CN113139771A - Logistics dispatching method - Google Patents
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Abstract
The application relates to a method for dispatching logistics, which comprises the following steps: acquiring scanning information; the scanning information comprises recipient information; judging whether the current freight note can be sent by a third party or not according to the information of the addressee; if the current freight note can be sent by a third party, the information of the addressees is input into a pre-established risk early warning model, and whether the current freight note is a high-risk logistics freight note is detected; if the current waybill is a high-risk logistics waybill, sending early warning information to prompt a dispatcher that the current waybill needs to be dispatched after contacting a receiver; and if the current waybill is not the high-risk logistics waybill, performing third-party dispatching on the current waybill. Therefore, effective identification and automatic early warning of high-risk freight notes can be realized, the probability of complaints of users is reduced, the delivery efficiency and the service quality are improved, and the user experience is effectively improved.
Description
Technical Field
The application relates to the technical field of logistics dispatching application, in particular to a logistics dispatching method.
Background
At present, with the high-speed development of the express industry, the quantity of express items is continuously increased, so that the delivery link at the tail end is easily caused, and the delivery efficiency and the service quality cannot be effectively considered. Although the appearance of third party express cabinets and stagers greatly relieves the pressure of end delivery, the problems of poor service quality and increased customer complaints are also accompanied. For example, some users require express delivery to home, but due to the large amount of cases and the time, the couriers have difficulty in distinguishing and noticing the requirements, and generally carry out uniform third-party sign-in, which finally leads to user complaints.
In the related art, there are generally two existing solutions: one is a post-incident intervention method, which usually intervenes and remedies after complaints of users, but can seriously affect the user experience and also can influence the square image of the dispatch; the other type is a prior rule tag, the prior rule tag is used for marking the express delivery arriving at the terminal delivery link by utilizing the existing set of user and waybill tag which are calculated based on rules, but the coverage of the tag is too wide, the number of the tags is too many, the workload of the service staff delivery is increased, the delivery efficiency is low, and the first-line enabling is not strong.
Disclosure of Invention
In view of the above, the present application aims to overcome the shortcomings of the prior art and provide a method for dispatching logistics.
In order to achieve the purpose, the following technical scheme is adopted in the application:
the application provides a method for logistics dispatching, which comprises the following steps:
acquiring scanning information; the scanning information comprises recipient information;
judging whether the current freight note can be sent by a third party or not according to the recipient information;
if the current freight note can be sent by a third party, the recipient information is input into a pre-established risk early warning model, and whether the current freight note is a high-risk logistics freight note is detected;
if the current waybill is a high-risk logistics waybill, sending early warning information to prompt a dispatcher that the current waybill needs to be dispatched after contacting a receiver; and if the current waybill is not the high-risk logistics waybill, performing third-party dispatching on the current waybill.
Optionally, after determining whether the current waybill can be sent by a third party, the method further includes:
and if the current waybill cannot be dispatched by a third party, the current waybill is dispatched to the home.
Optionally, the recipient information includes recipient address information;
the judging whether the current freight note can be sent by a third party or not according to the recipient information comprises the following steps:
according to the address information of the receiver, whether the receiver address is consistent with the receiver address is searched from the corresponding relation between the receiver address stored in advance in a related mode and the third party delivery address;
if the address of the receiver exists, the corresponding third party delivery address exists in the address of the receiver, and the current freight note can be delivered by the third party; and if the address does not exist, the address of the receiver does not have a corresponding third party dispatching address, and the current freight note cannot be dispatched by the third party.
Optionally, before the obtaining the scanning information, the method further includes:
acquiring customer information, service delivery information and website information as sample information, and performing feature selection and extraction on the sample information to obtain training information;
and performing risk model training by using the training information and a machine learning model to obtain the risk early warning model.
Optionally, the machine learning model includes an XGBoost model.
The technical scheme provided by the application can comprise the following beneficial effects:
according to the scheme, the risk early warning model is established in advance, the logistics waybill can be detected according to the model, whether high risk early warning needs to be carried out or not is judged, and on the basis, after the scanning information is obtained, preliminary judgment can be carried out on the current waybill according to the addressee information in the scanning information, and whether third party dispatching can be carried out or not is determined. If the current freight note is determined to be capable of being dispatched by a third party, the information of the addressee can be input into a pre-established risk early warning model, and whether the current freight note is a high-risk logistics freight note or not is detected so as to determine whether the current freight note is suitable for being dispatched by the third party or not. If the current freight note is a high-risk logistics freight note, early warning information is sent out to prompt a dispatcher that the current freight note cannot be directly dispatched by a third party, the dispatcher needs to contact with a receiver before dispatching, and dispatches after communicating a dispatching mode. And if the current waybill is not a high-risk logistics waybill, the third-party dispatch can be directly performed on the current waybill. Therefore, effective identification and automatic early warning of high-risk freight notes can be realized, the probability of complaints of users is reduced, the delivery efficiency and the service quality are improved, and the user experience is effectively improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for logistics distribution according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a flowchart of a method for logistics distribution according to an embodiment of the present application. The embodiment provides a method for logistics dispatching, as shown in the figure, the method at least comprises the following implementation steps:
In implementation, after the deliverer scans the current waybill by using the scanning equipment, the scanning information of the current waybill can be acquired.
The scanning device may include, but is not limited to, a rifle, a mobile phone, a tablet computer, and the like.
And step 12, judging whether the current freight note can be dispatched by a third party or not according to the information of the addressee.
And step 13, if the current waybill can be sent by a third party, inputting the information of the addressees into a pre-established risk early warning model, and detecting whether the current waybill is a high-risk logistics waybill.
In this embodiment, a risk early warning model is established in advance, and according to the model, the logistics waybill can be detected to judge whether high risk early warning needs to be performed, and based on this, after the scanning information is obtained, the current waybill can be preliminarily judged according to the recipient information in the scanning information to determine whether the current waybill can be sent by a third party. If the current freight note is determined to be capable of being dispatched by a third party, the information of the addressee can be input into a pre-established risk early warning model, and whether the current freight note is a high-risk logistics freight note or not is detected so as to determine whether the current freight note is suitable for being dispatched by the third party or not. If the current freight note is a high-risk logistics freight note, early warning information is sent out to prompt a dispatcher that the current freight note cannot be directly dispatched by a third party, the dispatcher needs to contact with a receiver before dispatching, and dispatches after communicating a dispatching mode. And if the current waybill is not a high-risk logistics waybill, the third-party dispatch can be directly performed on the current waybill. Therefore, effective identification and automatic early warning of high-risk freight notes can be realized, the probability of complaints of users is reduced, the delivery efficiency and the service quality are improved, and the user experience is effectively improved.
The execution main body of the scheme of the application can be a background management system, a software or hardware-based functional module in the background management system, other equipment and the like.
During implementation, in the step 12, whether the current freight note can be dispatched by a third party is judged, and the freight note which can be dispatched by the third party can be preliminarily screened out, so that the working efficiency of a dispatcher is improved. For the freight bill which can not be dispatched by the third party, the dispatcher is required to dispatch the freight bill at home. That is to say, after detecting that the current waybill cannot be sent by a third party, the current waybill can be sent by a home to ensure that a receiver can receive the express, and the service quality of sending is improved.
In some embodiments, the recipient information may include recipient address information. Correspondingly, when judging whether the current shipping bill can be sent by a third party or not according to the recipient information, whether the recipient address is consistent with the recipient address or not can be searched from the corresponding relation between the recipient address and the third party sending address stored in advance in an associated manner according to the recipient address information; if the address of the receiver exists, the corresponding third party delivery address exists in the address of the receiver, and the current freight note can be delivered by the third party; if the address does not exist, the address of the receiver does not have a corresponding third party dispatching address, and the current freight note cannot be dispatched by the third party.
In specific implementation, after the address of the recipient is determined, whether the current recipient address has a third party delivery address or not can be determined from the correspondence between the pre-associated and stored recipient address and the third party delivery address. If so, the current waybill can be preliminarily judged to be dispatched by a third party. If not, the current waybill can be preliminarily judged not to be dispatched by a third party. For example, when the recipient of one waybill is a, and when the recipient is judged, it is found that the recipient address corresponding to the address of the a cannot be found from the corresponding relation between the pre-associated and stored recipient address and the third party dispatching address, it can be determined that there is no third party dispatching address near the address of the a, that is, the waybill of the a cannot be dispatched by the third party.
In some embodiments, before acquiring the scanning information, the method for logistics distribution may further include: acquiring customer information, service delivery information and website information as sample information, and performing feature selection and extraction on the sample information to obtain training information; and training a risk model by using the training information and the machine learning model to obtain a risk early warning model.
The machine learning model may include an XGBoost model, among others.
Specifically, the specific implementation manner of performing risk model training by using the training information and the XGBoost model may refer to the prior art, and is not described herein again.
In specific implementation, in a historical complaint sample, through data grouping observation, more than 50% of dispatches are not sent to a third-party express delivery cabinet directly without telephone or short message, the average daily dispatch quantity of the last 7 days of a dispatcher is positively correlated with the complaint rate of the last 3 days, the total backlog quantity of dispatches of a network is positively correlated with whether a receiver is in an office area or a living cell, the age of the receiver and the gender of the receiver, the arrival time of a waybill at the network is positively correlated with the current time (48h), and the urgency degree of the receiver to express is correlated with, for example, express in which the receiver has consulted before dispatching is more likely to have complaints, and the complaints are correlated with the weight and the volume of the express. Therefore, the characteristics of the client related information, the business related information and the website related information are selected and extracted through data observation and important model classification for training the early warning model.
In specific implementation, in a historical complaint sample, through data grouping observation, a plurality of factors are mainly used, wherein the higher correlation with the complaint probability is higher, and the class of the service staff is as follows: non-standard dispatches (more than 50% of dispatches are not delivered by telephone or short message directly to a third party courier cabinet), average daily dispatch volume for the dispatcher on the last 7 days, complaint rate for the dispatcher on the last 3 days, and volume of backlog of dispatches at a branch; the recipient is: the addressee address of the addressee, the age of the addressee, the sex of the addressee, and the consultation times of the addressee in the waybill process; and (4) waybill condition: whether the time is later than the specified time for 48 hours, the weight of the express mail and the volume of the express mail. The described complaint probability is mainly related to relevant information of a receiver, a dispatcher, a website and an invoice, so that the information can be selected and extracted by a characteristic engineering method for training an early warning model. And then selecting an XBglost model to carry out early warning risk model training.
In addition, in order to evaluate the risk early warning model and ensure that the risk early warning model has higher precision, the risk early warning model can be used for carrying out online prediction on freight notes with preset quantity after the risk early warning model is established, and the obtained high-risk early warning freight notes are randomly divided into two groups: one group finishes signing after responding to the risk early warning, and the other group finishes signing after not responding to the risk early warning. And comparing the complaints of the two groups of waybills signed by the receiver, evaluating the online accuracy of the risk early warning model, and carrying out badcase search based on the accuracy so as to continuously improve the accuracy of the model.
The specific implementation manner of badcase finding may refer to the related art, and is not described herein again.
Based on the same technical concept, this embodiment provides a device for logistics distribution, and the device may specifically include: the acquisition module is used for acquiring scanning information; the scanning information comprises recipient information; the judging module is used for judging whether the current freight note can be sent by a third party or not according to the recipient information; the detection module is used for inputting the information of the addressees into a pre-established risk early warning model if the current waybill can be dispatched by a third party, and detecting whether the current waybill is a high-risk logistics waybill or not; the first execution module is used for sending out early warning information to prompt a dispatcher that the current freight note needs to be dispatched after contacting a receiver if the current freight note is a high-risk logistics freight note; and if the current waybill is not the high-risk logistics waybill, performing third-party dispatching on the current waybill.
Optionally, the device for logistics distribution further includes a second execution module, configured to: and if the current waybill cannot be dispatched by a third party, the current waybill is dispatched to the home.
Optionally, when the recipient information includes recipient address information; the determining module may specifically be configured to: according to the address information of the receiver, whether the receiver address is consistent with the receiver address is searched from the corresponding relation between the receiver address stored in advance in an associated mode and the third party delivery address; if the address of the receiver exists, the corresponding third party delivery address exists in the address of the receiver, and the current freight note can be delivered by the third party; if the address does not exist, the address of the receiver does not have a corresponding third party dispatching address, and the current freight note cannot be dispatched by the third party.
Optionally, the device for logistics distribution may further include a training module, and the training module is specifically configured to: acquiring customer information, service delivery information and website information as sample information, and performing feature selection and extraction on the sample information to obtain training information; and training a risk model by using the training information and the machine learning model to obtain a risk early warning model. Wherein the machine learning model comprises an XGboost model.
For the specific implementation of the device for dispatching logistics, which is provided in the embodiment of the present application, reference may be made to the implementation of the method for dispatching logistics described in any of the above embodiments, and details are not described here.
The embodiment of the present application further provides a device for logistics distribution, where the device may specifically include: a processor, and a memory coupled to the processor; the memory is used for storing a computer program; the processor is configured to invoke and execute a computer program in the memory to perform the method of logistics dispatching as described in any of the embodiments above.
For a specific implementation of the logistics dispatching device provided in the embodiment of the present application, reference may be made to the implementation of the logistics dispatching method described in any of the above embodiments, and details are not described here.
Embodiments of the present application further provide a storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of the method for logistics distribution as described in any of the above embodiments are implemented.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (5)
1. A method for dispatching logistics is characterized by comprising the following steps:
acquiring scanning information; the scanning information comprises recipient information;
judging whether the current freight note can be sent by a third party or not according to the recipient information;
if the current freight note can be sent by a third party, the recipient information is input into a pre-established risk early warning model, and whether the current freight note is a high-risk logistics freight note is detected;
if the current waybill is a high-risk logistics waybill, sending early warning information to prompt a dispatcher that the current waybill needs to be dispatched after contacting a receiver; and if the current waybill is not the high-risk logistics waybill, performing third-party dispatching on the current waybill.
2. The method of claim 1, wherein after determining whether the current waybill can be sent by a third party, the method further comprises:
and if the current waybill cannot be dispatched by a third party, the current waybill is dispatched to the home.
3. The method for logistics distribution according to claim 2, wherein the recipient information comprises recipient address information;
the judging whether the current freight note can be sent by a third party or not according to the recipient information comprises the following steps:
according to the address information of the receiver, whether the receiver address is consistent with the receiver address is searched from the corresponding relation between the receiver address stored in advance in a related mode and the third party delivery address;
if the address of the receiver exists, the corresponding third party delivery address exists in the address of the receiver, and the current freight note can be delivered by the third party; and if the address does not exist, the address of the receiver does not have a corresponding third party dispatching address, and the current freight note cannot be dispatched by the third party.
4. The method for logistics distribution according to claim 1, wherein before the obtaining of the scanning information, the method further comprises:
acquiring customer information, service delivery information and website information as sample information, and performing feature selection and extraction on the sample information to obtain training information;
and performing risk model training by using the training information and a machine learning model to obtain the risk early warning model.
5. The method of logistics distribution according to claim 4, wherein the machine learning model comprises an XGboost model.
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| CN202110439566.4A CN113139771A (en) | 2021-04-23 | 2021-04-23 | Logistics dispatching method |
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| CN202110439566.4A CN113139771A (en) | 2021-04-23 | 2021-04-23 | Logistics dispatching method |
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