CN112529486A

CN112529486A - Logistics processing method, device, equipment and machine readable medium

Info

Publication number: CN112529486A
Application number: CN201910883645.7A
Authority: CN
Inventors: 陈凯伟; 杨睿
Original assignee: Cainiao Smart Logistics Holding Ltd
Current assignee: Cainiao Smart Logistics Holding Ltd
Priority date: 2019-09-18
Filing date: 2019-09-18
Publication date: 2021-03-19

Abstract

The embodiment of the application provides a logistics processing method, a logistics processing device, logistics processing equipment and a machine readable medium, wherein the logistics processing method comprises the following steps: determining service quality information corresponding to a plurality of logistics service providers respectively; the service quality information is used for representing the service quality of the logistics service provider; updating the distribution parameters corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers; and distributing the logistics tasks to be distributed to the at least one logistics service provider according to the updated distribution parameters. The method and the device for distributing the logistics tasks can improve objectivity and accuracy of distribution parameters, and can improve processing efficiency and processing quality of the logistics tasks.

Description

Logistics processing method, device, equipment and machine readable medium

Technical Field

The present application relates to the field of logistics technology, and in particular, to a logistics processing method, a logistics processing apparatus, a device, and a machine-readable medium.

Background

With the rapid development of the mobile internet and the increasing demand of the electronic commerce, more and more users choose to use the electronic commerce platform to purchase goods and services, thereby driving the development of logistics business and logistics service providers. The express company is a common logistics service provider, the express company is a service company derived from door-to-door logistics activities with a mail delivery function, and the express company delivers logistics objects quickly through transportation means such as railways, roads and air transports.

At present, an electronic goods platform usually cooperates with a plurality of logistics service providers at the same time, and sets distribution parameters (parameters for distributing logistics tasks to the logistics service providers) for the plurality of logistics service providers respectively; therefore, in the distribution process of the logistics task, the logistics task can be distributed to the corresponding logistics service provider according to the distribution parameter.

Currently, the above-mentioned distribution parameters are usually set manually. The distribution parameters are set manually, so that certain labor cost is consumed on one hand; on the other hand, more subjective factors are easily introduced, so that the distribution parameters have the problems of subjectivity and inaccuracy, and the processing efficiency and the processing quality of the logistics task are influenced by using the subjective and inaccurate distribution parameters in the distribution process of the logistics task.

Disclosure of Invention

The technical problem to be solved by the embodiments of the present application is to provide a logistics processing method, which can improve the objectivity and accuracy of distribution parameters, and further improve the processing efficiency and the processing quality of logistics tasks.

Correspondingly, the embodiment of the application also provides a logistics processing device, equipment and a machine readable medium, which are used for ensuring the realization and application of the method.

In order to solve the above problem, an embodiment of the present application discloses a logistics processing method, including:

determining service quality information corresponding to a plurality of logistics service providers respectively; the service quality information is used for representing the service quality of the logistics service provider;

updating the distribution parameters corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers;

and distributing the logistics tasks to be distributed to the at least one logistics service provider according to the updated distribution parameters.

receiving a logistics task; the logistics task is determined by the server according to the updated distribution parameters; the server side updates the distribution parameters according to the service quality information respectively corresponding to the plurality of logistics service providers;

and sending the execution information corresponding to the logistics task.

On the other hand, the embodiment of the application also discloses a logistics processing device, which comprises:

the determining module is used for determining the service quality information corresponding to the plurality of logistics service providers respectively; the service quality information is used for representing the service quality of the logistics service provider; and

the updating module is used for updating the distribution parameters corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers;

and the distribution module is used for distributing the logistics tasks to be distributed to the at least one logistics service provider according to the updated distribution parameters.

the receiving module is used for receiving the logistics task; the logistics task is determined by the server according to the updated distribution parameters; the server side updates the distribution parameters according to the service quality information respectively corresponding to the plurality of logistics service providers;

and the sending module is used for sending the execution information corresponding to the logistics task.

In another aspect, an embodiment of the present application further discloses an apparatus, including:

one or more processors; and

one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform one or more of the methods described above.

In yet another aspect, embodiments of the present application disclose one or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform one or more of the methods described above.

The embodiment of the application has the following advantages:

according to the service quality information corresponding to a plurality of logistics service providers, the distribution parameters corresponding to at least one logistics service provider are automatically updated; since the service quality information is an objective factor of marketization, the distribution parameters are automatically updated based on the service quality information, and the objectivity and accuracy of the distribution parameters can be improved.

Under the condition of improving the objectivity and the accuracy of the distribution parameters, the logistics tasks to be distributed are distributed to the at least one logistics service provider according to the updated distribution parameters, so that the processing efficiency and the processing quality of the logistics tasks can be improved.

Drawings

FIG. 1 is a flow chart of the steps of a first embodiment of a method of processing a material flow according to the present application;

FIG. 2 is a flow chart of steps of a second embodiment of a method for processing a material flow according to the present application;

FIG. 3 is a schematic structural diagram of a logistics processing system according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a task execution module 305 according to an embodiment of the present application;

FIG. 5 is a flow chart of the steps of a third embodiment of the logistics processing method of the present application;

FIG. 6 is a block diagram of a logistics processing apparatus according to an embodiment of the present application;

FIG. 7 is a block diagram of a logistics processing apparatus according to an embodiment of the present application;

FIG. 8 is an exemplary device 1300 that may be used to implement the various embodiments described above in this application;

FIG. 9 is a schematic illustration of a logistics processing process of an embodiment of the present application;

FIG. 10 is an illustration of an interface for querying quality of service information in an embodiment of the present application;

FIG. 11 is an illustration of a configuration interface according to an embodiment of the present application.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments that can be derived from the embodiments given herein by a person of ordinary skill in the art are intended to be within the scope of the present disclosure.

While the concepts of the present application are susceptible to various modifications and alternative forms, specific embodiments thereof have been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that the description above is not intended to limit the application to the particular forms disclosed, but on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the application.

Reference in the specification to "one embodiment," "an embodiment," "a particular embodiment," or the like, means that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may or may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Further, where a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to affect such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. In addition, it should be understood that items in the list included in the form "at least one of a, B, and C" may include the following possible items: (A) (ii) a (B) (ii) a (C) (ii) a (A and B); (A and C); (B and C); or (A, B and C). Likewise, a listing of items in the form of "at least one of a, B, or C" may mean (a); (B) (ii) a (C) (ii) a (A and B); (A and C); (B and C); or (A, B and C).

In some cases, the disclosed embodiments may be implemented as hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be executed by one or more processors. A machine-readable storage medium may be implemented as a storage device, mechanism, or other physical structure (e.g., a volatile or non-volatile memory, a media disk, or other media other physical structure device) for storing or transmitting information in a form readable by a machine.

In the drawings, some structural or methodical features may be shown in a particular arrangement and/or ordering. Preferably, however, such specific arrangement and/or ordering is not necessary. Rather, in some embodiments, such features may be arranged in different ways and/or orders than as shown in the figures. Moreover, the inclusion of structural or methodical features in particular figures is not meant to imply that such features are required in all embodiments and that, in some embodiments, such features may not be included or may be combined with other features.

The embodiment of the application can be applied to logistics scenes. In a logistics scenario, a logistics task may be used to characterize the flow process of a logistics object from a first location to a second location. The logistics node of a logistics object generally comprises: collecting, transporting, dispatching, signing and the like.

As an example, the information of the logistics task may include: the weight, price and other related information of the sender, the receiver and the logistics object. In the flowing process of the logistics object, the logistics service provider firstly collects the logistics object according to the information of the sender; then, sorting, transferring and distributing the logistics objects according to the information of the receivers, and then delivering the logistics objects to the receivers.

At present, an electronic goods platform usually cooperates with a plurality of logistics service providers at the same time, and sets distribution parameters (parameters for distributing logistics tasks to the logistics service providers) for the plurality of logistics service providers respectively; therefore, in the distribution process of the logistics task, the logistics task can be distributed to the corresponding logistics service provider according to the distribution parameter. The logistics service provider can include: logistics companies, express companies, and the like.

Aiming at the technical problems that the distribution parameters are set manually at present and the processing efficiency and the processing quality of the logistics task are affected, the embodiment of the application provides a logistics processing scheme, which specifically comprises the following steps: determining service quality information corresponding to a plurality of logistics service providers respectively; the service quality information is used for representing the service quality of the logistics service provider; updating the distribution parameters corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers; and distributing the logistics tasks to be distributed to the at least one logistics service provider according to the updated distribution parameters.

The embodiment of the application can automatically update the distribution parameters aiming at a plurality of logistics service providers in the region. The area can be used to characterize the territorial extent of logistics management. For the logistics platform, the logistics platform can perform the processing of logistics tasks (including the distribution of the logistics tasks) in units of areas. For a logistics service provider, it can provide logistics service in an area.

In an application example of the present application, it is assumed that 2 courier companies exist in a certain area, and initially, the 2 courier companies occupy distribution probabilities of 50% and 50%, respectively. Then, based on the service quality information of the 2 express companies, the distribution probability of the 2 express companies is automatically updated, so that the express companies with better service quality obtain higher distribution probability, and further more logistics tasks are inclined to high-quality express companies, and the express companies can be stimulated to provide higher-quality services.

Method embodiment one

Referring to fig. 1, a flowchart illustrating steps of a first embodiment of a logistics processing method according to the present application is shown, which may specifically include the following steps:

step 101, determining service quality information corresponding to a plurality of logistics service providers respectively; the service quality information is used for representing the service quality of the logistics service provider;

102, updating distribution parameters corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers;

and 103, distributing the logistics tasks to be distributed to the at least one logistics service provider according to the updated distribution parameters.

According to the embodiment of the application, the distribution parameters corresponding to at least one logistics service provider can be updated based on the service quality information, so that the objectivity and the accuracy of the distribution parameters are improved.

In addition, the logistics tasks can be distributed according to the distribution parameters. The distribution parameters used in the distribution process are obtained by updating based on the service quality information, and the objectivity and the accuracy of the distribution parameters are updated by the service quality information, so that the logistics tasks are distributed according to the distribution parameters with higher objectivity and accuracy, the rationality and the distribution of the logistics tasks can be improved,

In step 101, a plurality of logistics providers may belong to one area, that is, the processing of the embodiment of the present application may be performed for a plurality of logistics providers in one area.

In this embodiment of the application, optionally, the method may further include: receiving execution information corresponding to the logistics task from a logistics service provider; and determining the service quality information corresponding to the logistics service provider according to the execution information.

The execution information can be used for representing the execution condition of the logistics task. The execution information may include: business data corresponding to the logistics task. The business data corresponding to the logistics task may include but is not limited to: logistics node data, and/or user evaluation data. The logistics node data may include: time data of the logistics node, for example, the receiving time of the logistics task, the collecting time of the logistics object, and the like. The user rating data may include: user satisfaction, logistics personnel attitude, whether to go upstairs or not, whether to dispatch according to points on time or not, whether to violently load and unload, claim settlement speed, anti-theft protection and the like.

In an alternative embodiment of the present application, multiple threads may be utilized to perform tasks to perform at least one of the steps included in the above-described method; the information of the task may include: region information and thread information; one of the areas corresponds to a plurality of logistics service providers.

According to the embodiment of the application, the information of the tasks can be preset, and the massive data corresponding to the multiple areas can be processed in a task execution mode.

The region information may include: region identification, etc. The thread information may include: the number of threads, etc. In this way, the tasks of the M regions can be handled by the N threads, and thus, processing of mass data can be realized. Wherein M, N are natural numbers.

In this embodiment of the present application, optionally, a modulo operation may be performed on the area identifier and the number of threads to obtain a processing thread corresponding to the area identifier.

In an application example of the present application, assuming that the area identifier of area 1 is 12001, and the area identifier of area 2 is 12002, 12001% 2 is 1, that is, the task of area 1 is handed to thread 1 for processing; 12002% 2 is 0, i.e. the task for region 2 is handed over to thread 0 for processing. a% b, meaning a modulo b.

Optionally, a task item may be generated for the area identifier and its corresponding processing thread, for example, the task item: thread 1[12001], characterization thread 1 is responsible for tasks in 12001 area, and task items: thread 0[12002], and thread 0 is responsible for tasks in 12002 areas.

In step 101, service quality information corresponding to the logistics service provider may be determined according to the service data corresponding to the logistics service provider.

The business data corresponding to the logistics service provider may include, but is not limited to: logistics node data, and/or user evaluation data. The logistics node data may include: time data of the logistics node, for example, the receiving time of the logistics task, the collecting time of the logistics object, and the like. The user rating data may include: user satisfaction, logistics personnel attitude, whether to go upstairs or not, whether to dispatch according to points on time or not, whether to violently load and unload, claim settlement speed, anti-theft protection and the like.

The embodiment of the application can analyze the business data corresponding to the logistics service provider to obtain the service quality information corresponding to the logistics service provider. The corresponding analysis method may include: statistical methods, machine learning methods, and the like.

In this embodiment of the present application, optionally, the mapping relationship between the service data and the service quality information may be characterized by the first data analyzer. Correspondingly, the method may further include: training the training data to obtain a first data analyzer; the first data analyzer may be configured to characterize a mapping relationship between the service data and the service quality information; the training data may include: service data samples and service quality information obtained by labeling.

In an alternative embodiment of the present application, the mathematical model may be trained based on training data to obtain a first data analyzer, which may characterize a mapping between input data (traffic data) and output data (quality of service information).

The mathematical model is a scientific or engineering model constructed by using a mathematical logic method and a mathematical language, and is a mathematical structure which is generally or approximately expressed by adopting the mathematical language aiming at the characteristic or quantity dependency relationship of a certain object system, and the mathematical structure is a relational structure which is described by means of mathematical symbols. The mathematical model may be one or a set of algebraic, differential, integral or statistical equations, and combinations thereof, by which the interrelationships or causal relationships between the variables of the system are described quantitatively or qualitatively. In addition to mathematical models described by equations, there are also models described by other mathematical tools, such as algebra, geometry, topology, mathematical logic, etc. Where the mathematical model describes the behavior and characteristics of the system rather than the actual structure of the system. The method can adopt methods such as machine learning and deep learning methods to train the mathematical model, and the machine learning method can comprise the following steps: linear regression, decision trees, random forests, etc., and the deep learning method may include: convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated cyclic units (GRU), and so on.

In this embodiment of the present application, optionally, the service quality information may include at least one of the following information:

whether the logistics task is collected within the specified collection time or not;

canceling the information of the logistics task after receiving the logistics task; and

and receiving the logistics task but not collecting the information of the logistics task.

Whether the logistics tasks are collected within the specified collection time can reflect the timeliness of the logistics service providers in collecting the logistics tasks. For example, the timely acquisition rate can be obtained according to the number of the logistics tasks acquired within the specified acquisition time and the total number of the logistics tasks acquired.

The information of canceling the logistics task after receiving the logistics task can reflect the service capability of the logistics service provider. For example, the cancellation rate after receiving the order may be determined according to the information of canceling the logistics task after receiving the logistics task.

The information of receiving the logistics task but not collecting the logistics task can reflect the integrity of the logistics service provider. For example, the odds rate may be determined based on information received from the logistics task but not included in the logistics task.

Under the condition of adopting various service quality information, the various service quality information can be fused, and the corresponding fusion mode can comprise: the average and weighted average are equal, and a weight corresponding to one kind of service quality information may be determined by those skilled in the art according to actual application requirements, and the embodiment of the present application does not limit the specific weight corresponding to the service quality information.

In an alternative embodiment of the present application, the service quality information of the logistics service provider in a period can be determined. Optionally, the service quality information of the logistics service provider in a plurality of period segments may be determined, and then the service quality information of the logistics service provider in a plurality of period segments may be fused to obtain the service quality information of the logistics service provider in one period. The corresponding fusion mode may include: average, weighted average, etc

The length of the period and the period segments can be determined by those skilled in the art according to the requirements of the actual application. For example, the length of a cycle is 7 days, the length of a cycle fragment is 1 day, and so on.

In step 102, the distribution parameters are used to characterize the distribution probability of distributing the logistics tasks to the corresponding logistics service providers. The distribution parameters may include: distribution proportion and the like, and the distribution proportion can be used for representing the proportion of the logistics tasks corresponding to one logistics service provider in all the logistics tasks corresponding to a plurality of logistics service providers.

Optionally, the step 102 of updating the distribution parameter corresponding to at least one logistics service provider may specifically include: increasing the distribution probability represented by the distribution parameters corresponding to the first logistics service provider; the first logistics service provider may include: at least one logistics service provider with service quality information meeting preset conditions in the plurality of logistics service providers.

In the embodiment of the application, the distribution probability represented by the distribution parameter corresponding to the first logistics service provider can be increased. The first logistics service provider may include: at least one logistics service provider with service quality information meeting preset conditions in the plurality of logistics service providers.

The preset condition can be used for restricting the service quality information corresponding to the first logistics service provider. Alternatively, the preset conditions may include: and at least one logistics facilitator positioned at the top in the sequencing result. The sorting result may be obtained by sorting a plurality of logistics service providers in the order of the quality of service information from good to bad. The number of the first logistics service providers can be one or more.

According to the method and the system, aiming at the first logistics service provider with the service quality information meeting the preset conditions in the plurality of logistics service providers, the distribution probability of the corresponding distribution parameter representation is increased, the probability of distributing the logistics tasks to the high-quality logistics service providers can be improved, and the high-quality logistics service providers can obtain more logistics tasks; on the basis, the processing efficiency and the processing quality of the logistics task can be improved.

In this embodiment of the present application, optionally, the service quality information corresponding to each of the plurality of logistics service providers may be sorted according to the order from the superior to the inferior of the service quality information, so as to obtain at least one of the logistics service providers ranked in the front as the first logistics service provider.

According to the method and the system, aiming at the first logistics service provider with the service quality information meeting the preset conditions in the plurality of logistics service providers, the distribution probability of the corresponding distribution parameter representation is increased, the probability of distributing the logistics tasks to the high-quality logistics service providers can be improved, and the high-quality logistics service providers can obtain more logistics tasks; on the basis, the logistics service provider can be stimulated to continuously improve the service quality (namely, provide higher-quality service), so that the processing efficiency and the processing quality of the logistics task can be improved.

In this embodiment of the application, optionally, the original distribution parameter corresponding to the first logistics service provider may be increased on the basis of the growth parameter. The growth parameters may include: growth rate, growth value, etc.

According to an embodiment, the growth parameter corresponding to the first logistics service provider may be a fixed value. The fixed value can be determined by those skilled in the art according to the requirements of practical application.

According to another embodiment, the growth parameter corresponding to the first logistics service provider can be a dynamic value.

In this embodiment of the application, optionally, the increasing of the distribution probability represented by the distribution parameter corresponding to the first logistics service provider may specifically include: determining a growth parameter corresponding to a first logistics service provider according to an original distribution parameter corresponding to the first logistics service provider; and increasing the original distribution parameters corresponding to the first logistics service provider according to the growth parameters corresponding to the first logistics service provider.

According to the method and the device, the growth parameters can be obtained according to the original distribution parameters, so that the growth parameters have dynamic property.

According to an embodiment, the growth parameter corresponding to the first logistics service provider is inversely proportional to the original distribution parameter corresponding to the first logistics service provider.

Along with the gradual increase of the original distribution parameters and the gradual decrease of the growth parameters, the market occupation of high-quality logistics service providers can be prevented from being rapidly completed in a short time to a certain extent. With the gradual decline of the original distribution parameters and the gradual increase of the growth parameters, the logistics service provider who is temporarily in the inferior stage can possibly improve the service quality and the distribution probability and the market share.

According to another embodiment, if the distribution probability represented by the original distribution parameter is within a first distribution probability range, the growth parameter is within a first growth parameter range; or

If the distribution probability represented by the original distribution parameter is within a second distribution probability range, the growth parameter is within a second growth parameter range;

if the lower end point of the first distribution probability range is greater than or equal to the lower end point of the second distribution probability range, the upper end point of the first growth parameter range is less than or equal to the lower end point of the second growth parameter range.

The first distribution probability range and the second distribution probability range are used to characterize different distribution probability ranges, the two distribution probability ranges having an upper endpoint and a lower endpoint, respectively.

If the lower end point of the first distribution probability range is greater than or equal to the lower end point of the second distribution probability range, it may be said that the distribution probability represented by the first distribution probability range exceeds the distribution probability represented by the second distribution probability range.

The first growth parameter range and the second growth parameter range are used to characterize different growth parameter ranges, the two growth parameter ranges having upper and lower endpoints, respectively.

The upper end point of the first growth parameter range is less than or equal to the lower end point of the second growth parameter range, which may indicate that the growth parameter represented by the second growth parameter range exceeds the growth parameter represented by the first growth parameter range, or that the growth parameter represented by the first growth parameter range does not exceed the growth parameter represented by the second growth parameter range.

According to the embodiment of the application, under the condition that the distribution probability represented by the first distribution probability range exceeds the distribution probability represented by the second distribution probability range, the growth parameter represented by the first growth parameter range does not exceed the growth parameter represented by the second growth parameter range, so that the growth parameter is gradually reduced along with the increase of the distribution probability, and a high-quality logistics service provider can be prevented from rapidly completing market occupation in a short time to a certain extent. From another perspective, as the distribution probability is reduced, the growth parameter is gradually increased, so that the logistics service provider which is temporarily in a disadvantaged stage can still improve the distribution probability and the market share by improving the service quality.

According to an embodiment, a segmented determination method may be adopted to determine an original distribution parameter corresponding to a first logistics service provider, and determine an increase parameter corresponding to the first logistics service provider.

The segmentation determination method may include: segmented mapping functions, etc., which may include: a logarithmic function, etc. The mapping relationship may represent a mapping relationship between the growth parameter and the original distribution parameter, wherein the original distribution parameter is gradually decreased as the original distribution parameter increases. Illustratively, the original distribution parameter is 10%, and the growth parameter is 50%; or, the original distribution parameter is 50%, and the growth parameter is 15%; or, the original distribution parameter is 95%, and the growth parameter is 1%. It can be understood that the embodiment of the present application does not impose a limitation on the specific mapping relationship between the growth parameter and the original distribution parameter.

In this embodiment of the present application, optionally, the mapping relationship between the growth parameter and the original distribution parameter may be characterized by the second data analyzer. Correspondingly, the method may further include: training the training data to obtain a second data analyzer; the second data analyzer can be used for representing the mapping relation between the growth parameters and the original distribution parameters; the training data may include: distributing parameter samples and marking the obtained growth parameters.

In an alternative embodiment of the present application, the mathematical model may be trained based on training data to derive a second data analyzer, which may characterize a mapping between input data (original distribution parameters) and output data (growth parameters).

In an optional embodiment of the application, the determining the growth parameter corresponding to the first logistics service provider specifically includes: and determining the growth parameter corresponding to the first logistics service provider according to the original distribution parameter and the growth factor corresponding to the first logistics service provider.

The growth factor is used for expanding the single growth amplitude and is convenient for adjusting the growth amplitude. If the single growth parameter is 10% and the growth factor is 1, the determined growth parameter may be 10%. As another example, if the single growth parameter is 10% and the growth factor is 2, then the determined growth parameter may be 20%.

In step 102, the distribution parameter corresponding to at least one logistics service provider is updated, and the updated distribution parameter may be output, so that the updated distribution parameter is applied to the distribution process of the logistics task.

In an optional embodiment of the present application, the above updating involves the logistics service provider entering the area for a period of time exceeding a time threshold. For the logistics service provider which enters the area and the time length of which does not exceed the time length threshold value, the logistics service provider does not participate in updating the distribution parameters, so that certain familiar time and buffering time can be provided for the logistics service provider which newly enters the area. The duration threshold may be determined by one skilled in the art according to actual application requirements, for example, the duration threshold may be 30 days, 15 days, etc.

In another optional embodiment of the present application, the distribution probability represented by the distribution parameter corresponding to the logistics service provider related to the update may exceed the probability threshold. For the logistics service provider which does not exceed the probability threshold, the logistics service provider does not participate in updating the distribution parameters. In this way, for the logistics service provider which does not exceed the probability threshold, even if the service quality is in a disadvantage, the logistics service provider does not participate in updating the distribution parameters, so that the elimination and replacement speed of the logistics service provider in the area can be reduced to a certain extent.

In an optional embodiment of the present application, before updating the distribution parameter corresponding to the at least one logistics service provider, the method may further include: and filtering the plurality of logistics service providers according to the business data corresponding to the logistics service providers to obtain the filtered target logistics service providers needing to be updated.

The filtering process of the plurality of logistics service providers may include at least one of the following processes:

filtering the logistics service provider with the time length of the logistics task not exceeding the cycle length;

filtering the logistics service provider with the distribution parameters with data abnormity;

filtering the logistics service providers with the time length of entering the area not exceeding the time length threshold;

filtering the logistics service providers with the processing capacity of the logistics tasks in the period not exceeding the threshold value;

and filtering the logistics service providers with the probability threshold value represented by the distribution parameter not exceeding the probability threshold value.

In step 103, the logistics task to be allocated may refer to a logistics task to be allocated. The number of logistics tasks to be allocated may be one or more.

As an example, a logistics task to be assigned may be received from an e-commerce platform. As another example, a logistics task to be allocated input by a user may be received to satisfy a pick-up request from a user. It is understood that the embodiments of the present application do not impose limitations on the specific sources of logistics tasks to be distributed.

In this embodiment of the application, optionally, a target logistics service provider may be determined from the plurality of logistics service providers according to the updated distribution parameter, and the target logistics service provider may be configured to process the logistics task to be distributed.

The distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers. Alternatively, the logistics tasks can be distributed according to the distribution parameters in one distribution period. For example, the distribution parameter is a distribution ratio, and the distribution ratio can be used to represent a ratio of the logistics task corresponding to one logistics service provider to all the logistics tasks corresponding to a plurality of logistics service providers. The distribution of the embodiment of the application can realize that the proportion of the logistics tasks distributed to the logistics service provider in all the distributed logistics tasks in one distribution cycle is matched with the distribution proportion.

In this embodiment of the present application, the logistics task may be distributed to the target logistics service provider, so that the target logistics service provider processes the distributed logistics task.

In an optional embodiment of the present application, the method may further include: and sending corresponding service quality information to the at least one logistics service provider. The embodiment of the application provides corresponding service quality information to the logistics service provider or service personnel (such as couriers) of the logistics service provider so that the logistics service provider or the service personnel can know the service quality information of the logistics service provider or the service personnel, and the logistics service provider or the service personnel can improve the working mode according to the corresponding service information to improve the service quality.

To sum up, the logistics processing method according to the embodiment of the application automatically updates the distribution parameters corresponding to at least one logistics service provider according to the service quality information corresponding to each of the plurality of logistics service providers; since the service quality information is an objective factor of marketization, the distribution parameters are automatically updated based on the service quality information, and the objectivity and accuracy of the distribution parameters can be improved.

In addition, according to the embodiment of the application, aiming at a first logistics service provider of which the service quality information accords with the preset conditions in a plurality of logistics service providers, the distribution probability represented by the corresponding distribution parameters is increased, so that the probability of distributing logistics tasks to high-quality logistics service providers can be improved, and the high-quality logistics service providers can obtain more logistics tasks; on the basis, the logistics service provider can be stimulated to continuously improve the service quality (namely, provide higher-quality service), so that the processing efficiency and the processing quality of the logistics task can be improved.

Method embodiment two

Referring to fig. 2, a flowchart illustrating steps of a second embodiment of the logistics processing method of the present application is shown, which may specifically include the following steps:

step 201, determining service quality information corresponding to a plurality of logistics service providers respectively; the service quality information is used for representing the service quality of the logistics service provider;

step 202, updating distribution parameters corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers;

step 202, updating the distribution parameters corresponding to at least one logistics service provider, which may specifically include:

step 221, increasing the distribution probability represented by the distribution parameters corresponding to the first logistics service provider; the first logistics service provider may include: at least one logistics service provider with service quality information meeting preset conditions in the plurality of logistics service providers;

step 222, reducing the distribution probability represented by the distribution parameters corresponding to the second stream service provider; the second logistics facilitator is different from the first logistics facilitator.

According to the embodiment of the application, the distribution probability represented by the distribution parameters corresponding to the first logistics service provider is increased, and the distribution probability represented by the distribution parameters corresponding to the second logistics service provider can be reduced, so that the distribution probability in the region is balanced.

The second logistics facilitator may be different from the first logistics facilitator. According to one embodiment, the second streaming service provider may comprise: and the logistics service provider in the area is different from the first logistics service provider. According to another embodiment, the second streaming service provider may comprise: and the logistics service providers which are different from the first logistics service provider in the area and enter the area for a time period exceeding the time period threshold value. According to yet another embodiment, the second streaming service provider may comprise: and the logistics service providers which are different from the first logistics service provider in the area and have the distribution probability represented by the corresponding distribution parameters exceeding the probability threshold. According to yet another embodiment, the second streaming service provider may comprise: and the logistics service providers are different from the first logistics service provider in the region, the time length of entering the region exceeds the time length threshold value, and the distribution probability represented by the corresponding distribution parameters exceeds the probability threshold value.

The determination process of the first and second logistics service providers is explained herein.

In an application example of the present application, it is assumed that a logistics service provider a and a logistics service provider B exist in an area, and a service quality of the logistics service provider a is better than a service quality of the logistics service provider B, the logistics service provider a is a first logistics service provider that needs to increase a distribution probability, and the logistics service provider B is a second logistics service provider that needs to decrease the distribution probability.

In another application example of the present application, it is assumed that a logistics service provider a, a logistics service provider B, and a logistics service provider C exist in an area, and the service quality is ranked as follows: the logistics service provider A, the logistics service provider B and the logistics service provider C are the logistics service providers needing to increase the distribution probability, and the logistics service provider B and the logistics service provider C are the logistics service providers needing to reduce the distribution probability.

In another application example of the present application, it is assumed that a logistics provider a, a logistics provider B, and a logistics provider C exist in an area, and the service quality is ranked as follows: the logistics service provider a is the logistics service provider B > the logistics service provider C, the logistics service provider a and the logistics service provider B are the logistics service providers which need to increase the distribution probability, and the logistics service provider C is the logistics service provider which needs to reduce the distribution probability.

The sum of the distribution probabilities of all the logistics service providers in one area may be a fixed value, for example, the fixed value may be 100%. Therefore, after the increase parameters of the first logistics service provider are determined, the decrease parameters of the second logistics service provider are also determined correspondingly. Examples are as follows:

for example, a logistics service provider a and a logistics service provider B exist in the area, and the service quality of the logistics service provider a is better than that of the logistics service provider B, that is, a is a first logistics service provider that needs to increase the distribution probability, and B is a second logistics service provider that needs to decrease the distribution probability. The increased distribution probability value of the distribution facilitator a may be matched with the decreased distribution probability value of the distribution facilitator B.

For another example, assume that a logistics service provider a, a logistics service provider B, and a logistics service provider C exist in an area, and the service quality is ranked as: the logistics service provider A, the logistics service provider B and the logistics service provider C are the logistics service providers needing to increase the distribution probability, and the logistics service provider B and the logistics service provider C are the logistics service providers needing to reduce the distribution probability.

The increased distribution probability value of the logistics service provider a may be matched to the sum of the decreased distribution probability values of the logistics service provider B and the logistics service provider C. The reduced values of the distribution probabilities of the logistics service provider B and the logistics service provider C can be obtained according to the original distribution parameters corresponding to the logistics service provider B and the logistics service provider C and the first weight. For example, the original distribution parameters corresponding to the logistics service provider B and the logistics service provider C are 30% and 20%, respectively, and the ratio of the first weight corresponding to the logistics service provider B and the logistics service provider C may be: 30%: 20 percent.

For another example, assume that a logistics provider a, a logistics provider B, and a logistics provider C exist in the area, and the service quality is ranked as: the logistics service provider a is the logistics service provider B > the logistics service provider C, the logistics service provider a and the logistics service provider B are the logistics service providers which need to increase the distribution probability, and the logistics service provider C is the logistics service provider which needs to reduce the distribution probability.

According to an embodiment, according to the method of the first method embodiment, the sum of the growth values of the distribution probabilities corresponding to the logistics service provider a and the logistics service provider B is determined, then, according to the original distribution parameters of the logistics service provider a and the logistics service provider B, the corresponding second weight is obtained, and then, according to the sum of the second weight and the growth values, the growth values of the logistics service provider a and the logistics service provider B are obtained.

According to another embodiment, the increase values of the distribution probability corresponding to the logistics service provider a and the logistics service provider B can be respectively determined according to the method of the first embodiment.

In summary, the logistics processing method of the embodiment of the application increases the distribution probability of the corresponding distribution parameter representation for a first logistics service provider, of which the service quality information meets the preset conditions, among a plurality of logistics service providers; and reducing the distribution probability represented by the corresponding distribution parameters for a second logistics facilitator different from the first logistics facilitator. Therefore, the probability of distributing the logistics tasks to the high-quality logistics service providers can be improved, the probability of distributing the logistics tasks to the poor-quality logistics service providers can be reduced, and the logistics tasks are transferred from the poor-quality logistics service providers to the high-quality logistics service providers; on the basis, the logistics service provider can be stimulated to continuously improve the service quality (namely, provide higher-quality service), so that the processing efficiency and the processing quality of the logistics task can be improved.

In order to make those skilled in the art better understand the logistics processing method implemented in the present application, referring to fig. 3, a schematic structural diagram of a logistics processing system according to an embodiment of the present application is shown, which may specifically include: a configuration module 301, a task creation module 302, a task data determination module 303, a task publication module 304, and a task execution module 305.

The configuration module 301 is configured to provide gray scale region data, processing rules, and task data.

And the gray scale area data is used for selecting a part of areas as test points at the on-line initial stage of the product corresponding to the method in the embodiment of the application, namely the gray scale area data is used for storing the area information of the test points.

And the processing rule is used for providing a rule in the task execution process.

Examples of processing rules may include: a duration threshold, a growth factor, a probability threshold, etc.

The protection period length of the logistics service provider newly entering the region may be referred to as a duration threshold. In this way, a certain familiarity and buffering time can be provided for logistics service providers in newly populated areas.

The probability threshold may be a lower limit of the distribution probability. In the case that the distribution probability of a certain logistics service provider in the area is reduced to the probability threshold value, the logistics service provider is not involved in updating the distribution parameters any more.

A task creating module 302, configured to read data from the configuration module 301, and create a task according to the read data.

A task data determining module 303, configured to determine task data for the created task.

The task data is used for task division configuration in a big data scene. Optionally, the task item represents how many threads are enabled for processing the region, so as to update the distribution parameters in the region in a multi-thread form, thereby increasing the operation speed.

And a task issuing module 304, configured to issue the generated task. In practical applications, the tasks and the task data may be stored in a database or the like.

And a task execution module 305, configured to execute a task according to the task data. Alternatively, the task execution module 305 may execute the task using multiple threads to improve the processing efficiency of the task.

In an application example of the present application, the gray scale region data may include: region 1 and region 2 under a city.

The information of the task may include: the number of threads. For example, the number of threads is 2, that is, thread 1 and thread 2 are started, for executing the task corresponding to the region.

After the program is started, the grayscale region data, that is, the data of 2 regions, may be read, and modulo operation is performed according to the region identifier to obtain a processing thread corresponding to the region identifier, that is, to complete the assignment of the task corresponding to the region to the thread.

Referring to fig. 4, a schematic structural diagram of a task execution module 305 according to an embodiment of the present application is shown, which may specifically include: quality of service information determination module 351, filtering module 352, adjustment parameter determination module 353, and updating module 354.

The service quality information determining module 351 may read service data corresponding to each of the plurality of logistics service providers in the area according to the cycle length, and determine service quality information corresponding to each of the plurality of logistics service providers according to the service data.

The filtering module 352 is configured to filter multiple logistics service providers according to the service data and/or the service quality information.

An adjustment parameter determining module 353, configured to determine, for the filtered logistics service provider, a corresponding adjustment parameter, where the adjustment parameter may include: an increase parameter or a decrease parameter.

The updating module 354 is configured to update the distribution parameters corresponding to the logistics service provider according to the adjustment parameters.

For example, in a scenario where an area includes 5 logistics service providers, the 5 logistics service providers are: the order of the quality of service information from good to bad is specifically: the CP1 ═ CP2> CP3 ═ CP4> CP5, the merging process may be performed for the logistics service providers with the same quality of service information. Specifically, the merging processing is performed for CP1 and CP2, the merging processing is performed for CP3 and CP4, and the independent processing is performed for CP 5.

According to the method of the first embodiment of the method, the sum of the increase values of the distribution probabilities corresponding to the CP1 and the CP2 is determined, then the corresponding second weight is obtained according to the original distribution parameters of the CP1 and the CP2, and then the increase values of the CP1 and the CP2 are obtained according to the sum of the second weight and the increase values.

The increased value of the distribution probability of (CP1+ CP2) may be matched to the sum of the decreased values of the distribution probabilities of (CP3+ CP4) and CP 5. The respective reduced values of the CP3, CP4 and CP5 can be determined according to the sum of the first weight corresponding to the original distribution parameter of the CP3, CP4 and CP5 and the reduced value.

Referring to fig. 9, which shows a schematic diagram of a logistics processing process according to an embodiment of the present application, in fig. 9, a cycle may include: cycle 1, cycle 2, … cycle i, … cycle N, etc., where i, N may be a natural number greater than 0.

The logistics process in fig. 9 may include: determining the service quality information corresponding to the multiple CPs in the area in the period i respectively, and updating the distribution parameters corresponding to the multiple CPs respectively according to the service quality information.

Assuming that the CPs participating in the update in the area include: the CPa, the CPb and the CPc assume that the service quality of the CPc is better than that of the CPa and the service quality of the CPa is better than that of the CPb, namely, the service quality information of the CPc meets the preset conditions, the distribution probability can be transferred from the CPa and the CPb to the CPc, and on the basis, a logistics service provider can be stimulated to continuously improve the service quality (namely, provide higher-quality service), so that the processing efficiency and the processing quality of a logistics task can be improved.

In this embodiment of the application, optionally, the corresponding service quality information may be provided to the logistics service provider or a service person (e.g., a courier) of the logistics service provider, so that the logistics service provider or the service person knows the service quality information of the logistics service provider or the service person, and further, the logistics service provider or the service person may improve a working manner according to the corresponding service information to improve the service quality.

Referring to fig. 10, a schematic diagram of an interface for querying quality of service information according to an embodiment of the present application is shown, where the interface may specifically include: an input box 1001, a query trigger control 1002, and a query result 1003.

Wherein, the input box 1001 can be used for inputting a query string, and can support inputting the identification of the logistics service provider or courier.

Query trigger control 1002 may be used to trigger a query for content in input box 1001.

The query result 1003 may be used to show a query result corresponding to the query string, that is, service quality information corresponding to the logistics service provider or the courier.

In the embodiment of the present application, the service quality information may be characterized by a service quality score or a service quality level. For example, the service quality score may range from 0 to 100, and the service quality level may include: good, medium and poor. Alternatively, the quality of service information may also be characterized by its ranking. In fig. 10, the query result 1003 shows 90 points, which may be used as an example of the qos information.

In this embodiment of the application, optionally, a configuration interface may be provided to the logistics service provider or the service staff, so as to configure whether to participate in updating the distribution parameters through the configuration interface.

Referring to fig. 11, a schematic diagram of a configuration interface according to an embodiment of the present application is shown, where the configuration interface may include: a first selection control 1101 and a second selection control 1102. The first selection control 1101 and the second selection control 1102 may be single selection controls. If the first selection control 1101 is selected, it indicates that the distribution parameter is involved in updating, or if the second selection control 1102 is selected, it indicates that the distribution parameter is not involved in updating.

It is understood that the first selection control 1101 and the second selection control 1102 shown in fig. 11 are only examples of the configuration interface, and in fact, a switch control may be included in the configuration interface, and if the state of the switch control is an on state, the switch control is characterized to participate in updating the distribution parameters, or if the state of the switch control is an off state, the switch control is not characterized to participate in updating the distribution parameters. It is to be understood that the embodiments of the present application are not limited to a specific configuration interface.

Method embodiment three

Referring to fig. 5, a flowchart illustrating steps of a third embodiment of the logistics processing method of the present application is shown, which may specifically include the following steps:

step 501, receiving a logistics task; the logistics task can be a task determined by the server according to the updated distribution parameters; the server side updates the distribution parameters according to the service quality information respectively corresponding to a plurality of logistics service providers;

and 502, sending execution information corresponding to the logistics task.

The embodiment of the application can be applied to the client, and the client can run on the equipment corresponding to the logistics service provider. The client can receive the logistics task from the server, determine execution information corresponding to the logistics task, and send the execution information to the server, so that the server determines the service quality information according to the execution information.

According to the distribution parameters after updating, the logistics tasks to be distributed are distributed to the at least one logistics service provider, so that the processing efficiency and the processing quality of the logistics tasks can be improved.

In this embodiment of the application, optionally, the method may further include: receiving quality of service information, wherein the quality of service information can be obtained according to the execution information.

The embodiment of the application provides corresponding service quality information to the logistics service provider or service personnel (such as couriers) of the logistics service provider so that the logistics service provider or the service personnel can know the service quality information of the logistics service provider or the service personnel, and the logistics service provider or the service personnel can improve the working mode according to the corresponding service information to improve the service quality.

It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.

The embodiment of the application also provides a logistics processing device.

Referring to fig. 6, a block diagram of a first embodiment of the logistics processing apparatus according to the present application is shown, and specifically, the first embodiment of the logistics processing apparatus may include the following modules:

a determining module 601, configured to determine service quality information corresponding to each of a plurality of logistics service providers; the service quality information is used for representing the service quality of the logistics service provider;

an updating module 602, configured to update a distribution parameter corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers; and

the allocating module 603 is configured to allocate the logistics task to be allocated to the at least one logistics service provider according to the updated distribution parameter.

Optionally, the updating module 602 may specifically include:

the first updating module is used for increasing the distribution probability represented by the distribution parameters corresponding to the first logistics service provider; the first logistics service provider may include: at least one logistics service provider with service quality information meeting preset conditions in the plurality of logistics service providers.

Optionally, the first updating module may include:

the system comprises a growth parameter determining module, a distribution parameter determining module and a distribution parameter determining module, wherein the growth parameter determining module is used for determining a growth parameter corresponding to a first logistics service provider according to an original distribution parameter corresponding to the first logistics service provider; and

and the adding module is used for adding the original distribution parameters corresponding to the first logistics service provider according to the growth parameters corresponding to the first logistics service provider.

Optionally, if the distribution probability represented by the original distribution parameter is within a first distribution probability range, the growth parameter is within a first growth parameter range; or

Optionally, the growth parameter determining module may include:

and the factor-based parameter determination module is used for determining the growth parameter corresponding to the first logistics service provider according to the original distribution parameter and the growth factor corresponding to the first logistics service provider.

Optionally, the growth parameter corresponding to the first logistics service provider is a fixed value; or

The growth parameter corresponding to the first logistics service provider is inversely proportional to the original distribution parameter corresponding to the first logistics service provider.

Optionally, the updating module 602 may further include:

the second updating module is used for reducing the distribution probability represented by the distribution parameters corresponding to the second stream service provider; the second logistics facilitator is different from the first logistics facilitator.

Optionally, the time length of entering the area by the logistics service provider related to the update exceeds the time length threshold.

Optionally, the distribution probability represented by the distribution parameter corresponding to the logistics service provider involved in the update exceeds a probability threshold.

Optionally, the service quality information may include at least one of the following information:

Optionally, the apparatus performs tasks using multiple threads to trigger at least one module included in the apparatus;

the information of the task may include: region information and thread information; one of the areas corresponds to a plurality of logistics service providers.

Optionally, the apparatus may further include:

the execution information receiving module is used for receiving execution information corresponding to the logistics task from the logistics service provider;

and the quality information determining module is used for determining the service quality information corresponding to the logistics service provider according to the execution information.

Optionally, the apparatus may further include:

and the quality information sending module is used for sending corresponding service quality information to the at least one logistics service provider.

Referring to fig. 7, a block diagram of a second embodiment of the logistics processing apparatus according to the present application is shown, and specifically, the second embodiment of the logistics processing apparatus may include the following modules:

a receiving module 701, configured to receive a logistics task; the logistics task can be a task determined by the server according to the updated distribution parameters; the server side updates the distribution parameters according to the service quality information respectively corresponding to a plurality of logistics service providers;

a sending module 702, configured to send execution information corresponding to the logistics task.

Optionally, the apparatus may further include: a quality information receiving module, configured to receive quality of service information, where the quality of service information may be obtained according to the execution information.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

Embodiments of the application can be implemented as a system or apparatus employing any suitable hardware and/or software for the desired configuration. Fig. 8 schematically illustrates an exemplary device 1300 that can be used to implement the various embodiments described above in this application.

For one embodiment, fig. 8 illustrates an exemplary apparatus 1300, which apparatus 1300 may comprise: one or more processors 1302, a system control module (chipset) 1304 coupled to at least one of the processors 1302, system memory 1306 coupled to the system control module 1304, non-volatile memory (NVM)/storage 1308 coupled to the system control module 1304, one or more input/output devices 1310 coupled to the system control module 1304, and a network interface 1312 coupled to the system control module 1306. The system memory 1306 may include: instruction 1362, the instruction 1362 executable by the one or more processors 1302.

Processor 1302 may include one or more single-core or multi-core processors, and processor 1302 may include any combination of general-purpose processors or special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In some embodiments, the device 1300 can be a server, a target device, a wireless device, etc. as described above in embodiments of the present application.

In some embodiments, device 1300 may include one or more machine-readable media (e.g., system memory 1306 or NVM/storage 1308) having instructions thereon and one or more processors 1302, which in combination with the one or more machine-readable media, are configured to execute the instructions to implement the modules included in the aforementioned means to perform the actions described above in embodiments of the present application.

System control module 1304 for one embodiment may include any suitable interface controller to provide any suitable interface to at least one of processors 1302 and/or any suitable device or component in communication with system control module 1304.

System control module 1304 for one embodiment may include one or more memory controllers to provide an interface to system memory 1306. The memory controller may be a hardware module, a software module, and/or a firmware module.

System memory 1306 for one embodiment may be used to load and store data and/or instructions 1362. For one embodiment, system memory 1306 may include any suitable volatile memory, such as suitable DRAM (dynamic random access memory). In some embodiments, system memory 1306 may include: double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

System control module 1304 for one embodiment may include one or more input/output controllers to provide an interface to NVM/storage 1308 and input/output device(s) 1310.

NVM/storage 1308 for one embodiment may be used to store data and/or instructions 1382. NVM/storage 1308 may include any suitable non-volatile memory (e.g., flash memory, etc.) and/or may include any suitable non-volatile storage device(s), e.g., one or more Hard Disk Drives (HDDs), one or more Compact Disc (CD) drives, and/or one or more Digital Versatile Disc (DVD) drives, etc.

The NVM/storage 1308 may include storage resources that are physically part of the device on which the apparatus 1300 is installed or may be accessible by the device and not necessarily part of the device. For example, the NVM/storage 1308 may be accessed over a network via the network interface 1312 and/or through the input/output devices 1310.

Input/output device(s) 1310 for one embodiment may provide an interface for device 1300 to communicate with any other suitable device, and input/output devices 1310 may include communication components, audio components, sensor components, and so forth.

Network interface 1312 of one embodiment may provide an interface for device 1300 to communicate with one or more networks and/or with any other suitable means, and device 1300 may communicate wirelessly with one or more components of a Wireless network according to any of one or more Wireless network standards and/or protocols, e.g., to access a Wireless network based on a communication standard, such as WiFi (Wireless Fidelity), 2G or 3G or 4G or 5G, or a combination thereof.

For one embodiment, at least one of the processors 1302 may be packaged together with logic for one or more controllers (e.g., memory controllers) of the system control module 1304. For one embodiment, at least one of the processors 1302 may be packaged together with logic for one or more controllers of the system control module 1304 to form a System In Package (SiP). For one embodiment, at least one of the processors 1302 may be integrated on the same novelty as the logic of one or more controllers of the system control module 1304. For one embodiment, at least one of processors 1302 may be integrated on the same chip with logic for one or more controllers of system control module 1304 to form a system on a chip (SoC).

In various embodiments, apparatus 1300 may include, but is not limited to: a computing device such as a desktop computing device or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet, a netbook, etc.). In various embodiments, device 1300 may have more or fewer components and/or different architectures. For example, in some embodiments, device 1300 may include one or more cameras, a keyboard, a Liquid Crystal Display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an Application Specific Integrated Circuit (ASIC), and speakers.

Wherein, if the display includes a touch panel, the display screen may be implemented as a touch screen display to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or slide action but also detect the duration and pressure associated with the touch or slide operation.

The present application also provides a non-transitory readable storage medium, where one or more modules (programs) are stored in the storage medium, and when the one or more modules are applied to an apparatus, the apparatus may be caused to execute instructions (instructions) of methods in the present application.

Provided in one example is an apparatus comprising: one or more processors; and, instructions stored thereon in one or more machine-readable media, which when executed by the one or more processors, cause the apparatus to perform a method as in embodiments of the present application, the method may comprise: the method shown in fig. 2 or fig. 3 or fig. 4 or fig. 5.

One or more machine-readable media are also provided in one example, having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform a method as in embodiments of the application, which may include: the method shown in fig. 2 or fig. 3 or fig. 4 or fig. 5.

The specific manner in which each module performs operations of the apparatus in the above embodiments has been described in detail in the embodiments related to the method, and will not be described in detail here, and reference may be made to part of the description of the method embodiments for relevant points.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable logistics processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable logistics processing apparatus, create means for implementing the functions specified in the flow diagram flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable logistics processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above detailed description is provided for a logistics processing method, a logistics processing apparatus, a device, and a machine-readable medium, and the principles and embodiments of the present application are explained herein by applying specific examples, and the descriptions of the above examples are only used to help understand the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims

1. A method for processing a material flow, comprising:

2. The method according to claim 1, wherein the updating the distribution parameters corresponding to the at least one logistics service provider comprises:

increasing the distribution probability represented by the distribution parameters corresponding to the first logistics service provider; the first logistics service provider comprises: and at least one logistics service provider of which the service quality information accords with preset conditions in the plurality of logistics service providers.

3. The method of claim 2, wherein the increasing the distribution probability of the distribution parameter characterization corresponding to the first logistics facilitator comprises:

determining a growth parameter corresponding to a first logistics service provider according to an original distribution parameter corresponding to the first logistics service provider;

and increasing the original distribution parameters corresponding to the first logistics service provider according to the growth parameters corresponding to the first logistics service provider.

4. The method of claim 3, wherein if the distribution probability represented by the original distribution parameter is within a first distribution probability range, the growth parameter is within a first growth parameter range; or

if the lower endpoint of the first distribution probability range is greater than or equal to the lower endpoint of the second distribution probability range, the upper endpoint of the first growth parameter range is less than or equal to the lower endpoint of the second growth parameter range.

5. The method of claim 3, wherein the determining the growth parameter corresponding to the first logistics facilitator comprises:

and determining a growth parameter corresponding to a first logistics service provider according to the original distribution parameter and the growth factor corresponding to the first logistics service provider.

6. The method of claim 2, wherein the growth parameter corresponding to the first logistics facilitator is a fixed value; or

7. The method of claim 2, wherein the updating the distribution parameters corresponding to the at least one logistics service provider further comprises:

reducing the distribution probability represented by the distribution parameters corresponding to the second stream service provider; the second logistics facilitator is different from the first logistics facilitator.

8. The method of claim 1, wherein the update involves the logistics facilitator entering the area for a period of time exceeding a time threshold.

9. The method of claim 1, wherein the distribution probability of the distribution parameter characterization corresponding to the logistics facilitator involved in the update exceeds a probability threshold.

10. The method according to any of claims 1 to 9, wherein the quality of service information comprises at least one of the following information:

11. Method according to any of claims 1 to 9, characterized in that tasks are executed with multiple threads to perform at least one of the steps comprised by the method;

the information of the task comprises: region information and thread information; one said area corresponds to a plurality of logistics service providers.

12. The method according to any one of claims 1 to 9, further comprising:

receiving execution information corresponding to the logistics task from a logistics service provider;

and determining the service quality information corresponding to the logistics service provider according to the execution information.

13. The method according to any one of claims 1 to 9, further comprising:

and sending corresponding service quality information to the at least one logistics service provider.

14. A method for processing a material flow, comprising:

and sending the execution information corresponding to the logistics task.

15. The method of claim 14, further comprising:

and receiving service quality information, wherein the service quality information is obtained according to the execution information.

16. A logistics processing apparatus, said apparatus comprising:

the determining module is used for determining the service quality information corresponding to the plurality of logistics service providers respectively; the service quality information is used for representing the service quality of the logistics service provider;

the updating module is used for updating the distribution parameters corresponding to at least one logistics service provider according to the service quality information; the distribution parameters are used for representing the distribution probability of distributing the logistics tasks to the corresponding logistics service providers; and

17. A logistics processing apparatus, said apparatus comprising:

the receiving module is used for receiving the logistics task; the logistics task is determined by the server according to the updated distribution parameters; the server side updates the distribution parameters according to the service quality information respectively corresponding to the plurality of logistics service providers; and

18. An apparatus, comprising:

one or more processors; and

one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of one or more of claims 1-13.

19. One or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform the method recited by one or more of claims 1-13.

20. An apparatus, comprising:

one or more processors; and

one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of claim 14 or 15.

21. One or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform the method of claim 14 or 15.