Method, system, electronic device and storage medium for assigning a delivery task

Abstract

The embodiment of the present application provides a distribution task allocation method and system, wherein the method comprises the following steps: obtaining distribution information of a distribution task and distribution data of a fixed point in a pre-divided fixed circle range, generating a task priority corresponding to the distribution task according to the distribution data of the fixed point, the distribution information and task information for each fixed point distribution object; selecting an optimal fixed point distribution object from the plurality of fixed point distribution objects according to the task priority and a merchant priority, and allocating the distribution task to the optimal fixed point distribution object. The embodiment of the present application allocates the optimal fixed point distribution object to the distribution task according to the task priority of the distribution task to the fixed point distribution object and the merchant priority of the fixed point merchant to the fixed point distribution object, selects the optimal fixed point distribution object from the fixed point distribution object and the distribution task, and the fixed point merchant and the fixed point distribution object, avoids allocating the distribution task to the fixed point distribution object far away from the fixed point merchant, and improves the timeliness of the fixed point distribution object.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to a method, system, electronic device, and computer-readable storage medium for allocating delivery tasks. Background Technology

[0002] In the field of short-distance logistics and delivery, some merchants with high requirements for delivery service quality and timeliness will employ dedicated delivery personnel. These dedicated personnel will prioritize fulfilling the merchant's delivery tasks. When there are many delivery tasks that cannot be completed on time by dedicated personnel, delivery platforms will typically assign the tasks to registered dedicated delivery personnel to ensure timely delivery. For example, pairing merchants or food courts with high daily order volumes with delivery riders increases the riders' familiarity with the merchants, reduces the time spent waiting for orders, and thus improves the riders' delivery efficiency. This star-shaped delivery station model can effectively improve the riders' efficiency (number of orders delivered daily) or timeliness (number of orders delivered per hour), and combined with reduced delivery fees, effectively lower delivery costs.

[0003] Because a single-vendor on-site delivery model can lead to inefficiencies and fluctuations in orders, it's difficult to achieve optimal efficiency for delivery riders and a satisfactory experience for merchants. Specifically, too many riders result in low rider efficiency, while too few riders lead to a poor merchant experience, with significant peak-to-valley differences. Therefore, by comprehensively considering factors such as merchant location, user order distribution, and the size of the business district, merchants are merged, grouping similar merchants into a single business district to form an on-site cluster model.

[0004] The "marketplace model" uses clustering algorithms to group different merchants into market districts, addressing issues like excessive peak-to-valley differences inherent in the traditional "marketplace model." However, the delivery task allocation scheme based on the marketplace model simply clusters multiple merchants together. While this increases the number of delivery riders, it also easily leads to riders being too far from the merchants, resulting in reduced delivery times. Furthermore, if the peak times of merchants within a marketplace are the same, there's a high possibility of cross-assignment affecting delivery times, impacting both efficiency and customer experience. Summary of the Invention

[0005] In view of the above problems, embodiments of the present invention are proposed to provide a method, system, electronic device, and computer-readable storage medium for allocating delivery tasks that overcomes or at least partially solves the above problems.

[0006] To address the aforementioned problems, according to a first aspect of the present invention, a method for allocating delivery tasks is disclosed. The method includes: acquiring delivery data for multiple delivery points within a pre-defined designated area; the delivery data includes a list of delivery merchants and the merchant priority of each delivery merchant in the list, the delivery center location of each delivery point, and its delivery range; acquiring delivery information for the delivery task, the delivery information including delivery merchant information and delivery user information; for each delivery point, generating a task priority corresponding to the delivery task based on the delivery data, the delivery information, and the current task information of the delivery point; and selecting an optimal delivery point from the multiple delivery points based on the task priority and the merchant priority carried in the delivery merchant information, and allocating the delivery task to the optimal delivery point.

[0007] Optionally, generating the task priority of the stationed delivery object corresponding to the delivery task based on the stationed delivery data, the delivery information, and the current task information of the stationed delivery object includes: generating the current delivery route information of the stationed delivery object based on the stationed delivery data and the task information, and generating the expected delivery route information of the stationed delivery object based on the stationed delivery data, the delivery information, and the task information; calculating the priority constraint parameters of the stationed delivery object based on the current delivery route information and the expected delivery route information; the priority constraint parameters include at least one of the following: distance increment, time increment, whether timeout occurs, timeout probability, and whether it is a reserved delivery object; and determining the task priority based on the priority constraint parameters and the corresponding parameter threshold.

[0008] Optionally, determining the task priority based on the priority constraint parameters and the corresponding parameter thresholds includes: determining the task priority as the highest level when the distance increment is less than the corresponding distance increment threshold, the time increment is less than the corresponding time increment threshold, the timeout is not exceeded, the timeout probability is less than the corresponding timeout probability threshold, the reserved delivery object is a reserved delivery object, and the number of reserved delivery objects within the designated area is greater than the number of reserved delivery objects threshold.

[0009] Optionally, the determination step of the reserved delivery object includes: obtaining the maximum number of pickup and delivery points and the pickup and delivery point threshold of the stationed delivery object, or obtaining the idle time, the current time and the idle time period threshold of the stationed delivery object; the idle time is the time when the stationed delivery object completes all delivery tasks and returns to the delivery center location; if the maximum number of pickup and delivery points is less than the pickup and delivery point threshold, or if the time difference between the idle time and the current time is less than the idle time period threshold, then the stationed delivery object is determined to be the reserved delivery object.

[0010] Optionally, the step of dividing the service area includes: calculating the number of missing delivery targets for each candidate merchant in each time period; expanding the delivery range of each candidate merchant in each time period according to a preset delivery radius; calculating the reduction in the number of missing delivery targets for all candidate merchants covered after the expansion of the delivery range of each candidate merchant in each time period; and dividing the service area for each time period from each candidate merchant based on the reduction value and the corresponding reduction value threshold.

[0011] Optionally, calculating the number of missing delivery targets for each candidate merchant in each time period includes: dividing the order volume of each candidate merchant in each time period by the order volume completed by each delivery target in a unit of time to obtain the ideal number of delivery targets for each candidate merchant in each time period; and subtracting the existing number of delivery targets for each candidate merchant in each time period from the ideal number of delivery targets to obtain the number of missing delivery targets.

[0012] Optionally, calculating the reduction in the number of missing delivery targets for all candidate merchants after the delivery range of the candidate merchants is expanded in each time period includes: dividing the average number of orders of all candidate merchants by the number of orders completed by each delivery target within a unit of time to obtain the average number of delivery targets; and selecting the minimum value from the average number of delivery targets, the number of missing delivery targets of all candidate merchants, and the number of surplus delivery targets as the maximum value of the reduction.

[0013] According to a second aspect of the present invention, a delivery task allocation system is also disclosed. The system includes: a delivery data acquisition module, configured to acquire delivery data of multiple stationed delivery objects within a pre-divided stationing area; the stationed delivery data includes a list of stationed merchants and the merchant priority of each stationed merchant in the list, the delivery center location of the stationed delivery object, and the delivery range; a delivery information acquisition module, configured to acquire delivery information of the delivery task, the delivery information including delivery merchant information and delivery user information; a task priority generation module, configured to generate a task priority for each stationed delivery object based on the stationed delivery data, the delivery information, and the current task information of the stationed delivery object; and a delivery task allocation module, configured to select the optimal stationed delivery object from the multiple stationed delivery objects based on the task priority and the merchant priority carried in the delivery merchant information, and allocate the delivery task to the optimal stationed delivery object.

[0014] Optionally, the task priority generation module includes: a delivery route generation module, used to generate current delivery route information of the stationed delivery object based on the stationed delivery data and the task information, and to generate expected delivery route information of the stationed delivery object based on the stationed delivery data, the delivery information, and the task information; a constraint parameter calculation module, used to calculate priority constraint parameters of the stationed delivery object based on the current delivery route information and the expected delivery route information; the priority constraint parameters include at least one of the following: distance increment, time increment, whether timeout occurs, timeout probability, and whether it is a reserved delivery object; and a task priority determination module, used to determine the task priority based on the priority constraint parameters and the corresponding parameter thresholds.

[0015] Optionally, the task priority determination module is used to determine the task priority as the highest level when the distance increment is less than the corresponding distance increment threshold, the time increment is less than the corresponding time increment threshold, the timeout is not overdue, the timeout probability is less than the corresponding timeout probability threshold, the reserved delivery object is a reserved delivery object, and the number of reserved delivery objects within the stationing area is greater than the reserved delivery object number threshold.

[0016] Optionally, the delivery task allocation system further includes: a reserved delivery object determination module, used to determine whether a stationed delivery object is a reserved delivery object; the reserved delivery object determination module is used to obtain the maximum number of pickup and delivery points and the pickup and delivery point threshold of the stationed delivery object, or to obtain the idle time, current time and idle time period threshold of the stationed delivery object; the idle time is the time when the stationed delivery object completes all delivery tasks and returns to the delivery center location; if the maximum number of pickup and delivery points is less than the pickup and delivery point threshold, or if the time difference between the idle time and the current time is less than the idle time period threshold, then the stationed delivery object is determined to be the reserved delivery object.

[0017] Optionally, the delivery task allocation system further includes: a hierarchical stationing area division module, used to divide the stationing area range; the hierarchical stationing area division module includes: a delivery object shortage quantity calculation module, used to calculate the delivery object shortage quantity of each candidate stationing merchant in each time period; a delivery range expansion module, used to expand the delivery range of each candidate stationing merchant in each time period according to a preset delivery radius; a reduction value calculation module, used to calculate the reduction value of the delivery object shortage quantity of all candidate stationing merchants covered after the delivery range of the candidate stationing merchants in each time period is expanded; and a stationing area division module, used to divide the stationing area range of each time period from each candidate stationing merchant according to the reduction value and the corresponding reduction value threshold.

[0018] Optionally, the delivery object shortage calculation module is used to divide the order volume of each candidate stationed merchant in each time period by the order volume completed by each stationed delivery object in a unit time period to obtain the ideal number of stationed delivery objects for each candidate stationed merchant in each time period; and to subtract the existing number of stationed delivery objects for each candidate stationed merchant in each time period from the ideal number of stationed delivery objects to obtain the shortage number of delivery objects.

[0019] Optionally, the reduction value calculation module is used to divide the average number of orders of all the candidate on-site merchants by the number of orders completed by each on-site delivery object in a unit time to obtain the average number of delivery objects; and select the minimum value from the average number of delivery objects, the number of delivery objects missing of all the candidate on-site merchants, and the number of delivery objects that are surplus as the maximum value of the reduction value.

[0020] According to a third aspect of the present invention, an electronic device is also disclosed, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a delivery task allocation method as described in the first aspect.

[0021] According to a fourth aspect of the present invention, a computer-readable storage medium is also disclosed, on which a computer program is stored, which, when executed by a processor, implements the delivery task allocation method described in the first aspect.

[0022] Compared with the prior art, the technical solution provided by the embodiments of the present invention has the following advantages:

[0023] This invention provides a delivery task allocation scheme. Within a pre-defined service area, it acquires delivery data and task information for multiple designated delivery points. For each delivery point, a task priority is generated based on the delivery data, delivery information, and the current task information. The optimal delivery point is selected from the multiple points based on the task priority and the merchant priority information, and the delivery task is assigned to the optimal delivery point.

[0024] In this embodiment of the invention, when allocating delivery tasks, a task priority is generated for each delivery object within the designated service area. Then, based on the task priority and the merchant priority of the corresponding service area merchant, the optimal service area delivery object is selected, and the delivery task is ultimately assigned to the optimal service area delivery object. Compared to the traditional service area model, which randomly assigns delivery tasks to service area delivery objects with fewer orders, this embodiment of the invention assigns the optimal service area delivery object based on the task priority of the service area delivery object to the delivery task and the merchant priority of the service area merchant to the service area delivery object. This selection of the optimal service area delivery object from both the perspectives of service area delivery object and delivery task, and service area merchant and service area delivery object, avoids assigning delivery tasks to service area delivery objects that are too far away from the service area merchant, thereby improving the timeliness of service area delivery. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the steps of a delivery task allocation method according to an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram illustrating the positional relationship between the delivery recipients and merchants within the designated service area according to an embodiment of the present invention.

[0027] Figure 3 This is a schematic diagram illustrating the delivery range of a certain merchant stationed within the designated area and other merchants stationed within the designated area, according to an embodiment of the present invention.

[0028] Figure 4 This is a flowchart illustrating the steps for dividing the stationary area according to an embodiment of the present invention;

[0029] Figure 5This is a schematic diagram of the operation flow of a hierarchical stationary scheduling system according to an embodiment of the present invention;

[0030] Figure 6 This is a structural block diagram of a delivery task allocation system according to an embodiment of the present invention;

[0031] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0032] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] Reference Figure 1 This diagram illustrates a flowchart of a delivery task allocation method according to an embodiment of the present invention. This delivery task allocation method can be applied to a terminal or a server. Specifically, the delivery task allocation method may include the following steps:

[0034] Step 101: Obtain delivery data for multiple delivery points within the pre-defined designated delivery area.

[0035] In an embodiment of the present invention, the pre-defined service area includes multiple service points. This service area differs from those obtained through traditional clustering algorithms. The method for obtaining this service area will be described in detail later in this embodiment. Each service point within this service area is configured with its own designated delivery recipients. The delivery data for each delivery recipient may include a list of service points corresponding to the delivery recipient, as well as the service point priority of each service point in the list, the delivery center location of the delivery recipient, and its delivery range.

[0036] Step 102: Obtain delivery information for the delivery task.

[0037] In embodiments of the present invention, a delivery task can be understood as a new delivery task received by the delivery platform, which has not yet been assigned a delivery recipient. The delivery information for a delivery task may include delivery merchant information and delivery user information. In practical applications, delivery merchant information may include, but is not limited to, information about the delivery merchant, such as the merchant's name, address, rating, etc. Delivery user information may include, but is not limited to, information about the delivery user, who can be understood as the user who generated the delivery task. The delivery user's information may include the delivery user's name, contact information, address, goods, services, price, etc.

[0038] It should be noted that steps 101 and 102 can be executed simultaneously, or steps 101 can be executed first and then steps 102, or steps 102 can be executed first and then steps 101. This embodiment of the invention does not impose specific restrictions on the execution order of steps 101 and 102.

[0039] Step 103: For each stationed delivery object, generate the task priority corresponding to the delivery task based on the stationed delivery data, delivery information and the current task information of the stationed delivery object.

[0040] In an embodiment of the present invention, when the delivery platform receives a new delivery task, it can determine the delivery objects corresponding to each delivery task based on the relevant information of the merchants stationed in the delivery task. For each determined delivery object, a task priority is generated for each delivery object corresponding to the delivery task received by the delivery platform. That is, when the delivery platform receives a new delivery task, the task priority of each delivery object relative to the delivery task is different. The higher the task priority, the more likely the delivery platform is to assign the delivery task to the delivery object; the lower the task priority, the less likely the delivery platform is to assign the delivery task to the delivery object. For example, if the task priority of delivery object a1 corresponding to delivery task T1 is R1, and the task priority of delivery object b2 corresponding to delivery task T1 is R2, and the task priority R1 is greater than the task priority R2, then the delivery platform is more likely to assign delivery task T1 to delivery object a1.

[0041] Step 104: Select the optimal delivery target from multiple delivery targets based on the task priority and the merchant priority carried in the delivery merchant information, and assign the delivery task to the optimal delivery target.

[0042] In embodiments of the present invention, there may be multiple station delivery objects that correspond to the same task priority for delivery tasks. Moreover, the task priority is insufficient to determine which station delivery object is most suitable for the delivery task. It is necessary to select the most suitable station delivery object based on the task priority and the merchant priority, that is, to select the optimal station delivery object, and then assign the delivery task to the optimal station delivery object.

[0043] This invention provides a delivery task allocation scheme. Within a pre-defined service area, it acquires delivery data and task information for multiple designated delivery points. For each delivery point, a task priority is generated based on the delivery data, delivery information, and the current task information. The optimal delivery point is selected from the multiple points based on the task priority and the merchant priority information, and the delivery task is assigned to the optimal delivery point.

[0044] In this embodiment of the invention, when allocating delivery tasks, a task priority is generated for each delivery object within the designated service area. Then, based on the task priority and the merchant priority of the corresponding service area merchant, the optimal service area delivery object is selected, and the delivery task is ultimately assigned to the optimal service area delivery object. Compared to the traditional service area model, which randomly assigns delivery tasks to service area delivery objects with fewer orders, this embodiment of the invention assigns the optimal service area delivery object based on the task priority of the service area delivery object to the delivery task and the merchant priority of the service area merchant to the service area delivery object. This selection of the optimal service area delivery object from both the perspectives of service area delivery object and delivery task, and service area merchant and service area delivery object, avoids assigning delivery tasks to service area delivery objects that are too far away from the service area merchant, thereby improving the timeliness of service area delivery.

[0045] In a preferred embodiment of the present invention, one method for generating the task priority of a stationed delivery object corresponding to a delivery task based on stationed delivery data, delivery information, and the current task information of the stationed delivery object is as follows: The current delivery path information of the stationed delivery object is generated based on the stationed delivery data and task information, and the expected delivery path information of the stationed delivery object is generated based on the stationed delivery data, delivery information, and task information. The current delivery path information can be understood as the delivery path information of the stationed delivery object before the delivery task is assigned to it. The expected delivery path information can be understood as the delivery path information of the stationed delivery object after the delivery task is assigned to it. Priority constraint parameters for the stationed delivery object are calculated based on the current delivery path information and the expected delivery path information. These priority constraint parameters include, but are not limited to, distance increment, time increment, whether it times out, timeout probability, whether it is a reserved delivery object, etc. Then, the task priority is determined based on the priority constraint parameters and the corresponding parameter thresholds.

[0046] In a preferred embodiment of the present invention, one way to determine task priority based on priority constraint parameters and corresponding parameter thresholds is as follows: when the distance increment is less than the corresponding distance increment threshold, the time increment is less than the corresponding time increment threshold, the timeout is not exceeded, the timeout probability is less than the corresponding timeout probability threshold, the task is a reserved delivery object, and the number of reserved delivery objects within the designated area is greater than the reserved delivery object quantity threshold, the task priority is determined to be the highest level. It should be noted that the above determination of the task priority as the highest level is merely an illustrative example. In practical applications, task priorities can also be determined as the second highest, first level, second level, etc. The specific level of task priority can be determined based on the number of priority constraint parameters that meet the conditions. For example, if all priority constraint parameters meet their respective priority conditions, the task priority is set to the highest level; if none of the priority constraint parameters meet their respective priority conditions, the task priority is set to the lowest level. The more priority constraint parameters that meet the priority conditions, the higher the task priority level; the fewer priority constraint parameters that meet the priority conditions, the lower the task priority level.

[0047] In a preferred embodiment of the present invention, the determination step of the reserved object may include: obtaining the maximum number of pickup and delivery points and the pickup and delivery point threshold of the stationed delivery object, or obtaining the idle time, current time, and idle time period threshold of the stationed delivery object. The pickup and delivery point number refers to the number of each stationed merchant and the number of each delivery user included in the current delivery route information of the stationed delivery object. The idle time is the time when the stationed delivery object completes all delivery tasks and returns to the delivery center. If the maximum number of pickup and delivery points is less than the pickup and delivery point threshold, or if the time difference between the idle time and the current time is less than the idle time period threshold, then the stationed delivery object is determined to be a reserved delivery object. That is, for a stationed delivery object to be a reserved delivery object, it needs to meet any one of the following conditions:

[0048] Condition 1: The maximum number of pick-up and delivery points is less than the threshold number of pick-up and delivery points.

[0049] Condition 2: (Idle time - current time) < idle time period threshold.

[0050] In practical applications, the number of reserved delivery targets for a merchant can be predicted based on its order volume. For example, machine learning algorithms such as XGBoost (an optimized distributed gradient boosting library) can be used to predict the order volume of the merchant with the highest priority among the merchants being served, over a future period. The number of reserved delivery targets can then be calculated based on the predicted order volume. Specifically, the order volume of a merchant can be predicted for the next 15 minutes. This predicted order volume can then be multiplied by 4 to obtain the order volume for one hour. Finally, the order volume for one hour can be divided by the average delivery time of the corresponding merchant to obtain the number of reserved delivery targets for that merchant.

[0051] In a preferred embodiment of the present invention, the above-described method for dividing the service area ensures that, while guaranteeing shared access for delivery personnel, these personnel operate around their designated delivery center location, primarily responsible for their assigned merchants. This improves the delivery efficiency of the delivery personnel while satisfying the fulfillment experience of the merchants. (Refer to...) Figure 2 This diagram illustrates the locational relationship between delivery recipients and merchants within the designated service area. Figure 2 In this system, the merchants stationed at the central point are considered central delivery providers, while other merchants are considered non-central delivery providers. When a central delivery provider has no orders to assign to a delivery provider, or when a delivery provider can handle more orders, the delivery provider may accept orders from non-central delivery providers. (Refer to...) Figure 3 This diagram illustrates the delivery range of a specific merchant within a designated service area and its comparison with other merchants. Figure 3 In this case, the merchant stationed at the location is within the delivery range of the other three merchants stationed at the location.

[0052] Reference Figure 4 The steps for delineating the above-mentioned area of ​​the stationing zone may include:

[0053] Step 401: Calculate the number of missing delivery recipients for each candidate merchant in each time period.

[0054] In practical applications, when calculating the number of delivery recipients needed, the order volume of each candidate merchant in each time period can be divided by the order volume completed by each delivery recipient in a unit of time to obtain the ideal number of delivery recipients for each candidate merchant in each time period. Subtracting the current number of delivery recipients for each candidate merchant in each time period from the ideal number of delivery recipients gives the number of delivery recipients needed.

[0055] Step 402: For each candidate merchant, expand the delivery range of the candidate merchant in each time period according to the preset delivery radius.

[0056] In practical applications, expanding the delivery range of candidate merchants aims to cover more of them. Typically, the preset delivery radius is 1 kilometer, and in practice, it is less than 5 kilometers.

[0057] Step 403: Calculate the reduction in the number of missing delivery targets for all candidate merchants covered after the delivery range of the candidate merchants is expanded in each time period.

[0058] In practical applications, when calculating the reduction value, the average number of orders from all candidate vendors can be divided by the number of orders completed by each delivery recipient per unit time to obtain the average number of delivery recipients. The minimum value among the average number of delivery recipients, the number of delivery recipients missing from all candidate vendors, and the number of delivery recipients that are surplus is selected as the maximum reduction value.

[0059] Step 404: Based on the reduction value and the corresponding reduction value threshold, the area of ​​each time period is divided from each candidate station merchant.

[0060] In practical applications, if the reduction value is greater than the corresponding reduction value threshold, it means that after the delivery range of a candidate merchant is expanded, the number of missing delivery targets for all candidate merchants covered meets the preset expectation. In this case, the delivery range of the next candidate merchant in each time period will be expanded according to the preset delivery radius. If the reduction value is less than or equal to the corresponding reduction value threshold, the delivery range of the candidate merchant will remain unchanged, that is, the delivery range of the candidate merchant will not be expanded.

[0061] Based on the above description of an embodiment of a delivery task allocation method, a scheduling system based on hierarchical clustering is introduced below. Hierarchical clustering is the clustering scheme proposed in this embodiment of the invention, and is not the traditional clustering method that relies solely on clustering algorithms. (Refer to...) Figure 5 This paper presents a schematic diagram of the operation process of a scheduling system based on hierarchical stationary loops.

[0062] First, input the delivery data for multiple delivery points within the tiered delivery network. This data includes a list of participating merchants, the merchant priority of each merchant, the delivery center location of each delivery point, and its delivery range.

[0063] Secondly, obtain the delivery information of the delivery tasks, and generate a task priority corresponding to the delivery task for each station delivery object according to the evaluation algorithm.

[0064] In practical applications, we can first determine whether the delivery task is an order from a merchant within the tiered service area. If the delivery task is an order from a merchant within the tiered service area, we can then determine whether to assign the delivery task to a delivery object within the tiered service area. If the delivery task is assigned to a delivery object within the tiered service area, we can then determine whether the priority constraint parameters of the delivery object meet the preset constraints. If the priority constraint parameters do not meet the preset constraints, we can set the task priority of the delivery object corresponding to the delivery task to the lowest level. If the priority constraint parameters meet the preset constraints, we can set the task priority of the delivery object corresponding to the delivery task to the highest level.

[0065] Next, select the optimal station delivery target based on task priority and other delivery scores, and assign the delivery task to the optimal station delivery target.

[0066] In practical applications, when selecting the optimal delivery target based on the matching algorithm, if a certain delivery target has the highest priority for multiple delivery tasks, then the optimal delivery task is further selected from multiple delivery tasks and assigned to the optimal delivery target based on the merchant priority, delivery score, and distance score of the delivery merchant corresponding to each delivery task.

[0067] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0068] Reference Figure 6 This diagram illustrates a structural block diagram of a delivery task allocation system according to an embodiment of the present invention. This delivery task allocation system can be applied to a terminal or a server. Specifically, the delivery task allocation system may include the following modules:

[0069] The delivery data acquisition module 61 is used to acquire delivery data of multiple delivery objects at designated locations within a pre-defined designated area; the delivery data includes a list of merchants at designated locations and the merchant priority of each merchant in the list, the delivery center location of each delivery object, and its delivery range;

[0070] The delivery information acquisition module 62 is used to acquire delivery information for delivery tasks, the delivery information including delivery merchant information and delivery user information;

[0071] The task priority generation module 63 is used to generate a task priority for each of the stationed delivery objects based on the stationed delivery data, the delivery information, and the current task information of the stationed delivery object;

[0072] The delivery task allocation module 64 is used to select the optimal on-site delivery object from multiple on-site delivery objects according to the task priority and the merchant priority carried in the delivery merchant information, and allocate the delivery task to the optimal on-site delivery object.

[0073] In a preferred embodiment of the present invention, the task priority generation module 63 includes:

[0074] The delivery route generation module is used to generate the current delivery route information of the stationed delivery object based on the stationed delivery data and the task information, and to generate the expected delivery route information of the stationed delivery object based on the stationed delivery data, the delivery information and the task information;

[0075] The constraint parameter calculation module is used to calculate the priority constraint parameters of the stationed delivery object based on the current delivery route information and the expected delivery route information; the priority constraint parameters include at least one of the following: distance increment, time increment, whether timeout occurs, timeout probability, and whether it is a reserved delivery object.

[0076] The task priority determination module is used to determine the task priority based on the priority constraint parameters and the corresponding parameter thresholds.

[0077] In a preferred embodiment of the present invention, the task priority determination module is used to determine the task priority as the highest level when the distance increment is less than the corresponding distance increment threshold, the time increment is less than the corresponding time increment threshold, the timeout is not overdue, the timeout probability is less than the corresponding timeout probability threshold, the reserved delivery object is a reserved delivery object, and the number of reserved delivery objects within the stationing area is greater than the number threshold of reserved delivery objects.

[0078] In a preferred embodiment of the present invention, the delivery task allocation system further includes: a reserved delivery object determination module, used to determine whether a stationed delivery object is a reserved delivery object; the reserved delivery object determination module is used to obtain the maximum number of pickup and delivery points and the pickup and delivery point threshold of the stationed delivery object, or to obtain the idle time, the current time and the idle time period threshold of the stationed delivery object; the idle time is the time when the stationed delivery object completes all delivery tasks and returns to the delivery center location; if the maximum number of pickup and delivery points is less than the pickup and delivery point threshold, or if the time difference between the idle time and the current time is less than the idle time period threshold, then the stationed delivery object is determined to be the reserved delivery object.

[0079] In a preferred embodiment of the present invention, the delivery task allocation system further includes: a hierarchical stationing area division module, used to divide the stationing area range; the hierarchical stationing area division module includes:

[0080] The module for calculating the number of missing delivery objects is used to calculate the number of missing delivery objects for each candidate merchant in each time period.

[0081] The delivery range expansion module is used to expand the delivery range of each candidate merchant in each time period according to a preset delivery radius.

[0082] The reduction value calculation module is used to calculate the reduction value of the number of missing delivery objects for all the candidate station merchants after the delivery range of the candidate station merchants is expanded in each time period;

[0083] The stationing area division module is used to divide the stationing area range for each time period from each of the candidate stationing merchants based on the reduction value and the corresponding reduction value threshold.

[0084] In a preferred embodiment of the present invention, the missing quantity calculation module for delivery objects is used to divide the order volume of each candidate stationed merchant in each time period by the order volume completed by each stationed delivery object in a unit time period to obtain the ideal quantity of stationed delivery objects for each candidate stationed merchant in each time period; and to subtract the existing quantity of stationed delivery objects for each candidate stationed merchant in each time period from the ideal quantity of stationed delivery objects to obtain the missing quantity of delivery objects.

[0085] In a preferred embodiment of the present invention, the reduction value calculation module is used to divide the average number of orders of all the candidate station merchants by the number of orders completed by each station delivery object in a unit time to obtain the average number of delivery objects; and select the minimum value from the average number of delivery objects, the number of delivery objects missing of all the candidate station merchants and the number of delivery objects that are surplus as the maximum value of the reduction value.

[0086] This invention also provides an electronic device, see [link to relevant documentation]. Figure 7 The system includes: a processor 701, a memory 702, and a computer program 7021 stored in the memory 702 and executable on the processor 701. When the processor 701 executes the program 7021, it implements the delivery task allocation method of the aforementioned embodiment.

[0087] This invention also provides a readable storage medium storing a computer program that, when executed by a processor, implements the delivery task allocation method of the foregoing embodiments.

[0088] It should be noted that all actions involving the acquisition of signals, information, or data in the embodiments of the present invention are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

[0089] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0090] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0091] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0096] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0097] The above provides a detailed description of the delivery task allocation method and system provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for allocating delivery tasks, characterized in that, The method includes: Within a pre-defined designated area, obtain delivery data for multiple delivery points; the delivery data includes a list of delivery merchants and the merchant priority of each delivery merchant in the list, the delivery center location of each delivery point, and its delivery range. Obtain delivery information for the delivery task, which includes delivery merchant information and delivery user information; For each of the stationed delivery objects, a task priority corresponding to the delivery task is generated based on the stationed delivery data, the delivery information, and the current task information of the stationed delivery object, including: The current delivery route information of the stationed delivery object is generated based on the stationed delivery data and the task information, and the expected delivery route information of the stationed delivery object is generated based on the stationed delivery data, the delivery information and the task information. The priority constraint parameters of the stationed delivery object are calculated based on the current delivery route information and the expected delivery route information; the priority constraint parameters include at least one of the following: distance increment, time increment, whether timeout occurs, timeout probability, and whether it is a reserved delivery object. When the distance increment is less than the corresponding distance increment threshold, the time increment is less than the corresponding time increment threshold, the timeout is not exceeded, the timeout probability is less than the corresponding timeout probability threshold, the reserved delivery object is a reserved delivery object, and the number of reserved delivery objects within the designated area is greater than the number of reserved delivery objects threshold, the task priority is determined to be the highest level. Based on the task priority and the merchant priority carried in the delivery merchant information, the optimal on-site delivery object is selected from multiple on-site delivery objects, and the delivery task is assigned to the optimal on-site delivery object.

2. The method according to claim 1, characterized in that, The steps for determining the reserved delivery recipients include: Obtain the maximum number of pickup and delivery points and the threshold number of pickup and delivery points for the stationed delivery object; or, obtain the idle time, current time, and idle time period threshold for the stationed delivery object; the idle time is the time when the stationed delivery object completes all delivery tasks and returns to the delivery center location; If the maximum number of pickup and delivery points is less than the threshold number of pickup and delivery points, or if the time difference between the idle time and the current time is less than the idle time period threshold, then the stationed delivery object is determined to be the reserved delivery object.

3. The method according to claim 1, characterized in that, The steps for dividing the area of ​​the stationing zone include: Calculate the number of missing delivery recipients for each candidate merchant in each time period; For each of the candidate merchants, the delivery range of the candidate merchants in each time period is expanded according to the preset delivery radius; Calculate the reduction in the number of missing delivery targets for all the candidate merchants after their delivery range is expanded in each time period; The stationing range for each time period is determined from each of the candidate stationing merchants based on the reduction value and the corresponding reduction value threshold.

4. The method according to claim 3, characterized in that, The calculation of the number of missing delivery recipients for each candidate merchant in each time period includes: Divide the order volume of each candidate merchant in each time period by the order volume completed by each delivery object in a unit of time to obtain the ideal number of delivery objects for each candidate merchant in each time period. The missing quantity of delivery objects is obtained by subtracting the current quantity of delivery objects for each candidate merchant in each time period from the ideal quantity of delivery objects at the station.

5. The method according to claim 3, characterized in that, The calculation of the reduction in the number of missing delivery targets for all candidate merchants covered after the expansion of their delivery range in each time period includes: The average number of orders from all the candidate on-site merchants is divided by the number of orders completed by each on-site delivery object within a unit of time to obtain the average number of delivery objects. The minimum value among the average number of delivery objects, the number of delivery objects missing from all the candidate merchants, and the number of delivery objects surplus is selected as the maximum value of the reduction value.

6. A delivery task allocation system, characterized in that, The system includes: The delivery data acquisition module is used to acquire delivery data of multiple delivery points within a pre-defined area; the delivery data includes a list of delivery points and the priority of each delivery point in the list, the delivery center location and delivery range of each delivery point. The delivery information acquisition module is used to acquire delivery information for delivery tasks, which includes delivery merchant information and delivery user information; The task priority generation module is used to generate a task priority for each of the stationed delivery objects based on the stationed delivery data, the delivery information, and the current task information of the stationed delivery object; The delivery task allocation module is used to select the optimal on-site delivery object from multiple on-site delivery objects based on the task priority and the merchant priority carried in the delivery merchant information, and to allocate the delivery task to the optimal on-site delivery object.

7. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the delivery task allocation method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the method for allocating delivery tasks as described in any one of claims 1 to 5.

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