CN111598486B - Task grouping method, platform, server and storage medium - Google Patents

Task grouping method, platform, server and storage medium Download PDF

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CN111598486B
CN111598486B CN202010518064.6A CN202010518064A CN111598486B CN 111598486 B CN111598486 B CN 111598486B CN 202010518064 A CN202010518064 A CN 202010518064A CN 111598486 B CN111598486 B CN 111598486B
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魏钰衡
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Lazas Network Technology Shanghai Co Ltd
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Abstract

The embodiment of the invention relates to the technical field of information processing, and discloses a task grouping method, a platform, a server and a storage medium, wherein the task grouping method comprises the following steps: combining all the tasks to obtain all the task combinations; sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps: determining whether the currently traversed task combination does not include the task of which the grouping is completed; in response to the currently traversed task combination not including the task of which the grouping is completed, acquiring each remaining task except the currently traversed task combination and the task of which the grouping is completed from each task; traversing each residual task in sequence, adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; the tasks in the task grouping are tasks which are already grouped, and task distribution can be performed based on the obtained task grouping by performing task grouping, so that distribution efficiency is improved.

Description

Task grouping method, platform, server and storage medium
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a task grouping method, a platform, a server, and a storage medium.
Background
Currently, in the field of real-time delivery, when a degree platform performs order scheduling, the order scheduling is performed according to a single order, that is, after a user places an order, a server distributes the order to delivery personnel for delivery.
However, the inventors found that there are at least the following problems in the related art: when the number of orders is large, if a single order is allocated, the allocation efficiency is low.
Disclosure of Invention
The embodiment of the invention aims to provide a task grouping method, a platform, a server and a storage medium, which enable task distribution to be performed based on the obtained task grouping by performing task grouping, and are beneficial to improving distribution efficiency.
In order to solve the above technical problems, an embodiment of the present invention provides a task grouping method, including: combining all the tasks to obtain all the task combinations; sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps: determining whether the currently traversed task combination does not include the task of which the grouping is completed; acquiring each residual task except the currently traversed task combination and the task which is completed in the group from each task in response to the currently traversed task combination not including the task which is completed in the group; sequentially traversing each residual task, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group.
The embodiment of the invention also provides a task grouping platform, which comprises the following components: the combination module is used for combining all the tasks to obtain all the task combinations; the first traversing module is used for sequentially traversing task combinations meeting preset traversing conditions and executing the following steps: determining whether the currently traversed task combination does not include the task of which the grouping is completed; acquiring each residual task except the currently traversed task combination and the task which is completed in the group from each task in response to the currently traversed task combination not including the task which is completed in the group; the second traversing module is used for traversing each residual task in sequence, adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group.
The embodiment of the invention also provides a server, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes when running the program: combining all the tasks to obtain all the task combinations; sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps: determining whether the currently traversed task combination does not include the task of which the grouping is completed; acquiring each residual task except the currently traversed task combination and the task which is completed in the group from each task in response to the currently traversed task combination not including the task which is completed in the group; traversing each residual task in sequence, adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group.
Embodiments of the present invention also provide a non-volatile storage medium storing a computer-readable program for causing a computer to execute the task grouping method described above.
Compared with the prior art, the embodiment of the invention has the main differences and effects that: each task is combined, each task combination meeting the preset traversing condition is traversed in sequence, and each traversing to one task combination is executed once, and the following steps are executed: determining whether the currently traversed task combination does not comprise the task with the completed group, responding to the fact that the currently traversed task combination does not comprise the task with the completed group, acquiring each residual task except the currently traversed task combination and the task with the completed group from each task, sequentially traversing each residual task, and adding the traversed residual task meeting the preset condition into the currently traversed task combination to obtain the task group; wherein the tasks in the task group are tasks that have completed the group. That is, each time a task combination that does not include a task that has completed a group is traversed, it is determined whether there are tasks that can be added to the task combination currently traversed among the remaining tasks that have not completed a group, thereby achieving task grouping. By performing task grouping, task distribution can be performed based on the obtained task grouping, and distribution efficiency is improved.
In addition, the sequentially traversing each residual task, adding the traversed residual task meeting the preset condition into the currently traversed task combination to obtain a task group, including: and traversing each residual task in sequence, adding the residual tasks meeting the preset conditions into the currently traversed task combination to obtain task groups, and stopping traversing until the number of tasks in the task groups reaches the preset task upper limit. By setting the time for stopping the traversal, namely stopping the traversal when the number of tasks in the task group reaches the preset upper limit of the tasks, the upper limit value of the number of the tasks in the task group can be set according to actual needs, and task grouping according to actual needs is facilitated.
In addition, the similarity of any two tasks in the task combination meeting the preset traversal condition is larger than the preset first similarity. Because the task with lower similarity is less likely to be finally divided into one task group, the similarity of any two tasks in the task combination meeting the preset traversal condition is larger than the preset first similarity, so that the method is beneficial to reducing the number of traversed task combinations and improving the traversal speed while not affecting the rationality of the task group, and further improves the task group speed.
In addition, each task is combined to obtain each task combination, which comprises the following steps: each task is combined pairwise to obtain each task combination; the sequentially traversing task combinations meeting preset traversing conditions comprises the following steps: calculating the similarity of each task combination; the similarity of each task combination is the similarity between two tasks in each task combination; and traversing task combinations meeting preset traversing conditions in sequence according to the similarity of the task combinations. That is, the tasks are combined in pairs at the beginning, so that the number of the combinations is not increased due to the increase of the upper limit of the tasks (i.e. the increase of the number of the tasks in each group), the complexity of the combination is reduced, and the complexity of calculating the similarity of each task pair is reduced, so that the complexity of the whole task group is reduced. The embodiment of the invention can not increase the task grouping complexity under the condition of improving the task upper limit of the task grouping; therefore, the processing burden of the server when task grouping is carried out can be reduced, and the processing efficiency is improved.
In addition, according to the similarity of each task combination, sequentially traversing the task combinations meeting the preset traversing condition, including: dividing the task combinations meeting the preset traversal conditions into a plurality of preset similarity intervals according to the similarity of each task combination; wherein the plurality of similarity intervals are divided by continuous numerical intervals; acquiring the arrangement sequence of the multiple similarity intervals; wherein, in the multiple similarity intervals, the higher the upper limit value, the earlier the arrangement order is; traversing task combinations meeting preset traversing conditions in the similarity intervals from front to back in sequence according to the arrangement sequence of the similarity intervals; wherein, the traversing sequence of the task combination which is divided into the same similarity interval and meets the preset traversing condition is random. Considering the small difference of the similarity, the task combination meeting the preset traversing condition in the similarity intervals is sequentially traversed from front to back according to the arrangement sequence of the similarity intervals. The traversing sequence of the task combinations which are divided into the same similarity interval and meet the preset traversing condition is random, namely the task combinations (task combinations with tiny similarity differences) falling in the same similarity interval are not further ordered, so that the speed of ordering is improved, the complexity of ordering is reduced, the complexity of task grouping is reduced, and the efficiency of task grouping is improved. In addition, the task combinations falling within the same similarity interval can be considered to have little difference in similarity, so that the influence thereof on the grouping result can be ignored.
In addition, the preset conditions include: the similarity between the traversed residual tasks and each task in the currently traversed task combination is larger than a preset second similarity; the tasks are combined in pairs, and the number of the obtained task combinations isWherein n is the total number of tasks to be combined pairwise; after the similarity of each task combination is calculated, the method further comprises the following steps: storing the similarity of each task combination; the similarity between the traversed remaining tasks and each task in the currently traversed task combination is obtained by: searching each any of the traversed residual task and the currently traversed task combination according to the stored similarity of the various task combinationsSimilarity of transactions. That is, all possible combination modes of combining each task two by two are obtained, and then after the similarity of each task combination is calculated, the similarity of each task combination is stored, so that the similarity of the residual task which is traversed and all tasks in the task combination which is traversed currently can be directly searched from the similarity of each task combination which is calculated previously. The similarity of all task combinations calculated at the beginning is fully utilized, and the direct searching of the similarity is beneficial to improving the speed of determining whether the traversed residual tasks can be added into the currently traversed task combination, so that the speed of completing task grouping is beneficial to improving.
Drawings
FIG. 1 is a flow chart of a task grouping method provided in accordance with a first embodiment of the present application;
FIG. 2 is a flow chart of a task grouping method provided in accordance with a second embodiment of the present application;
FIG. 3 is a flow chart of a task grouping method provided in accordance with a third embodiment of the present application;
FIG. 4 is a flow chart of a task grouping method provided in accordance with a fourth embodiment of the present application;
FIG. 5 is a schematic diagram of a task grouping platform provided in accordance with a fifth embodiment of the present application;
fig. 6 is a schematic structural diagram of a server according to a sixth embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the following detailed description of the embodiments of the present application will be given with reference to the accompanying drawings. However, those of ordinary skill in the art will understand that in various embodiments of the present application, numerous technical details have been set forth in order to provide a better understanding of the present application. However, the claimed application may be practiced without these specific details and with various changes and modifications based on the following embodiments. The following embodiments are divided for convenience of description, and should not be construed as limiting the specific implementation of the present application, and the embodiments can be mutually combined and referred to without contradiction.
The first embodiment of the invention relates to a task grouping method which is applied to a server. The application scenario of this embodiment can be understood as: when task allocation is performed, all tasks to be allocated are generally grouped first, and tasks allocated in the same task group are assigned to task executors at one time. The tasks may be a pick task or a delivery task, for example: order picking, order distribution, etc., however, the task in this embodiment is merely exemplified above, and the present invention is not limited thereto. Taking the delivery order as an example, in the process of assigning the delivery order, reasonable order grouping is beneficial to improving the order taking willingness of a rider, improving the order taking efficiency, shortening the order taking time length and reducing the delivery cost to a certain extent. Order grouping is part of the overall order assignment process, which can be significantly impacted if the complexity of the grouping is high, the assignment is delayed for a few seconds. The embodiment provides a task grouping method, which can not increase the complexity of task grouping under the condition of improving the upper limit of tasks of task grouping, so that the processing burden of a server in task grouping can be reduced, and the processing efficiency is improved.
The following details of implementation of the task grouping method of this embodiment are specifically described, and the following details are provided only for facilitating understanding, and are not necessary to implement this embodiment.
The flow chart of the task grouping method in this embodiment may refer to fig. 1, including:
step S101, combining the tasks to obtain the task combinations.
Wherein each task is a task to be assigned, which needs task grouping.
In one example, a server may receive task messages from various clients, which may be packetized into data packets at the clients and sent to the server by way of wireless communication. In this embodiment, the server obtains each task to be assigned, which needs to be task-grouped, according to the received task messages from each client.
Specifically, the mode of combining each task may be two-by-two combination, that is, one task combination includes two tasks; three combinations are also possible, i.e. three tasks are included in one task combination. However, in a specific implementation, the combination may be set according to actual needs, which is not specifically limited in this embodiment.
Step S102, sequentially traversing task combinations meeting preset traversing conditions, and determining whether the currently traversed task combinations do not comprise tasks which are already grouped; if yes, step S103 is performed, otherwise step S105 is performed.
In one example, the task combination that satisfies the preset traversal condition may be: combining all the resulting task combinations.
In another example, the similarity of any two tasks in the task combination that satisfies the preset traversal condition is greater than the preset first similarity. The preset first similarity can be set according to actual needs. For example, the task combination includes two tasks, and if the similarity between the two tasks in the task combination is greater than the first similarity, the task combination is a task combination that satisfies a preset traversal condition. For another example, more than two tasks in the task combination, for example, the task combination includes three tasks, and if the similarity between any two tasks in the three tasks is greater than the first similarity, the task combination is a task combination that satisfies a preset traversal condition.
In a specific implementation, the manner of calculating the similarity between two tasks may be as follows: and calculating the similarity between the two tasks according to the characteristic data of the two tasks. After receiving the task message sent by each client, the server may parse the received task message by at least one processor, thereby obtaining feature data of the task.
In one example, the task is a delivery task, and the feature data of the delivery task may include at least one of: the distribution task dimension feature data, the user dimension feature data, the merchant dimension feature data and the objective factor dimension feature data. And the similarity between two distribution tasks is calculated by considering various factors, so that the method is more comprehensive and accurate. The feature data of the distribution task dimension can comprise distribution distance, distribution route, distribution time period and the like; the feature data of the merchant dimension may include location information of the merchant and the feature data of the user dimension may include location information of the user. In addition, the feature data of the user dimension and the feature data of the merchant dimension may further include information that can represent the distribution difficulty level, for example, information such as whether the user address or the vicinity of the merchant address can be ridden, whether there is an elevator, or the like. The characteristic data of the objective factor dimension may include data of time, weather, and the like.
In one example, the manner of calculating the similarity of the task combination according to the feature data of two tasks in the task combination may be: and calling a pre-trained similarity model, inputting the characteristic data of each of the two tasks in the task combination into the similarity model, and outputting the similarity of the two tasks by the similarity model. Wherein, the value range of the similarity is usually between 0 and 1.
In one example, where the task is a distribution task, the training data for the similarity model may include: characteristic data of a delivery task carried by the specific delivery end, characteristic data of a next delivery task assigned by the specific delivery end, and a true refusing result of the specific delivery end on the next delivery task; the specific delivery end is a delivery end with the carried delivery task quantity of 1 determined from the historical data. And selecting the delivery end with the carried delivery task quantity of 1 determined in the historical data, and training the similarity model by considering the actual refusal result of the specific delivery end on the next delivery task, so that the training result of the model is more accurate. In another example, the training data for similarity may further include: characteristic data of a selected delivery task of the multi-single delivery end, characteristic data of a next delivery task assigned by the multi-single delivery end, and a true rejection result of the multi-single delivery end on the next delivery task; the multi-list delivery end is a delivery end with the carried delivery task quantity being more than 1, which is determined from the historical data, and the selected delivery task is a selected delivery task in a plurality of delivery tasks carried by the multi-list delivery end.
In a specific implementation, according to the feature data of two delivery tasks, the manner of calculating the similarity between the delivery tasks may be: and scoring the similarity according to a preset scoring strategy and characteristic data of the two distribution tasks. The scoring strategies may include different scoring items, such as scoring items for the dispensing task dimension, scoring items for the user dimension, scoring items for the merchant dimension, and scoring items for the objective factor dimension. According to the feature data of the distribution task dimension, calculating to obtain the similarity score of the scoring items of the two distribution tasks based on the distribution task dimension, and similarly, according to the feature data of other dimensions of the two distribution tasks, calculating to obtain the similarity score of the scoring items of the two distribution tasks based on other dimensions. And finally, carrying out weighting processing on the similarity scores of the scoring items of each dimension, and taking the similarity scores after the weighting processing as the calculated similarity between the two distribution tasks. The weighting coefficients corresponding to the scoring items of each dimension can be set according to actual needs.
In this embodiment, a task combination satisfying a preset traversal condition may be selected from the task combinations obtained by the combination, and then the task combinations satisfying the preset traversal condition may be traversed sequentially, and it is determined whether the task combination currently traversed does not include the task that has completed the grouping. If the task combination currently traversed does not include the task of which the grouping has been completed, step S103 is performed, otherwise step S105 is performed.
Step S103, in response to the currently traversed task combination not including the task of which the grouping is completed, acquiring each remaining task except the currently traversed task combination and the task of which the grouping is completed from each task.
In one example, if it is determined that the currently traversed task combination does not include a task that has completed the group, each remaining task other than the currently traversed task combination and the task that has completed the group may be obtained from each task. The remaining tasks are also understood to be tasks which are not grouped, except for the tasks in the currently traversed task combination, which are acquired from the individual tasks.
Step S104, traversing each residual task in sequence, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group.
Wherein the tasks in the task group are tasks that have completed the group.
Specifically, each time a residual task is traversed, whether the traversed residual task meets a preset condition or not can be judged, if so, the residual task is added into a task combination traversed currently to obtain a task group; if the preset condition is not met, continuing to traverse the next remaining task.
In one example, all remaining tasks may be traversed all the way through. That is, the conditions for stopping traversing the remaining orders are: all remaining tasks are traversed.
In another example, the traversal may be stopped upon determining that the number of tasks in the task group reaches a preset upper task limit. That is, the conditions for stopping traversing the remaining orders are: the number of tasks in the task group reaches a preset task upper limit. The preset upper limit of tasks can be set according to actual needs and is used for representing the maximum number of tasks allowed to exist in one task group. In a specific implementation, when determining the preset task upper limit, the total number of tasks to be grouped and the number of task executors capable of executing the tasks may be combined. The preset task upper limit can be dynamically adjusted according to actual needs, for example, under the conditions that the total number of tasks is large and the number of task executors is small, the preset task upper limit can be properly adjusted; the total number of tasks is small, and the preset upper limit of tasks can be properly reduced under the condition that the number of task executors is large. Taking a delivery order as an example, a task executor takes a rider as an example, when the total number of delivery orders is larger than the preset order number, and the number of the distributable riders is smaller than the preset rider number, the preset upper limit of the orders can be properly adjusted to enable more orders to be contained in one order group, so that the quick order distribution is facilitated. By setting the time for stopping the traversal, namely stopping the traversal when the number of tasks in the task group reaches the preset upper limit of the tasks, the upper limit value of the number of the tasks in the task group can be set according to actual needs, and task grouping according to actual needs is facilitated.
In a specific implementation, if the currently traversed task combination does not include the task that has completed the grouping, the currently traversed task combination may be referred to as a target task combination, and for one traversed target task combination, after determining that the condition for stopping traversing the remaining orders is met, the traversed remaining tasks meeting the preset condition are added to the target task combination to obtain a task grouping.
In one example, the preset condition includes at least one of:
preset condition 1: the similarity between each task in the traversed residual task and the current traversed task combination is larger than a preset second similarity;
preset condition 2: the distribution included angle between the traversed residual tasks and each task in the currently traversed task combination is smaller than a preset included angle; the task is a delivery task, and the delivery included angle is determined according to the delivery route corresponding to the traversed residual task and the delivery route corresponding to the task in the currently traversed task combination;
preset condition 3: the difference between the distribution distance corresponding to the traversed residual task and the distribution distance corresponding to each task in the currently traversed task combination is smaller than a preset first distance; wherein the task is a delivery task;
Preset condition 4: the distance between the distribution starting place corresponding to the traversed residual task and the distribution starting place corresponding to each task in the currently traversed task combination is smaller than a preset second distance; wherein the task is a delivery task.
Specifically, the preset second similarity mentioned in the preset condition 1 may be set according to actual needs.
In one example, the tasks are combined in step S101, and the number of task combinations obtained isI.e. each task is combined two by two, resulting in all possible combinations. The similarity of each task combination is stored after the similarity of each task combination is calculated. Therefore, when determining the similarity between each task in the traversed residual task and the currently traversed task combination, the similarity of the traversed residual task and the task in the currently traversed task combination can be directly searched according to the stored similarity of each task combination. For example, a task combination of each of the remaining tasks and the task combinations currently traversed may be determined first. And then, searching the similarity of the task combination consisting of the rest tasks and each task in the current traversing task combination according to the stored similarity of each task combination. Finally, judging whether the similarity of the searched task combination consisting of the residual task and each task in the currently traversed task combination is larger than a preset second similarity; if yes, the traversed residual task is indicated to meet the preset condition 1. That is, all possible combination modes of combining each task two by two are obtained, and then after the similarity of each task combination is calculated, the similarity of each task combination is stored, so that the similarity of the residual task which is traversed and all tasks in the task combination which is traversed currently can be directly searched from the similarity of each task combination which is calculated previously. The similarity of all task combinations calculated at the beginning is fully utilized, and the direct searching of the similarity is beneficial to improving the speed of determining whether the traversed residual tasks can be added into the currently traversed task combination, so that the speed of completing task grouping is beneficial to improving.
In another example, referring to the above manner of calculating the similarity between the two tasks, the similarity between the traversed remaining task and each task in the currently traversed task combination may be directly calculated, and whether the similarity of the task combination formed by the calculated remaining task and each task in the currently traversed task combination is greater than the preset second similarity may be determined; if yes, the traversed residual task is indicated to meet the preset condition 1.
It should be noted that, in order to ensure that the similarity between each task in the finally obtained task group is relatively high, so as to improve the effectiveness and practicality of the task group, the requirement on whether the traversed remaining tasks can be added to the currently traversed task combination is generally relatively strict, so that the preset second similarity can be set relatively large, and the specific size of the preset second similarity can be set by a person skilled in the art according to actual needs, which is not limited in this embodiment.
Specifically, the preset included angle mentioned in the preset condition 2 may be set according to actual needs. The task is a delivery task, and it can be understood that the delivery task has a corresponding delivery start location, such as a merchant location, and a delivery destination location, such as a user location, and a delivery route corresponding to the delivery task is: the delivery task corresponds to a line between a delivery start location and a delivery destination location, such as a line between a merchant location and a user location. Therefore, the distribution route corresponding to the traversed residual task can be obtained, and the distribution route corresponding to each task in the currently traversed task combination can be obtained. And then obtaining the distribution included angle between the distribution route corresponding to the traversed residual task and the distribution route corresponding to each task in the currently traversed task combination. It can be understood that each task in the traversed remaining task and the current traversed task combination has a corresponding distribution included angle, and if it is determined that the distribution included angle of each task in the traversed remaining task and the current traversed task combination is smaller than a preset included angle, it can be determined that the traversed remaining task meets the preset condition 2.
Specifically, the preset first distance mentioned in the preset condition 3 may be set according to actual needs. The task is a delivery task, and it can be understood that the delivery task has a corresponding delivery start position, such as a merchant position, and a delivery destination position, such as a user position, and the delivery distance corresponding to the delivery task is: the delivery distance between the delivery start position and the delivery destination position, such as the distance between the merchant position and the user position. Therefore, the distribution distance corresponding to the traversed residual tasks can be obtained, and the distribution distance corresponding to each task in the currently traversed task combination can be obtained. And then obtaining the difference between the distribution distance corresponding to the traversed residual task and the distribution distance corresponding to each task in the currently traversed task combination. It may be appreciated that each task in the traversed remaining task and the currently traversed task combination has a corresponding difference in distribution distance, and if it is determined that the difference in distribution distance between the traversed remaining task and each task in the currently traversed task combination is smaller than the preset first distance, it may be determined that the traversed remaining task meets the preset condition 3.
Specifically, the preset second distance mentioned in the preset condition 4 may be set according to actual needs. The task is a delivery task, and it is understood that the delivery task has a corresponding delivery start location, such as a merchant location. Therefore, the distribution start position corresponding to the traversed residual task can be obtained, and the distribution start position corresponding to each task in the currently traversed task combination can be obtained. And then acquiring the distance between the distribution starting place corresponding to the traversed residual task and the distribution starting place corresponding to each task in the currently traversed task combination. It may be appreciated that each task in the traversed remaining task and the currently traversed task combination has a distance between corresponding delivery start points, and if it is determined that the distance between the traversed remaining task and the delivery start point of each task in the currently traversed task combination is smaller than the preset second distance, it may be determined that the traversed remaining task satisfies the preset condition 4.
In a specific implementation, if the currently traversed task combination includes tasks which have completed grouping, processing is not performed on the currently traversed task combination, and the next task combination is traversed continuously.
Step S105: determining whether all task combinations meeting preset traversal conditions are traversed; if so, the flow ends, otherwise, the step S102 is continued to be executed, and the next task combination is continued to be traversed.
That is, when it is determined that all task combinations satisfying the preset traversal condition have not been traversed, the next task combination is then traversed until all task combinations satisfying the preset traversal condition have been traversed.
After all task combinations meeting the preset traversal conditions are traversed, a plurality of task groups can be obtained, and the remaining tasks which are not divided into any task group can be respectively self-organized into a group, and each self-organized into a group means that one task group comprises one task. Finally, all task groups can be integrated, namely, the results of the task groups are summarized, and the flow of the whole task groups is ended.
In the following, the present embodiment is explained by using a specific example, in this embodiment, for convenience of explanation, the task grouping is required to be performed on 5 tasks currently, and it is understood that, in a specific implementation, the number of tasks required to be performed on the task grouping is far greater than 5. After the 5 tasks are combined, 10 task combinations are obtained, and if the similarity of any two tasks in the task combinations meeting the preset traversal condition is greater than 0.8, the 9 task combinations except the task combination 10 are traversed in sequence because the similarity of the task combination 10 (task 1, task 3) is less than 0.8. The similarity calculation results of the 9 task combinations are shown in table 1:
TABLE 1
Task assembly Similarity degree
Task combination 1 (task 1, task 2) 0.99
Task combination 2 (task 1, task 5) 0.98
Task combination 3 (task 2, task 5) 0.97
Task combination 4 (task 3, task 4) 0.96
Task combination 5 (task 2, task 4) 0.9
Task combination 6 (task 1, task 4) 0.89
Task combination 7 (task 3, task 5) 0.84
Task combination 8 (task 2, task 3) 0.83
Task combination 9 (task 4, task 5) 0.81
Task group 1 and task group 9 may be sequentially traversed according to the similarity from large to small, and it may be understood that since task group 1 is the first task group traversed, i.e., the traversed task group 1 does not include the task that has already been grouped, the task group 1 currently traversed may be referred to as the target task group. Then, obtaining each remaining task except the target task combination, namely the currently traversed task combination 1 and the task which has completed grouping, from 5 tasks, traversing the tasks 3 to 5 in sequence according to the fact that the remaining tasks are the tasks 3 to 5, and adding the task 5 into the task combination 1 to obtain a task group 1 if the determined remaining tasks which meet the preset condition are the tasks 5, wherein the task group 1 comprises: task 1, task 2, task 5.
Then traversing task group 2, since task 1 and task 5 in task group 2 have both completed the grouping and have been divided into task group 1 described above, it may be determined that task group 2 is not the target task group, and task group 2 is disassembled. Wherein, from the perspective of the server, the task combination 2 is disassembled, which is understood as not processing the task combination 2.
Then traversing task group 3, since task 2 and task 5 in task group 3 have completed the grouping, and have been divided into task group 1 described above, it can be determined that task group 3 is not the target task group, and task group 3 is disassembled.
Then traversing the task group 4, wherein the task 3 and the task 4 in the task group 4 are tasks which are not grouped, the task group 4 which is traversed currently can be called as a target task group, and when no task which is not grouped is existed at the moment, the task 3 and the task 4 in the task group 4 are taken as tasks in the task group 2. It will be appreciated that if there are remaining tasks at this time, it will be continued to determine whether the remaining tasks meet the preset condition, i.e. whether they can be added to the task combination 4.
Finally, task group 1 and task group 2 are obtained. In task assignment, 3 tasks in task group 1 are assigned to the same task executor, and 2 tasks in task group 2 are assigned to the same task executor.
The above examples in this embodiment are all examples for easy understanding, and do not limit the technical configuration of the present invention.
Compared with the prior art, the method combines all tasks, sequentially traverses all task combinations meeting preset traversing conditions, and executes the following steps once from each traversing to one task combination: determining whether the currently traversed task combination does not comprise the task with the completed group, responding to the fact that the currently traversed task combination does not comprise the task with the completed group, acquiring each residual task except the currently traversed task combination and the task with the completed group from each task, sequentially traversing each residual task, and adding the traversed residual task meeting the preset condition into the currently traversed task combination to obtain the task group; wherein the tasks in the task group are tasks that have completed the group. That is, each time a task combination that does not include a task that has completed a group is traversed, it is determined whether there are tasks that can be added to the task combination currently traversed among the remaining tasks that have not completed a group, thereby achieving task grouping. By performing task grouping, task distribution can be performed based on the obtained task grouping, and distribution efficiency is improved.
The second embodiment of the present invention relates to a task grouping method, and implementation details of the task grouping method of this embodiment are specifically described below, which are provided for convenience of understanding only and are not necessary for implementing this embodiment.
The flow chart of the task grouping method in the present embodiment may refer to fig. 2, including:
step S201, each task is combined in pairs, and each task combination is obtained.
In one example, each task is combined two by two, so as to obtain an implementation manner of each task combination, which may be: any two tasks are selected from the tasks to be combined, and all combination forms are obtained to be used as the obtained task combinations. That is, after the tasks are combined in pairs, the number of the obtained task combinations isWhere n is the total number of tasks to be combined pairwise; />Represents the number of all task combinations obtained by combining optionally 2 tasks among the n tasks. For example, the number of all task combinations obtained by combining 2 optional tasks among 10 tasks is
In another example, the implementation manner of combining each task two by two to obtain each task combination may be: and carrying out pairwise combination on each task to obtain each task combination without the same task. That is, the tasks in the task combinations obtained after the combination of the two pairs do not have repeated tasks. For example, after the task a and the task b form a task combination, the task a and the task b can not appear in other task combinations.
Step S202, calculating the similarity of each task combination.
The similarity of each task combination is the similarity between two tasks in each task combination. The calculation method of the similarity between the two tasks may refer to the calculation method of the similarity in the first embodiment, which is not particularly limited in this embodiment.
Step S203, according to the similarity of each task combination, sequentially traversing the task combinations meeting the preset traversing conditions, and determining whether the currently traversed task combination does not comprise the task which has been completed in groups; if yes, step S204 is performed, otherwise step S206 is performed.
For example, task combinations meeting preset traversal conditions may be traversed in sequence according to the similarity of each task combination from large to small, and it is determined whether the currently traversed task combination does not include tasks that have completed the grouping, where tasks that do not include the grouping may be referred to as target task combinations.
In one example, the task combinations that meet the preset traversal condition are: all tasks for which the similarity is calculated are combined.
In another example, the task combinations that meet the preset traversal condition are: and task combinations with similarity larger than the preset first similarity in all the task combinations with calculated similarity.
In step S204, in response to the currently traversed task combination not including the task of which the grouping has been completed, each remaining task other than the currently traversed task combination and the task of which the grouping has been completed is acquired from each task.
Step S205, traversing each residual task in sequence, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group.
The steps S204 to S205 are substantially the same as the steps S103 to S104 in the first embodiment, and are not repeated here.
Step S206: determining whether all task combinations meeting preset traversal conditions are traversed; if so, the flow ends, otherwise, the process continues to step S203, where the next task combination is continued to be traversed.
The following explains the present embodiment with a specific example, the task group is exemplified by an order group, and 45 orders are combined assuming that there are 10 current orders; it is assumed that the sequences from big to small according to the similarity are: (1, 2), (1, 3), (1, 4), (1, 5) … … (1, 10), (2, 3), (2, 4), (2, 5), … … (2, 10), (3, 4), (3, 5), (3, 6), … … (2, 10) … … (8, 9), (8, 10), (9, 10)
If the first similarity is not set, the order combination meeting the preset traversal condition is as follows: all the order combinations with the calculated similarity are sequentially traversed according to the similarity sequence, and the traversed order combinations are processed as follows:
upon traversing to order combination (1, 2), it is found that neither order 1,2 is grouped, with the remaining orders at this time comprising: order 3-order 10; suppose that after traversing the remaining orders, an order group (1, 2, 3) is obtained
On traversing to the order combination (1, 3), it is found that the orders 1,3 have been grouped, and no processing is done for the order combination (1, 3);
on traversing to the order combination (1, 4), finding that the order 1 has been grouped, then not processing the order combination (1, 4);
……
when traversing to the order combination (4, 5), it is found that none of the orders 4,5 are grouped, the remaining orders at this time including orders 6-10 (i.e., all orders 1-10 except the currently traversed (4, 5) and the orders 1,2,3 that have been grouped); assume that after traversing the remaining orders, an order group (4, 5, 6) is obtained;
on traversing to the order combination (4, 6), it is found that the orders 4,6 have been grouped, and no processing is done for the order combination (4, 6);
……
Upon traversing to the order combination (7, 8), it is found that neither order 7,8 is grouped, with the remaining orders at this time comprising: order 9 to order 10; assume that after traversing the remaining orders, an order group (7, 8, 9, 10) is obtained; all order groupings are completed.
If the preset first similarity is 95%, screening all order combinations with the similarity greater than 95%, and if 25 screened order combinations exist, sequentially traversing the 25 order combinations according to the similarity ranking of the 25 order combinations; the manner of processing the traversed order combination is similar to that described above.
The above examples in this embodiment are all examples for easy understanding, and do not limit the technical configuration of the present invention.
Compared with the prior art, the method and the device for combining the tasks in the embodiment have the advantages that the tasks are combined in pairs at the beginning, the number of the combinations is not increased due to the fact that the upper limit of the tasks is increased (namely, the number of the tasks in each group is increased), the complexity of the combination is reduced, meanwhile, the complexity of calculating the similarity of the combination of the tasks is reduced, and further the overall complexity of the task group is reduced. The grouping mode provided by the embodiment of the invention adopts a grouping mode that new tasks are added into the task combination by taking the task combination (2 tasks) as a reference, so that the task grouping complexity is not increased under the condition of improving the task upper limit of the task grouping; therefore, the processing burden of the server when task grouping is carried out can be reduced, and the processing efficiency is improved.
Taking a delivery order as an example, the task is used for grouping the delivery orders by adopting the task grouping method in the embodiment. That is, based on the order pair (2 orders), new orders are added into the order pair, and order grouping supporting high order upper limit is realized. Combining up to 3 orders, 4 orders, or n orders into one order group can be achieved without increasing the complexity of the group, compared to earlier methods that can only be 2 orders into one group. It can be understood that a plurality of orders are combined into a group, which is beneficial to improving the order taking wish of a rider, improving the order taking efficiency and shortening the order taking time. More orders with high similarity are combined together, and one-time assignment can improve the experience of a rider and reduce the distribution cost.
A third embodiment of the present invention relates to a task grouping method, and implementation details of the task grouping method of this embodiment are specifically described below, which are provided for convenience of understanding only and are not essential to implementation of this embodiment.
The flow chart of the task grouping method in the present embodiment may refer to fig. 3, including:
step S301, each task is combined in pairs, and each task combination is obtained.
Step S302, each task combination is filtered according to a preset filtering rule, and each filtered task combination is obtained.
The filtering rules can be set according to actual needs. The filtering rules are used to filter out task combinations that consist of significantly different tasks. For example, if the task is a delivery order, filtering may be performed according to rules such as a generation time interval, an expected delivery time interval, a delivery start interval distance, and the like of two delivery orders in each task combination. For example, pairs of delivery orders that are generated too long, for which delivery time intervals are desired too long, and for which delivery origin intervals are too far apart, may be filtered out.
Step S303, calculating the similarity of each task combination after filtering.
Step S304, sequentially traversing task combinations meeting preset traversing conditions according to the similarity of each task combination after filtering, and determining whether the currently traversed task combination does not comprise tasks which are already grouped; if yes, step S305 is performed, otherwise step S307 is performed.
In step S305, in response to the currently traversed task combination not including the task of which the grouping has been completed, each remaining task other than the currently traversed task combination and the task of which the grouping has been completed is acquired from each task.
Step S306, traversing each residual task in sequence, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group.
Step S307, determining whether all task combinations meeting preset traversal conditions are traversed; if so, the flow ends, otherwise, the step S304 is continued to be executed, and the next task combination is continued to be traversed.
Compared with the prior art, in the embodiment, each task combination is filtered according to the preset filtering rule to obtain the filtered task combination, so that the task combination with obviously poor similarity is filtered before the similarity is calculated, the number of the task combinations with the similarity required to be calculated subsequently is reduced, and the execution speed is increased.
A fourth embodiment of the present invention relates to a task grouping method, and implementation details of the task grouping method of this embodiment are specifically described below, and the following description is merely provided for understanding implementation details, and is not necessary to implement this embodiment.
The flow chart of the task grouping method in this embodiment may refer to fig. 4, including:
and S401, combining the tasks in pairs to obtain the task combination.
In step S402, the similarity of each task combination is calculated.
The steps S401 to S402 are substantially the same as the steps S201 to S202 in the second embodiment, and are not repeated here.
Step S403, dividing the task combinations meeting the preset traversal conditions into a plurality of preset similarity intervals according to the similarity of each task combination.
Wherein, the plurality of similarity intervals are divided by continuous numerical intervals. In a specific implementation, the multiple divided similarity intervals are not overlapped with each other. For example, the plurality of similarity intervals into which the continuous numerical interval [0.8 ] is divided include: [ 0.8.0.84), [ 0.84.88), [ 0.88.92), [ 0.92.96), [0.96 1). The preset traversal conditions have been described in the above embodiments, and in order to avoid repetition, this embodiment will not be repeated.
In one example, task combinations that satisfy the preset traversal condition may be determined first according to the similarity of the task combinations. And then dividing the task combination meeting the preset traversal condition into a plurality of preset similarity intervals according to the task combination meeting the preset traversal condition.
In another example, whether the task combination is a task combination satisfying a preset traversal condition may be determined according to the similarity of the task combinations; if the task combination is a task combination meeting the preset traversal condition, dividing the task combination into a similarity interval in which the similarity of the task combination is located according to the similarity of the task combination. And if the task combination is the task combination which does not meet the preset traversal condition, not processing the task combination.
For example, currently, there are 5 tasks to be task-grouped, two-by-two combinations obtain 10 task combinations, the similarity of the 10 task combinations is calculated, and if the similarity of the task combinations meeting the preset traversal condition is greater than 0.8, since the similarity of the task combinations 10 (task 1, task 3) is less than 0.8, the other 9 task combinations except the task combination 10 are divided into a plurality of preset similarity intervals. The similarity of the 9 task combinations, and the similarity interval into which the 9 task combinations are divided, can be as shown in table 2 below. Note that, since the 5 tasks in table 1 are different from the 5 tasks in table 2, the similarity between the 5 tasks in table 1 is different from the similarity between the 5 tasks in table 2.
TABLE 2
Task assembly Similarity degree Similarity interval
Task combination 1 (task 1, task 2) 0.97 [0.96 1)
Task combination 2 (task 1, task 5) 0.98 [0.96 1)
Task combination 3 (task 2, task 5) 0.99 [0.96 1)
Task combination 4 (task 3, task 4) 0.96 [0.96 1)
Task combination 5 (task 2, task 4) 0.89 [0.88 0.92)
Task combination 6 (task 1, task 4) 0.9 [0.88 0.92)
Task combination 7 (task 3, task 5) 0.84 [0.84 0.88)
Task combination 8 (task 2, task 3) 0.81 [0.8 0.84)
Task combination 9 (task 4, task 5) 0.83 [0.8 0.84)
Step S404, obtaining the arrangement sequence of a plurality of similarity intervals.
Wherein, the higher the upper limit value, the higher the arrangement order among the plurality of similarity intervals. Referring to the example in table 2, the arrangement order of the plurality of similarity intervals is, in order from front to back: [0.96 1), [0.88 0.92), [0.84 0.88), [0.8 0.84).
It is understood that the sorting manner of each task combination in this embodiment may be understood as a bucket sorting. Each barrel represents a similarity interval, and the task combination meeting the preset traversal condition is divided into a plurality of preset similarity intervals, which can be understood as follows: dividing task combinations meeting preset traversal conditions into a plurality of preset barrels, and then sequencing similarity intervals represented by the barrels to obtain the arrangement sequence of the barrels.
Step S405, traversing task combinations meeting preset traversing conditions in a plurality of similarity intervals from front to back in sequence according to the arrangement sequence of the plurality of similarity intervals, and determining whether the currently traversed task combinations do not comprise tasks which have been completed in groups; if yes, step S406 is performed, otherwise step S408 is performed.
Wherein, the traversing sequence of the task combination which is divided into the same similarity interval and meets the preset traversing condition is random.
In one example, referring to Table 2, the task combination partitioned into [0.96 1) may be traversed first, followed by the task combination partitioned into [0.88 0.92), followed by the task combination partitioned into [0.84 0.88), and finally followed by the task combination partitioned into [0.8 0.84). When traversing 4 task combinations (task combination 1 to task combination 4) divided into [0.96 1 ], the traversing order of the 4 task combinations is random; when traversing 2 task combinations (task combination 5 and task combination 6) divided into [ 0.88.92 ], the traversing order of the 2 task combinations is random; when traversing 2 task combinations (task combination 8 and task combination 9) divided into [ 0.80.84), the traversing order of these 2 task combinations is random.
In addition, since the implementation manner of determining whether the currently traversed task combination does not include the task that has completed the grouping has been described in the first embodiment, this embodiment will not be described in detail.
In step S406, in response to the currently traversed task combination not including the task of the completed group, each remaining task other than the currently traversed task combination and the task of the completed group is acquired from each task.
Step S407, traversing each residual task in sequence, and adding the traversed residual tasks meeting the preset conditions into the current traversed task combination to obtain task groups.
Step S408, determining whether all task combinations meeting preset traversal conditions are traversed; if so, the flow ends, otherwise, the process continues to step S405, where the next task combination is continued to be traversed.
The steps S406 to S408 are substantially the same as the steps S204 to S206 in the second embodiment of the present invention, and are not repeated here.
In a specific implementation, after step S401 is performed, the steps may be performed: according to a preset filtering rule, each task combination is filtered to obtain each filtered task combination, and step S402 may be to calculate the similarity of each filtered task combination.
The above examples in this embodiment are all examples for easy understanding, and do not limit the technical configuration of the present invention.
Compared with the prior art, the method and the device consider small differences of the similarity, and can be ignored in practical application, so that a similar bucket sorting mode is adopted, and task combinations meeting preset traversing conditions in a plurality of similarity intervals are sequentially traversed from front to back according to the arrangement sequence of the similarity intervals. The traversing sequence of the task combinations which are divided into the same similarity interval and meet the preset traversing condition is random, namely the task combinations (task combinations with tiny similarity differences) falling in the same similarity interval are not further ordered, so that the speed of ordering is improved, the complexity of ordering is reduced, the complexity of task grouping is reduced, and the efficiency of task grouping is improved. In addition, the task combinations falling within the same similarity interval can be considered to have little difference in similarity, so that the influence thereof on the grouping result can be ignored.
The above steps of the methods are divided, for clarity of description, and may be combined into one step or split into multiple steps when implemented, so long as they include the same logic relationship, and they are all within the protection scope of this patent; it is within the scope of this patent to add insignificant modifications to the algorithm or flow or introduce insignificant designs, but not to alter the core design of its algorithm and flow.
A fifth embodiment of the present invention relates to a task grouping platform, as shown in fig. 5, including: the combination module 501 is configured to combine each task to obtain each task combination; the first traversing module 502 is configured to sequentially traverse task combinations that satisfy a preset traversing condition, and execute the following steps: determining whether the currently traversed task combination does not include the task of which the grouping is completed; acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode; a second traversing module 503, configured to sequentially traverse the remaining tasks, and add the traversed remaining tasks meeting the preset condition to the currently traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group
In one example, the similarity of any two tasks in the task combination meeting the preset traversal condition is greater than the preset first similarity.
In one example, the combining each task to obtain each task combination includes: each task is combined pairwise to obtain each task combination; the sequentially traversing task combinations meeting preset traversing conditions comprises the following steps: calculating the similarity of each task combination; the similarity of each task combination is the similarity between two tasks in each task combination; and traversing task combinations meeting preset traversing conditions in sequence according to the similarity of the task combinations.
In one example, the sequentially traversing the task combinations meeting the preset traversing condition according to the similarity of the task combinations includes: dividing the task combinations meeting the preset traversal conditions into a plurality of preset similarity intervals according to the similarity of each task combination; wherein the plurality of similarity intervals are divided by continuous numerical intervals; acquiring the arrangement sequence of the multiple similarity intervals; wherein, in the multiple similarity intervals, the higher the upper limit value, the earlier the arrangement order is; traversing task combinations meeting preset traversing conditions in the similarity intervals from front to back in sequence according to the arrangement sequence of the similarity intervals; wherein, the traversing sequence of the task combination which is divided into the same similarity interval and meets the preset traversing condition is random.
In one example, the preset condition includes at least one of: the similarity between the traversed residual tasks and each task in the currently traversed task combination is larger than a preset second similarity; the distribution included angle between the traversed residual tasks and each task in the currently traversed task combination is smaller than a preset included angle; the tasks are delivery tasks, and the delivery included angles are determined according to delivery routes corresponding to the traversed residual tasks and delivery routes corresponding to the tasks added to the current traversed task combination; the difference between the distribution distance corresponding to the traversed residual task and the distribution distance corresponding to each task in the currently traversed task combination is smaller than a preset first distance; the task is a delivery task; the distance between the distribution starting place corresponding to the traversed residual task and the distribution starting place corresponding to each task in the currently traversed task combination is smaller than a preset second distance; the task is a delivery task.
In one example, the preset condition includes: the similarity between the traversed residual tasks and each task in the currently traversed task combination is larger than a preset second similarity; the tasks are combined in pairs, and the number of the obtained task combinations is Wherein n is the total number of tasks to be combined pairwise; after the similarity of each task combination is calculated, the method further comprises the following steps: storing the similarity of each task combination; the similarity between the traversed remaining tasks and each task in the currently traversed task combination is obtained by: and searching the similarity of each task in the traversed residual task and the currently traversed task combination according to the stored similarity of each task combination.
In one example, the traversing the remaining tasks sequentially, adding the task meeting the preset condition to the currently traversed task combination to obtain a task group, including: and traversing the residual tasks in sequence, adding the residual tasks meeting preset conditions into the currently traversed task combination to obtain task groups, and stopping traversing until the number of tasks in the task groups reaches the preset task upper limit.
It is to be noted that this embodiment is an example of a device corresponding to the first to fourth embodiments, and can be implemented in cooperation with the first to fourth embodiments. The related technical details and technical effects mentioned in the first to fourth embodiments are still valid in the present embodiment, and are not repeated here for the sake of reducing repetition. Accordingly, the related-art details mentioned in the present embodiment can also be applied to the first to fourth embodiments.
It should be noted that each module in this embodiment is a logic module, and in practical application, one logic unit may be one physical unit, or may be a part of one physical unit, or may be implemented by a combination of multiple physical units. In addition, in order to highlight the innovative part of the present invention, units that are not so close to solving the technical problem presented by the present invention are not introduced in the present embodiment, but this does not indicate that other units are not present in the present embodiment.
A sixth embodiment of the present invention relates to a server, as shown in fig. 6, including: at least one processor 601; and a memory 602 communicatively coupled to the at least one processor 601; and a communication component 603 communicatively coupled to the scanning device, the communication component 603 receiving and transmitting data under control of the processor 601; wherein the memory 602 stores instructions executable by the at least one processor 601, the instructions being executable by the at least one processor 601 to implement:
combining all the tasks to obtain all the task combinations;
sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps:
Determining whether the currently traversed task combination does not include the task of which the grouping is completed;
acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode;
traversing each residual task in sequence, adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group.
Specifically, the server includes: one or more processors 601 and a memory 602, one processor 601 being illustrated in fig. 6. The processor 601, the memory 602 may be connected by a bus or otherwise, for example in fig. 6. The memory 602 is a non-volatile computer readable storage medium that can be used to store non-volatile software programs, non-volatile computer executable programs, and modules. The processor 601 executes various functional applications of the device and data processing, i.e., implements the above-described distribution task grouping method, by running nonvolatile software programs, instructions, and modules stored in the memory 602.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store a list of options, etc. In addition, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some implementations, the memory 602 optionally includes memory 602 located remotely from the processor 601, the remote memory 602 being connectable to an external device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 602 that, when executed by the one or more processors 601, perform the task grouping method of any of the method embodiments described above.
The above product may perform the method provided by the embodiment of the present application, and has the corresponding functional module and beneficial effect of the performing method, and technical details not described in detail in the embodiment of the present application may be referred to the method provided by the embodiment of the present application.
A seventh embodiment of the present application relates to a nonvolatile storage medium storing a computer-readable program for causing a computer to execute some or all of the above-described method embodiments.
That is, it will be understood by those skilled in the art that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, where the program includes several instructions for causing a device (which may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps in the methods of the embodiments of the application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific examples of carrying out the application and that various changes in form and details may be made therein without departing from the spirit and scope of the application.
The embodiment of the application provides a task grouping method, which comprises the following steps:
Combining all the tasks to obtain all the task combinations;
sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps:
determining whether the currently traversed task combination does not include the task of which the grouping is completed;
acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode;
sequentially traversing each residual task, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group.
A2. The task grouping method according to A1, wherein the sequentially traversing each remaining task, adding the traversed remaining task meeting the preset condition to the currently traversed task combination to obtain a task group, includes:
and traversing each residual task in sequence, adding the residual tasks meeting preset conditions into the currently traversed task combination to obtain task groups, and stopping traversing until the number of tasks in the task groups reaches the preset task upper limit.
A3. The task grouping method according to A1, wherein the combining each task to obtain each task combination includes:
each task is combined pairwise to obtain each task combination;
the sequentially traversing task combinations meeting preset traversing conditions comprises the following steps:
calculating the similarity of each task combination; the similarity of each task combination is the similarity between two tasks in each task combination;
and traversing task combinations meeting preset traversing conditions in sequence according to the similarity of the task combinations.
A4. The task grouping method according to A3, according to the similarity of each task combination, sequentially traversing the task combinations meeting the preset traversing condition, including:
dividing the task combinations meeting the preset traversal conditions into a plurality of preset similarity intervals according to the similarity of each task combination; wherein the plurality of similarity intervals are divided by continuous numerical intervals;
acquiring the arrangement sequence of the multiple similarity intervals; wherein, in the multiple similarity intervals, the higher the upper limit value, the earlier the arrangement order is;
traversing task combinations meeting preset traversing conditions in the similarity intervals from front to back in sequence according to the arrangement sequence of the similarity intervals; wherein, the traversing sequence of the task combination which is divided into the same similarity interval and meets the preset traversing condition is random.
A5. The task grouping method according to any one of A1 to A4, wherein the similarity of any two tasks in the task combination satisfying the preset traversal condition is greater than a preset first similarity.
A6. The task grouping method according to A1, wherein the preset condition includes at least one of the following:
the similarity between the traversed residual tasks and each task in the currently traversed task combination is larger than a preset second similarity;
the distribution included angle between the traversed residual tasks and each task in the currently traversed task combination is smaller than a preset included angle; the tasks are delivery tasks, and the delivery included angles are determined according to delivery routes corresponding to the traversed residual tasks and delivery routes corresponding to the tasks in the currently traversed task combination;
the difference between the distribution distance corresponding to the traversed residual task and the distribution distance corresponding to each task in the currently traversed task combination is smaller than a preset first distance; wherein the task is a delivery task;
the distance between the distribution starting place corresponding to the traversed residual task and the distribution starting place corresponding to each task in the currently traversed task combination is smaller than a preset second distance; wherein the task is a delivery task.
A7. The task grouping method according to A3 or A4, wherein the preset conditions include: the similarity between the traversed residual tasks and each task in the currently traversed task combination is larger than a preset second similarity;
the tasks are combined in pairs, and the number of the obtained task combinations isWherein n is the total number of tasks to be combined pairwise;
after the similarity of each task combination is calculated, the method further comprises the following steps:
storing the similarity of each task combination;
the similarity between the traversed remaining tasks and each task in the currently traversed task combination is obtained by:
and searching the similarity of each task in the traversed residual task and the currently traversed task combination according to the stored similarity of each task combination.
The embodiment of the application provides a task grouping platform, which comprises the following components:
the combination module is used for combining all the tasks to obtain all the task combinations;
the first traversing module is used for sequentially traversing task combinations meeting preset traversing conditions and executing the following steps:
determining whether the currently traversed task combination does not include the task of which the grouping is completed;
Acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode;
the second traversing module is used for traversing each residual task in sequence, adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group.
The embodiment of the application provides a server, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the following steps:
combining all the tasks to obtain all the task combinations;
sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps:
determining whether the currently traversed task combination does not include the task of which the grouping is completed;
acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode;
sequentially traversing each residual task, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks of the completed group.
C2. The server according to C1, the processor executing a program performs the task grouping method as described in any one of A2 to A7.
The embodiment of the present application provides d1. A nonvolatile storage medium storing a computer-readable program for causing a computer to execute the task grouping method according to any one of A1 to A7.

Claims (9)

1. A method of task grouping, comprising:
combining all the tasks to obtain all the task combinations, wherein the tasks are distribution tasks;
sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps:
determining whether the currently traversed task combination does not include the task of which the grouping is completed;
acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode;
sequentially traversing each residual task, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks which are already grouped;
Wherein, each task is combined to obtain each task combination, including:
each task is combined pairwise to obtain each task combination;
the sequentially traversing task combinations meeting preset traversing conditions comprises the following steps:
calculating the similarity of each task combination, wherein the similarity of each task combination is the similarity between two tasks in each task combination;
according to the similarity of each task combination, sequentially traversing the task combinations meeting preset traversing conditions;
according to the similarity of each task combination, sequentially traversing the task combinations meeting preset traversing conditions, wherein the method comprises the following steps:
dividing the task combinations meeting the preset traversal conditions into a plurality of preset similarity intervals according to the similarity of each task combination, wherein the plurality of similarity intervals are obtained by dividing continuous numerical intervals;
acquiring the arrangement sequence of the plurality of similarity intervals, wherein the arrangement sequence with the larger upper limit value is the earlier in the plurality of similarity intervals;
and traversing task combinations meeting preset traversing conditions in the similarity intervals from front to back according to the arrangement sequence of the similarity intervals, wherein the traversing sequence of the task combinations meeting the preset traversing conditions, which are divided into the same similarity intervals, is random.
2. The task grouping method according to claim 1, wherein traversing each of the remaining tasks in turn, adding the traversed remaining tasks satisfying a preset condition to the currently traversed task combination to obtain a task group, includes:
and traversing each residual task in sequence, adding the residual tasks meeting preset conditions into the currently traversed task combination to obtain task groups, and stopping traversing until the number of tasks in the task groups reaches the preset task upper limit.
3. The task grouping method according to any one of claims 1 to 2, wherein a similarity of any two tasks in the task combination satisfying a preset traversal condition is greater than a preset first similarity.
4. The task grouping method of claim 1, wherein the preset condition includes at least one of:
the similarity between the traversed residual tasks and each task in the currently traversed task combination is larger than a preset second similarity;
the distribution included angle between the traversed residual tasks and each task in the currently traversed task combination is smaller than a preset included angle; the tasks are delivery tasks, and the delivery included angles are determined according to delivery routes corresponding to the traversed residual tasks and delivery routes corresponding to the tasks in the currently traversed task combination;
The difference between the distribution distance corresponding to the traversed residual task and the distribution distance corresponding to each task in the currently traversed task combination is smaller than a preset first distance; wherein the task is a delivery task;
the distance between the distribution starting place corresponding to the traversed residual task and the distribution starting place corresponding to each task in the currently traversed task combination is smaller than a preset second distance; wherein the task is a delivery task.
5. The task grouping method of claim 1, wherein the preset condition includes: the similarity between the traversed residual tasks and each task in the currently traversed task combination is larger than a preset second similarity;
the tasks are combined in pairs, and the number of the obtained task combinations isThe method comprises the steps of carrying out a first treatment on the surface of the Wherein n is the total number of tasks to be combined pairwise;
after the similarity of each task combination is calculated, the method further comprises the following steps:
storing the similarity of each task combination;
the similarity between the traversed remaining tasks and each task in the currently traversed task combination is obtained by:
And searching the similarity of each task in the traversed residual task and the currently traversed task combination according to the stored similarity of each task combination.
6. A task grouping platform, comprising:
the combination module is used for combining all the tasks to obtain all the task combinations, wherein the tasks are distribution tasks;
the first traversing module is used for sequentially traversing task combinations meeting preset traversing conditions and executing the following steps:
determining whether the currently traversed task combination does not include the task of which the grouping is completed;
acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode;
the second traversing module is used for traversing each residual task in sequence, adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks which are already grouped;
the first traversing module is further configured to calculate a similarity of each task combination, where the similarity of each task combination is a similarity between two tasks in each task combination, and sequentially traverse task combinations that meet a preset traversing condition according to the similarity of each task combination;
The first traversing module is further configured to divide the task combinations meeting the preset traversing condition into a plurality of preset similarity intervals according to the similarity of each task combination, wherein the plurality of similarity intervals are obtained by dividing continuous numerical intervals, and the arrangement sequence of the plurality of similarity intervals is obtained, the arrangement sequence with the upper limit value larger in the plurality of similarity intervals is traversed forward, and the task combinations meeting the preset traversing condition in the plurality of similarity intervals are traversed sequentially from front to back according to the arrangement sequence of the plurality of similarity intervals, wherein the traversing sequence of the task combinations meeting the preset traversing condition in the plurality of similarity intervals divided into the same similarity interval is random.
7. A server comprising a memory and a processor, the memory storing a computer program, wherein the processor executes:
combining all the tasks to obtain all the task combinations, wherein the tasks are distribution tasks;
sequentially traversing task combinations meeting preset traversing conditions, and executing the following steps:
determining whether the currently traversed task combination does not include the task of which the grouping is completed;
Acquiring each residual task except the currently traversed task combination and the task which is completed in a grouping mode from each task in response to the currently traversed task combination not including the task which is completed in the grouping mode;
sequentially traversing each residual task, and adding the traversed residual task meeting the preset condition into the current traversed task combination to obtain a task group; wherein the tasks in the task group are the tasks which are already grouped;
wherein, each task is combined to obtain each task combination, including:
each task is combined pairwise to obtain each task combination;
the sequentially traversing task combinations meeting preset traversing conditions comprises the following steps:
calculating the similarity of each task combination, wherein the similarity of each task combination is the similarity between two tasks in each task combination;
according to the similarity of each task combination, sequentially traversing the task combinations meeting preset traversing conditions;
according to the similarity of each task combination, sequentially traversing the task combinations meeting preset traversing conditions, wherein the method comprises the following steps:
dividing the task combinations meeting the preset traversal conditions into a plurality of preset similarity intervals according to the similarity of each task combination, wherein the plurality of similarity intervals are obtained by dividing continuous numerical intervals;
Acquiring the arrangement sequence of the plurality of similarity intervals, wherein the arrangement sequence with the larger upper limit value is the earlier in the plurality of similarity intervals;
and traversing task combinations meeting preset traversing conditions in the similarity intervals from front to back according to the arrangement sequence of the similarity intervals, wherein the traversing sequence of the task combinations meeting the preset traversing conditions, which are divided into the same similarity intervals, is random.
8. The server according to claim 7, wherein the processor, when running a program, performs the task grouping method according to any one of claims 2 to 5.
9. A non-transitory storage medium storing a computer readable program for a computer to perform the task grouping method of any one of claims 1 to 5.
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