Order distribution method, device, readable storage medium and electronic device

Abstract

This disclosure relates to an order allocation method, apparatus, readable storage medium, and electronic device. The method includes: determining multiple delivery capacities associated with orders to be allocated; performing task point path planning for each delivery capacity based on the orders to be allocated and the current planned path of each delivery capacity to obtain a target planned path for each delivery capacity; obtaining delivery efficiency parameters for each target planned path, wherein the delivery efficiency parameters of the target planned path are determined based on fuzzy numbers of random delivery characteristic information corresponding to the target planned path; determining delivery efficiency variation parameters for the delivery capacity based on the delivery efficiency parameters of the target planned paths and the delivery efficiency parameters of the current planned paths; determining the optimal delivery capacity based on the delivery efficiency variation parameters, and allocating the orders to be allocated to the optimal delivery capacity. This improves the accuracy of order allocation and the delivery efficiency of delivery capacities.

Description

Technical Field

[0001] The present disclosure relates to the field of network technology, and in particular, to an order distribution method, device, readable storage medium and electronic device. Background Art

[0002] With the rapid development of network technology, network applications such as express delivery, food delivery, and shared power bank delivery have become widely popular. Users can place orders online and the delivery capacity will complete the delivery service. In order to improve delivery efficiency, the order scheduling system usually makes dispatch decisions based on the matching degree between the orders to be assigned and the delivery capacity.

[0003] Generally speaking, when analyzing the matching degree between the orders to be assigned and the delivery capacity, it is necessary to consider the deterministic information and random information related to the delivery. Deterministic information can include the merchant location, user location, order price, expected delivery time, etc. Random information can include the merchant's meal delivery time, driving time, meal pickup completion time, and delivery completion time. Among them, the delivery completion time can be affected by the delivery capacity or the user. Therefore, the delivery time can be the delivery time related to the delivery capacity behavior or the delivery time related to the user behavior. Due to the many factors affecting random information, it is difficult to obtain real data, and it is difficult to accurately estimate even by machine learning. Therefore, in the existing order scheduling system, in order to simplify the problem, random information is usually assumed to be deterministic data for processing to analyze the matching degree between the orders to be assigned and the delivery capacity, resulting in the inability to accurately determine the matching degree between the orders to be assigned and the delivery capacity, and thus it is impossible to make accurate order dispatching decisions, which affects the delivery efficiency of the delivery capacity. Summary of the invention

[0004] The purpose of the present disclosure is to provide an order distribution method, device, readable storage medium and electronic device to improve the distribution efficiency of distribution capacity.

[0005] In order to achieve the above-mentioned objectives, the first aspect of the present disclosure provides an order allocation method, including: determining multiple delivery capacities associated with an order to be allocated; performing task point path planning for each delivery capacity according to the order to be allocated and the current planned path of each delivery capacity, so as to obtain a target planned path for each delivery capacity; obtaining a delivery efficiency parameter of each target planned path, wherein the delivery efficiency parameter of the target planned path is determined based on a fuzzy number of random delivery feature information corresponding to the target planned path; determining a delivery efficiency change parameter of the target planned path of the same delivery capacity relative to the current planned path according to the delivery efficiency parameter of the target planned path and the delivery efficiency parameter of the current planned path; determining the optimal delivery capacity according to the delivery efficiency change parameter, and allocating the order to be allocated to the optimal delivery capacity.

[0006] Optionally, the random delivery characteristic information includes at least one of the following: item preparation completion time; item pickup completion time; delivery capacity movement time; item delivery time.

[0007] Optionally, the delivery efficiency parameter includes: an overtime duration parameter and / or a delivery distance parameter, and the delivery efficiency change parameter includes: a change in the overtime duration parameter and / or a change in the delivery distance parameter.

[0008] Optionally, the timeout duration parameter is determined by the following steps: for each order involved in the planned path, determine the fuzzy number of the estimated delivery time of the order, and determine the fuzzy number of the timeout duration of the order based on the fuzzy number of the estimated delivery time and the promised delivery time of the order; determine the timeout duration parameter of the planned path based on the fuzzy number of the timeout duration of each order involved in the planned path.

[0009] Optionally, according to the orders to be assigned and the current planned path of each of the delivery transport capacities, task point path planning is performed for each of the delivery transport capacities respectively to obtain the target planned path of each of the delivery transport capacities, including: for each of the delivery transport capacities, the delivery priority of each current delivery order and the order to be assigned involved in the current planned path is determined; the orders are taken as target orders in sequence according to the delivery priority, and the following steps are performed for the target orders: the delivery parameters of the path are determined according to the fuzzy number of the random delivery feature information corresponding to the path after the target order is inserted into each feasible position, wherein the feasible position is a position where the pickup task point that meets the target order is located before the item delivery task point, and the number of waybills of the delivery transport capacity at this position is less than or equal to the waybill threshold, and the delivery parameters include delivery time parameters and / or delivery distance parameters; the position of the target order corresponding to the minimum delivery parameter is determined as the current target position of the target order; according to the current target positions of the current delivery order and the order to be assigned, the task point path planning is performed for the delivery transport capacity to obtain the target planned path of each of the delivery transport capacities.

[0010] Optionally, the task point path planning of the distribution capacity is performed according to the current target positions of the current distribution order and the order to be allocated, so as to obtain the target planning path of each distribution capacity, including: sorting the current distribution order and the order to be allocated according to their respective current target positions to obtain the planned path of the distribution capacity and the first path-related parameters of the planned path; searching the planned path through a preset neighborhood operator to obtain the second path-related parameters of the path after each search under each neighborhood operator; for each neighborhood operator, determining the optimal path-related parameters under the neighborhood operator from the second path-related parameters under the neighborhood operator, and when the optimal path-related parameters are better than the first path-related parameters, determining the optimal path-related parameters as the third path-related parameters of the path under the neighborhood operator to obtain the third path-related parameters of the path under each neighborhood operator; determining the optimal path-related parameters from the third path-related parameters of the path under each neighborhood operator, and using the optimal path-related parameters as the target path-related parameters; determining the target planning path of the distribution capacity according to the target path-related parameters.

[0011] Optionally, determining the target planning path of the distribution capacity according to the target path-related parameters includes: determining, based on the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, when there are overdue orders in the orders involved in the path, calculating the path-related parameters of the path after inserting the item delivery task point of the order with the largest timeout into each feasible position after its current position, so as to obtain the optimal path-related parameters under backward search, and when the optimal path-related parameters are better than the target path-related parameters, updating the target path-related parameters to the optimal path-related parameters; and / or calculating, based on the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, inserting the pickup task point and / or item delivery task point of the order with the largest delivery margin among the orders involved in the path into the pickup task point and / or Or the path-related parameters of the path after each feasible position before the current position of each of the item delivery task points, so as to obtain the optimal path-related parameters under the forward search, and when the optimal path-related parameters are better than the target path-related parameters, the target path-related parameters are updated to the optimal path-related parameters; judging whether the update termination condition is met, wherein the update termination condition includes that the time for executing the task point path planning is less than or equal to the preset time; when the update termination condition is not met, returning to the step of sorting according to the current target positions of the current delivery order and the order to be allocated, so as to obtain the planned path of the distribution capacity and the first path-related parameters of the planned path, until the update termination condition is met; when the update termination condition is met, the path corresponding to the target path-related parameters is determined as the target planned path of the distribution capacity.

[0012] Optionally, the path-related parameters of the path include distribution efficiency change parameters of the path relative to the currently planned path; accordingly, the optimal path-related parameters are determined by the following steps: comparing the distribution efficiency change parameters in the path-related parameters, and determining the path-related parameters with the smallest distribution efficiency change parameters as the optimal path-related parameters; or when the distribution efficiency change parameters are the same, comparing the robustness characteristic parameters included in the path-related parameters, and determining the path-related parameters with the largest robustness characteristic parameters as the optimal path-related parameters.

[0013] Optionally, the robustness characteristic parameter of the path is determined by the following steps: determining the target order with the largest timeout according to the fuzzy number of the estimated delivery time of each order involved in the path; determining the robustness characteristic parameter of the path according to the membership function of the fuzzy number of the estimated delivery time of the target order and the promised delivery time of the target order by the following formula:

[0014]

[0015] AI=[area(D(x)∩A(x))] / areaA(x)

[0016] Wherein, x is time, d is the promised delivery time of the target order, D(x) is the step function of the target order with respect to the promised time, A(x) is the membership function of the fuzzy number of the estimated delivery time of the target order, and AI is the robustness characteristic parameter of the path.

[0017] Optionally, there is one order to be allocated, and determining the optimal delivery capacity according to the delivery efficiency change parameter, and allocating the order to be allocated to the optimal delivery capacity, includes: comparing the delivery efficiency change parameter of each delivery capacity, determining the delivery capacity with the smallest delivery efficiency change parameter as the optimal delivery capacity, and allocating the order to be allocated to the optimal delivery capacity; or when the delivery efficiency change parameters are the same, comparing the robustness characteristic parameters of the target planning path of each delivery capacity, determining the delivery capacity corresponding to the target planning path with the largest robustness characteristic parameter as the optimal delivery capacity, and allocating the order to be allocated to the optimal delivery capacity.

[0018] Optionally, there are multiple orders to be allocated, and each delivery capacity has a target planning path for each order to be allocated; accordingly, determining the optimal delivery capacity according to the delivery efficiency change parameter, and allocating the orders to be allocated to the optimal delivery capacity, includes: among the multiple target planning paths of the multiple delivery capacities, determining the delivery capacity with the smallest delivery efficiency change parameter as the optimal delivery capacity; among the multiple target planning paths of the optimal delivery capacity, determining the target planning path with a delivery efficiency change parameter whose difference with the smallest delivery efficiency change parameter is less than or equal to a preset difference as a candidate planning path; comparing the robustness characteristic parameters of each candidate planning path, and allocating the order to be allocated corresponding to the candidate planning path with the largest robustness characteristic parameter to the optimal delivery capacity.

[0019] The second aspect of the present disclosure also provides an order allocation device, including: a first determination module, configured to determine multiple delivery capacities associated with an order to be allocated; a path planning module, configured to perform task point path planning for each delivery capacity according to the order to be allocated and the current planned path of each delivery capacity, so as to obtain a target planned path for each delivery capacity; an acquisition module, configured to obtain a delivery efficiency parameter of each target planned path, wherein the delivery efficiency parameter of the target planned path is determined based on a fuzzy number of random delivery feature information corresponding to the target planned path; a second determination module, configured to determine a delivery efficiency change parameter of the target planned path of the same delivery capacity relative to the current planned path according to the delivery efficiency parameter of the target planned path and the delivery efficiency parameter of the current planned path; an order allocation module, configured to determine the optimal delivery capacity according to the delivery efficiency change parameter, and allocate the order to be allocated to the optimal delivery capacity.

[0020] Optionally, the device also includes: a third determination module, configured to determine the fuzzy number of the estimated delivery time of each order involved in the planned path, and determine the fuzzy number of the timeout duration of the order based on the fuzzy number of the estimated delivery time and the promised delivery time of the order; a fourth determination module, configured to determine the timeout duration parameter of the planned path based on the fuzzy number of the timeout duration of each order involved in the planned path.

[0021] Optionally, the path planning module includes: a first determination submodule, configured to determine, for each of the distribution transport capacity, the distribution priority of each current distribution order and the order to be assigned involved in the currently planned path; an execution submodule, configured to take the orders as target orders in sequence according to the distribution priority, and perform the following steps for the target order: determining the distribution parameters of the path according to the fuzzy number of the random distribution feature information corresponding to the path after the target order is inserted into each feasible position, wherein the feasible position is a position where the pickup task point that meets the target order is located before the item delivery task point, and the number of waybills of the distribution transport capacity at this position is less than or equal to the waybill threshold, and the distribution parameters include delivery time parameters and / or delivery distance parameters; determining the position of the target order corresponding to the minimum distribution parameter as the current target position of the target order; a path planning submodule, configured to perform task point path planning for the distribution transport capacity according to the current target positions of the current distribution order and the order to be assigned, so as to obtain the target planned path for each of the distribution transport capacity.

[0022] Optionally, the path planning submodule includes: a sorting submodule, configured to sort the current delivery order and the to-be-allocated order according to their respective current target locations, so as to obtain the planned path of the delivery capacity and the first path-related parameters of the planned path; a searching submodule, configured to search the planned path through a preset neighborhood operator, so as to obtain the second path-related parameters of the path after each search under each neighborhood operator; and a second determining submodule, configured to determine, for each neighborhood operator, the optimal path under the neighborhood operator from the second path-related parameters under the neighborhood operator. and determining the optimal path-related parameters as the third path-related parameters of the path under the neighborhood operator when the optimal path-related parameters are better than the first path-related parameters, so as to obtain the third path-related parameters of the path under each of the neighborhood operators; a third determining submodule is configured to determine the optimal path-related parameters from the third path-related parameters of the path under each of the neighborhood operators, and use the optimal path-related parameters as the target path-related parameters; and a fourth determining submodule is configured to determine the target planning path of the distribution capacity according to the target path-related parameters.

[0023] Optionally, the fourth determination submodule includes: an updating submodule, which is configured to determine, based on the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, that when there are overdue orders among the orders involved in the path, calculate the path-related parameters of the path after inserting the item delivery task point of the order with the largest timeout into each feasible position after its current position, so as to obtain the optimal path-related parameters under backward search, and when the optimal path-related parameters are better than the target path-related parameters, update the target path-related parameters to the optimal path-related parameters; and / or, based on the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, calculate the insertion of the pickup task point and / or item delivery task point of the order with the largest delivery margin among the orders involved in the path into each feasible position before the current position of the pickup task point and / or the item delivery task point. The path-related parameters of the subsequent path are obtained to obtain the optimal path-related parameters under the forward search, and when the optimal path-related parameters are better than the target path-related parameters, the target path-related parameters are updated to the optimal path-related parameters; a judgment submodule is configured to judge whether an update termination condition is met, wherein the update termination condition includes that the time for executing the task point path planning is less than or equal to a preset time; a return submodule is configured to return to the sorting submodule to execute the step of sorting according to the current target positions of the current delivery order and the order to be allocated to obtain the planned path of the distribution capacity and the first path-related parameters of the planned path when the update termination condition is not met, until the update termination condition is met; a fifth determination submodule is configured to determine the path corresponding to the target path-related parameters as the target planned path of the distribution capacity when the update termination condition is met.

[0024] Optionally, the path-related parameters of the path include distribution efficiency change parameters of the path relative to the currently planned path; accordingly, the device also includes: a fifth determination module, configured to compare the distribution efficiency change parameters in the path-related parameters, and determine the path-related parameters with the smallest distribution efficiency change parameters as the optimal path-related parameters; or when the distribution efficiency change parameters are the same, compare the robustness characteristic parameters included in the path-related parameters, and determine the path-related parameters with the largest robustness characteristic parameters as the optimal path-related parameters.

[0025] Optionally, the device further includes: a sixth determination module configured to determine the target order with the largest timeout according to the fuzzy number of the estimated delivery time of each order involved in the path; and a seventh determination module configured to determine the robustness characteristic parameter of the path according to the membership function of the fuzzy number of the estimated delivery time of the target order and the promised delivery time of the target order by the following formula:

[0026]

[0027] AI=[area(D(x)∩A(x))] / areaA(x)

[0028] Wherein, x is time, d is the promised delivery time of the target order, D(x) is the step function of the target order with respect to the promised time, A(x) is the membership function of the fuzzy number of the estimated delivery time of the target order, and AI is the robustness characteristic parameter of the path.

[0029] Optionally, there is one order to be allocated, and the order allocation module is configured to compare the delivery efficiency change parameters of each delivery capacity, determine the delivery capacity with the smallest delivery efficiency change parameter as the optimal delivery capacity, and allocate the order to be allocated to the optimal delivery capacity; or when the delivery efficiency change parameters are the same, compare the robustness characteristic parameters of the target planning path of each delivery capacity, determine the delivery capacity corresponding to the target planning path with the largest robustness characteristic parameter as the optimal delivery capacity, and allocate the order to be allocated to the optimal delivery capacity.

[0030] Optionally, there are multiple orders to be allocated, and each of the delivery transport capabilities has a target planning path for each of the orders to be allocated; accordingly, the order allocation module includes: a sixth determination submodule, configured to determine the delivery transport capability with the smallest delivery efficiency change parameter as the optimal delivery transport capability among the multiple target planning paths of the multiple delivery transport capabilities;

[0031] The seventh determination submodule is configured to determine, among the multiple target planning paths of the optimal delivery capacity, a target planning path whose difference with the minimum delivery efficiency change parameter is less than or equal to a preset difference as a candidate planning path; the order allocation submodule is configured to compare the robustness characteristic parameters of each of the candidate planning paths, and allocate the to-be-allocated orders corresponding to the candidate planning path with the largest robustness characteristic parameters to the optimal delivery capacity.

[0032] The third aspect of the present disclosure further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method provided in the first aspect of the present disclosure are implemented.

[0033] The fourth aspect of the present disclosure also provides an electronic device, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the steps of the method provided in the first aspect of the present disclosure.

[0034] Through the above technical solution, the distribution efficiency parameters of the target planning path are determined according to the fuzzy number of the random distribution feature information corresponding to the target planning path, and the distribution efficiency change parameters of the target planning path relative to the current planning path are determined, and the optimal distribution capacity is determined according to the distribution efficiency change parameters. Since the fuzzy number of the random distribution feature information corresponding to the planning path is considered when allocating orders, the distribution efficiency change parameters of the target planning path with the same distribution capacity relative to the current planning path can be accurately determined, which improves the accuracy of determining the optimal distribution capacity according to the distribution efficiency change parameters, and thus improves the accuracy of order allocation. In addition, the orders to be allocated are allocated to the optimal distribution capacity, which improves the distribution efficiency of the distribution capacity.

[0035] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings:

[0037] Figure 1 The invention is a flow chart of an order allocation method according to an exemplary embodiment.

[0038] Figure 2A is a schematic diagram of a target planning path for delivery capacity A according to an exemplary embodiment.

[0039] Figure 2B is a schematic diagram of a currently planned path of delivery capacity A according to an exemplary embodiment.

[0040] Figure 3 The present invention is a flowchart of a method for determining a target planning path according to an exemplary embodiment.

[0041] Figure 4 The present invention is a flowchart of a method for determining a target planning path according to another exemplary embodiment.

[0042] Figure 5The diagram is a schematic diagram showing a membership function of a triangular fuzzy number for estimating a delivery time and a step function for a commitment time according to an exemplary embodiment.

[0043] Figure 6 The invention is a flowchart showing a method for allocating to-be-allocated orders to optimal delivery capacity according to an exemplary embodiment.

[0044] Figure 7 It is a block diagram of an order distribution device according to an exemplary embodiment.

[0045] Figure 8 It is a block diagram of an electronic device according to an exemplary embodiment.

[0046] Fig. 9 is a block diagram of an electronic device according to another exemplary embodiment. DETAILED DESCRIPTION

[0047] The present disclosure is mainly applied to business scenarios such as takeaway delivery, supermarket delivery, same-city delivery, and express delivery. In these business scenarios, it is usually necessary to comprehensively consider many factors to allocate and schedule the orders to be allocated, so as to allocate the orders to be allocated to the appropriate delivery capacity. In the related art, when allocating the orders to be allocated, the random information is usually assumed to be deterministic information for processing to analyze the matching degree between the orders to be allocated and the delivery capacity. This processing method is relatively simple. However, according to this method, it is impossible to accurately determine the impact of the order to be allocated on the delivery efficiency of the delivery capacity, and thus it is impossible to accurately allocate the order to be allocated to the delivery capacity with the least impact on the delivery efficiency. For example, in real scenarios, there are many factors affecting random information and many emergencies, and it is impossible to replace it with a certain scenario. For example, if the meal delivery time is regarded as a certain value, if the merchant blocks the meal, it will cause the deliveryman to wait for the meal at the merchant. In the case of fewer delivery orders, the merchant blocking the meal will not have a relatively bad impact. However, during the lunch rush hour when delivery capacity is tight, merchants blocking meals may lead to the following problems: 1. Merchants blocking meals causes delivery capacity orders to time out, reduces delivery punctuality, and affects user experience. 2. Merchants blocking meals is a waste of delivery capacity. The delivery time of delivery capacity during the lunch rush hour (for example, 10:00 to 14:00) is fixed. The longer the delivery capacity waits for meals at the merchant, the shorter the effective delivery time, thereby reducing the number of orders completed by the delivery capacity per unit time, seriously affecting delivery efficiency. 3. It intensifies the contradiction between delivery capacity and merchants.

[0048] In view of this, the present disclosure provides an order allocation method, device, readable storage medium and electronic device to improve the distribution efficiency of distribution capacity.

[0049] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.

[0050] Please refer to Figure 1 , Figure 1 FIG. 1 is a flow chart of an order allocation method according to an exemplary embodiment. Figure 1 As shown, the method may include steps 101 to 105.

[0051] In step 101, a plurality of delivery capacities associated with an order to be allocated are determined.

[0052] In the present disclosure, the order allocation method can be executed by a server or electronic device of an order scheduling system or a distribution platform. Since it is necessary to determine which distribution capacity to allocate the order to be allocated from multiple distribution capacities, it is first necessary to determine multiple distribution capacities associated with the order to be allocated. Among them, the distribution capacity can be a delivery person or a distribution device (such as a distribution robot, an unmanned delivery vehicle, a drone, etc.). The multiple distribution capacities associated with the order to be allocated can be distribution capacities corresponding to the delivery location or pickup location of the order to be allocated. For example, assuming that the delivery location of the order to be allocated is Haidian District, Beijing, the above-mentioned multiple distribution capacities can be distribution capacities belonging to Haidian District. Further, the multiple distribution capacities associated with the order to be allocated can also be candidate distribution capacities preliminarily determined by the server or electronic device. For example, the matching degree between the order to be allocated and the distribution capacity can be determined by using relevant technologies, and the distribution capacity with a matching degree greater than the preset matching degree can be determined as a candidate distribution capacity, and the candidate distribution capacity can be determined as multiple distribution capacities associated with the order to be allocated. The present disclosure does not explain and limit this in detail.

[0053] In step 102, task point path planning is performed for each delivery transport capacity according to the orders to be assigned and the current planned path of each delivery transport capacity, so as to obtain the target planned path of each delivery transport capacity.

[0054] After determining multiple delivery capacities associated with the order to be allocated, further obtain the current planned path of the delivery capacity, wherein the current planned path of the delivery capacity is the planned path that only includes the current delivery order of the delivery capacity, and the current delivery order refers to an order that has been assigned to the delivery capacity but has not yet been completed (including orders where both the pickup task and the item delivery task are not completed, and the pickup task of the order is completed but the delivery task is not yet completed). Since the current delivery task of the delivery capacity is the order that the delivery capacity needs to complete, the current delivery order must be considered when determining the planned path after the order to be allocated is to be allocated to the delivery capacity. In the present disclosure, a task point can represent a pickup task point and an item delivery task point.

[0055] In step 103, the delivery efficiency parameter of each target planning path is obtained, wherein the delivery efficiency parameter of the target planning path is determined based on the fuzzy number of the random delivery feature information corresponding to the target planning path.

[0056] In order to ensure that when the to-be-allocated order is allocated to a certain delivery transport capacity, the to-be-allocated order has the least impact on the delivery efficiency of the delivery transport capacity in completing the current delivery task, therefore, in the present disclosure, it is necessary to obtain the delivery efficiency parameter of each target planning path. The delivery efficiency parameter can characterize the delivery efficiency of the delivery transport capacity corresponding to the target planning path.

[0057] For example, the delivery efficiency parameter may include: an overtime duration parameter and / or a delivery distance parameter. In one embodiment, the delivery efficiency parameter may be directly represented by the delivery distance parameter or the overtime duration parameter, or may be represented by a parameter obtained by weighted summation of the delivery distance parameter and the overtime duration parameter, which is not specifically limited in the present disclosure.

[0058] It should be noted that the distribution efficiency parameters can be determined in the process of determining the target planning path, or after determining the target planning path for the distribution capacity. However, no matter when the distribution efficiency parameters of the target planning path are determined, they are all determined based on the fuzzy numbers of the random distribution feature information corresponding to the target planning path.

[0059] In addition, in order to solve the problem in the related art that the distribution impact of the to-be-allocated orders on the distribution capacity cannot be accurately determined due to the assumption that the random information is processed as deterministic information, therefore, in the disclosure, the distribution efficiency parameter of the target planning path is determined according to the fuzzy number of the random distribution feature information corresponding to the target planning path. Among them, the disclosure does not specifically limit the method for determining the fuzzy number of the random distribution feature information. Moreover, in the disclosure, the fuzzy number can be a triangular fuzzy number, a trapezoidal fuzzy number, etc. For the sake of convenience, in the disclosure, the fuzzy number is taken as a triangular fuzzy number as an example for explanation.

[0060] In the present disclosure, random delivery feature information may include at least one of the following: item preparation completion time; item pickup completion time; delivery transport movement time; item delivery time. Among them, item preparation completion time refers to the time it takes for the merchant to prepare the item from the time the order is received; item pickup completion time refers to the time it takes for the delivery transport to arrive at the merchant and pick up the goods; delivery transport movement time refers to the time it takes for the delivery transport to arrive at one task point to another; item delivery time refers to the time it takes for the delivery transport to arrive at the agreed delivery location and hand over the item to the user. For example, assuming that the order is a takeaway order, the triangular fuzzy number of the item preparation completion time can be the triangular fuzzy number of the merchant's meal delivery time, wherein the triangular fuzzy number can be determined based on the merchant's historical meal delivery time.

[0061] It should be noted that, when the random delivery feature information includes the time for completing item preparation, the triangular fuzzy number of the random delivery feature information corresponding to the target planning path may be the triangular fuzzy number of the meal delivery time of each merchant involved in the target planning path, or may be the triangular fuzzy number of the meal delivery time of one or several merchants among the multiple merchants included in the target planning path. The present disclosure does not make any specific limitations on this.

[0062] In step 104, based on the delivery efficiency parameters of the target planned path of the same delivery capacity and the delivery efficiency parameters of the current planned path, the delivery efficiency change parameters of the target planned path of the delivery capacity relative to the current planned path are determined.

[0063] For example, the delivery efficiency change parameter may include: the change in the timeout duration parameter and / or the change in the delivery distance parameter. In one embodiment, the delivery efficiency change parameter may be directly represented by the change in the delivery distance parameter or the change in the timeout duration parameter, or may be represented by a parameter obtained by the weighted sum of the change in the delivery distance parameter and the change in the timeout duration parameter, which is not specifically limited in the present disclosure.

[0064] In the present disclosure, in order to determine the degree of influence of the order to be allocated on the delivery efficiency of the delivery capacity, it is necessary to obtain the current planned path of the delivery capacity before the order to be allocated is allocated to the delivery capacity, and the target planned path of the delivery capacity after the order to be allocated is allocated to the delivery capacity, and based on the delivery efficiency parameter of the target planned path and the delivery efficiency parameter of the current planned path, determine the delivery efficiency change parameter of the target planned path relative to the current planned path. Among them, the delivery efficiency change parameter can characterize the degree of influence of the order to be allocated on the delivery efficiency of the delivery capacity, and the larger the delivery efficiency change parameter, the greater the influence of the order to be allocated on the delivery efficiency of the delivery capacity.

[0065] In step 105, the optimal delivery capacity is determined according to the delivery efficiency change parameter, and the orders to be distributed are allocated to the optimal delivery capacity.

[0066] As shown above, the delivery efficiency change parameter can characterize the degree of influence of the orders to be allocated on the delivery efficiency of the delivery capacity. Therefore, according to the delivery efficiency change parameter, the target planning path with the least impact of the orders to be allocated on the delivery efficiency of the delivery capacity can be determined, that is, the target planning path with the least impact of the orders to be allocated on the on-time delivery of the delivery capacity and / or the driving distance of the delivery capacity, and the delivery capacity corresponding to the target planning path is determined as the optimal delivery capacity, and the order to be allocated is then allocated to the optimal delivery capacity.

[0067] Through the above technical solution, the distribution efficiency parameters of the target planning path are determined according to the triangular fuzzy number of the random distribution feature information corresponding to the target planning path, and the distribution efficiency change parameter of the target planning path relative to the current planning path is determined, and the optimal distribution capacity is determined according to the distribution efficiency change parameter. Since the triangular fuzzy number of the random distribution feature information corresponding to the planning path is considered when allocating orders, the distribution efficiency change parameter of the target planning path with the same distribution capacity relative to the current planning path can be accurately determined, which improves the accuracy of determining the optimal distribution capacity according to the distribution efficiency change parameter, and thus improves the accuracy of order allocation. In addition, the orders to be allocated are allocated to the optimal distribution capacity, which improves the distribution efficiency of the distribution capacity.

[0068] In order to enable those skilled in the art to better understand the order allocation method provided by the present disclosure, the above steps are described and explained in detail below.

[0069] First, the method for determining the timeout duration parameter and the delivery path parameter, which are included in the delivery efficiency parameters, is explained.

[0070] For example, a method for determining a timeout duration parameter may include:

[0071] For each order involved in the planning path, determine the triangular fuzzy number of the estimated delivery time of the order, and determine the triangular fuzzy number of the overtime duration of the order based on the triangular fuzzy number of the estimated delivery time and the promised delivery time of the order;

[0072] The timeout duration parameter of the planned path is determined according to the triangular fuzzy number of the timeout duration of each order involved in the planned path.

[0073] First, the operation rules of triangular fuzzy numbers are explained. For two triangular fuzzy numbers A = [a1, a2, a3] and B = [b1, b2, b3] and a definite number c, the addition operation is: A+B = [a1+b1, a2+b2, a3+b3], A+c = [a1+c, a2+c, a3+c]. Subtraction operation: Ac = [a1-c, a2-c, a3-c]. The maximum operation is: max(A, B) = (max(a1, b1), max(a2, b2), max(a3, b3)), max(A, c) = (max(a1, c), max(a2, c), max(a3, c)). The expected expression of triangular fuzzy numbers is: E(A) = (a1+2*a2+a3) / 4.

[0074] In one embodiment, assuming that the target planning path of delivery capacity A is as follows Figure 2A As shown in the figure, 0 in the target planning path represents the current location of the delivery capacity, 1 + Represents the pickup location of order W1, 2 + Represents the pickup location of order W2, 1 - Represents the item delivery location of order W1, 2 - The triangular fuzzy number representing the delivery location of the item in order W2 and assuming that the time for the delivery capacity to leave the current location is [0, 0, 0], ranging from 0 to 1 + The travel time is 2 and the distance is 10, then the destination is 1 + The triangular fuzzy number of time is [2, 2, 2], and the triangular fuzzy number of the preparation and completion time of the item corresponding to order W1 is [3, 5, 10]. + The triangular fuzzy number of the departure time is [3, 5, 10]; from 1 + To 2 + The travel time is 5 and the distance is 18, so the destination is 2 + The triangular fuzzy number of time is [8, 10, 15], and the triangular fuzzy number of the preparation and completion time of the item corresponding to order W2 is [7, 10, 15]. + The triangular fuzzy number of the departure time is [8, 10, 15]; from 2 + to 1 - The travel time is 3 and the distance is 20, then the arrival and departure time is 1 - The triangular fuzzy number of time (the triangular fuzzy number of the estimated delivery time of order W1) is [11, 13, 18]; from 1 - To 2 - The travel time is 2 and the distance is 15, so the arrival and departure time is 2 -The triangular fuzzy number of time (the triangular fuzzy number of the estimated delivery time of order W2) is [13, 15, 20], where the promised delivery time of order W1 and order W2 is 15.

[0075] It can be calculated Figure 2A The delivery distance parameter DC of the target route shown 目标 =10+18+20+15=63; the triangular fuzzy number of the timeout duration of order W1 is OT1=max(0,[11,13,18]-15)=[0,0,3], and the triangular fuzzy number of the timeout duration of order W2 is OT2=max(0,[[13,15,20]-15)=[0,0,5], that is, Figure 2A The timeout parameter TC of the target path shown 目标 =E([0,0,3])+E([0,0,5])=2. Where E represents expectation.

[0076] Assume that the current planned path of delivery capacity A is as follows Figure 2B As shown. In the current planning path, from 0 to 1 + The travel time is 2 and the distance is 10, then the destination is 1 + The triangular fuzzy number of time is [2, 2, 2], and the triangular fuzzy number of the preparation and completion time of the item corresponding to order W1 is [3, 5, 10]. + The triangular fuzzy number of the departure time is [3, 5, 10]; from 1 + to 1 - The travel time is 6 and the distance is 32, so the arrival and departure time is 1 - The triangular fuzzy numbers of time (the triangular fuzzy numbers of the estimated delivery time of order W1) are all [9, 11, 16], and the promised delivery time of order W1 is 15. It can be calculated that Figure 2B The delivery distance parameter DC of the current planned route is shown 当前 =10+32=42; Figure 2B The timeout duration parameter TC of the currently planned path is shown 当前 =E([0,0,1])=0.25.

[0077] In addition, the change in the timeout duration parameter and / or the change in the delivery distance parameter of the target planned path of the delivery capacity A relative to the current planned path can be further determined. For example, the change in the timeout duration parameter TC=TC 目标 -TC 当前 =1.75. Change in delivery distance parameter = DC 目标 -DC 当前 =21.

[0078] The following describes a specific implementation method of step 102 of performing task point path planning for each delivery transport capacity according to the orders to be allocated and the current planned path of each delivery transport capacity to obtain the target planned path of each delivery transport capacity.

[0079] In one embodiment, relevant path planning technology may be used to perform path planning for each delivery transport capacity according to the orders to be assigned and the current planned path of each delivery transport capacity, so as to obtain a target planned path for each delivery transport capacity.

[0080] In another embodiment, based on the properties of the fuzzy path planning problem, a fruit fly optimization algorithm may be used to perform task point path planning for each delivery capacity.

[0081] For example, Figure 3 FIG. 1 is a flow chart showing a method for determining a target planning path according to an exemplary embodiment. Figure 3 As shown, the above step 102 may include the following steps.

[0082] In step 1021, for each delivery capacity, the delivery priority of each current delivery order and to-be-allocated order involved in the currently planned route is determined.

[0083] It is worth noting that after the order to be allocated is assigned to the delivery capacity, the delivery capacity can have multiple planned paths. However, in the present disclosure, in order to further improve the accuracy of order allocation, the target planned path of the delivery capacity refers to the optimal planned path of the delivery capacity after the order to be allocated is assigned to the delivery capacity.

[0084] When determining the target planning path of the delivery capacity, it is necessary to obtain each current delivery order involved in the current planning path of the delivery capacity, and for the current delivery order and the order to be assigned, determine the delivery priority of each order according to the urgency of the promised time. For example, the smaller the promised time, the more urgent the order, and the higher the corresponding delivery priority. Therefore, in the present disclosure, the delivery priority of each order can be determined according to the size of the promised time of each order.

[0085] In step 1022, orders are taken as target orders in turn according to the delivery priority, and for the target orders, the delivery parameters of the path are determined based on the fuzzy number of the random delivery feature information corresponding to the path after the target order is inserted into each feasible position.

[0086] In step 1023, the location of the target order corresponding to the minimum delivery parameter is determined as the current target location of the target order.

[0087] In practical applications, for any order, its pickup task point should be located before the item delivery task point, and, usually, the delivery capacity is limited. For example, the takeaway box of a takeaway deliveryman can only hold up to 10 portions of food. Then there should be at least one item delivery task point before the pickup task point of the eleventh order to ensure that the takeaway box can hold the food corresponding to the eleventh order. Based on the above considerations, when inserting the target order, it is necessary to control its insertion into a feasible position, where the feasible position is the position where the pickup task point that satisfies the target order is located before the item delivery task point, and the number of waybills of the delivery capacity at this position is less than or equal to the waybill threshold.

[0088] For example, let's take the current delivery order as order W1 and the order to be assigned as order W2 as an example. Assuming that order W1 has a higher priority than order W2, order W1 is first used as the target order and inserted into each feasible position. Since no task point has been planned in the current path, at this time, the task point 1 of order W1 is + , Item Delivery Mission Point 1 - There is only one situation where Figure 2B As shown, and calculated Figure 2B The delivery parameters of the route shown may include a delivery time parameter and / or a delivery distance parameter. The delivery time parameter of the route is E([9, 11, 16])=11.75, and the delivery distance parameter is 42.

[0089] After that, order W2 is taken as the target order, and the pickup task point 2 of order W2 is + For , the feasible positions include 0 and 1 + The position between + and 1 - The position between - Then, in the task point 2 + Insert 0 and 1 respectively + When the position between + and 1 - When the position between - When the location is reached, calculate the delivery parameters of the current path respectively, and take the task point 2 of the path corresponding to the minimum delivery parameter + The location is used as the fetch mission point 2 + For example, when taking the object task point 2 + Insert 1 + and 1 - The delivery parameter of the calculated route is the minimum when the location between + and 1 - The location between the two is the object-collecting task point 2 +The current target location. Then, insert the item delivery task point 2 as above - , and determine the item delivery task point 2 - The current target location of each order. In this way, the target location of each order can be determined in the above manner.

[0090] In step 1024, task point path planning is performed on the delivery capacity according to the current target locations of the current delivery order and the order to be assigned, so as to obtain the target planning path for each delivery capacity.

[0091] In one embodiment, the path obtained by sorting the current target locations of the current delivery orders and the orders to be allocated is the target planning path for the delivery capacity.

[0092] Since the current target location of a certain order is determined based on the locations of other orders with higher priority than the order, the current target location determined may be inaccurate, and the path obtained by sorting the current target locations of the current delivery order and the order to be allocated is not necessarily the optimal path, that is, it is not necessarily the target planning path for the delivery capacity. Therefore, in another embodiment, the optimal path can also be determined based on the olfactory search of multi-operation collaboration.

[0093] For example, Figure 4 As shown, the above step 1024 may further include the following steps.

[0094] In step 401, the current delivery orders and the orders to be allocated are sorted according to their respective current target locations to obtain a planned path for the delivery capacity and a first path-related parameter of the planned path.

[0095] The first path-related parameter may include a distribution efficiency change parameter of the planned path relative to the current planned path. + and 1 - The location between them is the object-collecting task point 2 + The current target position, 1 - The next location is item delivery task point 2 - The current target location of the distribution capacity is Figure 2A The planned path shown in 2B is the current planned path of the distribution capacity, and the first path related parameters may include Figure 2A The planned path shown is relative to Figure 2B The delivery efficiency change parameters of the current planned route are shown.

[0096] In step 402, the planned path is searched by using a preset neighborhood operator to obtain a second path-related parameter of the path after each search under each neighborhood operator.

[0097] The preset neighborhood operator may include, but is not limited to, an Insert operator, a SwapN operator, and a SwapC operator. The following description is made by taking the preset neighborhood operator including the Insert operator, the SwapN operator, and the SwapC operator as an example.

[0098] When searching using the Insert operator, a task point is randomly selected in the planned path determined in step 401 (the task point can be a pickup task point or an item delivery task point), the selected task point is moved out of its current position, and inserted into each feasible position in the planned path. Figure 2A Pickup point 1 + Move out from the current position and insert into every possible position (e.g., 2 + and 1 - The position between - and 2 - Position between 2 - The location after that). Among them, the fetching task point 1 + Each insertion of a feasible position is recorded as a search, and the second path-related parameters of the path after each search are calculated. In this way, three second path-related parameters can be obtained.

[0099] When searching using the SwapN operator, a task point (which may be a pickup task point or an item delivery task point) is randomly selected from the planned path determined in step 401 and exchanged with the nearest feasible task point. Figure 2A Pickup point 1 + With the fetch mission point 2 + By exchanging, a second path related parameter under this kind of neighborhood operator can be obtained.

[0100] When searching using the SwapC operator, consecutive pickup task points or item delivery task points are bound in the planned path determined in step 401, and a group of binding points are randomly selected and exchanged with their adjacent binding points to obtain the second path related parameters under this kind of neighborhood operator.

[0101] It should be noted that when the number of orders involved in the planning path is large, the SwapC operator can be used for search, but when the number of orders involved in the planning path is small, for example, there are only two orders involved, the SwapC operator cannot be used for search.

[0102] In step 403, for each neighborhood operator, the optimal path-related parameters under the neighborhood operator are determined from the second path-related parameters under the neighborhood operator, and when the optimal path-related parameters are better than the first path-related parameters, the optimal path-related parameters are determined to be the third path-related parameters of the path under the neighborhood operator, so as to obtain the third path-related parameters of the path under each neighborhood operator.

[0103] For each neighborhood operator, an optimal path-related parameter is selected therefrom. If the optimal path-related parameter is not better than the first path-related parameter, the first path-related parameter is determined as the third path-related parameter of the path under the neighborhood operator. If the optimal path-related parameter is better than the first path-related parameter, the optimal path-related parameter is determined as the third path-related parameter of the path under the neighborhood operator.

[0104] For example, a specific implementation of determining the optimal path-related parameters may include: comparing the delivery efficiency variation parameters in the path-related parameters, and determining the path-related parameters with the smallest delivery efficiency variation parameters as the optimal path-related parameters; or when the delivery efficiency variation parameters are the same, comparing the robustness characteristic parameters included in the path-related parameters, and determining the path-related parameters with the largest robustness characteristic parameters as the optimal path-related parameters.

[0105] Among them, the robustness characteristic parameters of the path can be determined by the following steps:

[0106] According to the fuzzy number of the estimated delivery time of each order involved in the path, determine the target order with the largest timeout;

[0107] According to the membership function of the fuzzy number of the estimated delivery time of the target order and the promised delivery time of the target order, the robustness characteristic parameter of the path is determined by the following formula:

[0108]

[0109] AI=[area(D(x)∩A(x))] / areaA(x)

[0110] Where x is time, d is the promised delivery time of the target order, D(x) is the step function of the target order with respect to the promised time, A(x) is the membership function of the fuzzy number of the estimated delivery time of the target order, and AI is the robustness characteristic parameter of the path.

[0111] For example, continue with Figure 2ATaking the planning path shown in the figure as an example, the timeout parameter of order W1 is E([0, 0, 3]) = 0.75, and the timeout parameter of order W2 is E([0, 0, 5]) = 1.25. Then order W2 is determined to be the target order with the largest timeout, and the triangular fuzzy number of the estimated delivery time of the target order is [13, 15, 20]. The membership function is as follows: Figure 5 As shown in A(x) in , the promised delivery time of the target order is 15, then AI is Figure 5 The ratio of the area of ​​region a1 in to the area enclosed by A(x) and the x-axis, for example, AI=2 / 7.

[0112] In step 404, an optimal path-related parameter is determined from the third path-related parameters of the paths under each neighborhood operator, and the optimal path-related parameter is used as the target path-related parameter.

[0113] In step 405, the target planning path of the distribution capacity is determined according to the target path related parameters.

[0114] In one embodiment, after the target path related parameters are determined, the path corresponding to the target path related parameters can be directly determined as the target planning path of the distribution capacity.

[0115] In another embodiment, the target path related parameters may be cyclically updated through local enhanced search until the loop termination condition is met, and then the path corresponding to the last updated target path related parameters is determined as the target planning path for the distribution capacity.

[0116] For example, the above step 405 may further include:

[0117] According to the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameter, when determining that there are overdue orders in the orders involved in the path, calculate the path-related parameters of the path after inserting the item delivery task point of the order with the largest timeout into each feasible position after its current position, so as to obtain the optimal path-related parameters under backward search, and when the optimal path-related parameters are better than the target path-related parameters, update the target path-related parameters to the optimal path-related parameters; and / or

[0118] According to the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, the path-related parameters of the path after inserting the pickup task point and / or item delivery task point of the order with the largest delivery margin among the orders involved in the path into each feasible position before the current position of the pickup task point and / or item delivery task point are calculated to obtain the optimal path-related parameters under forward search, and when the optimal path-related parameters are better than the target path-related parameters, the target path-related parameters are updated to the optimal path-related parameters; in the present disclosure, the delivery margin is the difference between the promised delivery time and the estimated delivery time for orders that complete the delivery of items in advance, for example, the promised time is 15, and the estimated delivery time parameter is 10, then the delivery margin is 15-10=5.

[0119] Determine whether the update termination condition is met, wherein the update termination condition includes that the time for executing the task point path planning is less than or equal to the preset time; wherein the preset time can be determined according to the number of orders to be assigned, in seconds. For example, the preset time can be 0.1×the number of orders to be assigned.

[0120] If the update termination condition is not met, return to the step of sorting the current delivery order and the order to be allocated according to their respective current target locations to obtain the planned path of the delivery capacity and the first path-related parameters of the planned path until the update termination condition is met;

[0121] It is worth noting that each time the step of returning to sort the current delivery order and the order to be allocated according to their respective current target locations to obtain the planned path of the delivery capacity and the first path-related parameters of the planned path is based on the current delivery order and the order to be allocated corresponding to the optimal path-related parameters determined in this cycle. The planned path of the delivery capacity and the first path-related parameters of the planned path are obtained. That is, the planned path of the delivery capacity involved in this cycle is the path corresponding to the optimal path-related parameters determined in the previous cycle.

[0122] When the update termination conditions are met, the path corresponding to the target path related parameters is determined as the target planning path for the distribution capacity.

[0123] By adopting the above technical scheme, the target path related parameters can be updated cyclically. In each cycle, the optimal path related parameters are further determined based on the target path related parameters obtained in the previous cycle, that is, the target path related parameters obtained after each cycle are better than or equal to the target path related parameters obtained in the previous cycle. Therefore, by cyclically updating the target path related parameters, the target planning path of the distribution capacity can be determined more accurately, thereby improving the accuracy of order allocation.

[0124] In one embodiment, if there is one order to be allocated, Figure 1 Step 105 in the above method may further include: comparing the delivery efficiency variation parameters of each delivery capacity, determining the delivery capacity with the smallest delivery efficiency variation parameter as the optimal delivery capacity, and allocating the orders to be allocated to the optimal delivery capacity; or when the delivery efficiency variation parameters are the same, comparing the robustness characteristic parameters of the target planning path of each delivery capacity, determining the delivery capacity corresponding to the target planning path with the largest robustness characteristic parameter as the optimal delivery capacity, and allocating the orders to be allocated to the optimal delivery capacity.

[0125] As shown above, the delivery efficiency change parameter can characterize the influence of the to-be-allocated order on the delivery efficiency of the delivery capacity. Specifically, it can characterize the influence of the to-be-allocated order on the overtime duration of the delivery capacity or the delivery distance of the delivery capacity. That is, the smaller the delivery efficiency change parameter is, the smaller the impact of the to-be-allocated order on the overtime duration of the delivery capacity or the delivery distance is. Therefore, in the present disclosure, the delivery capacity with the smallest delivery efficiency change parameter is determined as the optimal delivery capacity, and the to-be-allocated order is allocated to the optimal delivery capacity.

[0126] In the present disclosure, the robustness characteristic parameter can characterize the possibility of delivery capacity timeout when delivering along the path. The larger the robustness characteristic parameter, the smaller the possibility of timeout. Therefore, under the condition that the delivery efficiency change parameters are the same, the delivery capacity corresponding to the target planning path with the largest robustness characteristic parameter is determined as the optimal delivery capacity, and the orders to be distributed are allocated to the optimal delivery capacity.

[0127] In one embodiment, there are multiple orders to be allocated, and each delivery transport capacity has a target planning path for each order to be allocated, that is, each delivery transport capacity has multiple target planning paths. Figure 6 As shown above Figure 1 Step 105 in the method may further include the following steps.

[0128] In step 1051, among multiple target planning paths of multiple delivery transport capacities, the delivery transport capacity with the smallest delivery efficiency variation parameter is determined as the optimal delivery transport capacity.

[0129] In step 1052, among the multiple target planning paths with the optimal delivery capacity, the target planning path with the distribution efficiency change parameter whose difference with the smallest distribution efficiency change parameter is less than or equal to the preset difference is determined as the candidate planning path;

[0130] In step 1053, the robustness characteristic parameters of the candidate planning paths are compared, and the to-be-allocated order corresponding to the candidate planning path with the largest robustness characteristic parameter is allocated to the optimal delivery capacity.

[0131] In this embodiment, each distribution capacity has multiple target planning paths, and for each target planning path, the distribution efficiency variation parameters and robustness characteristic parameters of the target planning path can be calculated. For example, assuming that the orders to be allocated are order W1, order W2, and order W3, the multiple distribution capacities associated with these orders to be allocated include distribution capacity V1, distribution capacity V2, and distribution capacity V3. Correspondingly, each distribution capacity has three target planning paths. For example, the three target planning paths of distribution capacity v1 are: W1-V1 (the target planning path for allocating order W1 to distribution capacity V1), W2-V1 (the target planning path for allocating order W2 to distribution capacity V1), and W3-V1 (the target planning path for allocating order W3 to distribution capacity V1), and for each target planning path, the distribution efficiency variation parameters and robustness characteristic parameters of the target planning path can be determined in the above manner. For example, C Wi，Vi Characterize the distribution efficiency change parameter of the target planning path that allocates orders Wi to distribution capacity Vi, AI Wi，Vi Characterize the robustness characteristic parameters of the target planning path that assigns orders Wi to delivery transport capacity Vi. Table 1 shows the distribution efficiency variation parameters and robustness characteristic parameters of the target planning path that assigns each order to each delivery transport capacity.

[0132] Table 1

[0133] <![CDATA[(C Wi，Vi ,AI Wi，Vi )]]> Delivery capacity V1 Delivery Capacity V2 Delivery Capacity V3 Order W1 (4.0，0.1) (3.0，0.5) (2.0，0.7) Order W2 (2.6，0.8) (1.5，0.6) (5.0，1.0) Order W3 (3.0，0.7) (4.0，1.0) (8.0，0.5)

[0134] Taking Table 1 as an example, the above steps 1051 to 1053 are explained. For example, according to the distribution efficiency change parameters of each target planning path shown in Table 1, the distribution capacity V2 is determined as the optimal distribution capacity. Assuming that the preset difference is 3, the three target planning paths of the distribution capacity V2 are all candidate planning paths. Afterwards, the robustness characteristic parameters of each candidate planning path are compared, and the to-be-allocated order corresponding to the candidate planning path with the largest robustness characteristic parameter is allocated to the optimal distribution capacity, that is, the order W3 is allocated to the distribution capacity V2. In this way, it is possible to allocate order W3 to the optimal distribution capacity corresponding to order W3.

[0135] It is worth noting that after order W3 is assigned to delivery capacity V2 in the above manner, order W3 is the current delivery order of delivery capacity V2, and W3-V2 (the target planning path for assigning order W3 to delivery capacity V2) is updated to the current planning path of delivery capacity V2, and the orders to be assigned only include order W1 and order W2. After that, the above order assignment method is executed again until both order W1 and order W2 are assigned to the optimal delivery capacity.

[0136] By adopting the above technical solution, in the order allocation process, the distribution efficiency variation parameters and robustness characteristic parameters of the target planning path of the distribution capacity are comprehensively considered to determine the optimal distribution capacity for each order to be allocated, and each order to be allocated is allocated to the corresponding optimal distribution capacity, further improving the accuracy of order allocation. In addition, the allocation of the orders to be allocated to the optimal distribution capacity improves the robustness of the distribution path, thereby improving the distribution efficiency of the distribution capacity.

[0137] Based on the same inventive concept, the present disclosure also provides an order allocating device. Figure 7 FIG. 1 is a block diagram of an order distribution device according to an exemplary embodiment. Figure 7 As shown, the apparatus 700 may include:

[0138] The first determination module 701 is configured to determine a plurality of delivery transport capacities associated with the order to be allocated;

[0139] The path planning module 702 is configured to perform task point path planning for each of the delivery transport capabilities according to the to-be-assigned orders and the current planned path of each of the delivery transport capabilities, so as to obtain a target planned path for each of the delivery transport capabilities;

[0140] The acquisition module 703 is configured to acquire a delivery efficiency parameter of each of the target planning paths, wherein the delivery efficiency parameter of the target planning path is determined based on a fuzzy number of random delivery feature information corresponding to the target planning path;

[0141] The second determination module 704 is configured to determine a distribution efficiency change parameter of the target planned path of the distribution capacity relative to the current planned path according to the distribution efficiency parameter of the target planned path of the same distribution capacity and the distribution efficiency parameter of the current planned path;

[0142] The order allocation module 705 is configured to determine the optimal delivery capacity according to the delivery efficiency change parameter, and allocate the to-be-allocated order to the optimal delivery capacity.

[0143] Optionally, the device may further include:

[0144] a third determining module configured to determine, for each order involved in the planned route, a fuzzy number of an estimated delivery time of the order, and determine a fuzzy number of a timeout duration of the order according to the fuzzy number of the estimated delivery time and the promised delivery time of the order;

[0145] The fourth determination module is configured to determine a timeout duration parameter of the planned path according to a fuzzy number of a timeout duration of each order involved in the planned path.

[0146] Optionally, the path planning module 702 may include:

[0147] A first determination submodule is configured to determine, for each of the delivery transport capacities, a delivery priority of each current delivery order and the to-be-allocated order involved in the currently planned route;

[0148] The execution submodule is configured to sequentially select orders as target orders according to the delivery priority, and perform the following steps for the target orders: determine the delivery parameters of the path according to the fuzzy number of the random delivery feature information corresponding to the path after the target order is inserted into each feasible position, wherein the feasible position is a position where the pickup task point that satisfies the target order is located before the item delivery task point, and the number of waybills of the delivery capacity at the position is less than or equal to the waybill threshold, and the delivery parameters include delivery time parameters and / or delivery distance parameters; determine the position of the target order corresponding to the minimum delivery parameter as the current target position of the target order;

[0149] The path planning submodule is configured to perform task point path planning for the delivery transport capacity according to the current target locations of the current delivery order and the order to be assigned, so as to obtain a target planning path for each of the delivery transport capacity.

[0150] Optionally, the path planning submodule includes:

[0151] A sorting submodule is configured to sort the current delivery order and the to-be-allocated order according to their respective current target locations, so as to obtain a planned path for the delivery capacity and a first path-related parameter of the planned path;

[0152] A search submodule is configured to search the planned path through a preset neighborhood operator to obtain a second path-related parameter of the path after each search under each neighborhood operator;

[0153] a second determining submodule configured to determine, for each neighborhood operator, an optimal path-related parameter under the neighborhood operator from the second path-related parameter under the neighborhood operator, and, if the optimal path-related parameter is better than the first path-related parameter, determine the optimal path-related parameter as a third path-related parameter of the path under the neighborhood operator, so as to obtain a third path-related parameter of the path under each neighborhood operator;

[0154] A third determination submodule is configured to determine an optimal path-related parameter from the third path-related parameters of the paths under each of the neighborhood operators, and use the optimal path-related parameter as the target path-related parameter;

[0155] The fourth determination submodule is configured to determine the target planning path of the distribution capacity according to the target path related parameters.

[0156] Optionally, the fourth determining submodule includes:

[0157] The updating submodule is configured to, based on the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, determine that there are overdue orders in the orders involved in the path, calculate the path-related parameters of the path after inserting the item delivery task point of the order with the largest timeout into each feasible position after its current position, so as to obtain the optimal path-related parameters under backward search, and if the optimal path-related parameters are better than the target path-related parameters, update the target path-related parameters to the optimal path-related parameters; and / or, based on the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, calculate the path-related parameters of the path after inserting the pickup task point and / or item delivery task point of the order with the largest delivery margin in the orders involved in the path into each feasible position before the current position of the pickup task point and / or the item delivery task point, so as to obtain the optimal path-related parameters under forward search, and if the optimal path-related parameters are better than the target path-related parameters, update the target path-related parameters to the optimal path-related parameters;

[0158] A judgment submodule is configured to judge whether an update termination condition is met, wherein the update termination condition includes that the time for executing the task point path planning is less than or equal to a preset time;

[0159] a return submodule configured to, if the update termination condition is not met, return to the sorting submodule to execute the step of sorting the current delivery order and the order to be allocated according to their respective current target locations to obtain the planned path of the delivery capacity and the first path-related parameter of the planned path, until the update termination condition is met;

[0160] The fifth determination submodule is configured to determine the path corresponding to the target path related parameters as the target planning path of the distribution capacity when the update termination condition is met.

[0161] Optionally, the path-related parameters of the path include a distribution efficiency change parameter of the path relative to the currently planned path; accordingly, the device may further include:

[0162] The fifth determination module is configured to compare the delivery efficiency change parameters in the path-related parameters, and determine the path-related parameters with the smallest delivery efficiency change parameters as the optimal path-related parameters; or when the delivery efficiency change parameters are the same, compare the robustness characteristic parameters included in the path-related parameters, and determine the path-related parameters with the largest robustness characteristic parameters as the optimal path-related parameters.

[0163] Optionally, the device may further include:

[0164] A sixth determination module is configured to determine a target order with the largest timeout according to a fuzzy number of an estimated delivery time of each order involved in the path;

[0165] The seventh determination module is configured to determine the robustness characteristic parameter of the path according to the membership function of the fuzzy number of the estimated delivery time of the target order and the promised delivery time of the target order by the following formula:

[0166]

[0167] AI=[area(D(x)∩A(x))] / areaA(x)

[0168] Wherein, x is time, d is the promised delivery time of the target order, D(x) is the step function of the target order with respect to the promised time, A(x) is the membership function of the fuzzy number of the estimated delivery time of the target order, and AI is the robustness characteristic parameter of the path.

[0169] Optionally, there is one order to be allocated, and the order allocation module 705 is configured to compare the delivery efficiency change parameters of each delivery capacity, determine the delivery capacity with the smallest delivery efficiency change parameters as the optimal delivery capacity, and allocate the order to be allocated to the optimal delivery capacity; or when the delivery efficiency change parameters are the same, compare the robustness characteristic parameters of the target planning path of each delivery capacity, determine the delivery capacity corresponding to the target planning path with the largest robustness characteristic parameters as the optimal delivery capacity, and allocate the order to be allocated to the optimal delivery capacity.

[0170] Optionally, there are multiple orders to be allocated, and each of the delivery transport capacity has a target planning path for each of the orders to be allocated; accordingly, the order allocation module 705 may include:

[0171] A sixth determination submodule is configured to determine, among the plurality of target planning paths of the plurality of delivery transport capacities, the delivery transport capacity with the smallest delivery efficiency variation parameter as the optimal delivery transport capacity;

[0172] A seventh determination submodule is configured to determine, among the plurality of target planning paths of the optimal delivery capacity, a target planning path whose distribution efficiency change parameter difference with the minimum distribution efficiency change parameter is less than or equal to a preset difference as a candidate planning path;

[0173] The order allocation submodule is configured to compare the robustness characteristic parameters of the candidate planning paths and allocate the to-be-allocated orders corresponding to the candidate planning path with the largest robustness characteristic parameter to the optimal delivery capacity.

[0174] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0175] Figure 8 FIG. 1 is a block diagram of an electronic device according to an exemplary embodiment. Figure 8 As shown, the electronic device 800 may include: a processor 801 and a memory 802. The electronic device 800 may also include one or more of a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805.

[0176] The processor 801 is used to control the overall operation of the electronic device 800 to complete all or part of the steps in the above-mentioned order allocation method. The memory 802 is used to store various types of data to support the operation of the electronic device 800, and these data may include, for example, instructions for any application or method used to operate on the electronic device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the electronic device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 805 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0177] In an exemplary embodiment, the electronic device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned order allocation method.

[0178] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned order allocation method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the electronic device 800 to complete the above-mentioned order allocation method.

[0179] Fig. 9 900 is a block diagram of an electronic device according to an exemplary embodiment. For example, the electronic device 900 may be provided as a server. Fig. 9 The electronic device 900 includes a processor 922, which may be one or more, and a memory 932 for storing a computer program executable by the processor 922. The computer program stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 922 may be configured to execute the computer program to perform the above-mentioned order allocation method.

[0180] In addition, the electronic device 900 may further include a power supply component 926 and a communication component 950. The power supply component 926 may be configured to perform power management of the electronic device 900, and the communication component 950 may be configured to implement communication of the electronic device 900, for example, wired or wireless communication. In addition, the electronic device 900 may further include an input / output (I / O) interface 958. The electronic device 900 may operate based on an operating system stored in the memory 932, such as Windows Server 2008. TM , Mac OSX TM , Unix TM , Linux TM etc.

[0181] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned order allocation method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 932 including program instructions, and the above-mentioned program instructions can be executed by the processor 922 of the electronic device 900 to complete the above-mentioned order allocation method.

[0182] In another exemplary embodiment, a computer program product is also provided. The computer program product includes a computer program executable by a programmable device, and the computer program has a code portion for executing the above-mentioned order allocation method when executed by the programmable device.

[0183] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0184] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0185] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

Claims

1. An order allocation method, characterized in that: include: Determining multiple delivery transport capacities associated with the order to be assigned; According to the to-be-assigned orders and the currently planned path of each of the delivery transport capabilities, task point path planning is performed for each of the delivery transport capabilities to obtain a target planned path for each of the delivery transport capabilities; Obtaining a delivery efficiency parameter of each of the target planning paths, wherein the delivery efficiency parameter of the target planning path is determined based on a fuzzy number of random delivery feature information corresponding to the target planning path; Determine, based on the distribution efficiency parameter of the target planned path of the same distribution capacity and the distribution efficiency parameter of the current planned path, a distribution efficiency change parameter of the target planned path of the distribution capacity relative to the current planned path; Determine the optimal delivery capacity according to the delivery efficiency change parameter, and assign the to-be-assigned order to the optimal delivery capacity; The step of performing task point path planning for each of the delivery transport capabilities according to the orders to be allocated and the current planned path of each of the delivery transport capabilities to obtain the target planned path of each of the delivery transport capabilities includes: For each of the delivery transport capacities, determining the delivery priority of each current delivery order and the to-be-allocated order involved in the currently planned route; The orders are selected as target orders in order according to the delivery priorities, and the following steps are performed for the target orders: Determine the delivery parameters of the path according to the fuzzy number of the random delivery feature information corresponding to the path after the target order is inserted into each feasible position, wherein the feasible position is a position where the pickup task point that satisfies the target order is located before the item delivery task point, and the number of waybills of the delivery capacity at the position is less than or equal to the waybill threshold, and the delivery parameters include a delivery time parameter and / or a delivery distance parameter; Determine the location of the target order corresponding to when the delivery parameter is minimum as the current target location of the target order; According to the current target locations of the current delivery order and the order to be assigned, the delivery transport capacity is subjected to task point path planning to obtain a target planning path for each of the delivery transport capacity; The step of performing task point path planning for the delivery transport capacity according to the current target locations of the current delivery order and the order to be allocated, so as to obtain the target planning path of each delivery transport capacity, includes: Sort the current delivery order and the to-be-allocated order according to their respective current target locations to obtain a planned path for the delivery capacity and a first path-related parameter of the planned path; Searching the planned path by using a preset neighborhood operator to obtain a second path-related parameter of the path after each search under each neighborhood operator; For each neighborhood operator, determine the optimal path-related parameters under the neighborhood operator from the second path-related parameters under the neighborhood operator, and when the optimal path-related parameters are better than the first path-related parameters, determine the optimal path-related parameters as the third path-related parameters of the path under the neighborhood operator, so as to obtain the third path-related parameters of the path under each of the neighborhood operators; Determine an optimal path-related parameter from the third path-related parameters of the paths under each of the neighborhood operators, and use the optimal path-related parameter as the target path-related parameter; The target planning path of the distribution capacity is determined according to the target path related parameters.

2. The method according to claim 1, characterized in that The random delivery characteristic information includes at least one of the following: item preparation completion time; item pickup completion time; delivery capacity movement time; item delivery time.

3. The method according to claim 1, characterized in that The delivery efficiency parameters include: an overtime duration parameter and / or a delivery distance parameter, and the delivery efficiency change parameters include: a change in the overtime duration parameter and / or a change in the delivery distance parameter.

4. The method according to claim 3, characterized in that The timeout duration parameter is determined by the following steps: For each order involved in the planned route, determine a fuzzy number of the estimated delivery time of the order, and determine a fuzzy number of the timeout duration of the order according to the fuzzy number of the estimated delivery time and the promised delivery time of the order; A timeout duration parameter of the planned path is determined according to a fuzzy number of a timeout duration of each order involved in the planned path.

5. The method according to claim 1, characterized in that Determining the target planning path of the distribution capacity according to the target path related parameters includes: According to the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameter, when it is determined that there are overdue orders in the orders involved in the path, the path-related parameters of the path after inserting the item delivery task point of the order with the largest timeout into each feasible position after its current position are calculated to obtain the optimal path-related parameters under backward search, and when the optimal path-related parameters are better than the target path-related parameters, the target path-related parameters are updated to the optimal path-related parameters; and / or According to the fuzzy number of the estimated delivery time of each order involved in the path corresponding to the target path-related parameters, calculate the path-related parameters of the path after inserting the pickup task point and / or the item delivery task point of the order with the largest delivery margin among the orders involved in the path into each feasible position before the current position of the pickup task point and / or the item delivery task point, so as to obtain the optimal path-related parameters under the forward search, and if the optimal path-related parameters are better than the target path-related parameters, update the target path-related parameters to the optimal path-related parameters; Determine whether an update termination condition is met, wherein the update termination condition includes that the time for executing the task point path planning is less than or equal to a preset time; If the update termination condition is not met, returning to the step of sorting the current delivery order and the to-be-allocated order according to their respective current target locations to obtain the planned path of the delivery capacity and the first path-related parameters of the planned path, until the update termination condition is met; When the update termination condition is met, the path corresponding to the target path related parameters is determined as the target planning path of the distribution capacity.

6. The method according to claim 1, characterized in that The path-related parameters of the path include the distribution efficiency change parameters of the path relative to the currently planned path; accordingly, the optimal path-related parameters are determined by the following steps: comparing the delivery efficiency variation parameters among the path-related parameters, and determining the path-related parameter with the smallest delivery efficiency variation parameter as the optimal path-related parameter; or When the delivery efficiency variation parameters are the same, the robustness characteristic parameters included in the path-related parameters are compared, and the path-related parameter with the largest robustness characteristic parameter is determined as the optimal path-related parameter.

7. The method according to claim 6, characterized in that The robustness characteristic parameters of the path are determined by the following steps: Determine the target order with the largest timeout according to the fuzzy number of the estimated delivery time of each order involved in the path; According to the membership function of the fuzzy number of the estimated delivery time of the target order and the promised delivery time of the target order, the robustness characteristic parameter of the path is determined by the following formula: in, For time, is the promised delivery time of the target order, is a step function of the target order with respect to the commitment time, is the membership function of the fuzzy number of the estimated delivery time of the target order, is the robustness characteristic parameter of the path.

8. The method according to claim 1, characterized in that The order to be allocated is one, determining the optimal delivery capacity according to the delivery efficiency change parameter, and allocating the order to be allocated to the optimal delivery capacity, including: Compare the delivery efficiency change parameters of each delivery transport capacity, determine the delivery transport capacity with the smallest delivery efficiency change parameter as the optimal delivery transport capacity, and allocate the to-be-allocated order to the optimal delivery transport capacity; or When the distribution efficiency variation parameters are the same, the robustness characteristic parameters of the target planning paths of each distribution capacity are compared, the distribution capacity corresponding to the target planning path with the largest robustness characteristic parameter is determined as the optimal distribution capacity, and the orders to be distributed are allocated to the optimal distribution capacity.

9. The method according to claim 1, characterized in that: There are multiple orders to be allocated, and each of the delivery transport capacity has a target planning path for each order to be allocated; Accordingly, determining the optimal delivery capacity according to the delivery efficiency change parameter and allocating the to-be-allocated orders to the optimal delivery capacity includes: Among the multiple target planning paths of the multiple delivery transport capacities, determining the delivery transport capacity with the smallest delivery efficiency change parameter as the optimal delivery transport capacity; Among the multiple target planning paths of the optimal delivery capacity, the target planning path with a distribution efficiency change parameter whose difference with the minimum distribution efficiency change parameter is less than or equal to a preset difference is determined as a candidate planning path; The robustness characteristic parameters of the candidate planning paths are compared, and the to-be-allocated orders corresponding to the candidate planning path with the largest robustness characteristic parameter are allocated to the optimal delivery capacity.

10. An order distribution device, characterized in that: include: A first determination module is configured to determine a plurality of delivery transport capacities associated with the order to be allocated; A path planning module is configured to perform task point path planning for each of the delivery transport capabilities according to the to-be-assigned orders and the current planned path of each of the delivery transport capabilities, so as to obtain a target planned path for each of the delivery transport capabilities; An acquisition module is configured to acquire a delivery efficiency parameter of each of the target planning paths, wherein the delivery efficiency parameter of the target planning path is determined based on a fuzzy number of random delivery feature information corresponding to the target planning path; A second determination module is configured to determine a distribution efficiency change parameter of the target planned path of the distribution capacity relative to the current planned path based on the distribution efficiency parameter of the target planned path of the same distribution capacity and the distribution efficiency parameter of the current planned path; An order allocation module is configured to determine an optimal delivery capacity according to the delivery efficiency change parameter, and allocate the to-be-allocated order to the optimal delivery capacity; The step of performing task point path planning for each of the delivery transport capabilities according to the orders to be allocated and the current planned path of each of the delivery transport capabilities to obtain the target planned path of each of the delivery transport capabilities includes: For each of the delivery transport capacities, determining the delivery priority of each current delivery order and the to-be-allocated order involved in the currently planned route; The orders are selected as target orders in order according to the delivery priorities, and the following steps are performed for the target orders: Determine the delivery parameters of the path according to the fuzzy number of the random delivery feature information corresponding to the path after the target order is inserted into each feasible position, wherein the feasible position is a position where the pickup task point that satisfies the target order is located before the item delivery task point, and the number of waybills of the delivery capacity at the position is less than or equal to the waybill threshold, and the delivery parameters include a delivery time parameter and / or a delivery distance parameter; Determine the location of the target order corresponding to when the delivery parameter is minimum as the current target location of the target order; According to the current target locations of the current delivery order and the order to be assigned, the delivery transport capacity is subjected to task point path planning to obtain a target planning path for each of the delivery transport capacity; The step of performing task point path planning for the delivery transport capacity according to the current target locations of the current delivery order and the order to be allocated, so as to obtain the target planning path of each delivery transport capacity, includes: Sort the current delivery order and the to-be-allocated order according to their respective current target locations to obtain a planned path for the delivery capacity and a first path-related parameter of the planned path; Searching the planned path by using a preset neighborhood operator to obtain a second path-related parameter of the path after each search under each neighborhood operator; For each neighborhood operator, determine the optimal path-related parameters under the neighborhood operator from the second path-related parameters under the neighborhood operator, and when the optimal path-related parameters are better than the first path-related parameters, determine the optimal path-related parameters as the third path-related parameters of the path under the neighborhood operator, so as to obtain the third path-related parameters of the path under each of the neighborhood operators; Determine an optimal path-related parameter from the third path-related parameters of the paths under each of the neighborhood operators, and use the optimal path-related parameter as the target path-related parameter; The target planning path of the distribution capacity is determined according to the target path related parameters.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 9 are implemented.

12. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the steps of the method according to any one of claims 1 to 9.

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