CN111667181B

CN111667181B - Task processing method, device, electronic equipment and computer readable storage medium

Info

Publication number: CN111667181B
Application number: CN202010514622.1A
Authority: CN
Inventors: 罗浩
Original assignee: Lazas Network Technology Shanghai Co Ltd
Current assignee: Lazas Network Technology Shanghai Co Ltd
Priority date: 2020-06-08
Filing date: 2020-06-08
Publication date: 2023-04-28
Anticipated expiration: 2040-06-08
Also published as: CN111667181A

Abstract

The embodiment of the disclosure discloses a task processing method, a device, an electronic device and a computer readable storage medium, wherein the task processing method comprises the following steps: acquiring task execution history behavior data of one or more task receivers in a preset history time period through at least one processor; calculating the embedded similarity between the task to be distributed and the distributed task distributed to the target task receiver through at least one processor according to the task execution history behavior data; in response to the task to be distributed to the target task recipient, adjusting, by at least one processor, a task execution plan of the target task recipient based on an embedded similarity between the task to be distributed and the distributed task that has been distributed to the target task recipient. According to the technical scheme, the service efficiency can be effectively improved under the condition of guaranteeing the service quality.

Description

Task processing method, device, electronic equipment and computer readable storage medium

Technical Field

The disclosure relates to the technical field of data processing, and in particular relates to a task processing method, a task processing device, electronic equipment and a computer readable storage medium.

Background

With the development of internet technology, more and more service providers provide services for users or other service demanders through an internet platform, and many internet services all require distribution operators to distribute, so how to improve distribution efficiency and reduce distribution cost under the condition of guaranteeing distribution quality is important for guaranteeing the service quality of the internet platform and reducing the service cost of the internet platform. In the prior art, the degree of similarity between an order to be distributed and one of the orders received by a dispatcher but not distributed is generally examined, and the order to be distributed is distributed according to the degree of similarity, wherein the adjustment of the task execution route of the dispatcher after the distribution is an important factor for improving the distribution efficiency.

Disclosure of Invention

The embodiment of the disclosure provides a task processing method, a task processing device, electronic equipment and a computer readable storage medium.

In a first aspect, a task processing method is provided in an embodiment of the present disclosure.

Specifically, the task processing method includes:

acquiring task execution history behavior data of one or more task receivers in a preset history time period through at least one processor;

Calculating, by at least one processor, an embedding similarity between the task to be allocated and the allocated task allocated to the target task receiver according to the task execution history behavior data, wherein the embedding similarity is calculated based on embedding vectors of the task to be allocated and the allocated task;

in response to the task to be distributed to the target task recipient, adjusting, by at least one processor, a task execution plan of the target task recipient based on an embedded similarity between the task to be distributed and the distributed task that has been distributed to the target task recipient.

With reference to the first aspect, in a first implementation manner of the first aspect, the calculating, by at least one processor, an embedded similarity between the task to be allocated and the allocated task allocated to the target task receiver according to the task execution history behavior data includes:

calculating, by at least one processor, a first embedded vector of a task execution location based on the task execution historical behavior data;

acquiring a second embedded vector corresponding to the execution place of the task to be distributed through at least one processor;

The similarity between the second embedding vector and the first embedding vector is calculated by at least one processor, and is determined as the embedding similarity between the task to be allocated and the allocated task allocated to the target task receiver.

With reference to the first aspect and the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the calculating, by at least one processor, a first embedded vector of a task execution place according to the task execution history behavior data includes:

generating a task execution weighted undirected graph according to the task execution history behavior data through at least one processor, wherein the task execution weighted undirected graph comprises two or more nodes used for representing task execution places and edges connected with the nodes, and the nodes are correspondingly provided with corresponding geographic areas;

and calculating node embedded vectors of all nodes in the task execution weighted undirected graph by at least one processor, and taking the node embedded vectors as first embedded vectors of corresponding task execution places.

With reference to the first aspect, the first implementation manner of the first aspect, and the second implementation manner of the first aspect, in a third implementation manner of the first aspect, the calculating, by at least one processor, a node embedding vector of each node in the task execution weighted undirected graph is implemented as:

Calculating, by at least one processor, a random walk behavior sequence of the task execution weighted undirected graph;

and determining an embedded vector learning model through at least one processor, and learning the random walk behavior sequence as input of the embedded vector learning model to obtain node embedded vectors of all nodes in the task execution weighted undirected graph.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, and the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the obtaining, by at least one processor, the second embedded vector corresponding to the execution location of the task to be allocated is implemented as follows:

acquiring an execution place of the task to be distributed through at least one processor;

matching, by at least one processor, the execution location of the task to be allocated with a geographic area corresponding to a node in the task execution weighted undirected graph;

and determining, by at least one processor, a node embedding vector corresponding to the task execution weighted undirected graph node that matches the geographical area of the task execution location to be allocated as a second embedding vector corresponding to the execution location of the task to be allocated.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, and the fourth implementation manner of the first aspect, in a fifth implementation manner of the first aspect, before the calculating, by at least one processor, the similarity between the second embedded vector and the first embedded vector, the embodiment of the disclosure further includes:

and acquiring side information and corresponding weights through at least one processor, and carrying out weighting adjustment on the first embedded vector and the second embedded vector according to the side information and the weights.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, the fourth implementation manner of the first aspect, and the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, the embodiment of the present disclosure further includes:

and calculating an initial evaluation value of the task to be allocated by at least one processor, and adjusting the initial evaluation value of the task to be allocated according to the embedded similarity.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, the fourth implementation manner of the first aspect, the fifth implementation manner of the first aspect, and the sixth implementation manner of the first aspect, in a seventh implementation manner of the first aspect, the calculating, by at least one processor, an initial evaluation value of a task to be allocated includes:

Determining, by at least one processor, an evaluation value per unit time corresponding to the target task recipient;

acquiring the execution distance of the task to be distributed and the historical execution speed of the target task receiver by at least one processor, and calculating to obtain the estimated execution time of the task to be distributed according to the execution distance and the historical execution speed;

and multiplying the estimated execution time by the unit time evaluation value through at least one processor to obtain an initial evaluation value of the task to be allocated.

With reference to the first aspect, the first implementation manner of the first aspect, the second implementation manner of the first aspect, the third implementation manner of the first aspect, the fourth implementation manner of the first aspect, the fifth implementation manner of the first aspect, the sixth implementation manner of the first aspect, and the seventh implementation manner of the first aspect, in an eighth implementation manner of the first aspect, the embodiment of the present disclosure performs, according to the embedding similarity, an adjustment process on the initial evaluation value of the task to be allocated, where the adjustment process is implemented:

and when the embedded similarity is higher than a preset similarity threshold, performing down-regulation processing on the initial evaluation value of the task to be distributed, and enabling the evaluation value of unit time corresponding to the down-regulation processed evaluation value to be not lower than the evaluation value of unit time corresponding to the target task receiver.

In a second aspect, a task processing device is provided in an embodiment of the present disclosure.

Specifically, the task processing device includes:

the acquisition module is configured to acquire task execution history behavior data of one or more task receivers in a preset history time period through at least one processor;

a calculation module configured to calculate, by at least one processor, an embedding similarity between the task to be allocated and the allocated task allocated to the target task receiver according to the task execution history behavior data, wherein the embedding similarity is calculated based on embedding vectors of the task to be allocated and the allocated task;

and the adjustment module is configured to respond to the task to be distributed to the target task receiver, and adjust a task execution plan of the target task receiver through at least one processor based on the embedded similarity between the task to be distributed and the distributed task distributed to the target task receiver.

With reference to the second aspect, in a first implementation manner of the second aspect, the computing module includes:

a computing sub-module configured to compute, by at least one processor, a first embedded vector of a task execution place from the task execution history behavior data;

The acquisition sub-module is configured to acquire a second embedded vector corresponding to the execution place of the task to be distributed through at least one processor;

a determination submodule configured to calculate, by at least one processor, a similarity between the second embedding vector and the first embedding vector, and determine it as an embedding similarity between the task to be allocated and the allocated task that has been allocated to the target task recipient.

With reference to the second aspect and the first implementation manner of the second aspect, in a second implementation manner of the second aspect, the computing submodule is configured to:

acquiring task execution history behavior data of the one or more task recipients in a preset history time period through at least one processor;

With reference to the second aspect, the first implementation manner of the second aspect, and the second implementation manner of the second aspect, in a third implementation manner of the second aspect, the calculating, by at least one processor, a portion of a node embedding vector of each node in the task execution weighted undirected graph is configured to:

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, and the third implementation manner of the second aspect, in a fourth implementation manner of the second aspect, the obtaining sub-module is configured to:

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, and the fourth implementation manner of the second aspect, in a fifth implementation manner of the second aspect, the determining submodule further includes, before:

and the adjustment sub-module is configured to acquire side information and corresponding weight through at least one processor, and perform weighted adjustment on the first embedded vector and the second embedded vector according to the side information and the weight.

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, the fourth implementation manner of the second aspect, and the fifth implementation manner of the second aspect, in a sixth implementation manner of the second aspect, an embodiment of the present disclosure further includes:

and the processing module is configured to calculate an initial evaluation value of the task to be allocated through at least one processor, and adjust the initial evaluation value of the task to be allocated according to the embedded similarity.

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, the fourth implementation manner of the second aspect, the fifth implementation manner of the second aspect, and the sixth implementation manner of the second aspect, in a seventh implementation manner of the second aspect, the processing module is configured to calculate, by at least one processor, a portion of an initial evaluation value of a task to be assigned:

With reference to the second aspect, the first implementation manner of the second aspect, the second implementation manner of the second aspect, the third implementation manner of the second aspect, the fourth implementation manner of the second aspect, the fifth implementation manner of the second aspect, the sixth implementation manner of the second aspect, and the seventh implementation manner of the second aspect, in an eighth implementation manner of the second aspect, the portion of the processing module that performs adjustment processing on the initial evaluation value of the task to be allocated according to the embedding similarity is configured to:

In a third aspect, an embodiment of the present disclosure provides an electronic device, including a memory and at least one processor, where the memory is configured to store one or more computer instructions, and where the one or more computer instructions are executed by the at least one processor to implement the method steps of the task processing method in the first aspect.

In a fourth aspect, an embodiment of the present disclosure provides a computer readable storage medium storing computer instructions for use by a task processing device, including computer instructions for performing the task processing method of the first aspect, as referred to by the task processing device.

The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:

according to the technical scheme, the embedding similarity between the task to be allocated and the task allocated to the target task receiver is calculated based on the embedding vector of the task to be allocated and the task allocated, and finally the task execution plan of the target task receiver is adjusted according to the embedding similarity. According to the technical scheme, the similarity between the task to be distributed and all the distributed tasks of a certain target task receiver is considered, and the task execution plan of the target task receiver is adjusted based on the calculated similarity, so that the service efficiency is effectively improved under the condition of guaranteeing the service quality.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

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

FIG. 1 illustrates a flow chart of a task processing method according to an embodiment of the present disclosure;

FIG. 2 illustrates a delivery route adjustment schematic according to an embodiment of the present disclosure;

FIG. 3 shows a block diagram of a task processing device according to an embodiment of the present disclosure;

FIG. 4 shows a block diagram of an electronic device according to an embodiment of the present disclosure;

FIG. 5 is a schematic diagram of a computer system suitable for use in implementing a task processing method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, portions irrelevant to description of the exemplary embodiments are omitted in the drawings.

In this disclosure, it should be understood that terms such as "comprises" or "comprising," etc., are intended to indicate the presence of features, numbers, steps, acts, components, portions, or combinations thereof disclosed in this specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, acts, components, portions, or combinations thereof are present or added.

In addition, it should be noted that, without conflict, the embodiments of the present disclosure and features of the embodiments may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

According to the technical scheme provided by the embodiment of the disclosure, the embedding similarity between the task to be allocated and the task allocated to the target task receiver is calculated based on the embedding vector of the task to be allocated and the task allocated, and finally the task execution plan of the target task receiver is adjusted according to the embedding similarity. According to the technical scheme, the similarity between the task to be distributed and all the distributed tasks of a certain target task receiver is considered, and the task execution plan of the target task receiver is adjusted based on the calculated similarity, so that the service efficiency is effectively improved under the condition of guaranteeing the service quality.

Fig. 1 shows a flowchart of a task processing method according to an embodiment of the present disclosure, which is applicable to a task processing server, as shown in fig. 1, and includes the following steps S101 to S103:

in step S101, task execution history behavior data of one or more task recipients in a preset history period is obtained by at least one processor;

In step S102, according to the task execution history behavior data, calculating, by at least one processor, an embedding similarity between the task to be allocated and the task allocated to the target task receiver, where the embedding similarity is calculated based on the embedding vectors of the task to be allocated and the task allocated;

in step S103, in response to the task to be distributed to the target task receiver, the task execution plan of the target task receiver is adjusted by at least one processor based on the embedded similarity between the task to be distributed and the distributed task that has been distributed to the target task receiver.

As mentioned above, with the development of internet technology, more and more service providers provide services for users or other service consumers through an internet platform, and many internet services require distribution operators to distribute, so how to improve distribution efficiency and reduce distribution cost under the condition of guaranteeing distribution quality is important to guarantee the service quality of the internet platform and reduce the service cost of the internet platform. In the prior art, the degree of similarity between an order to be distributed and one of the orders received by a dispatcher but not distributed is generally examined, and the order to be distributed is distributed according to the degree of similarity, wherein the adjustment of the task execution route of the dispatcher after the distribution is an important factor for improving the distribution efficiency.

In view of the above-described drawbacks, in this embodiment, a task processing method is proposed that calculates an embedding similarity between a task to be allocated and an allocated task allocated to a target task receiver based on embedding vectors of the task to be allocated and the allocated task, and finally adjusts a task execution plan of the target task receiver according to the embedding similarity. According to the technical scheme, the similarity between the task to be distributed and all the distributed tasks of a certain target task receiver is considered, and the task execution plan of the target task receiver is adjusted based on the calculated similarity, so that the service efficiency is effectively improved under the condition of guaranteeing the service quality.

In an alternative implementation manner of this embodiment, the task refers to a task object that may be allocated, may be executed after allocation, and may generate a certain execution result after execution, for example, the task may be a pick-up task, a delivery task, an order task, or the like. For ease of description, the embodiments of the present disclosure are explained and illustrated below with respect to a pick-up task as an example.

In an alternative implementation manner of this embodiment, the task receiver refers to a party that receives a task and performs an operation on the received task. When the task is a picking task, the task receiving party is a picking resource for executing the picking task, where the picking resource refers to a resource that can be used for executing the picking task, such as a picker, a picking device, a picking robot, etc., and it should be noted that the picking resource may include a picking resource that provides a service provider with a shared picking service, or may include a crowd-sourced picking resource that provides a plurality of service providers with a picking service, and flexibly bears the picking task across service providers according to the requirements of different service providers. The target task receiver refers to a task receiver for which tasks are currently required to be allocated.

In an optional implementation manner of this embodiment, the task execution history behavior data refers to behavior data that a certain task receiver may take place when executing a task in a preset history period, for example, the task execution history behavior data may include one or more of the following data: task execution location information, task execution start location information, task execution end location information, total task execution duration, and the like. In consideration of that the continuous tasks have a certain similarity, in order to ensure the accuracy of the subsequent embedded vectors, the historical behavior data of the continuous tasks in the preset historical time period is determined as the historical behavior data of the subsequent embedded vectors.

In an optional implementation manner of this embodiment, the embedding similarity is a type of similarity, and is calculated based on the embedding vectors corresponding to the task to be allocated and the allocated task, and is used for evaluating the similarity between the task to be allocated and the allocated task allocated to the target task receiver.

In this embodiment, after the task to be allocated to the target task receiver is allocated to the target task receiver, the task execution plan of the target task receiver needs to be adjusted based on the embedding similarity between the task to be allocated and the allocated task allocated to the target task receiver according to all the task information allocated to the target task receiver, considering that the task execution route of the target task receiver may be changed due to the calculation of the embedding similarity. When the task is a pickup task, the task execution plan may be, for example, pickup route adjustment, estimated pickup time adjustment, or the like. Taking the pick-up and delivery route adjustment as an example, as shown in fig. 2, assuming that before a new task is not allocated, the number of allocated tasks of the target task receiver is 2, the corresponding pick-up task execution locations are location 1 and location 2, respectively, the corresponding delivery task execution locations are location 1 and location 2, respectively, and before a new task is not allocated, the task execution route of the target task receiver is shown by the solid line in fig. 2, namely, from the pick-up location 1 to the pick-up location 2 to the delivery user 1 and finally to the delivery user 2, due to the execution location of the new pick-up task: the embedding similarity between the location 3 and the location 2 is greater than the embedding similarity between the location 3 and the location 1, and the execution location of the new delivery task: the embedding similarity between the user 3 and the user 1 is greater than the embedding similarity between the user 3 and the user 2, so that after the target task receiver is allocated with a new pick-up and delivery task, the task execution route of the target task receiver becomes as shown by the dotted line in fig. 2, namely, from the location 1 to the location 2, to the location 3 closer to the location 2, to the user 1, to the user 3 closer to the user 1, and to the user 2 farther from the user 1.

In an optional implementation manner of this embodiment, the step S102, that is, the step of calculating, by at least one processor, the embedded similarity between the task to be allocated and the allocated task allocated to the target task receiver according to the task execution history behavior data, includes the following steps:

The above-mentioned embedding similarity is calculated based on the embedding vectors corresponding to the task to be allocated and the allocated task, and is used for evaluating the similarity between the task to be allocated and the allocated task allocated to the target task receiver. In this embodiment, when calculating the embedding similarity between the task to be allocated and the allocated task that has been allocated to the target task receiver, first, calculating, by at least one processor, a first embedding vector of a task execution place according to the task execution history behavior data; then, obtaining a second embedded vector corresponding to the execution place of the task to be distributed through at least one processor; and finally, calculating the similarity between the second embedded vector and the first embedded vector through at least one processor, and determining the similarity as the embedded similarity between the task to be distributed and the distributed task distributed to the target task receiver.

In an alternative implementation manner of this embodiment, the embedded vector refers to a vector data expression for characterizing a feature of an object, for example, the object may be provided with an embedded vector corresponding to a task execution location. More specifically, the execution location of the task to be allocated may correspond to an embedded vector, that is, a second embedded vector, and one or more task execution locations may be calculated according to the task execution history behavior data to obtain one or more embedded vectors, that is, one or more first embedded vectors, where the similarity between the first embedded vector and the second embedded vector may be considered as the similarity between the task to be allocated and the task allocated to the target task receiver, and may be used to characterize the similarity between the task to be allocated and the task allocated to the target task receiver.

In an optional implementation manner of this embodiment, the step of calculating, by at least one processor, a first embedded vector of a task execution location according to the task execution history behavior data includes the steps of:

In order to obtain a vector data representation that can accurately characterize the characteristics of an object, in this embodiment, a weighted undirected graph is used to calculate a first embedded vector for all task execution sites from the task execution history behavior data. Specifically, a task execution weighted undirected graph is generated according to the task execution history behavior data through at least one processor, wherein the task execution weighted undirected graph comprises two or more nodes used for representing task execution places and edges connected with the nodes, and the nodes are correspondingly provided with corresponding geographic areas; and then calculating node embedded vectors of all nodes in the task execution weighted undirected graph by at least one processor, wherein the node embedded vectors can be used as first embedded vectors of corresponding task execution places.

In an optional implementation manner of this embodiment, when the task is a pickup task, when a task execution weighted undirected graph is generated, identification information of a geographical area where a task execution location is located in the task execution history behavior data is used as nodes in the task execution weighted undirected graph, where the number of the nodes is one or more, and then edges are formed between the nodes according to a task execution path of a task receiver, and the number of connection times between the two nodes is the weight of the edges. The geographical area where the task execution place is located is obtained by dividing the geographical area in advance according to a preset geographical area dividing rule, and the identification information of the geographical area where the task execution place is located is also obtained according to the preset geographical area identification rule.

In an optional implementation manner of this embodiment, the step of calculating, by at least one processor, a node embedding vector of each node in the task execution weighted undirected graph may be implemented as:

In this embodiment, when calculating the first embedded vector of each node in the task execution weighted undirected graph, first calculating, by at least one processor, a random walk behavior sequence of the task execution weighted undirected graph; and then determining an embedded vector learning model through at least one processor, and learning by taking the random walk behavior sequence as the input of the embedded vector learning model to obtain the node embedded vector of each node in the task execution weighted undirected graph.

Specifically, based on the task execution weighted undirected graph, the deep walk algorithm may be used to learn to obtain a node embedded vector expression of each node in the task execution weighted undirected graph: firstly, a task execution undirected graph is randomly walked through a random walk algorithm to obtain a task execution behavior sequence, and then the obtained task execution behavior sequence is used as training data of a Skip-Gram algorithm, so that node embedded vectors of all nodes in the task execution undirected graph can be obtained through learning, wherein the deep walk algorithm, the random walk algorithm and the Skip-Gram algorithm are common algorithms in the prior art, and a person skilled in the art can obtain node embedded vector expression of each node in the task execution undirected graph by using the algorithms, so that the description of the node embedded vectors of each node in the task execution undirected graph is omitted.

In an optional implementation manner of this embodiment, the step of obtaining, by at least one processor, a second embedded vector corresponding to an execution location of the task to be allocated may be implemented as:

The above mentioned nodes in the task execution weighted undirected graph are correspondingly provided with corresponding geographical areas, which are represented by the identification information of the geographical areas where the task execution places are located in the task execution history behavior data, so that when the second embedded vector corresponding to the execution places of the tasks to be distributed is obtained, the execution places of the tasks to be distributed can be determined first; then, matching the execution place of the task to be distributed with the geographical area corresponding to the node in the task execution weighted undirected graph respectively; and determining a node embedding vector corresponding to a node matched with the geographical area where the execution place of the task to be distributed is located in the task execution weighted undirected graph as a second embedding vector corresponding to the execution place of the task to be distributed.

After the similarity between the second embedding vector and each first embedding vector is calculated by at least one processor, the similarity can be determined as the embedding similarity between the task to be allocated and all allocated tasks allocated to the target task receiver. In an optional implementation manner of this embodiment, before the step of calculating, by at least one processor, the similarity between the second embedded vector and the first embedded vector and determining the similarity as the embedded similarity between the task to be allocated and the task allocated to the target task receiver, the step of weighting and adjusting the first embedded vector and the second embedded vector based on the side information may further include:

In order to further improve accuracy of the embedded vectors, in this embodiment, side information and corresponding weights are also acquired, and weighting adjustment is performed on the first embedded vector and the second embedded vector according to the side information and the weights. Wherein the side information may include one or more of the following information: the identification information of the geographical area where the new joining task execution place is located, the task execution distance, the task content type, the task content quantity, the time slicing serial number information of the task execution, and the like. The task execution distance refers to the distance that a task receiver needs to move when executing a task; the task content category refers to a category to which the task includes content, for example, if the task is a meal pick-up task, the task content category refers to meal pick-up; the task content number refers to the number of the content contained in the task, for example, if the task is a meal pickup task, the task content number refers to the number of the meal to be picked; the time slicing sequence number information of the task execution refers to a sequence number of the task belonging to the checked time slicing when the task is executed, for example, if 15 minutes is taken as one time slicing, when a certain task is executed at the 2 nd 15 minutes, the time slicing sequence number information of the task execution is 2.

After the above side information is determined, the first and second embedded vectors may be weighted according to the influence degree of the side information on the embedded vectors. Specifically, the side information is firstly respectively transformed into a vector form, wherein the transformation mode of the information vector belongs to a method commonly used in the prior art, and the description is omitted in the present disclosure; then, respectively setting corresponding weights for the side information and the embedded vector, determining a deep learning network and a loss function, taking the side information, the embedded vector and the weights as the input of the deep learning network, and learning to obtain the weight which enables the loss function to be minimum; and finally, weighting and adjusting the embedded vector by utilizing each side information vector obtained through learning, the weight corresponding to the side information vector and the weight corresponding to the embedded vector. The weights corresponding to the side information vectors obtained by the learning are assumed to be respectively: the weight corresponding to the task execution distance vector is 0.4, the weight corresponding to the task content type vector is 0.2, the weight corresponding to the task content quantity vector is 0.2, the weight corresponding to the time slicing sequence number information vector to which the task execution belongs is 0.2, the weight corresponding to the embedding vector is 1, and the adjusted embedding vector can be expressed as: the adjusted embedding vector= (embedding vector before adjustment×1+task execution distance vector×0.4+task content category vector×0.2+task content number vector×0.2+time slicing number information vector to which task execution belongs×0.2)/(1+0.4+0.2+0.2+0.2).

In an alternative implementation of this embodiment, the method further includes the steps of:

In the prior art, the similarity degree between an order to be distributed and one of the orders received by the distributor but not distributed is generally examined, and the distribution price of the order to be distributed is deducted according to the similarity degree, so that the receiving efficiency of the order by the distributor is improved, and meanwhile, the distribution cost is reduced. However, the processing manner does not consider the similarity degree between the to-be-dispensed order and all orders which are received by the dispenser but not dispensed, and the deduction of the to-be-dispensed order dispensing price mainly depends on subjective experience, so that the accuracy of the to-be-dispensed order dispensing price adjustment is greatly affected. Therefore, in this embodiment, the initial evaluation value of the task to be allocated is first calculated, and then the adjustment processing is performed on the initial evaluation value of the task to be allocated according to the embedding similarity calculated as described above.

In an alternative implementation of this embodiment, the evaluation value refers to the cost that is spent executing the task, i.e. the execution value of the task. When the task is a picking task, the evaluation value of the task is the picking price of the picking task.

In an optional implementation manner of this embodiment, the initial evaluation value refers to an initial evaluation value to be adjusted of a certain task to be allocated, which is calculated according to a preset rule.

In an optional implementation manner of this embodiment, the step of calculating, by at least one processor, an initial evaluation value of the task to be allocated includes the following steps:

In this embodiment, in order to determine an initial and subsequent evaluation value to be adjusted of the task to be allocated, first, determining or acquiring, by at least one processor, a unit time evaluation value corresponding to the target task receiver, where when the task is a pickup task, the target task receiver is a pickup resource, then the unit time evaluation value corresponding to the target task receiver refers to a unit time pickup value of the pickup resource, such as a time salary of the pickup resource; then, acquiring an execution distance of the task to be distributed and a historical execution speed of the target task receiver through at least one processor, and calculating to obtain the estimated execution time of the task to be distributed according to the execution distance and the historical execution speed, wherein the execution distance of the task to be distributed refers to the distance from the current position of the target task receiver to the execution position of the task to be distributed when the task to be distributed is executed, and the historical execution speed of the target task receiver can be obtained by calculating the average speed of the target task receiver when the task is executed in a preset historical time period; and finally, multiplying the estimated execution time by the unit time evaluation value through at least one processor to obtain an initial evaluation value of the task to be allocated, for example, if the estimated execution time of a certain task to be allocated is 0.5 hour and the unit time evaluation value corresponding to the target task receiver is 20 yuan/hour, the initial evaluation value of the task to be allocated is 0.5 hour×20 yuan/hour=10 yuan.

In an optional implementation manner of this embodiment, the step of performing adjustment processing on the initial evaluation value of the task to be allocated according to the embedding similarity may be implemented as:

and when the embedded similarity is higher than a preset similarity threshold, performing down-regulation processing on the initial evaluation value of the task to be distributed through at least one processor, and enabling the evaluation value in unit time corresponding to the evaluation value after the down-regulation processing to be not lower than the evaluation value in unit time corresponding to the target task receiver.

When the embedding similarity is higher than a preset similarity threshold, the task to be allocated and all allocated tasks allocated to the target task receiver are considered to be relatively similar, if the target task receiver receives the task to be allocated, a certain task execution time can be saved when the target task receiver executes the task, in order to improve the task receiving efficiency of the target task receiver and reduce the task execution cost, the initial evaluation value of the task to be allocated can be subjected to down-regulation according to the similarity degree between the task to be allocated and all allocated tasks allocated to the target task receiver, but the down-regulation needs to meet a certain rule, namely, the evaluation value of unit time corresponding to the evaluation value after the down-regulation is not lower than the evaluation value of unit time corresponding to the target task receiver, namely, the down-regulation of the initial evaluation value of the task to be allocated needs to meet the following formula:

Wherein P is ₁ And P ₂ An evaluation value, t, representing the task assigned to the target task recipient ₁₂ Indicating that the target task receiver has completed executing the assigned task P ₁ And P ₂ Total task execution time required, P ₃ Representing the evaluation value, t, of the task to be allocated after adjustment ₁₂₃ Indicating that the target task receiver has completed executing the assigned task P ₁ 、P ₂ And the total task execution time required by the task to be allocated.

The technical scheme of the present disclosure is explained and illustrated below by taking two application scenarios as examples.

Application scenario one

The task is a goods taking task, the target task receiver is a goods taking resource, the unit time evaluation value corresponding to the target task receiver is the goods taking value of the goods taking resource in unit time, namely the time salary of the goods taking resource, and the task to be allocated is the goods taking task to be allocated. Firstly, calculating an initial price of a to-be-allocated goods taking task, specifically, firstly determining a time salary corresponding to the goods taking resource, then obtaining an execution distance of the to-be-allocated goods taking task and a historical execution speed of the goods taking resource, calculating to obtain an estimated execution time of the to-be-allocated goods taking task according to the execution distance and the historical execution speed, and multiplying the estimated execution time by the time salary to obtain the initial price of the to-be-allocated goods taking task. Then, according to task execution history behavior data of the goods taking resources, calculating the embedding similarity between the goods taking tasks to be allocated and the goods taking tasks allocated to the goods taking resources, specifically, calculating a first embedding vector of a goods taking task execution place according to the task execution history behavior data of the goods taking resources; then obtaining a second embedded vector corresponding to the execution place of the goods taking task to be distributed; and finally, calculating the similarity between the second embedded vector and the first embedded vector, and taking the similarity as the embedded similarity between the to-be-allocated picking task and the allocated picking task allocated to the picking resource. Finally, adjusting the action route of the goods-taking resource and the initial price of the goods-taking task to be distributed according to the calculated embedded similarity, for example, when the embedded similarity is higher than a preset similarity threshold, performing down-regulating processing on the initial price of the goods-taking task to be distributed, wherein the time-firewood corresponding to the price after the down-regulating processing is not lower than the time-firewood of the goods-taking resource determined before.

Application scene two

The task is a delivery task, namely, a guest is sent from a starting point where the guest is located to an appointed end point, the target task receiver is a delivery resource, the unit time evaluation value corresponding to the target task receiver is the unit time delivery value of the delivery resource, namely, the time salary of the delivery resource, and the task to be allocated is the task to be allocated. Firstly, calculating an initial price of a to-be-allocated passenger delivering task, specifically, firstly determining a time salary corresponding to the passenger delivering resource, then obtaining an execution distance of the to-be-allocated passenger delivering task and a historical execution speed of the passenger delivering resource, calculating to obtain an estimated execution time of the to-be-allocated passenger delivering task according to the execution distance and the historical execution speed, and multiplying the estimated execution time by the time salary to obtain the initial price of the to-be-allocated passenger delivering task. Then, according to the task execution history behavior data of the passenger resource, calculating the embedding similarity between the passenger task to be allocated and the passenger task allocated, specifically, firstly calculating a first embedding vector of a passenger task starting/stopping execution place according to the task execution history behavior data of the passenger resource; then obtaining a second embedded vector corresponding to the initial execution place of the to-be-allocated passenger task; and finally, calculating the similarity between the second embedded vector and the first embedded vector, and taking the similarity as the embedded similarity between the to-be-allocated passenger task and the allocated passenger task allocated to the passenger resource. Finally, adjusting the action route of the passenger resource and the initial price of the passenger task to be allocated according to the calculated embedded similarity, for example, when the embedded similarity is higher than a preset similarity threshold, performing down-regulating processing on the initial price of the passenger task to be allocated, wherein the time salary corresponding to the price after the down-regulating processing is not lower than the time salary of the passenger resource determined before.

The following are device embodiments of the present disclosure that may be used to perform method embodiments of the present disclosure.

Fig. 3 shows a block diagram of a task processing device according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device, as a task processing server, by software, hardware, or a combination of both. As shown in fig. 3, the task processing device includes:

an obtaining module 301 configured to obtain, by at least one processor, task execution history behavior data of one or more task recipients within a preset history period;

a calculating module 302, configured to calculate, according to the task execution history behavior data, an embedding similarity between the task to be allocated and the task allocated to the target task receiver, where the embedding similarity is calculated based on the embedding vectors of the task to be allocated and the task allocated;

an adjustment module 303 configured to adjust, by at least one processor, a task execution plan of the target task recipient based on an embedded similarity between the task to be allocated and the task allocated to the target task recipient in response to the task to be allocated being allocated to the target task recipient.

In view of the above-described drawbacks, in this embodiment, a task processing device is proposed that calculates an embedding similarity between a task to be allocated and an allocated task allocated to a target task receiver based on embedding vectors of the task to be allocated and the allocated task, and finally adjusts a task execution plan of the target task receiver according to the embedding similarity. According to the technical scheme, the similarity between the task to be distributed and all the distributed tasks of a certain target task receiver is considered, and the task execution plan of the target task receiver is adjusted based on the calculated similarity, so that the service efficiency is effectively improved under the condition of guaranteeing the service quality.

In an alternative implementation of the present embodiment, the computing module 302 includes:

a computing sub-module configured to obtain, by at least one processor, task execution history behavior data of the one or more task recipients within a preset history period, and compute a first embedded vector of a task execution place according to the task execution history behavior data;

The above-mentioned embedding similarity is calculated based on the embedding vectors corresponding to the task to be allocated and the allocated task, and is used for evaluating the similarity between the task to be allocated and the allocated task allocated to the target task receiver. In this embodiment, when calculating the embedding similarity between the task to be allocated and the allocated task that has been allocated to the target task receiver, the calculation submodule calculates, by at least one processor, a first embedding vector of a task execution place according to the task execution history behavior data; the acquisition sub-module acquires a second embedded vector corresponding to the execution place of the task to be distributed through at least one processor; the determining submodule calculates the similarity between the second embedding vector and the first embedding vector through at least one processor, and determines the similarity as the embedding similarity between the task to be allocated and the allocated task allocated to the target task receiver.

In an alternative implementation of this embodiment, the computing sub-module may be configured to:

In an optional implementation manner of this embodiment, the calculating, by at least one processor, the portion of the node embedding vector of each node in the task execution weighted undirected graph may be configured to:

In an alternative implementation of this embodiment, the acquiring sub-module may be configured to:

In the determining submodule, after the similarity between the second embedding vector and each first embedding vector is calculated by at least one processor, the similarity can be respectively determined as the embedding similarity between the task to be allocated and all allocated tasks allocated to the target task receiver.

In an optional implementation manner of this embodiment, before the determining submodule, a portion for performing weighting adjustment on the first embedded vector and the second embedded vector based on side information may be further included, that is, the calculating module 302 includes:

the adjusting sub-module is configured to acquire side information and corresponding weight through at least one processor, and carry out weighting adjustment on the first embedded vector and the second embedded vector according to the side information and the weight;

In an alternative implementation of this embodiment, the apparatus further includes:

In an optional implementation manner of this embodiment, the portion of the processing module that calculates, by at least one processor, the initial evaluation value of the task to be allocated may be configured to:

In an optional implementation manner of this embodiment, the portion of the processing module that performs adjustment processing on the initial evaluation value of the task to be allocated according to the embedded similarity may be configured to:

The present disclosure also discloses an electronic device, fig. 4 shows a block diagram of the electronic device according to an embodiment of the present disclosure, and as shown in fig. 4, the electronic device 400 includes a memory 401 and a processor 402; wherein,,

the memory 401 is used to store one or more computer instructions, which are executed by the processor 402 to implement the above-described method steps.

As shown in fig. 5, the computer system 500 includes a processing unit 501 that can execute various processes in the above-described embodiments in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM503, various programs and data required for the operation of the system 500 are also stored. The processing unit 501, the ROM502, and the RAM503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

The following components are connected to the I/O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output portion 507 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The drive 510 is also connected to the I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as needed so that a computer program read therefrom is mounted into the storage section 508 as needed. The processing unit 501 may be implemented as a processing unit such as CPU, GPU, TPU, FPGA, NPU.

In particular, according to embodiments of the present disclosure, the methods described above may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program tangibly embodied on a medium readable thereby, the computer program comprising program code for performing the object recognition method. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 509, and/or installed from the removable medium 511.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units or modules described in the embodiments of the present disclosure may be implemented by software, or may be implemented by hardware. The units or modules described may also be provided in a processor, the names of which in some cases do not constitute a limitation of the unit or module itself.

As another aspect, the present disclosure also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the apparatus described in the above embodiment; or may be a computer-readable storage medium, alone, that is not assembled into a device. The computer-readable storage medium stores one or more programs for use by one or more processors in performing the methods described in the present disclosure.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by those skilled in the art that the scope of the invention referred to in this disclosure is not limited to the specific combination of features described above, but encompasses other embodiments in which any combination of features described above or their equivalents is contemplated without departing from the inventive concepts described. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Claims

1. A task processing method, comprising:

Calculating, by at least one processor, a first embedded vector of a task execution location based on the task execution historical behavior data; acquiring an execution place of a task to be allocated by at least one processor, matching the execution place of the task to be allocated with a geographic area corresponding to a node in a task execution weighted undirected graph by at least one processor, and determining a node embedding vector corresponding to the node of the task execution weighted undirected graph, which is matched with the geographic area of the execution place of the task to be allocated, as a second embedding vector corresponding to the execution place of the task to be allocated by at least one processor; calculating the similarity between the second embedded vector and the first embedded vector through at least one processor, and determining the similarity as the embedded similarity between the task to be distributed and the distributed task distributed to the target task receiver, wherein the task execution weighted undirected graph is generated according to the task execution historical behavior data;

2. The method of claim 1, the computing, by at least one processor, a first embedded vector of task execution places from the task execution historical behavior data, comprising:

3. The method of claim 2, the computing, by at least one processor, node embedding vectors for each node in the task execution weighted undirected graph, implemented as:

4. The method of claim 1, further comprising, prior to the computing, by at least one processor, a similarity between the second embedded vector and the first embedded vector:

5. The method of any of claims 1-4, further comprising:

6. The method of claim 5, the calculating, by at least one processor, an initial evaluation value of a task to be assigned, comprising:

7. The method of claim 6, wherein the adjusting the initial evaluation value of the task to be allocated according to the embedded similarity is implemented as:

8. A task processing device comprising:

a computing module, comprising: a computing sub-module configured to compute, by at least one processor, a first embedded vector of a task execution place from the task execution history behavior data; the acquisition sub-module is configured to acquire an execution place of a task to be allocated through at least one processor, match the execution place of the task to be allocated with a geographic area corresponding to a node in a task execution weighted undirected graph through at least one processor, and determine a node embedding vector corresponding to the node of the task execution weighted undirected graph matched with the geographic area of the execution place of the task to be allocated as a second embedding vector corresponding to the execution place of the task to be allocated through at least one processor; a determining submodule configured to calculate, by at least one processor, a similarity between the second embedding vector and the first embedding vector, determine it as an embedding similarity between the task to be allocated and an allocated task allocated to a target task receiver, wherein the task execution weighted undirected graph is generated from the task execution historical behavior data;

9. The apparatus of claim 8, the computing submodule configured to:

10. The apparatus of claim 9, the computing, by the at least one processor, the portion of the node embedding vector for each node in the task execution weighted undirected graph configured to:

11. The apparatus of claim 8, prior to the determining the sub-module, further comprising:

12. The apparatus of any of claims 8-11, further comprising:

13. The apparatus of claim 12, wherein the portion of the processing module that calculates, by the at least one processor, the initial evaluation value for the task to be assigned is configured to:

14. The apparatus of claim 13, wherein the portion of the processing module that performs adjustment processing on the initial evaluation value of the task to be allocated according to the embedded similarity is configured to:

15. An electronic device comprising a memory and at least one processor; wherein the memory is for storing one or more computer instructions, wherein the one or more computer instructions are executed by the at least one processor to implement the steps of the method of any of claims 1-7.

16. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any of claims 1-7.