183] In one possible implementation, the result determination module 901 is used to obtain a third allocation result of multiple reference orders, the third allocation result including the capacity allocated to the reference orders; for the reference orders among the multiple reference orders that have been allocated the target type capacity, the step of determining the first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate capacity is executed.

[0184] It should be noted that the order allocation device provided in the above embodiments is only illustrated by the division of the above functional modules when allocating orders. In actual applications, the above functional allocation can be completed by different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the order allocation device and the order allocation method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0185] In an exemplary embodiment, a computer device is provided, the computer device including one or more processors and one or more memories, the one or more memories storing at least one piece of program code, the at least one piece of program code being loaded and executed by the one or more processors to implement the order allocation method as described in the above embodiments.

[0186] Optionally, the computer device is provided as a terminal. Figure 11 A structural block diagram of a terminal 1100 provided in an exemplary embodiment of this application is shown. The terminal 1100 may be a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The terminal 1100 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names.

[0187] Terminal 1100 includes a processor 1101 and a memory 1102.

[0188] Processor 1101 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 1101 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1101 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1101 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 1101 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0189] The memory 1102 may include one or more computer-readable storage media, which may be non-transitory. The memory 1102 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1102 are used to store at least one program code, which is executed by the processor 1101 to implement the order allocation method provided in the method embodiments of this application.

[0190] In some embodiments, the terminal 1100 may also optionally include: a peripheral device interface 1103 and at least one peripheral device. The processor 1101, memory 1102, and peripheral device interface 1103 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1103 via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of: a radio frequency circuit 1104, a display screen 1105, a camera 1106, an audio circuit 1107, a positioning component 1108, and a power supply 1109.

[0191] Peripheral device interface 1103 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1101 and memory 1102. In some embodiments, processor 1101, memory 1102 and peripheral device interface 1103 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1101, memory 1102 and peripheral device interface 1103 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0192] The radio frequency (RF) circuit 1104 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1104 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1104 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 1104 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1104 can communicate with other terminals through at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: metropolitan area networks (MANs), various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks (WLANs), and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1104 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0193] Display screen 1105 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1105 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1101 for processing. In this case, display screen 1105 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen 1105, which serves as the front panel of terminal 1100; in other embodiments, there may be at least two display screens, respectively disposed on different surfaces of terminal 1100 or in a folded design; in still other embodiments, display screen 1105 may be a flexible display screen, disposed on a curved or folded surface of terminal 1100. Furthermore, display screen 1105 may also be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1105 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0194] The camera assembly 1106 is used to acquire images or videos. Optionally, the camera assembly 1106 includes a front-facing camera and a rear-facing camera. The front-facing camera is disposed on the front panel of the terminal, and the rear-facing camera is disposed on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1106 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm light flash and a cool light flash, which can be used for light compensation at different color temperatures.

[0195] The audio circuit 1107 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1101 for processing, or input to the radio frequency circuit 1104 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each positioned at a different location on the terminal 1100. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1101 or the radio frequency circuit 1104 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1107 may also include a headphone jack.

[0196] The positioning component 1108 is used to locate the current geographical location of the terminal 1100 in order to enable navigation or LBS (Location Based Service). The positioning component 1108 can be a positioning component based on the US GPS (Global Positioning System), China's BeiDou system, Russia's Granas system, or the European Union's Galileo system.

[0197] Power supply 1109 is used to power the various components in terminal 1100. Power supply 1109 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1109 includes a rechargeable battery, the rechargeable battery can support wired charging or wireless charging. The rechargeable battery can also be used to support fast charging technology.

[0198] In some embodiments, the terminal 1100 further includes one or more sensors 1110. The one or more sensors 1110 include, but are not limited to: an accelerometer 1111, a gyroscope 1112, a pressure sensor 1113, a fingerprint sensor 1114, an optical sensor 1115, and a proximity sensor 1116.

[0199] Accelerometer 1111 can detect the magnitude of acceleration along the three axes of a coordinate system established with terminal 1100. For example, accelerometer 1111 can be used to detect the components of gravitational acceleration along the three axes. Processor 1101 can control display screen 1105 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1111. Accelerometer 1111 can also be used for collecting game or user motion data.

[0200] The gyroscope sensor 1112 can detect the orientation and rotation angle of the terminal 1100. The gyroscope sensor 1112 can work in conjunction with the accelerometer sensor 1111 to collect the user's 3D movements on the terminal 1100. Based on the data collected by the gyroscope sensor 1112, the processor 1101 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0201] The pressure sensor 1113 can be disposed on the side bezel of the terminal 1100 and / or on the lower layer of the display screen 1105. When the pressure sensor 1113 is disposed on the side bezel of the terminal 1100, it can detect the user's grip signal on the terminal 1100, and the processor 1101 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 1113. When the pressure sensor 1113 is disposed on the lower layer of the display screen 1105, the processor 1101 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 1105. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0202] The fingerprint sensor 1114 is used to collect the user's fingerprint. The processor 1101 identifies the user's identity based on the fingerprint collected by the fingerprint sensor 1114, or the fingerprint sensor 1114 identifies the user's identity based on the collected fingerprint. When the user's identity is identified as trusted, the processor 1101 authorizes the user to perform relevant sensitive operations, including unlocking the screen, viewing encrypted information, downloading software, making payments, and changing settings. The fingerprint sensor 1114 can be located on the front, back, or side of the terminal 1100. When the terminal 1100 has physical buttons or a manufacturer's logo, the fingerprint sensor 1114 can be integrated with the physical buttons or manufacturer's logo.

[0203] An optical sensor 1115 is used to collect ambient light intensity. In one embodiment, the processor 1101 can control the display brightness of the display screen 1105 based on the ambient light intensity collected by the optical sensor 1115. Specifically, when the ambient light intensity is high, the display brightness of the display screen 1105 is increased; when the ambient light intensity is low, the display brightness of the display screen 1105 is decreased. In another embodiment, the processor 1101 can also dynamically adjust the shooting parameters of the camera assembly 1106 based on the ambient light intensity collected by the optical sensor 1115.

[0204] The proximity sensor 1116, also known as a distance sensor, is installed on the front panel of the terminal 1100. The proximity sensor 1116 is used to detect the distance between the user and the front of the terminal 1100. In one embodiment, when the proximity sensor 1116 detects that the distance between the user and the front of the terminal 1100 is gradually decreasing, the processor 1101 controls the display screen 1105 to switch from a screen-on state to a screen-off state; when the proximity sensor 1116 detects that the distance between the user and the front of the terminal 1100 is gradually increasing, the processor 1101 controls the display screen 1105 to switch from a screen-off state to a screen-on state.

[0205] Those skilled in the art will understand that Figure 11 The structure shown does not constitute a limitation on terminal 1100 and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0206] Alternatively, the computer device may be provided as a server. Figure 12 This is a schematic diagram of a server structure provided in an embodiment of this application. The server 1200 can vary considerably due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 1201 and one or more memories 1202. The memory 1202 stores at least one line of program code, which is loaded and executed by the processor 1201 to implement the methods provided in the above-described method embodiments. Of course, the server may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server may also include other components for implementing device functions, which will not be elaborated here.

[0207] The server 1200 is used to execute the steps performed by the server in the above method embodiments.

[0208] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including program code that can be executed by a processor in a computer device to perform the order allocation method in the above embodiments. For example, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0209] In an exemplary embodiment, a computer program or computer program product is also provided, which includes computer program code that, when executed by a computer, causes the computer to implement the order allocation method in the above embodiments.

[0210] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0211] The above description is merely an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An order allocation method, characterized in that, The method includes: Based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities, a first allocation result is determined. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity is assigned to one order, and the total cost parameter of the first allocation result is the lowest. Determine the cost parameters of a one-to-one combination of a target order and the plurality of candidate capacity, wherein the target order includes at least one of a first order that was not assigned a candidate capacity and a second order whose total cost parameter is reduced after the candidate capacity is reallocated; Based on the cost parameters of the one-to-one combination of the target order and the multiple candidate transport capacities, a second allocation result is determined. The second allocation result includes the candidate transport capacities allocated to the target order. In the second allocation result, one candidate transport capacity is allocated to one target order, and the total cost parameter of the second allocation result is the lowest. Based on the first allocation result and the second allocation result, candidate transportation capacity is allocated to the multiple orders.

2. The method according to claim 1, characterized in that, The process of determining the second order includes: Based on the first allocation result, orders that were not allocated the optimal candidate capacity will be treated as second orders; or, Based on the first allocation result, orders that have not been allocated the optimal candidate capacity are identified. From the orders that have not been allocated the optimal candidate capacity, orders that meet the first screening condition are selected as the second orders. The first screening condition is used to screen orders that have not been allocated the optimal candidate capacity.

3. The method according to claim 2, characterized in that, The step of selecting orders that meet the first screening criteria from the orders that have not been assigned optimal candidate capacity as the second order includes at least one of the following: The first screening criterion indicates that the optimal candidate transport capacity corresponding to the second order has the ability to accept the order; From the orders that have not been assigned the optimal candidate capacity, select the orders with the corresponding optimal candidate capacity that have the ability to accept orders, and use them as the second order; The first filtering condition indicates that the quantity of the second order does not exceed a first threshold; Based on the orders that have not been assigned the optimal candidate capacity, determine the cost parameter difference between the candidate capacity assigned to the order and the corresponding optimal candidate capacity; Based on the determined cost parameter difference, orders not exceeding a first threshold are selected from the orders that have not been assigned optimal candidate capacity as the second order. The cost parameter difference of the selected orders is greater than the cost parameter difference of the unselected orders among the orders that have not been assigned optimal candidate capacity.

4. The method according to claim 3, characterized in that, After selecting orders from the orders not allocated optimal candidate capacity based on the determined cost parameter difference, with a number not exceeding a first threshold, as the second order, the method further includes: If the number of the second orders does not exceed the first threshold, the orders allocated the optimal candidate capacity are determined based on the first allocation result; Based on the orders that have been allocated the optimal candidate capacity, determine the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity for the orders; Based on the determined cost parameter difference, orders not exceeding a target number are selected from the orders allocated with optimal candidate capacity as the second orders. The cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders among the orders allocated with optimal candidate capacity. The target number is the difference between the first threshold and the number of second orders determined from the orders not allocated with optimal candidate capacity.

5. The method according to claim 1, characterized in that, The process of determining the second order includes: Based on the first allocation result, the orders allocated the optimal candidate capacity were determined; From the orders allocated the optimal candidate capacity, the orders that meet the second screening condition are selected as the second order. The second screening condition is used to screen the orders allocated the optimal candidate capacity.

6. The method according to claim 5, characterized in that, The step of selecting an order that meets the second screening condition from the orders allocated with the optimal candidate capacity as the second order includes at least one of the following: The second filtering condition indicates that the optimal candidate capacity for the second order is the optimal candidate capacity for other orders to be assigned; From the orders that have been allocated the optimal candidate capacity, select the order whose corresponding optimal candidate capacity is used as the optimal candidate capacity for other orders to be allocated, and designate it as the second order; The second filtering condition indicates that the quantity of the second order does not exceed the first threshold; Based on the orders that have been allocated the optimal candidate capacity, determine the cost parameter difference between the optimal candidate capacity and the second-best candidate capacity for the orders; Based on the determined cost parameter difference, orders with a number not exceeding the first threshold are selected from the orders allocated the optimal candidate capacity as the second orders. The cost parameter difference of the selected orders is less than the cost parameter difference of the unselected orders among the orders allocated the optimal candidate capacity.

7. The method according to claim 1, characterized in that, The determination of the first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities includes: Based on the starting point and destination of the multiple orders, the multiple orders are aggregated to obtain multiple order groups, each order group including at least one order; Determine the cost parameters for a one-to-one combination of the multiple order groups and the multiple candidate transport capacities; Based on the cost parameters of the one-to-one combination of the multiple order groups and the multiple candidate capacity, the first allocation result is determined. The first allocation result includes multiple one-to-one combinations, and one candidate capacity in the multiple one-to-one combinations is assigned to an order group.

8. The method according to claim 1, characterized in that, The process of allocating candidate capacity to the multiple orders based on the first allocation result and the second allocation result includes: If the iteration termination condition is met, candidate capacity is allocated to the plurality of orders based on the first allocation result and the second allocation result; If the iteration termination condition is not met, the process of re-determining the target order is repeated, determining the one-to-one combination parameters of the target order and the multiple candidate transport capacities, determining the next allocation result based on the one-to-one combination parameters of the target order and the multiple candidate transport capacities, until the iteration termination condition is met, and allocating candidate transport capacities to the multiple orders based on the determined allocation result.

9. The method according to claim 8, characterized in that, The iteration termination condition includes at least one of the following: The target number of iterations has been reached. The total cost parameter of the multiple orders is the same as the total cost parameter of the multiple orders after the previous iteration; The determined allocation result is that all candidate transport capacities allocated to the multiple orders are the optimal candidate transport capacities.

10. The method according to claim 1, characterized in that, The multiple candidate capacities are target type capacities; The determination of the first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities includes: Obtain a third allocation result for multiple reference orders, the third allocation result including the capacity allocated to the reference orders; For the reference orders among the plurality of reference orders that have been allocated the target type of capacity, the step of determining the first allocation result based on the cost parameters of the one-to-one combination of the plurality of orders and the plurality of candidate capacity is executed.

11. An order allocation device, characterized in that, The device includes: The result determination module is used to determine a first allocation result based on the cost parameters of a one-to-one combination of multiple orders and multiple candidate transport capacities. The first allocation result includes multiple one-to-one combinations, in which one candidate transport capacity in the multiple one-to-one combinations is assigned to one order, and the total cost parameter of the first allocation result is the lowest. The parameter determination module is used to determine the cost parameters of a one-to-one combination of a target order and the plurality of candidate transport capacities. The target order includes at least one of a first order that was not assigned a candidate transport capacity and a second order whose total cost parameter is reduced after the candidate transport capacity is reallocated. The result determination module is used to determine a second allocation result based on the cost parameters of a one-to-one combination of the target order and the plurality of candidate transport capacities. The second allocation result includes candidate transport capacities allocated to the target order. In the second allocation result, one candidate transport capacity is allocated to one target order, and the total cost parameter of the second allocation result is the lowest. The allocation module is used to allocate candidate capacity to the multiple orders based on the first allocation result and the second allocation result.

12. A computer device, characterized in that, The computer device includes one or more processors and one or more memories, wherein at least one piece of program code is stored in the one or more memories, and the at least one piece of program code is loaded and executed by the one or more processors to perform the operations performed by the order allocation method as described in any one of claims 1 to 10.

13. A computer-readable storage medium, characterized in that, The storage medium stores at least one piece of program code, which is loaded and executed by a processor to perform the operations of the order allocation method as described in any one of claims 1 to 10.

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