Transportation order allocation method, device, readable program product, and navigation method

Abstract

The embodiment of the present disclosure discloses a kind of transport capacity order distribution method, device, readable program product and navigation method, the method comprises: obtaining order information to be distributed;According to the order information to be distributed, the order to be distributed is formed into one or more order chains, wherein the number of orders in the order chain is less than or equal to the first preset number threshold;The order chain is pre-assigned to the transport capacity end corresponding to the number of order chain, wherein each transport capacity end obtains an order chain;Calculate the global income target value of the order chain after the order chain is pre-assigned to the transport capacity end corresponding to the number of order chain;According to the global income target value, the distribution object of the order chain is adjusted, and the final distribution result of order chain is obtained.The technical scheme can quickly realize the real-time, unified optimization distribution of online car-hailing order, so as to effectively improve the dispatching efficiency of online car-hailing platform and the response rate of online car-hailing driver, effectively improve the user experience.

Description

Technical Field

[0001] This disclosure relates to the field of transportation technology, specifically to a method, apparatus, readable program product, and navigation method for allocating transportation capacity orders. Background Technology

[0002] With social development and progress, many users choose ride-hailing services. Ride-hailing orders are highly real-time, and users have high requirements for order fulfillment time. Therefore, how to quickly and uniformly optimize the allocation of ride-hailing orders in real-time to improve the platform's dispatch efficiency, driver response rates, and ultimately, enhance the user experience, is a problem that urgently needs to be solved. Summary of the Invention

[0003] This disclosure provides a method, apparatus, readable program product, and navigation method for allocating transportation capacity orders.

[0004] Firstly, this disclosure provides a method for allocating transportation capacity orders.

[0005] Specifically, the method for allocating transport capacity orders includes:

[0006] Get information on orders to be assigned;

[0007] The orders to be assigned are grouped into one or more order chains based on the order information to be assigned, wherein the number of orders in the order chain is less than or equal to a first preset quantity threshold;

[0008] The order chains are pre-allocated to the capacity terminals corresponding to the number of order chains, wherein each capacity terminal is allocated one order chain;

[0009] Calculate the global revenue target value of the order chain after pre-allocating the order chain to the capacity terminal corresponding to the number of order chains;

[0010] The allocation objects of the order chain are adjusted according to the global revenue target value to obtain the final allocation result of the order chain.

[0011] In conjunction with the first aspect, in a first implementation of the first aspect of this disclosure, the step of pre-allocating the order chain to the capacity terminal corresponding to the number of order chains includes:

[0012] The order chains are randomly pre-allocated to the capacity terminals corresponding to the number of order chains;

[0013] or,

[0014] The order chain is pre-assigned to the transport terminal that is closest to the departure point of the first order in the order chain, or has the shortest estimated arrival time at the departure point of the first order in the order chain, wherein the number of orders already accepted by the transport terminal is less than or equal to a second preset quantity threshold; or...

[0015] The order information of the order chain is broadcast to the transport terminals located within a preset geographical range, and in response to receiving an order acceptance request from the transport terminal, the order chain is pre-allocated to the transport terminal, wherein the order information of the order chain includes the departure location of the sequential orders in the order chain.

[0016] In conjunction with the first aspect and the first implementation of the first aspect, in the second implementation of the first aspect of this disclosure, the calculation of the global revenue target value of the order chain after pre-allocating the order chain to the capacity terminals corresponding to the number of order chains includes:

[0017] Under the constraint of the order chain, calculate the difference between the total revenue of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total revenue target value of the order chain.

[0018] In conjunction with the first aspect and the above-described implementations of the first aspect, the third implementation of the first aspect of this disclosure further includes:

[0019] Order chains that do not meet the aforementioned order chain constraints will be discarded.

[0020] In conjunction with the first aspect and the above-described implementation methods, in the fourth implementation method of the first aspect of this disclosure, the order chain constraints include order chain local constraints and order chain global constraints, wherein,

[0021] The local constraints of the order chain include local time constraints and local location constraints, wherein:

[0022] The local time constraints of the order chain include:

[0023] The estimated arrival time of the previous order in the order chain is less than or equal to the estimated departure time of the next order;

[0024] The estimated arrival time from the arrival location of the previous order in the order chain to the departure location of the next order is less than or equal to the first preset time threshold.

[0025] The local location constraints of the order chain include:

[0026] The distance between the arrival location of the previous order and the departure location of the next order in the order chain is less than a first preset distance threshold;

[0027] The global constraints of the order chain include global time constraints and global location constraints, wherein:

[0028] The global time constraints of the order chain include:

[0029] The total estimated time for completing all orders in the order chain on the transportation side is less than or equal to the second preset time threshold.

[0030] The global location constraints of the order chain include:

[0031] The estimated total distance of all orders in the order chain completed by the transportation capacity side is less than or equal to the second preset distance threshold.

[0032] In conjunction with the first aspect and the above-described implementations of the first aspect, in the fifth implementation of the first aspect of this disclosure, the adjustment of the allocation object of the order chain according to the global revenue target value includes:

[0033] The allocation object of the order chain is adjusted, and the order chain allocation result corresponding to the maximum global revenue target value obtained after adjusting the allocation object of the order chain is taken as the final order chain allocation result, or the order chain allocation result corresponding to the maximum global revenue target value obtained when the number of adjustments reaches a preset number threshold.

[0034] In conjunction with the first aspect and the above-described implementations of the first aspect, the sixth implementation of the first aspect of this disclosure further includes:

[0035] Based on the order chain allocation results, an order chain navigation route is generated and sent to the corresponding transportation capacity client for broadcasting and display.

[0036] Secondly, this disclosure provides a capacity order allocation device.

[0037] Specifically, the capacity order allocation device includes:

[0038] The acquisition module is configured to acquire information about orders to be assigned.

[0039] The combination module is configured to combine the orders to be assigned into one or more order chains based on the order information to be assigned, wherein the number of orders in the order chain is less than or equal to a first preset quantity threshold.

[0040] The allocation module is configured to pre-allocate the order chain to the capacity end corresponding to the number of order chains, wherein each capacity end is allocated one order chain;

[0041] The calculation module is configured to calculate the global revenue target value of the order chain after the order chain is pre-allocated to the capacity terminal corresponding to the number of the order chain;

[0042] The adjustment module is configured to adjust the allocation objects of the order chain according to the global revenue target value to obtain the final allocation result of the order chain.

[0043] Thirdly, embodiments of this disclosure provide an electronic device, including a memory and a processor. The memory stores one or more computer instructions that support a capacity order allocation device in executing the aforementioned capacity order allocation method. The processor is configured to execute the computer instructions stored in the memory. The capacity order allocation device may further include a communication interface for communicating with other devices or communication networks.

[0044] Fourthly, embodiments of this disclosure provide a computer-readable storage medium for storing computer instructions used by a capacity order allocation device, which includes computer instructions for executing the aforementioned capacity order allocation method in connection with the capacity order allocation device.

[0045] Fifthly, embodiments of this disclosure provide a navigation method, wherein a navigation route based at least on a starting point and an ending point is obtained, and navigation guidance is performed based on the navigation route, wherein the navigation route is generated based on any of the methods described above.

[0046] The technical solutions provided in this disclosure may have the following beneficial effects:

[0047] The aforementioned technical solution allocates the transportation capacity order chain to the transportation capacity side by setting and optimizing a global revenue target value. This solution enables rapid, real-time, and unified optimized allocation of ride-hailing orders, thereby effectively improving the dispatch efficiency of ride-hailing platforms and the response rate of ride-hailing drivers, and significantly enhancing the user experience.

[0048] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the embodiments of this disclosure. Attached Figure Description

[0049] Other features, objects, and advantages of embodiments of this disclosure will become more apparent from the following detailed description of non-limiting implementations, taken in conjunction with the accompanying drawings. In the drawings:

[0050] Figure 1 A flowchart illustrating a capacity order allocation method according to an embodiment of the present disclosure is shown;

[0051] Figure 2 A structural block diagram of a capacity order allocation device according to an embodiment of the present disclosure is shown;

[0052] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0053] Figure 4 This is a schematic diagram of the structure of a computer system suitable for implementing a capacity order allocation method according to an embodiment of the present disclosure. Detailed Implementation

[0054] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings to enable those skilled in the art to readily implement them. Furthermore, for clarity, portions unrelated to the description of the exemplary embodiments have been omitted from the drawings.

[0055] In embodiments disclosed herein, it should be understood that terms such as “comprising” or “having” are intended to indicate the presence of features, numbers, steps, behaviors, components, portions or combinations thereof disclosed herein, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, portions or combinations thereof are present or added.

[0056] It should also be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings and examples.

[0057] The technical solution provided in this disclosure allocates the transportation capacity order chain to the global revenue target value of the transportation capacity side by setting and optimizing the global revenue target value. This technical solution can quickly achieve real-time and unified optimized allocation of ride-hailing orders, thereby effectively improving the dispatching efficiency of the ride-hailing platform and the response rate of ride-hailing drivers, and effectively enhancing the user experience.

[0058] Figure 1 A flowchart illustrating a capacity order allocation method according to an embodiment of the present disclosure is shown, as follows: Figure 1 As shown, the capacity order allocation method includes the following steps S101-S105:

[0059] In step S101, the information of the orders to be assigned is obtained;

[0060] In step S102, the orders to be assigned are grouped into one or more order chains according to the order information to be assigned, wherein the number of orders in the order chain is less than or equal to a first preset quantity threshold.

[0061] In step S103, the order chain is pre-allocated to the capacity terminals corresponding to the number of order chains, wherein each capacity terminal is allocated one order chain;

[0062] In step S104, the global revenue target value of the order chain is calculated after the order chain is pre-allocated to the capacity terminal corresponding to the number of order chains;

[0063] In step S105, the allocation objects of the order chain are adjusted according to the global revenue target value to obtain the final allocation result of the order chain.

[0064] As mentioned above, with social development and progress, many users choose ride-hailing services. Ride-hailing orders are highly real-time, and users have high requirements for order fulfillment time. How to quickly achieve real-time and unified optimized allocation of ride-hailing orders to improve the dispatch efficiency of ride-hailing platforms, the response rate of ride-hailing drivers, and the user experience is an urgent problem to be solved.

[0065] Considering the aforementioned issues, this implementation proposes a capacity order allocation method. This method allocates capacity order chains to the global revenue target value of the capacity side by setting and optimizing the global revenue target value. This technical solution can quickly achieve real-time and unified optimized allocation of ride-hailing orders, thereby effectively improving the order dispatch efficiency of the ride-hailing platform and the response rate of ride-hailing drivers, and effectively enhancing the user experience.

[0066] In one embodiment of this disclosure, the capacity order allocation method can be applied to computers, computing devices, electronic devices, servers, etc., that can perform capacity order allocation.

[0067] In one embodiment of this disclosure, the capacity order refers to an order for the purpose of transportation, such as a ride-hailing order.

[0068] In one embodiment of this disclosure, the pending order refers to an order that is waiting to be assigned, and after assignment, it requires traveling to the departure location according to the order departure time to transport the user from the departure location to the arrival location. The pending order information may include one or more of the following: the order placement time of the pending order, the departure location of the pending order, the departure time of the pending order, the arrival location of the scheduled order, etc.

[0069] In one embodiment of this disclosure, the order chain refers to a combination of orders that can be allocated together, where there are no time or location conflicts between the multiple orders. The multiple orders are allocated collectively as a unit within the order chain and executed sequentially within the chain. This combined implementation is more efficient than allocating and executing individual orders separately. To ensure the execution efficiency of the orders in the order chain, the number of orders in the order chain cannot be excessive; that is, the number of orders in the order chain must be less than or equal to a first preset quantity threshold. The first preset quantity threshold can be set according to the needs of actual application, and this disclosure does not specifically limit its value.

[0070] In one embodiment of this disclosure, the "capacity terminal" refers to the terminal that receives, executes, and ultimately completes the capacity order and is capable of providing transportation capacity. The capacity terminal may, for example, be a ride-hailing driver capable of transporting passengers. For ease of explanation, the following explanation and illustration will use a ride-hailing driver as an example of the capacity terminal.

[0071] In one embodiment of this disclosure, the global revenue target value refers to the revenue value that can be achieved in a global sense after the pre-allocation of the capacity order chain.

[0072] In the above implementation, firstly, order information of orders to be assigned is obtained; then, based on the order information, the orders to be assigned are grouped into one or more order chains as allocation units; then, the order chains are pre-allocated to the capacity terminals corresponding to the number of order chains, so that each capacity terminal can be allocated an order chain; then, the global revenue target value of the order chains that can be achieved after pre-allocating the order chains to the capacity terminals corresponding to the number of order chains is calculated, and the allocation objects of the order chains are adjusted according to the global revenue target value, that is, the allocation of which order chain to which capacity terminal is adjusted, to obtain the final order chain allocation result, so as to optimize the global revenue target value.

[0073] In one embodiment of this disclosure, step S103, which is the step of pre-allocating the order chain to the capacity terminal corresponding to the number of order chains, may include the following steps:

[0074] The order chains are randomly pre-allocated to the delivery capacity corresponding to the number of order chains; or...

[0075] The order chain is pre-assigned to the transport terminal that is closest to the departure point of the first order in the order chain, or has the shortest estimated arrival time at the departure point of the first order in the order chain, wherein the number of orders already accepted by the transport terminal is less than or equal to a second preset quantity threshold; or...

[0076] The order information of the order chain is broadcast to the transport terminals located within a preset geographical range, and in response to receiving an order acceptance request from the transport terminal, the order chain is pre-allocated to the transport terminal, wherein the order information of the order chain includes the departure location of the sequential orders in the order chain.

[0077] In this implementation, the pre-allocation of the order chain can be achieved through order dispatching and order bidding by the transportation capacity side. Specifically:

[0078] When dispatching orders, orders can be dispatched randomly, that is, the order chain can be randomly pre-assigned to the capacity terminal corresponding to the number of order chains. Alternatively, orders can be pre-assigned in a targeted manner based on the order information of the order chain. That is, the order chain can be pre-assigned to the capacity terminal whose current location is closest to the departure point of the first order to be completed in the order chain, or the order chain can be pre-assigned to the capacity terminal with the shortest estimated time to reach the departure point of the first order to be completed in the order chain. In order to avoid the capacity terminal being overwhelmed with too many orders, the number of orders already accepted by the capacity terminal must be less than or equal to a second preset quantity threshold. The second preset quantity threshold can be set according to the needs of actual application, and this disclosure does not specifically limit its value.

[0079] When accepting an order, the order information of the order chain can be broadcast to the delivery terminals located within a preset geographical range. The preset geographical range refers to the geographical range centered on the departure location of the first order in the order chain and with a preset distance as the radius. The order information of the order chain refers to the sequential order information of all orders that make up the order chain, such as the departure locations of sequential orders in the order chain. Then, the order chain can be pre-assigned to the delivery terminal corresponding to the first order acceptance request received.

[0080] In one embodiment of this disclosure, step S104, which involves calculating the global revenue target value of the order chain after pre-allocating the order chain to the capacity terminals corresponding to the number of order chains, may include the following steps:

[0081] Under the constraint of the order chain, calculate the difference between the total revenue of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total revenue target value of the order chain.

[0082] To optimize the global revenue target value of order chain allocation and effectively improve the order dispatch efficiency and response rate of ride-hailing platforms, this implementation sets constraints on the order chain. Specifically, the global revenue target value after pre-allocation of the order chain is calculated only after ensuring that the order chain meets certain conditions. The global revenue target value after pre-allocation refers to the difference between the total revenue obtained by all the assigned ride-hailing units after completing all orders in the order chain, and the total cost incurred by all the assigned ride-hailing units after completing all orders in the order chain. This is the global revenue value of the order chain.

[0083] For one of the ride-hailing vehicles, the order chain is pre-assigned to that driver. The total revenue (function1) that the driver receives after completing all orders in the order chain can be expressed as:

[0084]

[0085] in, This represents the charge for the i-th order. Let N represent the total number of orders in the order chain for vehicle j.

[0086] The total cost, function2, required for the ride-hailing driver to complete all orders in the aforementioned order chain can be represented as:

[0087]

[0088] Among them, Car j This represents the current position of the j-th vehicle out of all vehicles, and Order1Origin represents the origin of order 1. This represents the cost required to travel from the current location of vehicle j to the departure point of order1; Where β is the cost per unit kilometer and γ is the cost per unit minute. This represents the distance from the current position of vehicle j to the departure point of order1. This represents the estimated arrival time from the current location of vehicle j to the departure point of order1; α represents the vehicle homecoming factor, α∈{0,1}, that is, if the cost of the vehicle returning home needs to be considered, then α=1, otherwise α=0.

[0089] Therefore, the global revenue target value after the pre-allocation of the order chains, that is, the global revenue value obtained after all ride-hailing drivers who have been pre-allocated the order chains to the number of ride-hailing drivers corresponding to the number of order chains, complete all orders in the order chains, can be expressed as:

[0090]

[0091] To ensure the efficiency of order execution in the order chain, in one embodiment of this disclosure, order chains that do not meet the order chain constraints are discarded. Orders in the discarded order chains can be reassembled into order chains and participate in the allocation process again during the next allocation.

[0092] In one embodiment of this disclosure, the order chain constraints include local order chain constraints and global order chain constraints.

[0093] The local constraints of the order chain include local time constraints that constrain the temporal relationships between orders in the order chain, and local location constraints that constrain the locational relationships between orders in the order chain. An order chain that satisfies both the local time and local location constraints can be considered an order chain without time and location conflicts and capable of executing subsequent allocation processes. Wherein:

[0094] The local time constraints of the order chain include:

[0095] The estimated arrival time of the previous order in the order chain is less than or equal to the estimated departure time of the next order;

[0096] The estimated arrival time from the arrival location of the previous order in the order chain to the departure location of the next order is less than or equal to a first preset time threshold.

[0097] In this order chain, the estimated arrival time of the previous order is less than or equal to the estimated departure time of the next order. This is to ensure that after completing the previous order at its destination, the ride-hailing driver has sufficient time to reach the departure point of the next order, thus guaranteeing the timely completion of the next order. This can be represented as:

[0098]

[0099] in, This represents the i-th order. i The estimated arrival time, This represents the (i+1)th order. i+1 The estimated departure time.

[0100] The condition that the estimated arrival time from the arrival location of the previous order to the departure location of the next order in the order chain is less than or equal to a first preset time threshold is designed to ensure that the time it takes for a ride-hailing driver to arrive at the departure location of the next order after completing the previous order is not too long. Otherwise, it would be detrimental to improving order completion efficiency and user experience. This can be expressed as:

[0101]

[0102] Among them, Order i Dest represents the i-th order. i Arrival location, Order i+1 Origin represents the (i+1)th order. i+1 Departure point This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 The estimated arrival time of Origin, EtaThres represents the first preset time threshold, wherein the first preset time threshold can be set according to the needs of actual application, and this disclosure does not specifically limit it.

[0103] The local location constraints of the order chain include:

[0104] The distance between the arrival location of the previous order and the departure location of the next order in the order chain is less than a first preset distance threshold.

[0105] The condition that the distance between the arrival point of the previous order and the departure point of the next order is less than a first preset distance threshold is to avoid the distance between the arrival point of the previous order and the departure point of the next order being too long, thereby effectively controlling the cost of order completion and thus improving the efficiency of order completion. This can be expressed as:

[0106]

[0107] Among them, Order i Dest represents the i-th order. i Arrival location, Order i+1 Origin represents the (i+1)th order. i+1 Departure point This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 The distance between Origins, DistThres represents the first preset distance threshold, which can be set according to the needs of actual application, and this disclosure does not specifically limit it.

[0108] The above local constraints on the order chain can be expressed as a whole as follows:

[0109]

[0110] Among them, Rule (Order) i Order i+1 Car j ) represents the Car currently assigned to the j-th car. j The i-th order in the order chain i And the next order thereafter i+1 The constraints can be set according to the needs of the actual application.

[0111] Furthermore, the above constraints are required for the order chain of all vehicles. Therefore, the local constraint condition for the order chain of all vehicles can be expressed as:

[0112]

[0113] Where CarNum represents the total number of ride-hailing vehicles, and N represents the total number of orders in the order chain allocated to a particular ride-hailing vehicle.

[0114] The global constraints on the order chain include a global time constraint that constrains the total execution time of orders from a global perspective, and a global location constraint that constrains the distance traveled to complete all orders in the order chain from a global perspective. Wherein:

[0115] The global time constraints of the order chain include:

[0116] The total estimated time for completing all orders in the order chain on the transportation side is less than or equal to the second preset time threshold.

[0117] The requirement that the total estimated time for a ride-hailing driver to complete all orders in the order chain is less than or equal to a second preset time threshold is to prevent excessive driving time for ride-hailing drivers, which could lead to safety hazards. This can be expressed as:

[0118]

[0119] in, This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 Origin's estimated arrival time, This represents the total time a ride-hailing driver needs to travel between all the orders in their order chain, i.e., the pick-up and drop-off time. This indicates the starting point of the i-th order. i Departure point Order i Origin to the i-th order i Arrival Location Order i The estimated arrival time of Dest is the time required for a ride-hailing driver to complete all the orders in its order chain, also known as the order duration; X represents the second preset time threshold, which can be set according to the needs of actual application. This disclosure does not specifically limit its value, for example, it can be set to 4-8.

[0120] The global location constraints of the order chain include:

[0121] The estimated total distance of all orders in the order chain completed by the transportation capacity side is less than or equal to the second preset distance threshold.

[0122] The requirement that the total estimated distance of all orders completed by a ride-hailing driver in the order chain be less than or equal to a second preset distance threshold is to prevent ride-hailing drivers from driving excessively long distances, thereby avoiding safety hazards. This can be expressed as:

[0123]

[0124] in, This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 The distance to Origin This represents the total distance traveled by a ride-hailing driver across all orders in their order chain, i.e., the pick-up distance. This indicates the starting point of the i-th order. i Departure point Order i Origin to the i-th order i Arrival Location Order i Dest is the distance that a ride-hailing driver needs to travel to complete all the orders in their order chain, which is also the order distance; Y represents the second preset distance threshold, which can be set according to the needs of actual application. This disclosure does not specifically limit its value, for example, it can be set to 200-400 kilometers.

[0125] In summary, the global constraints of the order chain can be expressed as follows:

[0126]

[0127] In one embodiment of this disclosure, step S105, which involves adjusting the allocation objects of the order chain based on the global revenue target value, may include the following steps:

[0128] The allocation object of the order chain is adjusted, and the order chain allocation result corresponding to the maximum global revenue target value obtained after adjusting the allocation object of the order chain is taken as the final order chain allocation result, or the order chain allocation result corresponding to the maximum global revenue target value obtained when the number of adjustments reaches a preset number threshold.

[0129] In this embodiment, in order to optimize the global revenue target value of order chain allocation, the allocation of the order chain, i.e. the allocation relationship between the order chain and the transportation capacity, can be adjusted. After each adjustment, the corresponding global revenue target value is calculated. Finally, the order chain allocation result corresponding to the maximum global revenue target value, or the order chain allocation result corresponding to the maximum global revenue target value obtained when the number of adjustments reaches a preset number threshold, can be used as the final order chain allocation result. The value of the preset number threshold can be set according to the needs of actual application, and this disclosure does not impose any special limitations on it.

[0130] In one embodiment of this disclosure, the method may further include the following steps:

[0131] Based on the order chain allocation results, an order chain navigation route is generated and sent to the corresponding transportation capacity client for broadcasting and display.

[0132] To provide better service to the transportation capacity side, save their time, reduce the complexity of order-grabbing operations, improve order execution efficiency, and ensure traffic safety, this embodiment can also generate an order chain navigation route based on the order chain allocation result. This route is then sent to the transportation capacity client for broadcast and display, allowing the transportation capacity to execute orders sequentially according to the navigation route, thereby improving the completion efficiency of the order chain. The order chain navigation route refers to a navigation route generated based on the order information of the order chain. For example, if the order chain includes N orders, the order chain navigation route based on the order information can be represented as: Order 1 departure location → Order 1 arrival location → Order 2 departure location → Order 2 arrival location → … → Order N departure location → Order N arrival location.

[0133] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein.

[0134] Figure 2The diagram shows a structural block diagram of a capacity order allocation device according to an embodiment of the present disclosure. This device can be implemented as part or all of an electronic device through software, hardware, or a combination of both. Figure 2 As shown, the capacity order allocation device includes:

[0135] Module 201 is configured to retrieve information on orders to be assigned.

[0136] The combination module 202 is configured to combine the orders to be assigned into one or more order chains based on the order information to be assigned, wherein the number of orders in the order chain is less than or equal to a first preset quantity threshold.

[0137] The allocation module 203 is configured to pre-allocate the order chain to the capacity end corresponding to the number of order chains, wherein each capacity end is allocated one order chain;

[0138] The calculation module 204 is configured to calculate the global revenue target value of the order chain after the order chain is pre-allocated to the capacity terminal corresponding to the number of the order chain;

[0139] The adjustment module 205 is configured to adjust the allocation object of the order chain according to the global revenue target value to obtain the final allocation result of the order chain.

[0140] As mentioned above, with social development and progress, many users choose ride-hailing services. Ride-hailing orders are highly real-time, and users have high requirements for order fulfillment time. How to quickly achieve real-time and unified optimized allocation of ride-hailing orders to improve the dispatch efficiency of ride-hailing platforms, the response rate of ride-hailing drivers, and the user experience is an urgent problem to be solved.

[0141] In view of the above problems, this embodiment proposes a capacity order allocation device. This device allocates the capacity order chain to the global revenue target value of the capacity side by setting and optimizing the global revenue target value. This technical solution can quickly realize the real-time and unified optimized allocation of ride-hailing orders, thereby effectively improving the order dispatch efficiency of the ride-hailing platform and the response rate of the capacity side, and effectively enhancing the user experience.

[0142] In one embodiment of this disclosure, the capacity order allocation device can be applied to computers, computing devices, electronic devices, servers, etc., that can perform capacity order allocation.

[0143] In one embodiment of this disclosure, the capacity order refers to an order for the purpose of transportation, such as a ride-hailing order.

[0144] In one embodiment of this disclosure, the pending order refers to an order that is waiting to be assigned, and after assignment, it requires traveling to the departure location according to the order departure time to transport the user from the departure location to the arrival location. The pending order information may include one or more of the following: the order placement time of the pending order, the departure location of the pending order, the departure time of the pending order, the arrival location of the scheduled order, etc.

[0145] In one embodiment of this disclosure, the order chain refers to a combination of orders that can be allocated together, where there are no time or location conflicts between the multiple orders. The multiple orders are allocated collectively as a unit within the order chain and executed sequentially within the chain. This combined implementation is more efficient than allocating and executing individual orders separately. To ensure the execution efficiency of the orders in the order chain, the number of orders in the order chain cannot be excessive; that is, the number of orders in the order chain must be less than or equal to a first preset quantity threshold. The first preset quantity threshold can be set according to the needs of actual application, and this disclosure does not specifically limit its value.

[0146] In one embodiment of this disclosure, the "capacity terminal" refers to the terminal that receives, executes, and ultimately completes the capacity order and is capable of providing transportation capacity. The capacity terminal may, for example, be a ride-hailing driver capable of transporting passengers. For ease of explanation, the following explanation and illustration will use a ride-hailing driver as an example of the capacity terminal.

[0147] In one embodiment of this disclosure, the global revenue target value refers to the revenue value that can be achieved in a global sense after the pre-allocation of capacity orders, which is the order chain.

[0148] In the above implementation, firstly, order information of orders to be assigned is obtained; then, based on the order information, the orders to be assigned are grouped into one or more order chains as allocation units; then, the order chains are pre-allocated to the capacity terminals corresponding to the number of order chains, so that each capacity terminal can be allocated an order chain; then, the global revenue target value of the order chains that can be achieved after pre-allocating the order chains to the capacity terminals corresponding to the number of order chains is calculated, and the allocation objects of the order chains are adjusted according to the global revenue target value, that is, the allocation of which order chain to which capacity terminal is adjusted, to obtain the final order chain allocation result, so as to optimize the global revenue target value.

[0149] In one embodiment of this disclosure, the allocation module 203 may be configured as follows:

[0150] The order chains are randomly pre-allocated to the delivery capacity corresponding to the number of order chains; or...

[0151] The order chain is pre-assigned to the transport terminal that is closest to the departure point of the first order in the order chain, or has the shortest estimated arrival time at the departure point of the first order in the order chain, wherein the number of orders already accepted by the transport terminal is less than or equal to a second preset quantity threshold; or...

[0152] The order information of the order chain is broadcast to the transport terminals located within a preset geographical range, and in response to receiving an order acceptance request from the transport terminal, the order chain is pre-allocated to the transport terminal, wherein the order information of the order chain includes the departure location of the sequential orders in the order chain.

[0153] In this implementation, the pre-allocation of the order chain can be achieved through order dispatching and order bidding by the transportation capacity side. Specifically:

[0154] When dispatching orders, orders can be dispatched randomly, that is, the order chain can be randomly pre-assigned to the capacity terminal corresponding to the number of order chains. Alternatively, orders can be pre-assigned in a targeted manner based on the order information of the order chain. That is, the order chain can be pre-assigned to the capacity terminal whose current location is closest to the departure point of the first order to be completed in the order chain, or the order chain can be pre-assigned to the capacity terminal with the shortest estimated time to reach the departure point of the first order to be completed in the order chain. In order to avoid the capacity terminal being overwhelmed with too many orders, the number of orders already accepted by the capacity terminal must be less than or equal to a second preset quantity threshold. The second preset quantity threshold can be set according to the needs of actual application, and this disclosure does not specifically limit its value.

[0155] When accepting an order, the order information of the order chain can be broadcast to the delivery terminals located within a preset geographical range. The preset geographical range refers to the geographical range centered on the departure location of the first order in the order chain and with a preset distance as the radius. The order information of the order chain refers to the sequential order information of all orders that make up the order chain, such as the departure locations of sequential orders in the order chain. Then, the order chain can be pre-assigned to the delivery terminal corresponding to the first order acceptance request received.

[0156] In one embodiment of this disclosure, the portion of the calculation module 204 that calculates the global revenue target value of the order chain after pre-allocating the order chain to the capacity terminals corresponding to the number of order chains can be configured as follows:

[0157] Under the constraint of the order chain, calculate the difference between the total revenue of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total revenue target value of the order chain.

[0158] To optimize the global target value and global revenue target value of order chain allocation, and effectively improve the order dispatch efficiency of the ride-hailing platform and the response rate of ride-hailing drivers, this embodiment sets constraints on the order chain. That is, under certain conditions, the global target value and global revenue target value after pre-allocation of the order chain are calculated. The global target value and global revenue target value after pre-allocation of the order chain refers to the difference between the total revenue obtained by all ride-hailing drivers who have been allocated order chains and all orders in the order chain after completing all orders in the order chain, and the total cost required for all ride-hailing drivers who have been allocated order chains to complete all orders in the order chain. That is, the global revenue value of the order chain.

[0159] For one of the ride-hailing vehicles, the order chain is pre-assigned to that driver. The total revenue (function1) that the driver receives after completing all orders in the order chain can be expressed as:

[0160]

[0161] in, This represents the charge for the i-th order. Let N represent the total number of orders in the order chain for vehicle j.

[0162] The total cost, function2, required for the ride-hailing driver to complete all orders in the aforementioned order chain can be represented as:

[0163]

[0164] Among them, Car j This represents the current position of the j-th vehicle out of all vehicles, and Order1Origin represents the origin of order 1. This represents the cost required to travel from the current location of vehicle j to the departure point of order1;

[0165]

[0166] Where β is the cost per unit kilometer and γ is the cost per unit minute. This represents the distance from the current position of vehicle j to the departure point of order1. This represents the estimated arrival time from the current location of vehicle j to the departure point of order1; α represents the vehicle homecoming factor, α∈{0,1}, that is, if the cost of the vehicle returning home needs to be considered, then α=1, otherwise α=0.

[0167] Therefore, the global revenue target value after the pre-allocation of the order chains, that is, the global revenue value obtained after all ride-hailing drivers who have been pre-allocated the order chains to the number of ride-hailing drivers corresponding to the number of order chains, complete all orders in the order chains, can be expressed as:

[0168]

[0169] To ensure the efficiency of order execution in the order chain, in one embodiment of this disclosure, order chains that do not meet the order chain constraints are discarded. Orders in the discarded order chains can be reassembled into order chains and participate in the allocation process again during the next allocation.

[0170] In one embodiment of this disclosure, the order chain constraints include local order chain constraints and global order chain constraints.

[0171] The local constraints of the order chain include local time constraints that constrain the temporal relationships between orders in the order chain, and local location constraints that constrain the locational relationships between orders in the order chain. An order chain that satisfies both the local time and local location constraints can be considered an order chain without time and location conflicts and capable of executing subsequent allocation processes. Wherein:

[0172] The local time constraints of the order chain include:

[0173] The estimated arrival time of the previous order in the order chain is less than or equal to the estimated departure time of the next order;

[0174] The estimated arrival time from the arrival location of the previous order in the order chain to the departure location of the next order is less than or equal to a first preset time threshold.

[0175] In this order chain, the estimated arrival time of the previous order is less than or equal to the estimated departure time of the next order. This is to ensure that after completing the previous order at its destination, the ride-hailing driver has sufficient time to reach the departure point of the next order, thus guaranteeing the timely completion of the next order. This can be represented as:

[0176]

[0177] in, This represents the i-th order. i The estimated arrival time, This represents the (i+1)th order. i+1 The estimated departure time.

[0178] The condition that the estimated arrival time from the arrival location of the previous order to the departure location of the next order in the order chain is less than or equal to a first preset time threshold is designed to ensure that the time it takes for a ride-hailing driver to arrive at the departure location of the next order after completing the previous order is not too long. Otherwise, it would be detrimental to improving order completion efficiency and user experience. This can be expressed as:

[0179]

[0180] Among them, Order i Dest represents the i-th order. i Arrival location, Order i+1 Origin represents the (i+1)th order. i+1 Departure point This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 The estimated arrival time of Origin, EtaThres represents the first preset time threshold, wherein the first preset time threshold can be set according to the needs of actual application, and this disclosure does not specifically limit it.

[0181] The local location constraints of the order chain include:

[0182] The distance between the arrival location of the previous order and the departure location of the next order in the order chain is less than a first preset distance threshold.

[0183] The condition that the distance between the arrival point of the previous order and the departure point of the next order is less than a first preset distance threshold is to avoid the distance between the arrival point of the previous order and the departure point of the next order being too long, thereby effectively controlling the cost of order completion and thus improving the efficiency of order completion. This can be expressed as:

[0184]

[0185] Among them, Order i Dest represents the i-th order. i Arrival location, Order i+1 Origin represents the (i+1)th order. i+1Departure point This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 The distance between Origins, DistThres represents the first preset distance threshold, which can be set according to the needs of actual application, and this disclosure does not specifically limit it.

[0186] The above local constraints on the order chain can be expressed as a whole as follows:

[0187]

[0188] Among them, Rule (Order) i Order i+1 Car j ) represents the Car currently assigned to the j-th car. j The i-th order in the order chain i And the next order thereafter i+1 The constraints can be set according to the needs of the actual application.

[0189] Furthermore, the above constraints are required for the order chain of all vehicles. Therefore, the local constraint condition for the order chain of all vehicles can be expressed as:

[0190]

[0191] Where CarNum represents the total number of ride-hailing vehicles, and N represents the total number of orders in the order chain allocated to a particular ride-hailing vehicle.

[0192] The global constraints on the order chain include a global time constraint that constrains the total execution time of orders from a global perspective, and a global location constraint that constrains the distance traveled to complete all orders in the order chain from a global perspective. Wherein:

[0193] The global time constraints of the order chain include:

[0194] The total estimated time for completing all orders in the order chain on the transportation side is less than or equal to the second preset time threshold.

[0195] The requirement that the total estimated time for a ride-hailing driver to complete all orders in the order chain is less than or equal to a second preset time threshold is to prevent excessive driving time for ride-hailing drivers, which could lead to safety hazards. This can be expressed as:

[0196]

[0197] in, This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 Origin's estimated arrival time, This represents the total time a ride-hailing driver needs to travel between all the orders in their order chain, i.e., the pick-up and drop-off time. This indicates the starting point of the i-th order. i Departure point Order i Origin to the i-th order i Arrival Location Order i The estimated arrival time of Dest is the time required for a ride-hailing driver to complete all the orders in its order chain, also known as the order duration; X represents the second preset time threshold, which can be set according to the needs of actual application. This disclosure does not specifically limit its value, for example, it can be set to 4-8.

[0198] The global location constraints of the order chain include:

[0199] The estimated total distance of all orders in the order chain completed by the transportation capacity side is less than or equal to the second preset distance threshold.

[0200] The requirement that the total estimated distance of all orders completed by a ride-hailing driver in the order chain be less than or equal to a second preset distance threshold is to prevent ride-hailing drivers from driving excessively long distances, thereby avoiding safety hazards. This can be expressed as:

[0201]

[0202] in, This indicates the starting point of the i-th order. i Arrival location Order i Orders from Dest to the (i+1)th order i+1 Departure Point Order i+1 The distance to Origin This represents the total distance traveled by a ride-hailing driver across all orders in their order chain, i.e., the pick-up distance. This indicates the starting point of the i-th order. i Departure point Order i Origin to the i-th order i Arrival Location Order iDest is the distance that a ride-hailing driver needs to travel to complete all the orders in their order chain, which is also the order distance; Y represents the second preset distance threshold, which can be set according to the needs of actual application. This disclosure does not specifically limit its value, for example, it can be set to 200-400 kilometers.

[0203] In summary, the global constraints of the order chain can be expressed as follows:

[0204]

[0205] In one embodiment of this disclosure, the portion of the adjustment module 205 that adjusts the allocation objects of the order chain according to the global revenue target value can be configured as follows:

[0206] The allocation object of the order chain is adjusted, and the order chain allocation result corresponding to the maximum global revenue target value obtained after adjusting the allocation object of the order chain is taken as the final order chain allocation result, or the order chain allocation result corresponding to the maximum global revenue target value obtained when the number of adjustments reaches a preset number threshold.

[0207] In this embodiment, in order to optimize the global revenue target value of order chain allocation, the allocation of the order chain, i.e. the allocation relationship between the order chain and the transportation capacity, can be adjusted. After each adjustment, the corresponding global revenue target value is calculated. Finally, the order chain allocation result corresponding to the maximum global revenue target value, or the order chain allocation result corresponding to the maximum global revenue target value obtained when the number of adjustments reaches a preset number threshold, can be used as the final order chain allocation result. The value of the preset number threshold can be set according to the needs of actual application, and this disclosure does not impose any special limitations on it.

[0208] In one embodiment of this disclosure, the apparatus may further include:

[0209] The generation module is configured to generate an order chain navigation route based on the order chain allocation result and send it to the corresponding capacity client for broadcasting and display.

[0210] To provide better service to the transportation capacity side, save their time, reduce the complexity of order-grabbing operations, improve order execution efficiency, and ensure traffic safety, this embodiment can also generate an order chain navigation route based on the order chain allocation result. This route is then sent to the transportation capacity client for broadcast and display, allowing the transportation capacity to execute orders sequentially according to the navigation route, thereby improving the completion efficiency of the order chain. The order chain navigation route refers to a navigation route generated based on the order information of the order chain. For example, if the order chain includes N orders, the order chain navigation route based on the order information can be represented as: Order 1 departure location → Order 1 arrival location → Order 2 departure location → Order 2 arrival location → … → Order N departure location → Order N arrival location.

[0211] This disclosure also discloses a navigation method, wherein a navigation route based at least on a starting point, intermediate points, and an ending point is obtained, and navigation guidance is performed based on the navigation route. The navigation route is obtained based on the capacity order allocation result generated by any of the above methods. The starting point of the navigation route is the current location of the capacity terminal, the ending point is the arrival location of the last order in the order chain allocated to the capacity terminal, and the intermediate points are the arrival location of the first order in the order chain, the departure and arrival locations of the intermediate orders, and the departure location of the last order.

[0212] This disclosure also discloses an electronic device. Figure 3 This diagram illustrates a structural block diagram of an electronic device according to an embodiment of the present disclosure, such as... Figure 3 As shown, the electronic device 300 includes a memory 301 and a processor 302; wherein,

[0213] The memory 301 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 302 to implement the above method steps.

[0214] Figure 4 This is a schematic diagram of the structure of a computer system suitable for implementing a capacity order allocation method according to an embodiment of the present disclosure.

[0215] like Figure 4As shown, the computer system 400 includes a processing unit 401, which can execute various processes described above based on a program stored in a read-only memory (ROM) 402 or a program loaded from a storage section 408 into a random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the system 400. The processing unit 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0216] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed. The processing unit 401 can be implemented as a CPU, GPU, TPU, FPGA, NPU, etc.

[0217] In particular, according to embodiments of this disclosure, the methods described above can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program tangibly embodied on a readable medium thereof, the computer program containing program code for performing the data inspection method. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411.

[0218] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0219] The units or modules described in the embodiments of this disclosure can be implemented in software or hardware. The described units or modules can also be located in a processor, and the names of these units or modules do not necessarily constitute a limitation on the unit or module itself.

[0220] In another aspect, embodiments of this disclosure also provide a computer-readable storage medium, which may be a computer-readable storage medium included in the apparatus described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to perform the methods described in embodiments of this disclosure.

[0221] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for allocating transportation capacity orders, wherein, include: Get information on orders to be assigned; The orders to be assigned are grouped into one or more order chains based on the order information to be assigned, wherein the number of orders in the order chain is less than or equal to a first preset quantity threshold; The order chains are pre-allocated to the capacity terminals corresponding to the number of order chains, wherein each capacity terminal is allocated one order chain; Calculate the global revenue target value of the order chain after pre-allocating the order chain to the capacity terminal corresponding to the number of order chains; The allocation objects of the order chain are adjusted according to the global revenue target value to obtain the final allocation result of the order chain.

2. The method according to claim 1, wherein pre-allocating the order chain to the capacity corresponding to the number of order chains comprises: The order chains are randomly pre-allocated to the capacity terminals corresponding to the number of order chains; or, The order chain is pre-assigned to the transport terminal that is closest to the departure point of the first order in the order chain, or has the shortest estimated arrival time at the departure point of the first order in the order chain, wherein the number of orders already accepted by the transport terminal is less than or equal to a second preset quantity threshold; or... The order information of the order chain is broadcast to the transport terminals located within a preset geographical range, and in response to receiving an order acceptance request from the transport terminal, the order chain is pre-allocated to the transport terminal, wherein the order information of the order chain includes the departure location of the sequential orders in the order chain.

3. The method according to claim 1 or 2, wherein calculating the global revenue target value of the order chain after pre-allocating the order chain to the capacity terminals corresponding to the number of order chains includes: Under the constraint of the order chain, calculate the difference between the total revenue of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total cost of all the capacity terminals that have been allocated the order chain and the total revenue target value of the order chain.

4. The method according to claim 3, further comprising: Order chains that do not meet the aforementioned order chain constraints will be discarded.

5. The method according to claim 3, wherein the order chain constraints include local order chain constraints and global order chain constraints, wherein, The local constraints of the order chain include local time constraints and local location constraints, wherein: The local time constraints of the order chain include: The estimated arrival time of the previous order in the order chain is less than or equal to the estimated departure time of the next order; The estimated arrival time from the arrival location of the previous order in the order chain to the departure location of the next order is less than or equal to the first preset time threshold. The local location constraints of the order chain include: The distance between the arrival location of the previous order and the departure location of the next order in the order chain is less than a first preset distance threshold; The global constraints of the order chain include global time constraints and global location constraints, wherein: The global time constraints of the order chain include: The total estimated time for completing all orders in the order chain on the transportation side is less than or equal to the second preset time threshold. The global location constraints of the order chain include: The estimated total distance of all orders in the order chain completed by the transportation capacity side is less than or equal to the second preset distance threshold.

6. The method according to any one of claims 1-3, wherein adjusting the allocation object of the order chain according to the global revenue target value includes: The allocation object of the order chain is adjusted, and the order chain allocation result corresponding to the maximum global revenue target value obtained after adjusting the allocation object of the order chain is taken as the final order chain allocation result, or the order chain allocation result corresponding to the maximum global revenue target value obtained when the number of adjustments reaches a preset number threshold.

7. The method according to any one of claims 1-3, further comprising: Based on the order chain allocation results, an order chain navigation route is generated and sent to the corresponding transportation capacity client for broadcasting and display.

8. A capacity order allocation device, wherein, include: The acquisition module is configured to acquire information about orders to be assigned. The combination module is configured to combine the orders to be assigned into one or more order chains based on the order information to be assigned, wherein the number of orders in the order chain is less than or equal to a first preset quantity threshold. The allocation module is configured to pre-allocate the order chain to the capacity end corresponding to the number of order chains, wherein each capacity end is allocated one order chain; The calculation module is configured to calculate the global revenue target value of the order chain after the order chain is pre-allocated to the capacity terminal corresponding to the number of the order chain; The adjustment module is configured to adjust the allocation objects of the order chain according to the global revenue target value to obtain the final allocation result of the order chain.

9. A computer program product comprising a computer program / instructions, wherein, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-7.

10. A navigation method, wherein, Obtain a navigation route based at least on a starting point, intermediate points, and an ending point, and provide navigation guidance based on the navigation route. The navigation route is obtained based on the capacity order allocation result generated by any one of the methods described in claims 1-7. The starting point of the navigation route is the current location of the capacity terminal, the ending point is the arrival location of the last order in the order chain allocated to the capacity terminal, and the intermediate points are the arrival location of the first order, the departure and arrival locations of the intermediate orders, and the departure location of the last order in the order chain.

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