CN110717690A

CN110717690A - Distribution resource management method, distribution resource management device, electronic equipment and computer storage medium

Info

Publication number: CN110717690A
Application number: CN201911007936.6A
Authority: CN
Inventors: 彭涛
Original assignee: Rajax Network Technology Co Ltd
Current assignee: Rajax Network Technology Co Ltd
Priority date: 2019-10-22
Filing date: 2019-10-22
Publication date: 2020-01-21

Abstract

The embodiment of the disclosure discloses a method and a device for managing distribution resources, electronic equipment and a computer storage medium, wherein the method for managing the distribution resources comprises the following steps: acquiring first historical distribution data in a first preset historical time period from a storage through at least one processor, and determining a first evaluation value according to the first historical distribution data through at least one processor; acquiring second historical distribution data in a second preset historical time period from the storage through at least one processor, and determining a second evaluation value according to the second historical distribution data through at least one processor; managing, by at least one processor, the delivery resources according to the first and second evaluation values. The technical scheme can carry out comprehensive balanced management on the distribution resources of various grades, eliminate the phenomenon of abnormal positive feedback, ensure the stability of the transport capacity of the distribution resources, promote the comprehensive development of the distribution resources, ensure the operation efficiency of a distribution system and improve the distribution quality of the distribution resources.

Description

Distribution resource management method, distribution resource management device, electronic equipment and computer storage medium

Technical Field

The present disclosure relates to the field of distributed resource management technologies, and in particular, to a distributed resource management method and apparatus, an electronic device, and a computer storage medium.

Background

With the development of internet technology, more and more merchants or service providers provide services for users through internet platforms, and many internet services need to be delivered by distributors, so that the delivery quality is very important for improving the service quality of the internet platforms. In order to ensure efficient operation of the distribution system, the prior art generally evaluates the distribution resources according to the capability and behavior data of the distribution resources, and manages the distribution resources according to the evaluation result.

Although the above processing method can realize the hierarchical management of the distributed resources, the purpose of balanced management cannot be achieved, so that the newly-entered distributed resources with capacity are difficult to promote to higher levels, and the excitation and balanced protection of the low-level distributed resources are also not facilitated, thereby affecting the overall operation efficiency of the distribution system and being not beneficial to the balanced development of the distributed resources.

Disclosure of Invention

The embodiment of the disclosure provides a distribution resource management method and device, electronic equipment and a computer storage medium.

In a first aspect, an embodiment of the present disclosure provides a method for managing delivery resources.

Specifically, the method for managing the distribution resources includes:

acquiring first historical distribution data in a first preset historical time period from a storage through at least one processor, and determining a first evaluation value according to the first historical distribution data through at least one processor;

acquiring second historical distribution data in a second preset historical time period from the storage through at least one processor, and determining a second evaluation value according to the second historical distribution data through at least one processor;

managing the distribution resources according to the first evaluation value and the second evaluation value through at least one processor, wherein the managing at least comprises the step of performing preset processing on the determined regression distribution resources according to a preset proportion;

wherein the first preset historical time period is longer than the second preset historical time period.

With reference to the first aspect, the present disclosure in a first implementation manner of the first aspect, the acquiring, by at least one processor, first historical delivery data within a first preset historical time period from a memory, and determining, by at least one processor, a first evaluation value according to the first historical delivery data, includes:

obtaining, by at least one processor, first historical distribution data within a first preset historical time period from a memory, wherein the first historical distribution data includes one or more first historical distribution sub-data;

determining, by at least one processor, a first weight value corresponding to the first historical delivery child data;

digitizing, by at least one processor, the first historical distribution subdata;

and calculating to obtain the first evaluation value according to the digitized first historical distribution subdata and the corresponding first weight value thereof by at least one processor.

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 acquiring, by the at least one processor, first historical delivery data within a first preset historical time period from a memory, and determining, by the at least one processor, a first evaluation value according to the first historical delivery data includes:

acquiring third history distribution data and a corresponding third evaluation value label in a third preset history time period from a memory through at least one processor, wherein the third history distribution data comprises one or more third history distribution subdata;

training by using the third history distribution data and the corresponding third evaluation value label as training data through at least one processor to obtain a first evaluation model;

and inputting the first historical distribution data into the first evaluation model through at least one processor to obtain a first evaluation value.

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 acquiring, by at least one processor, second historical delivery data within a second preset historical time period from a memory, and determining, by at least one processor, a second evaluation value according to the second historical delivery data includes:

obtaining, by at least one processor, second historical distribution data within a second preset historical time period from a memory, wherein the second historical distribution data includes one or more second historical distribution sub-data;

determining, by at least one processor, a second weight value corresponding to the second historical delivery child data;

digitizing, by at least one processor, the second historical distribution sub-data;

and calculating by at least one processor according to the digitized second historical distribution subdata and a second weight value corresponding to the second historical distribution subdata to obtain the second evaluation value.

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 the at least one processor, second historical delivery data within a second preset historical time period from the memory, and determining, by the at least one processor, a second evaluation value according to the second historical delivery data includes:

acquiring fourth historical distribution data and a corresponding fourth evaluation value label in a fourth preset historical time period from a memory through at least one processor, wherein the fourth historical distribution data comprises two or more fourth historical distribution subdata;

training by using the fourth historical distribution data and a corresponding fourth evaluation value label as training data through at least one processor to obtain a second evaluation model;

acquiring second historical distribution data in a second preset historical time period from a memory through at least one processor, wherein the second historical distribution data comprises two or more second historical distribution subdata;

and inputting the second historical distribution data into the second evaluation model through at least one processor to obtain a second evaluation value.

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, the managing, by the at least one processor, the delivery resources according to the first evaluation value and the second evaluation value includes:

determining, by at least one processor, a first evaluation weight value corresponding to the first evaluation value and a second evaluation weight value corresponding to the second evaluation value;

managing, by at least one processor, the delivery resources based on the first evaluation value and its corresponding first evaluation weight value, the second evaluation value and its corresponding second evaluation weight value.

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 managing, by the at least one processor, the delivery resources based on the first evaluation value and the corresponding first evaluation weight value, the second evaluation value and the corresponding second evaluation weight value, is implemented as:

when the management is first preset management, the first evaluation weight value is higher than the second evaluation weight value;

when the management is a second preset management, the first evaluation weight value is lower than a second evaluation weight value.

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 managing, by the at least one processor, the delivery resources according to the first evaluation value and the second evaluation value includes:

obtaining, by at least one processor, a distribution resource regression feature;

determining, by at least one processor, a regression delivery resource based on the first and second evaluation values and a delivery resource regression feature;

and performing preset processing on the regression distribution resources according to a preset proportion through at least one processor.

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 preset processing is step excitation processing.

In a second aspect, an apparatus for managing delivery resources is provided in the embodiments of the present disclosure.

Specifically, the delivery resource management device includes:

the system comprises a first determination module, a second determination module and a third determination module, wherein the first determination module is used for acquiring first historical distribution data in a first preset historical time period from a storage through at least one processor and determining a first evaluation value according to the first historical distribution data through at least one processor;

the second determination module is configured to acquire second historical distribution data in a second preset historical time period from the storage through at least one processor and determine a second evaluation value according to the second historical distribution data through at least one processor;

a management module configured to manage, by at least one processor, the delivery resources according to the first evaluation value and the second evaluation value, wherein the management at least includes performing preset processing on the determined regression delivery resources according to a preset proportion;

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

a first obtaining submodule configured to obtain, by at least one processor, first historical distribution data within a first preset historical time period from a memory, wherein the first historical distribution data includes one or more first historical distribution sub-data;

a first determining submodule configured to determine, by at least one processor, a first weight value corresponding to the first historical distribution sub-data;

a first numeralization submodule configured to numerate, by at least one processor, the first historical dispatch sub-data;

and the first calculation submodule is configured to calculate the first evaluation value according to the digitized first historical distribution subdata and the corresponding first weight value thereof through at least one processor.

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 embodiment of the present invention includes:

a second obtaining sub-module configured to obtain, by at least one processor, third history distribution data and a corresponding third evaluation value tag within a third preset history time period from a memory, where the third history distribution data includes one or more third history distribution sub-data;

a first training sub-module configured to train, by at least one processor, the third history distribution data and a corresponding third evaluation value label as training data to obtain a first evaluation model;

a third obtaining sub-module configured to obtain, by at least one processor, first historical distribution data within a first preset historical time period from a memory, wherein the first historical distribution data includes one or more first historical distribution sub-data;

a first input sub-module configured to input, by at least one processor, the first historical delivery data to the first evaluation model, resulting in a first evaluation value.

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 embodiment of the present invention includes:

a fourth obtaining sub-module configured to obtain, by the at least one processor, second historical distribution data within a second preset historical time period from the memory, wherein the second historical distribution data includes one or more second historical distribution sub-data;

a second determining submodule configured to determine, by at least one processor, a second weight value corresponding to the second historical distribution child data;

a second numeralization submodule configured to numerate, by at least one processor, the second historical dispatch sub-data;

and the second calculation submodule is configured to calculate, by at least one processor, the second evaluation value according to the digitized second historical distribution subdata and the second weight value corresponding to the second historical distribution subdata.

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 embodiment of the present invention includes:

a fifth obtaining sub-module, configured to obtain, by at least one processor, fourth historical distribution data and a corresponding fourth evaluation value tag within a fourth preset historical time period from a memory, where the fourth historical distribution data includes two or more fourth historical distribution sub-data;

a second training sub-module configured to train, by using the at least one processor, the fourth historical distribution data and a corresponding fourth evaluation value label as training data to obtain a second evaluation model;

a sixth obtaining sub-module configured to obtain, by the at least one processor, second historical distribution data within a second preset historical time period from the memory, where the second historical distribution data includes two or more second historical distribution sub-data;

a second input sub-module configured to input, by at least one processor, the second historical delivery data to the second evaluation model, resulting in a second evaluation value.

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 embodiment of the present invention includes:

a third determining sub-module configured to determine, by at least one processor, a first evaluation weight value corresponding to the first evaluation value and a second evaluation weight value corresponding to the second evaluation value;

a management sub-module configured to manage, by at least one processor, the delivery resources based on the first evaluation value and its corresponding first evaluation weight value, the second evaluation value and its corresponding second evaluation weight value.

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, the management submodule 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, 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 management module includes:

a seventh obtaining submodule configured to obtain, by the at least one processor, the distribution resource regression feature;

a fourth determination submodule configured to determine, by the at least one processor, a regression delivery resource from the first evaluation value, the second evaluation value, and a delivery resource regression feature;

a processing submodule configured to perform, by at least one processor, a preset process on the regression distribution resource according to a preset ratio.

In a third aspect, the disclosed embodiments provide an electronic device, including a memory and a processor, where the memory is configured to store one or more computer instructions, where the one or more computer instructions are executed by the processor to implement the method steps of the delivery resource management method in the first aspect.

In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium for storing computer instructions for a distributed resource management apparatus, where the computer instructions are used to execute the distributed resource management method in the first aspect as a distributed resource management apparatus.

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

according to the technical scheme, the first evaluation value and the second evaluation value which are obtained based on different considerations are set for the distribution resources, so that the distribution resources are comprehensively and uniformly managed. According to the technical scheme, the comprehensive and balanced management can be performed on the distribution resources of various grades, so that the abnormal positive feedback phenomenon is eliminated, the stability of the transport capacity of the distribution resources is guaranteed, the comprehensive and balanced development of the distribution resources is promoted, the overall operation efficiency of a distribution system is ensured, and the distribution quality of the distribution resources is improved.

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 when taken in conjunction with the accompanying drawings. In the drawings:

FIG. 1 illustrates a flow diagram of a method of delivery resource management according to an embodiment of the present disclosure;

FIG. 2 shows a flowchart of step S101 of the delivery resource management method according to the embodiment shown in FIG. 1;

FIG. 3 shows a flowchart of step S101 of a method for delivery resource management according to another embodiment shown in FIG. 1;

FIG. 4 is a flowchart illustrating step S102 of the distributed resource management method according to the embodiment shown in FIG. 1;

FIG. 5 is a flowchart illustrating a step S102 of a distributed resource management method according to another embodiment illustrated in FIG. 1;

FIG. 6 is a flowchart illustrating a step S103 of the distributed resource management method according to the embodiment illustrated in FIG. 1;

FIG. 7 is a flowchart illustrating a step S103 of a distributed resource management method according to another embodiment illustrated in FIG. 1;

fig. 8 is a block diagram showing the configuration of a distribution resource management apparatus according to an embodiment of the present disclosure;

FIG. 9 is a block diagram showing the configuration of a first determination module 801 of the distribution resource management apparatus according to the embodiment shown in FIG. 8;

FIG. 10 is a block diagram showing the configuration of a first determination module 801 of the distributed resource management apparatus according to another embodiment shown in FIG. 8;

FIG. 11 is a block diagram illustrating a second determining module 802 of the apparatus for distributing resource management according to the embodiment shown in FIG. 8;

FIG. 12 is a block diagram illustrating a second determining module 802 of the apparatus for distributing resource management according to another embodiment shown in FIG. 8;

FIG. 13 is a block diagram showing the configuration of a management module 803 of the distributed resource management apparatus according to the embodiment shown in FIG. 8;

FIG. 14 is a block diagram showing the structure of a management module 803 of a distributed resource management apparatus according to another embodiment shown in FIG. 8;

FIG. 15 shows a block diagram of an electronic device according to an embodiment of the present disclosure;

FIG. 16 is a block diagram of a computer system suitable for implementing a method for delivery resource management according to one 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. Also, for the sake of clarity, parts not relevant to the description of the exemplary embodiments are omitted in the drawings.

In the present disclosure, it is to be understood that terms such as "including" or "having," etc., are intended to indicate the presence of the disclosed features, numbers, steps, behaviors, components, parts, or combinations thereof, and are not intended to preclude the possibility that one or more other features, numbers, steps, behaviors, components, parts, or combinations thereof may be present or added.

It should be further noted that the embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict. 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 first evaluation value and the second evaluation value which are obtained based on different considerations are set for the distribution resources, so that the distribution resources are comprehensively and uniformly managed. According to the technical scheme, the comprehensive and balanced management can be performed on the distribution resources of various grades, so that the abnormal positive feedback phenomenon is eliminated, the stability of the transport capacity of the distribution resources is guaranteed, the comprehensive and balanced development of the distribution resources is promoted, the overall operation efficiency of a distribution system is ensured, and the distribution quality of the distribution resources is improved.

Fig. 1 shows a flowchart of a delivery resource management method according to an embodiment of the present disclosure, and as shown in fig. 1, the delivery resource management method includes the following steps S101 to S103:

in step S101, acquiring, by at least one processor, first historical distribution data within a first preset historical time period from a memory, and determining, by at least one processor, a first evaluation value according to the first historical distribution data;

in step S102, acquiring, by at least one processor, second historical distribution data within a second preset historical time period from a memory, and determining, by at least one processor, a second evaluation value according to the second historical distribution data;

in step S103, managing, by at least one processor, the delivery resources according to the first evaluation value and the second evaluation value, where the managing at least includes performing preset processing on the determined regression delivery resources according to a preset proportion;

As mentioned above, with the development of internet technology, more and more merchants or service providers provide services for users through internet platforms, and many internet services require distributors to distribute, so that the distribution quality is very important for improving the service quality of the internet platforms. In order to ensure efficient operation of the distribution system, the prior art generally evaluates the distribution resources according to the capability and behavior data of the distribution resources, and manages the distribution resources according to the evaluation result. Although the above processing method can realize the hierarchical management of the distributed resources, the purpose of balanced management cannot be achieved, so that the newly-entered distributed resources with capacity are difficult to promote to higher levels, and the excitation and balanced protection of the low-level distributed resources are also not facilitated, thereby affecting the overall operation efficiency of the distribution system and being not beneficial to the balanced development of the distributed resources.

In view of the above-mentioned drawbacks, in this embodiment, a delivery resource management method is proposed that performs comprehensive balance management of delivery resources by setting a first evaluation value and a second evaluation value, which are obtained based on different considerations, for the delivery resources. According to the technical scheme, the comprehensive and balanced management can be performed on the distribution resources of various grades, so that the abnormal positive feedback phenomenon is eliminated, the stability of the transport capacity of the distribution resources is guaranteed, the comprehensive and balanced development of the distribution resources is promoted, the overall operation efficiency of a distribution system is ensured, and the distribution quality of the distribution resources is improved.

In an alternative implementation manner of this embodiment, the delivery resource refers to a resource that can be used to perform a delivery task, such as a dispenser, a delivery device, a delivery robot, and the like. The distribution resources comprise distribution resources which provide exclusive distribution service for a certain merchant or service provider, and crowd-sourced distribution resources which provide distribution service for a plurality of merchants or service providers and flexibly undertake distribution tasks of different merchants or service providers according to the requirements of different merchants or service providers.

In an optional implementation manner of this embodiment, the first preset historical time period is longer than the second preset historical time period, for example, the first preset historical time period may be the time of the last year, the last 6 months, and the second preset historical time period may be the time of the last two days, the last week, or the last month. That is to say, the first evaluation value determined according to the first historical distribution data in the first preset historical time period is used for representing the long-term behavior characteristics of the distribution resources, and the grade information of the distribution resources can be subsequently evaluated mainly according to the long-term behavior characteristics; the second evaluation value determined according to the second historical distribution data in the second preset historical time period is used for representing the short-term behavior characteristics of the distribution resources, and then the short-term incentive information of the distribution resources can be evaluated mainly according to the short-term behavior characteristics.

In this implementation, the first historical delivery data used for determining the long-term behavior characteristics of the delivery resource may include one or more of the following first historical delivery sub-data capable of embodying the long-term behavior characteristics of the delivery resource: the historical total number of delivered tasks, the historical daily maximum delivered task number, the historical average received delivered task time, the historical average received delivered task timeout time, the historical received delivered task timeout rate, the historical average delivered time, the historical average delivered timeout time, the historical delivered timeout rate, the historical delivered task completion rate, the historical complained number, the historical rewarded rate, and the like. The first historical distribution data can be used for counting and analyzing normal weather and abnormal weather respectively.

In this implementation, the second historical delivery data used for determining the short-term behavior characteristics of the delivered resources may include one or more of the following second historical delivery sub-data capable of characterizing the short-term behavior of the delivered resources: the historical distribution task total amount, the historical receiving distribution task overtime rate, the historical receiving distribution task overtime frequency, the historical distribution overtime rate, the historical daily receiving distribution task amount, the historical daily average distribution task completion rate, the historical peak daily receiving distribution task amount, the historical peak daily average distribution task completion rate, the historical special time period receiving distribution task amount, the historical special time period distribution task completion rate, the historical attendance rate and the like.

In an alternative implementation manner of this embodiment, as shown in fig. 2, the step S101 of acquiring, by at least one processor, first historical distribution data in a first preset historical time period from a memory, and determining, by at least one processor, a first evaluation value according to the first historical distribution data includes the following steps S201 to S204:

in step S201, obtaining, by at least one processor, first historical distribution data within a first preset historical time period from a memory, where the first historical distribution data includes one or more first historical distribution sub-data;

in step S202, determining, by at least one processor, a first weight value corresponding to the first historical distribution child data;

in step S203, digitizing, by at least one processor, the first historical distribution sub-data;

in step S204, the at least one processor calculates the first evaluation value according to the digitized first historical distribution sub-data and the corresponding first weight value thereof.

As mentioned above, the first historical distribution data includes one or more first historical distribution sub-data, and in order to comprehensively consider the one or more first historical distribution sub-data, in this implementation, the first evaluation value is calculated based on the one or more first historical distribution sub-data by means of weighted average. Specifically, firstly, selecting the type of first historical distribution subdata according to the requirements of practical application through at least one processor, and acquiring first historical distribution data in a first preset historical time period from a memory through at least one processor; then, determining a first weight value corresponding to the first historical distribution subdata according to the importance degree of the first historical distribution subdata on the distribution resource evaluation through at least one processor; digitizing, by at least one processor, the first historical distribution child data; and finally, calculating by at least one processor according to the digitized first historical distribution subdata and the corresponding first weight value thereof through a weighted average algorithm to obtain the first evaluation value.

For example, if the historical total number of delivery tasks, the historical daily maximum delivery task number, the historical average delivery timeout duration, the historical delivery timeout rate, the historical complaint rate, and the historical rewarded rate are considered to be important for the evaluation of the delivery resources, and the historical average reception delivery task timeout duration, the historical reception delivery task timeout rate, the historical average reception delivery task duration, the historical delivery task completion rate, the historical complaint times, and the historical rewarded times are relatively less important for the evaluation of the delivery resources, the first weight values corresponding to the historical total number of delivery tasks, the historical daily maximum delivery task number, the historical average delivery timeout duration, the historical delivery timeout rate, the historical complaint rate, and the historical rewarded rate may be set higher, and the historical average reception delivery task timeout duration, the historical reception delivery timeout rate, the historical delivery delay time, the historical reception delivery delay time, the historical delivery delay time, And setting first weighted values corresponding to the historical average receiving and distributing task time length, the historical average distributing time length, the historical distributing task completion rate, the historical complained times and the historical rewarded times to be lower.

In another optional implementation manner of the present embodiment, the first evaluation value is calculated by means of a model calculation method. As shown in fig. 3, the step S101 of acquiring, by at least one processor, first historical distribution data in a first preset historical time period from a memory, and determining, by at least one processor, a first evaluation value according to the first historical distribution data includes the following steps S301 to S304:

in step S301, third history distribution data and a corresponding third evaluation value tag within a third preset history time period are acquired from a memory by at least one processor, where the third history distribution data includes one or more third history distribution sub-data;

in step S302, training by using the third history distribution data and the corresponding third evaluation value label as training data through at least one processor to obtain a first evaluation model;

in step S303, obtaining, by at least one processor, first historical distribution data within a first preset historical time period from a memory, where the first historical distribution data includes one or more first historical distribution sub-data;

in step S304, the first historical distribution data is input to the first evaluation model by at least one processor, so as to obtain a first evaluation value.

In this implementation, the first evaluation value is obtained by a model calculation method, specifically, first, third history distribution data and a corresponding third evaluation value label in a third preset history time period are obtained from a memory as model training data by at least one processor, where the third history distribution data includes one or more third history distribution sub-data similar to the first history distribution data, and it should be noted that the third history distribution sub-data should be the same as the first history distribution sub-data for which evaluation value calculation is subsequently performed; then, training by using the third history distribution data and the corresponding third evaluation value label as training data through at least one processor to obtain a first evaluation model, wherein an initial model of the first evaluation model can be selected according to the needs of practical application, and the method is not specifically limited by the disclosure; acquiring, by at least one processor, first historical distribution data within a first preset historical time period for evaluation value calculation from a memory, wherein the first historical distribution data includes one or more first historical distribution sub-data; and finally, inputting the first historical distribution data into the first evaluation model through at least one processor to obtain the first evaluation value.

The second evaluation value may be calculated by a weighted average method or a model calculation method, similar to the first evaluation value calculation method, and will not be described in detail herein, but it is noted that, unlike the first evaluation value calculation method, the data used in the second evaluation value calculation process is short-term feature data, not long-term feature data.

That is, in an optional implementation manner of this embodiment, the second evaluation value is calculated based on the one or more second historical delivery sub-data by means of a weighted average, as shown in fig. 4, and the step S102 of acquiring, by at least one processor, second historical delivery data in a second preset historical time period from a memory, and determining, by at least one processor, the second evaluation value according to the second historical delivery data includes the following steps S401 to S404:

in step S401, obtaining, by at least one processor, second historical distribution data within a second preset historical time period from a memory, where the second historical distribution data includes one or more second historical distribution sub-data;

in step S402, determining, by at least one processor, a second weight value corresponding to the second historical distribution child data;

in step S403, digitizing, by at least one processor, the second historical distribution sub-data;

in step S404, the at least one processor calculates the second evaluation value according to the digitized second historical distribution sub-data and the second weight value corresponding to the second historical distribution sub-data.

In another optional implementation manner of the present embodiment, the second evaluation value is calculated by means of a model calculation method. As shown in fig. 5, the step S102 of acquiring, by at least one processor, second historical distribution data in a second preset historical time period from a memory, and determining, by at least one processor, a second evaluation value according to the second historical distribution data includes the following steps S501 to S504:

in step S501, at least one processor obtains, from a memory, fourth historical distribution data and a corresponding fourth evaluation value tag in a fourth preset historical time period, where the fourth historical distribution data includes two or more fourth historical distribution sub-data;

in step S502, training, by at least one processor, the fourth historical distribution data and the corresponding fourth evaluation value label as training data to obtain a second evaluation model;

in step S503, obtaining, by at least one processor, second historical distribution data within a second preset historical time period from a memory, where the second historical distribution data includes two or more second historical distribution sub-data;

in step S504, the second historical distribution data is input to the second evaluation model by at least one processor, so as to obtain a second evaluation value.

In an optional implementation manner of this embodiment, as shown in fig. 6, the step S103 of managing, by at least one processor, the delivery resource according to the first evaluation value and the second evaluation value includes the following steps S601 to S602:

in step S601, determining, by at least one processor, a first evaluation weight value corresponding to the first evaluation value and a second evaluation weight value corresponding to the second evaluation value;

in step S602, the delivery resource is managed by at least one processor based on the first evaluation value and a corresponding first evaluation weight value, and the second evaluation value and a corresponding second evaluation weight value.

As mentioned above, the present disclosure is intended to comprehensively balance management of delivery resources using the first evaluation value and the second evaluation value obtained based on different considerations. In the implementation mode, the comprehensive balance management of the distribution resources is realized in a weighted average mode. Specifically, first, determining, by at least one processor, a first evaluation weight value corresponding to the first evaluation value and a second evaluation weight value corresponding to the second evaluation value; the delivery resources are then managed by at least one processor based on the first evaluation value and its corresponding first evaluation weight value, the second evaluation value and its corresponding second evaluation weight value.

Further, the step S602 of managing, by at least one processor, the delivery resources based on the first evaluation value and the corresponding first evaluation weight value, the second evaluation value and the corresponding second evaluation weight value may be implemented as:

In this implementation, the first evaluation value and the second evaluation value are flexibly used and the weight values corresponding to the first evaluation value and the second evaluation value are flexibly adjusted according to the difference of the management contents. For example, when distributing a distribution task to a distribution resource, considering that the long-term behavior feature of the distribution resource can better reflect the capability of the distribution resource to complete the task, a first evaluation value representing the long-term behavior feature of the distribution resource is taken as a main evaluation value, and a second evaluation value representing the short-term behavior feature of the distribution resource is taken as a subsidiary evaluation value, that is, a first evaluation weight value corresponding to the first evaluation value is set to be higher than a second evaluation weight value corresponding to the second evaluation value. For example, when an immediate or short-term reward is set for a delivery resource, considering that the short-term behavior feature of the delivery resource can reflect the recent performance of the delivery resource, the first evaluation value representing the long-term behavior feature of the delivery resource is set to be auxiliary, that is, the first evaluation value corresponding to the first evaluation value is set to be lower than the second evaluation value corresponding to the second evaluation value, mainly the second evaluation value representing the short-term behavior feature of the delivery resource.

Based on the scheme, the comprehensive balance management of the distribution resources can be realized, for example, short-term performance of high-grade distribution resources is reduced due to some special reasons, and based on the comprehensive balance management, the condition mainly influences the short-term reward of the distribution resources and does not greatly influence the grade evaluation of the distribution resources. Therefore, the phenomenon that the evaluation level of the distributed resources directly influences the task distribution amount of the distributed resources, and the task distribution amount in turn directly influences the evaluation level of the distributed resources is avoided, so that the newly-entered distributed resources with the capability have the opportunity to promote the level, the stability of the capacity of the distributed resources is further ensured, the comprehensive and balanced development of the distributed resources is promoted, the overall operation efficiency of a distribution system is ensured, and the distribution quality of the distributed resources is improved.

Considering that some industries have delivery peaks, for example, for the take-away industry, delivery tasks are basically concentrated in noon and evening, especially the number of the delivery tasks at noon exceeds 50% of the total number of the delivery tasks all day long, during the delivery peak, delivery resources often have insufficient resources, and at this time, besides timely scheduling the delivery resources providing dedicated delivery services for a certain merchant or service provider, it is also necessary to reasonably and actively schedule and manage crowd-sourced delivery resources providing delivery services for a plurality of merchants or service providers and flexibly assuming delivery tasks across merchants or service providers according to the needs of different merchants or service providers. Therefore, in an optional implementation manner of this embodiment, a regression incentive policy is implemented for the crowdsourced distribution resources to schedule and manage the crowdsourced distribution resources, that is, as shown in fig. 7, the step S103 of managing, by at least one processor, the distribution resources according to the first evaluation value and the second evaluation value includes the following steps S701 to S703:

in step S701, obtaining, by at least one processor, a regression feature of a distribution resource;

in step S702, determining, by at least one processor, a regression delivery resource according to the first evaluation value, the second evaluation value, and a delivery resource regression feature;

in step S703, at least one processor performs a preset process on the regression distribution resource according to a preset ratio.

Wherein, the regression refers to the condition that the crowd-sourced distribution resource returns to a certain merchant or service provider from other merchants or service providers to receive distribution tasks.

In an optional implementation manner of this embodiment, the distribution resource regression feature refers to a feature for characterizing the crowd-sourced distribution resource regression value and the regression likelihood, for example, the distribution resource regression feature may include one or more of the following features: the method comprises the steps of collecting a maximum receiving and distributing task quantity of crowdsourced distribution resources in a first preset historical time period in a preset peak time period, collecting a minimum receiving and distributing task quantity of crowdsourced distribution resources in the first preset historical time period in the preset peak time period, receiving and distributing task quantity variance of crowdsourced distribution resources in the first preset historical time period in the preset peak time period, receiving and distributing task quantity daily mean of crowdsourced distribution resources in a second preset historical time period in the preset peak time period, receiving and distributing task quantity daily mean of crowdsourced distribution resources in the second preset historical time period, receiving and distributing task, A difference value between a daily average of the number of received distribution tasks of the crowdsourced distribution resources in a first preset historical time period in a preset peak time period and a daily average of the number of received distribution tasks of the crowdsourced distribution resources in a second preset historical time period in a preset peak time period, a difference value between a maximum number of received distribution tasks of the crowdsourced distribution resources in the first preset historical time period in the preset peak time period and a minimum number of received distribution tasks of the crowdsourced distribution resources in the first preset historical time period in the preset peak time period, and the like, wherein the maximum number of received distribution tasks can represent the task receiving capacity of the crowdsourced distribution resources, the minimum number of received distribution tasks can represent the condition that the crowdsourced distribution resources receive other merchants or service providers, and the distribution task number variance and the daily average distribution task number variance can represent the change condition that the crowdsourced distribution resources receive the tasks of different merchants or service providers, the daily average of the number of delivery tasks can represent the condition that the crowd-sourced delivery resource receives the delivery tasks of a certain merchant or service provider.

The preset peak time period may be set according to the needs of practical applications and the characteristics of the distribution tasks, and is not particularly limited by the present disclosure, for example, the preset peak time period may be 10:30 to 12:30, and 17:00 to 19: 00.

After obtaining the regression feature of the distribution resources, at least one processor may determine regression distribution resources according to the first evaluation value, the second evaluation value, and the regression feature of the distribution resources, and perform regression processing, where the regression processing may allocate distribution tasks to the regressed distribution resources, send regression messages, and the like.

In an optional implementation manner of this embodiment, the step S702, namely determining, by at least one processor, a regression delivery resource according to the first evaluation value, the second evaluation value, and the delivery resource regression feature, may include the following steps:

setting corresponding regression evaluation weights for the first evaluation value, the second evaluation value and the distribution resource regression feature respectively through at least one processor;

calculating, by at least one processor, a regression evaluation value of the delivery resource according to the first evaluation value, the second evaluation value, the delivery resource regression feature and a regression evaluation weight corresponding thereto;

the regression distribution resources are determined by the at least one processor according to the regression evaluation value, for example, a preset number of distribution resources can be selected as the regression distribution resources according to the level of the regression evaluation value.

In order to avoid that the distribution resources illegally strive for incentives by using the regression strategy and increase the distribution cost, in an optional implementation manner of this embodiment, after the regression distribution resources are determined by the at least one processor, preset processing such as incentives is not performed on all the regression distribution resources, but a preset ratio is set, and the preset processing such as incentives is performed on the regression distribution resources by the at least one processor according to the preset ratio. The preset ratio can be selected according to the needs of practical application, and the preset ratio is not specifically limited in the present disclosure, and for example, the preset ratio can be set to 50%.

In order to excite the distribution capability of the regression distribution resources, in an optional implementation manner of this embodiment, for regression distribution resources of a preset proportion, a step incentive processing manner is adopted, that is, at least one processor performs incentive corresponding to distribution tasks according to the number of distribution tasks completed within a preset time period of the regression distribution resources, the larger the number of distribution tasks completed within the preset time period is, the larger an incentive value obtained based on each distribution task is, for example, for regression distribution resources with a strong task receiving capability, when a certain number of distribution tasks is completed within the preset time period after regression, the incentive is performed on the regression distribution resources, the larger the number of distribution tasks completed within the preset time period is, and the larger the incentive value obtained based on each distribution task is. For example, when the number of completed delivery tasks within a preset time period exceeds a threshold value c (1), delivery tasks in a number interval of step thresholds c (i) -c (i-1) may obtain an incentive value p (i), where 1< i < n, and n is the total number of incentive steps. For example, assuming that the total number n of excitation steps is 3, the step thresholds c (1), c (2), and c (3) are set to 30, 50, and 80, respectively, the excitation value p (1) of the delivery task number in the first step number section c (2) -c (1) is 1-ary, the excitation value p (2) in the second step number section c (3) -c (2) is 2-ary, the excitation value p (3) above the third step number section c (3) is 3-ary, and the excitation values available for the delivery resources are the sum of the different step excitation values. For example, if the number of the four dispatching resources a, b, c, and d completing the dispatching task in the preset time period is 30, 35, 56, and 90, respectively, the four dispatching resources finally obtain the excitation values of (30-30) × 1 ═ 0 element, (35-30) × 1 ═ 5 element, (50-30) × 1+ (56-50) × 2 ═ 32 element, (50-30) × 1+ (80-50) × 2+ (90-80) × 3 ═ 110 element, respectively.

The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods.

Fig. 8 is a block diagram illustrating a configuration of a distributed resource management apparatus according to an embodiment of the present disclosure, which may be implemented as part or all of an electronic device by software, hardware, or a combination of the two. As shown in fig. 8, the distributed resource management apparatus includes:

a first determining module 801 configured to obtain, by at least one processor, first historical delivery data within a first preset historical time period from a memory, and determine, by at least one processor, a first evaluation value according to the first historical delivery data;

a second determining module 802, configured to obtain, by at least one processor, second historical delivery data within a second preset historical time period from the memory, and determine, by at least one processor, a second evaluation value according to the second historical delivery data;

a management module 803, configured to manage, by at least one processor, the delivery resources according to the first evaluation value and the second evaluation value, where the managing includes at least performing preset processing on the determined regression delivery resources according to a preset proportion;

In view of the above-described drawbacks, in this embodiment, a distributed resource management apparatus is proposed that performs comprehensive balance management of distributed resources by setting a first evaluation value and a second evaluation value, which are obtained based on different considerations, for the distributed resources. According to the technical scheme, the comprehensive and balanced management can be performed on the distribution resources of various grades, so that the abnormal positive feedback phenomenon is eliminated, the stability of the transport capacity of the distribution resources is guaranteed, the comprehensive and balanced development of the distribution resources is promoted, the overall operation efficiency of a distribution system is ensured, and the distribution quality of the distribution resources is improved.

In an optional implementation manner of this embodiment, as shown in fig. 9, the first determining module 801 includes:

a first obtaining sub-module 901 configured to obtain, by at least one processor, first historical distribution data in a first preset historical time period from a memory, where the first historical distribution data includes one or more first historical distribution sub-data;

a first determining submodule 902 configured to determine, by at least one processor, a first weight value corresponding to the first historical distribution sub-data;

a first digitizing sub-module 903 configured to digitize, by at least one processor, the first historical dispatch sub-data;

the first calculating sub-module 904 is configured to calculate, by at least one processor, the first evaluation value according to the digitized first historical distribution sub-data and the corresponding first weight value thereof.

As mentioned above, the first historical distribution data includes one or more first historical distribution sub-data, and in order to comprehensively consider the one or more first historical distribution sub-data, in this implementation, the first evaluation value is calculated based on the one or more first historical distribution sub-data by means of weighted average. Specifically, the first obtaining sub-module 901 selects, through the at least one processor, a type of the first historical distribution sub-data according to a requirement of an actual application, and obtains, through the at least one processor, the first historical distribution data in a first preset historical time period from the memory; the first determining submodule 902 determines, by at least one processor, a first weight value corresponding to first historical distribution sub-data according to the importance degree of the first historical distribution sub-data on the distribution resource evaluation; the first numeralization submodule 903 is used for numerating the first historical distribution subdata through at least one processor; the first calculating sub-module 904 calculates, by at least one processor, the first evaluation value according to the digitized first historical distribution sub-data and the first weight value corresponding to the digitized first historical distribution sub-data through a weighted average algorithm.

In another optional implementation manner of the present embodiment, the first evaluation value is calculated by means of a model calculation method. That is, as shown in fig. 10, the first determining module 801 includes:

a second obtaining sub-module 1001 configured to obtain, by at least one processor, third history distribution data and a corresponding third evaluation value tag within a third preset history time period from a memory, where the third history distribution data includes one or more third history distribution sub-data;

a first training sub-module 1002 configured to train, by at least one processor, the third history distribution data and the corresponding third evaluation value label as training data to obtain a first evaluation model;

a third obtaining sub-module 1003 configured to obtain, by the at least one processor, first historical distribution data within a first preset historical time period from the memory, where the first historical distribution data includes one or more first historical distribution sub-data;

a first input sub-module 1004 configured to input, by at least one processor, the first historical delivery data to the first evaluation model, resulting in a first evaluation value.

In this implementation, the first evaluation value is obtained by a model calculation method, specifically, the second obtaining sub-module 1001 obtains, from the memory through at least one processor, third history distribution data and a corresponding third evaluation value label in a third preset history time period as model training data, where the third history distribution data includes one or more third history distribution sub-data similar to the first history distribution data, and it should be noted that the third history distribution sub-data should be the same as the first history distribution sub-data subjected to evaluation value calculation; the first training sub-module 1002 trains through at least one processor by using the third history distribution data and the corresponding third evaluation value label as training data to obtain a first evaluation model, wherein an initial model of the first evaluation model can be selected according to the needs of practical application, and the disclosure does not specifically limit the initial model; the third obtaining sub-module 1003 obtains, by at least one processor, from a memory, first historical distribution data in a first preset historical time period for performing evaluation value calculation, where the first historical distribution data includes one or more first historical distribution sub-data; the first input sub-module 1004 may input the first historical distribution data into the first evaluation model through at least one processor, so as to obtain the first evaluation value.

That is, in an optional implementation manner of this embodiment, the second evaluation value is calculated based on the one or more second historical distribution sub-data by using a weighted average manner, as shown in fig. 11, the second determining module 802 includes:

a fourth obtaining sub-module 1101 configured to obtain, by the at least one processor, second historical distribution data within a second preset historical time period from the memory, wherein the second historical distribution data includes one or more second historical distribution sub-data;

a second determining submodule 1102 configured to determine, by at least one processor, a second weight value corresponding to the second historical distribution child data;

a second numeralization submodule 1103 configured to numerate, by at least one processor, the second historical distribution sub-data;

the second calculating sub-module 1104 is configured to calculate, by at least one processor, the second evaluation value according to the digitized second historical distribution sub-data and the second weight value corresponding to the second historical distribution sub-data.

In another optional implementation manner of the present embodiment, the second evaluation value is calculated by means of a model calculation method. That is, as shown in fig. 12, the second determining module 802 includes:

a fifth obtaining sub-module 1201 configured to obtain, by at least one processor, fourth historical delivery data and a corresponding fourth evaluation value tag within a fourth preset historical time period from a memory, where the fourth historical delivery data includes two or more fourth historical delivery sub-data;

a second training sub-module 1202 configured to train, by at least one processor, the fourth historical distribution data and a corresponding fourth evaluation value label as training data to obtain a second evaluation model;

a sixth obtaining sub-module 1203 configured to obtain, by the at least one processor, second historical distribution data in a second preset historical time period from the memory, where the second historical distribution data includes two or more second historical distribution sub-data;

a second input sub-module 1204 configured to input, by at least one processor, the second historical distribution data into the second evaluation model, resulting in a second evaluation value.

In an optional implementation manner of this embodiment, as shown in fig. 13, the management module 803 includes:

a third determining submodule 1301 configured to determine, by at least one processor, a first evaluation weight value corresponding to the first evaluation value and a second evaluation weight value corresponding to the second evaluation value;

a management submodule 1302 configured to manage, by at least one processor, the delivery resource based on the first evaluation value and a corresponding first evaluation weight value thereof, and the second evaluation value and a corresponding second evaluation weight value thereof.

As mentioned above, the present disclosure is intended to comprehensively balance management of delivery resources using the first evaluation value and the second evaluation value obtained based on different considerations. In the implementation mode, the comprehensive balance management of the distribution resources is realized in a weighted average mode. Specifically, the third determining sub-module 1301 determines, by at least one processor, a first evaluation weight value corresponding to the first evaluation value and a second evaluation weight value corresponding to the second evaluation value; the management sub-module 1302 manages the delivery resource based on the first evaluation value and the corresponding first evaluation weight value, the second evaluation value and the corresponding second evaluation weight value by at least one processor.

Further, the management submodule 1302 may be configured to:

Considering that some industries have delivery peaks, for example, for the take-away industry, delivery tasks are basically concentrated in noon and evening, especially the number of the delivery tasks at noon exceeds 50% of the total number of the delivery tasks all day long, during the delivery peak, delivery resources often have insufficient resources, and at this time, besides timely scheduling the delivery resources providing dedicated delivery services for a certain merchant or service provider, it is also necessary to reasonably and actively schedule and manage crowd-sourced delivery resources providing delivery services for a plurality of merchants or service providers and flexibly assuming delivery tasks across merchants or service providers according to the needs of different merchants or service providers. Therefore, in an alternative implementation manner of this embodiment, a regression incentive policy is implemented for the crowdsourced distribution resources to schedule and manage them, that is, as shown in fig. 14, the management module 803 includes:

a seventh obtaining submodule 1401 configured to obtain, by the at least one processor, the distribution resource regression feature;

a fourth determination submodule 1402 configured to determine, by the at least one processor, a regression delivery resource from the first evaluation value, the second evaluation value, and the delivery resource regression feature;

a processing submodule 1403 configured to perform, by at least one processor, a preset processing on the regression distribution resources according to a preset proportion.

In an optional implementation manner of this embodiment, the fourth determining sub-module 1402 may be configured to:

The present disclosure also discloses an electronic device, fig. 15 shows a block diagram of an electronic device according to an embodiment of the present disclosure, as shown in fig. 15, the electronic device 1500 includes a memory 1501 and a processor 1502; wherein the content of the first and second substances,

the memory 1501 is used to store one or more computer instructions that are executed by the processor 1502 to perform the above-described method steps.

FIG. 16 is a schematic diagram of a computer system suitable for implementing a method for distributed resource management according to an embodiment of the present disclosure.

As shown in fig. 16, the computer system 1600 includes a Central Processing Unit (CPU)1601 which can execute various processes in the above-described embodiments in accordance with a program stored in a Read Only Memory (ROM)1602 or a program loaded from a storage portion 1608 into a Random Access Memory (RAM) 1603. In the RAM1603, various programs and data necessary for the operation of the system 1600 are also stored. The CPU1601, ROM1602, and RAM1603 are connected to each other via a bus 1604. An input/output (I/O) interface 1605 is also connected to the bus 1604.

The following components are connected to the I/O interface 1605: an input portion 1606 including a keyboard, a mouse, and the like; an output portion 1607 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage portion 1608 including a hard disk and the like; and a communication section 1609 including a network interface card such as a LAN card, a modem, or the like. The communication section 1609 performs communication processing via a network such as the internet. The driver 1610 is also connected to the I/O interface 1605 as needed. A removable medium 1611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 1610 as necessary, so that a computer program read out therefrom is mounted in the storage portion 1608 as necessary.

In particular, the above described methods may be implemented as computer software programs, according to embodiments of the present disclosure. 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 above-described delivery resource management method. In such embodiments, the computer program may be downloaded and installed from a network via the communication portion 1609, and/or installed from the removable media 1611.

The flowchart 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 flowcharts or block diagrams may represent a module, a program segment, or a 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 hardware. The units or modules described may also be provided in a processor, and the names of the units or modules do not in some cases constitute a limitation of the units or modules themselves.

As another aspect, the present disclosure also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiment; or it may be a separate computer readable storage medium not incorporated into the 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 exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is possible without departing from the inventive concept. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Claims

1. A method for delivery resource management, comprising:

2. The method of claim 1, wherein the obtaining, by at least one processor, first historical dispatch data from a memory for a first preset historical time period and determining, by at least one processor, a first rating value based on the first historical dispatch data comprises:

3. The method of claim 1, wherein the obtaining, by at least one processor, first historical dispatch data from a memory for a first preset historical time period and determining, by at least one processor, a first rating value based on the first historical dispatch data comprises:

4. The method of claim 1, wherein the obtaining, by the at least one processor, second historical dispatch data from the memory for a second predetermined historical period of time and determining, by the at least one processor, a second rating value based on the second historical dispatch data comprises:

5. A distribution resource management apparatus, comprising:

6. The apparatus of claim 5, wherein the first determining module comprises:

7. The apparatus of claim 5, wherein the first determining module comprises:

8. The apparatus of claim 5, wherein the second determining module comprises:

9. An electronic device comprising a memory and a processor; wherein the memory is configured to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method steps of any of claims 1-4.

10. A computer-readable storage medium having stored thereon computer instructions, characterized in that the computer instructions, when executed by a processor, carry out the method steps of any of claims 1-4.