CN115222476A

CN115222476A - Data processing method and electronic equipment

Info

Publication number: CN115222476A
Application number: CN202210648384.2A
Authority: CN
Inventors: 刘哲宇; 曾美霖; 张志利; 陈晓欣; 刘玥; 徐瑛
Original assignee: Alibaba China Co Ltd
Current assignee: Alibaba China Co Ltd
Priority date: 2022-06-09
Filing date: 2022-06-09
Publication date: 2022-10-21

Abstract

The embodiment of the application discloses a data processing method and electronic equipment, wherein the method comprises the following steps: identifying at least one target geographical area with logistics abnormality according to logistics state characteristics of a plurality of logistics orders in a target time period; determining at least one commodity and/or trade order affected by the logistics abnormal situation of the target geographic area; and performing exception processing on the at least one affected commodity and/or trade order according to an exception processing strategy corresponding to the target geographic area. Through the embodiment of the application, the occupation of manpower and time cost of merchants and platform sides can be reduced in the process of carrying out duty-free processing on merchant users.

Description

Data processing method and electronic equipment

Technical Field

The present application relates to the field of information processing technologies, and in particular, to a data processing method and an electronic device.

Background

In a goods information service system, in order to perform standardized management on merchant behaviors and ensure rights and interests and experiences of consumers, a platform side usually requires a merchant to deliver goods or delivery goods to consumers according to the promised logistics timeliness of the merchant. If the time efficiency is exceeded, the evaluation of the platform side on the merchant may be influenced, and meanwhile, an automatic reimbursement process may be triggered, that is, a certain amount of money is required to be reimbursed to the consumer by the merchant for compensation, and the like.

However, in practical applications, an overtime condition caused by some unexpected events and other non-merchant reasons often occurs, for example, due to the fact that a certain city needs to execute a regulatory policy and the like, the influence on logistics is so large that packages are retained or normal receiving and sending are not possible, and further logistics timeliness exceeding the promise of merchants is caused, and the like. In such cases, merchants should be exempted from responsibility, but the merchant may be lost because the system may have a lag in perceiving such incidents or it may be difficult to perceive the incidents in all areas in a timely manner.

In the prior art, a preparation approach can be provided for a merchant, when the merchant finds that the order of a certain city may be delayed for delivery due to an emergency, the merchant can prepare the order for the platform by himself, the platform side can manually check the preparation application of the merchant, and if the check is passed, the merchant can perform exemption processing on the specific transaction order. However, such an application process needs to be performed separately by the merchant in units of transaction orders, and a certain time cost is required for the application process for each order. For the platform side, because a large number of merchant preparation applications need to be docked, the consumption of manpower and time cost is more serious.

Disclosure of Invention

The application provides a data processing method and electronic equipment, which can reduce occupation of manpower and time cost on a merchant and a platform side in a process of carrying out duty-free processing on a merchant user.

The present application provides the following:

a method of data processing, comprising:

identifying at least one target geographical area with logistics abnormality according to logistics state characteristics of a plurality of logistics orders in a target time period;

determining at least one commodity and/or trade order affected by the logistics abnormal condition of the target geographic area;

and performing exception processing on the at least one affected commodity and/or transaction order according to an exception processing strategy corresponding to the target geographic area.

The identifying at least one target geographical area with logistics abnormality according to the logistics state characteristics of the plurality of logistics orders in the target time period comprises the following steps:

predicting the logistics abnormal probability of the logistics orders according to the logistics state characteristics of the logistics orders in the target time period;

performing step-by-step upward aggregation according to the upstream and downstream logistics node relation of the logistics line corresponding to the logistics order with logistics abnormality, and determining the logistics node with logistics abnormality;

and determining at least one target geographical area with logistics abnormity according to the geographical area where the logistics node with logistics abnormity is located.

Wherein the determining at least one commodity and/or trade order affected by the logistics abnormal situation of the target geographic area comprises:

and for the transaction order in the shipped state, determining whether the transaction order is affected by the logistics abnormal condition of the corresponding target geographic area according to whether the current logistics node corresponding to the transaction order and/or the logistics node which is about to arrive in the future hit the target geographic area.

predicting a delivery place for the transaction order in the to-be-delivered state;

predicting a plurality of logistics nodes of the corresponding logistics line according to the predicted delivery location and the receiving location information corresponding to the transaction order;

and determining whether the transaction order in the to-be-delivered state is the transaction order influenced by the logistics abnormal condition of the corresponding target geographic area according to whether the predicted logistics nodes hit the target geographic area.

Wherein the forecasting of the delivery location for the transaction order in the pending delivery status comprises:

if the corresponding merchant user only associates a single delivery warehouse, determining the geographical area where the single delivery warehouse is located as the delivery place;

and if the corresponding merchant user is associated with a plurality of delivery warehouses, predicting a delivery place according to the commodity corresponding to the transaction order and the historical delivery record of the merchant user for the commodity.

Wherein, still include:

after at least one target geographical area with logistics abnormality is identified, determining merchant users taking the target geographical area as a delivery place according to historical delivery records of a plurality of merchant users;

according to the condition of the delivery warehouse associated with the merchant user, determining the delivery probability of the merchant user for delivering the specific commodity in the target geographic area, predicting and storing the delivery probability;

the forecasting of the delivery place for the transaction order in the pending delivery state comprises the following steps:

and predicting the delivery place of the transaction order in the to-be-delivered state by inquiring the stored delivery probability according to the merchant user and commodity information associated with the transaction order.

predicting delivery places of the plurality of commodities according to the stored delivery probability information;

and determining at least one commodity influenced by the logistics abnormal condition of the target geographic area according to the delivery place prediction result of the commodity.

The target geographic area corresponds to multiple different area-level granularities, wherein different target geographic areas correspond to different exception handling strategies according to different area-level granularities and/or different goods receiving and dispatching ratios.

Wherein the disclaimer handling policy comprises a first exception handling policy, and the first exception handling policy comprises: the commodities and/or trade orders influenced by the logistics abnormal conditions in the same geographic area are all used as commodities and/or trade orders which are suitable for carrying out disclaimer-free processing on merchant users when logistics delay conditions occur.

Wherein, still include:

and displaying a list of target geographical areas applicable to the first exception handling strategy.

Wherein, still include:

and providing the transaction order information for performing exemption processing on the merchant user according to the first exception handling strategy to the corresponding merchant user client.

The exception handling policy comprises a second exception handling policy, and the second exception handling policy is as follows: aiming at commodities and/or transaction orders influenced by logistics abnormal conditions in the same geographic area, providing preparation notification information for corresponding merchant users, and after receiving preparation requests of the merchant users for the commodities and/or the transaction orders, determining the commodities and/or the transaction orders as the commodities and/or the transaction orders which can be subjected to exemption processing when logistics time delay conditions occur.

Wherein, still include:

and if the same merchant user has a plurality of commodities and/or trade orders which are suitable for the second exception handling strategy, providing operation options for batch preparation of the commodities and/or the trade orders through the client corresponding to the merchant user.

Wherein, still include:

and providing the information of the commodity and/or the trade order list to related downstream application modules so as to realize the synchronization of the exception handling information among the downstream application modules.

Wherein said providing information of said inventory of goods and/or trade orders to a related downstream application module comprises:

and providing the information of the commodity and/or the trade order list to an automatic claim payment application module and/or a merchant auditing application module so as to cancel the triggering of an automatic claim payment flow aiming at the trade order and/or eliminate the influence of the trade order on a merchant auditing result when the trade order generation flow in the list is delayed.

Wherein said providing information of said goods and/or trade order listings to a related downstream application module comprises:

and providing the information of the commodity and/or trade order list to an application module related to the logistics aging expression for the consumer user, so as to provide prompt information related to possible delay of the logistics aging when the logistics aging is displayed for the consumer user aiming at the commodity and/or trade order in the list.

and providing the information of the commodity and/or trade order list to a complaint application module and/or a consultation application module so as to reply according to the commodity and/or trade order list when a complaint or consultation request aiming at a specified commodity or trade order is received.

An order information processing method comprises the following steps:

receiving provisioning notification information provided by a server, wherein the provisioning notification information comprises: at least one commodity and/or trade order associated with a current merchant user that is affected by a logistical anomaly in the target geographic area; the target geographic area is determined after identification is carried out according to logistics state characteristics of a plurality of logistics orders in a target time period;

displaying the information of the at least one commodity and/or the trade order and providing a preparation operation option;

and after receiving an allocation operation request of the merchant user for the at least one commodity and/or transaction order, submitting the allocation operation request to the server so as to add the at least one commodity and/or transaction order into a transaction order list capable of performing exemption processing on the merchant user when a logistics aging delay condition is generated.

Wherein, the provision operation options include: an operational option for batch preparation of the at least one item and/or trade order.

Wherein, still include:

receiving and displaying the automatically exempted commodity and/or transaction order information which is provided by the server and is related to the current merchant user; wherein the automatically disclaimed goods and/or trade orders comprise: automatically processing the disclaimed merchandise and/or trade orders based on the identified target geographic area of higher area-level granularity or higher pick-and-place ratio.

A data processing apparatus comprising:

the area identification unit is used for identifying at least one target geographical area with logistics abnormality according to logistics state characteristics of a plurality of logistics orders in a target time period;

the commodity/order hitting unit is used for determining at least one commodity and/or transaction order influenced by the logistics abnormal condition of the target geographic area;

and the strategy execution unit is used for performing exception handling on the at least one affected commodity and/or trade order according to an exception handling strategy corresponding to the target geographic area.

An order information processing apparatus comprising:

the provisioning prompt information receiving unit is configured to receive provisioning notification information provided by a server, where the provisioning notification information includes: at least one commodity and/or trade order associated with a current merchant user that is affected by a logistical anomaly in the target geographic area; the target geographic area is determined after identification is carried out according to logistics state characteristics of a plurality of logistics orders in a target time period;

the display unit is used for displaying the information of the at least one commodity and/or the transaction order and providing a preparation operation option;

and the preparation request submitting unit is used for submitting the preparation operation request of the merchant user for the at least one commodity and/or transaction order to the server after receiving the preparation operation request of the merchant user for the at least one commodity and/or transaction order, so that the at least one commodity and/or transaction order is added into a transaction order list capable of performing exemption processing on the merchant user when a logistics time delay condition is generated.

A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the preceding claims.

An electronic device, comprising:

one or more processors; and

memory associated with the one or more processors for storing program instructions which, when read and executed by the one or more processors, perform the steps of the method of any of the preceding claims.

According to the specific embodiments provided herein, the present application discloses the following technical effects:

according to the embodiment of the application, at least one target geographical area with logistics abnormality can be identified according to the logistics state characteristics of the logistics orders in the target time period, so that the logistics abnormality condition of the specific area can be sensed in advance. And then, at least one commodity and/or trade order influenced by the logistics abnormal condition of the target geographic area can be determined, and then, the at least one commodity and/or trade order influenced by the logistics abnormal condition can be subjected to abnormal processing according to an abnormal processing strategy corresponding to the target geographic area. Therefore, the target geographic area with logistics abnormality can be automatically determined, so that the system can more timely and more quickly know which commodities and/or transaction orders are possibly required to be subjected to exemption processing, the occurrence probability of the damage condition caused by automatic claims and the like of a merchant user is reduced, and the occupation of manpower and time cost of the merchant and a platform side is reduced.

In a preferred embodiment, the specifically identified target geographic area may include a plurality of different area-level granularities, and the exception handling policy is layered based on the difference of the granularities or the difference of the ratio of the received goods to the sent goods, that is, the goods and/or the trade orders hit in different target geographic areas may be handled by using different exception handling policies. Wherein, for some target geographic areas with larger granularity (for example, some secondary areas at the city level), or those with higher ratio of receiving and dispatching goods (for example, tertiary areas at the county level, etc.) although the granularity is smaller, the goods and/or trade orders hitting the target geographic areas can be automatically determined as disclainable, and a list of the target geographic areas can be also publicly shown. For a target geographic area with smaller granularity or a target geographic area with lower receiving and dispatching duty ratio although the granularity is larger, the hit commodities and/or transaction orders can be provided for the merchant user and prompted to be prepared, and after the merchant user initiates preparation, the merchant user can automatically check the goods to pass. By the aid of the hierarchical strategy mechanism, the completeness of exception identification and application can be guaranteed.

Of course, it is not necessary for any product to achieve all of the above-described advantages at the same time for the practice of the present application.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required in the embodiments will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic diagram of a system architecture provided by an embodiment of the present application;

FIG. 2 is a flow chart of a first method provided by an embodiment of the present application;

FIG. 3 is a schematic interface diagram of a platform staff end according to an embodiment of the present disclosure;

FIG. 4 is a schematic interface diagram of a merchant user side according to an embodiment of the present disclosure;

fig. 5 is a schematic interface diagram of a customer end according to an embodiment of the present application;

FIG. 6 is a flow chart of a second method provided by embodiments of the present application;

FIG. 7 is a schematic diagram of a first apparatus provided by an embodiment of the present application;

FIG. 8 is a schematic view of a second apparatus provided by an embodiment of the present application;

fig. 9 is a schematic view of an electronic device provided in an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of protection of the present application.

In the embodiment of the present application, it is considered that when there is a logistics abnormality in some geographic areas, the logistics orders related to these geographic areas usually exhibit some corresponding characteristics, for example, the stay time at a certain site is too long, and the like. Therefore, firstly, the regions where the logistics abnormality may exist can be predicted by the related algorithm according to the logistics state characteristics of a plurality of logistics orders in the target time period (for example, in the case of performing prediction every day, T-1, that is, the previous day is used as the target time period). In addition, the prediction result can be verified by combining information such as weather conditions, special events and the like in a specific geographic area, and even the reason of the logistics abnormity can be roughly predicted. It may then be determined which goods and/or trade orders currently hit in these abnormal areas (including the abnormal areas as a receiving or delivery location, or a destination location on the logistics route, etc.). Furthermore, an exception handling strategy corresponding to the specific geographic area can be determined, and at least one commodity and/or transaction order affected by the logistics abnormal condition of the specific geographic area is subjected to exception handling according to the exception handling strategy. Therefore, the platform side can find the geographical area possibly with logistics abnormality in time, and further can automatically produce the commodity and/or transaction order list suitable for the exemption processing of the merchant user, so that the probability of paying or generating poor assessment records of the merchant user caused by the logistics abnormality in the geographical area is reduced, and meanwhile, the labor and time cost in the implementation process is reduced.

For example, the order associated with the abnormal area may be automatically determined as an order that needs to be exempted from liability, or the order associated with the abnormal area is provided to a merchant, and the merchant performs exemption processing after preparation. Or, in a preferred embodiment of the present application, since there may be multiple different granularities in a specifically identified geographic area where a logistics anomaly exists, and the number of specifically identified geographic areas where a logistics anomaly exists may be many, different anomaly handling policies may also be adopted for geographic areas with different granularities or different receiving and dispatching duty ratios. For example, if some event may cause a logistic anomaly to exist in the whole city, the corresponding identified geographic region may be the universe of the city; some events may affect only a certain county or even a certain street within the city, and the corresponding identified geographic area may also be a county or a street, and so on. That is, the size of the influence surface of the logistics is different for different events, so that different granularities exist in the identified geographic area, and accordingly, different exception handling strategies can be adopted for the geographic areas with different granularities or different receiving and dispatching duty ratios.

For example, in a geographic area with a large granularity such as a city or the like, since the influence surface is generally large, the affected goods and/or transaction orders can be processed in an automatic exempt manner directly. That is, if a certain city is found to have logistics abnormality, the commodities and/or transaction orders affected by the city can be automatically exempted directly without requiring the specific merchant user to perform processes such as preparation and the like. Of course, for some large cities or cities with a high ratio of transmitting/receiving areas, the logistics may be further refined to a level of county, for example, a certain county of a certain city has abnormal logistics, and since the ratio of the transmitting areas of the county is high, that is, a large number of merchants transmit goods or transfer goods from the county, the responsibility of goods and/or transaction orders affected by the county can be automatically avoided, and the specific merchant user is not required to perform processes such as preparation and the like.

In addition, for the geographic area directly subjected to the exemption processing, the information can be published to the public in a public way, so that all parties have uniform perception of the exemption processing range. Moreover, the merchant user corresponding to the commodity and/or the trade order which are specifically applicable to automatic disclaimer processing can be determined, and the information of the commodity and/or the trade order can be provided for the corresponding merchant user, so that the specific merchant user can know which commodities and/or trade orders are automatically disclaimer processed.

For geographical areas below a city, including streets, counties and the like with low transmit-receive floor occupation ratio, since the number of affected merchants may be small, and since the number of these geographical areas may be very large and is not suitable for being published to the whole society in a public manner, the exception handling policy adopted in the embodiment of the present application may be: after the commodities and/or the trade orders which are affected by the geographical area with the logistics abnormality are identified, a notification message about that the commodities and/or the trade orders possibly cause the delay of the logistics can be provided for the merchant users according to the merchant users corresponding to the commodities and/or the trade orders, and the merchant users are prompted to carry out disclaimer free reporting on the commodities and/or the trade orders. In this case, it is also possible to perform "one-touch preparation" of a plurality of commodities and/or trade orders in bulk, without initiating preparation applications for each order. If the merchant user performs disclaimer free reporting on the commodities and/or the trade orders, the corresponding commodities and/or the trade orders are determined by prediction in advance and are actually influenced by the logistics abnormal area, so that the commodities and/or the trade orders can be automatically checked to pass without manual checking. Therefore, the labor and time cost in the preparation process can be saved for both the merchant and the platform.

Through the above manner, different exception handling strategies can be adopted according to the granularity of the geographical area with logistics exception or the difference of the ratio of goods to be received and sent. For the geographic area with larger granularity or the geographic area with higher transmitting-receiving area occupation ratio, a mode of directly carrying out automatic duty-free processing on related commodities and/or transaction orders can be adopted, and the geographic areas can be disclosed, so that the attitude of the platform can be better shown. For a geographic area with smaller granularity or a geographic area with lower receiving and sending occupation ratio, a mode of providing a list of commodities and/or transaction orders influenced by the specific geographic area for the merchant user and prompting the merchant to actively register can be adopted to carry out exemption processing. Therefore, the integrity of the abnormal recognition and the application can be better ensured through a layered strategy mechanism.

From the perspective of system architecture, referring to fig. 1, the embodiment of the present application may provide a background data processing service for a commodity information service system, where the service may include identification of an abnormal geographic area through an algorithm, hit of a specific commodity and/or a transaction order, and produce a list of commodities and/or transaction orders that are suitable for being handled without responsibility for a merchant user when a time delay situation of a commodity flow occurs, and finally provide information to a downstream application module by using the list.

The specific algorithm may output the abnormal probabilities of a plurality of geographic areas, and in combination with an abnormal probability threshold configured by background staff (for example, may be referred to as "Xiaobi") (which may be determined according to daily or historical contemporaneous logistics characteristics in the specific area, including daily or historical contemporaneous stay time and the like), the geographic area where the logistics abnormality exists may be determined. In order to facilitate the hit of the goods and/or trade orders, a shipping probability that a particular merchant will ship for a particular good in a particular geographic area may also be identified during the identification of the anomalous geographic area. That is, two data tables can be obtained by the algorithm, the first data table is the corresponding relation between the geographical area and the abnormal probability, and the second data table is the corresponding relation between the specific geographical area, the merchant, the commodity and the delivery probability. Then, after determining the geographical areas with logistics anomalies according to the first data table and the anomaly probability threshold, the commodities and/or trade orders (only shown as trade orders in the figure) which hit the geographical areas can be determined, and in the process, the second data table can be used. Then, a list of goods and/or trade orders suitable for disclaimer processing of the merchant user can be further determined from the hit goods and/or trade orders.

In the process of specifically producing the commodity and/or trade order list suitable for disclaimer processing of the merchant user, if different exception handling strategies need to be used for exception areas with different granularities, a strategy determination and execution related module can be further included.

The method can be applied to an automatic liability-free processing strategy for the abnormal area with larger granularity, and certainly, in practical application, a list of the abnormal area can be provided to a background worker of the platform for checking, further confirming or completing some information and the like. At this time, a specific work order system may be provided so that the background worker may perform a specific task according to the work order, and after the task is completed, a specific geographic area requiring automatic task-exempting processing may be determined. Subsequent deadlines, etc. for such automatic disclaimer may also be configured and maintained by such back office personnel through the small end. For the geographic areas that are automatically exempt from liability processing, the goods and/or trade orders affected by these geographic areas may be automatically determined as being suitable for liability processing for merchant users.

For the abnormal area with smaller granularity, after the specific affected commodities and/or transaction orders are determined, the abnormal area can be provided for the client of the corresponding merchant user, and the merchant user is prompted to send a disclaimer declaration application. After the merchant user initiates a disclaimer application for a specific commodity and/or transaction order, the merchant user may be audited by an automatic auditing system, where the identified and hit commodity and/or transaction order may be automatically audited and determined as a commodity and/or transaction order that needs to be processed disclaimer. In addition, when a specific merchant user is provided with a list of transaction orders to be prepared, information of the automatically exempted transaction orders can be provided for the merchant user, and an abnormal area map and the like can be provided.

Thereafter, information output can be performed for related application modules at the downstream based on the goods and/or trade order list which is suitable for disclaimer processing of the merchant user. For example, the method comprises the steps of providing the automatic claim payment application module and the merchant assessment application module, so that when the corresponding transaction orders exceed the logistics time limit, the automatic claim payment flow is not triggered, and meanwhile specific transaction orders can be removed from the merchant assessment data. Merchant users may also be provided with information specific to the goods and/or trade orders that are automatically disclaimed. In addition, a specific list can be provided for an application module which is used for carrying out time-based expression correlation on the user of the consumer. For example, a product detail application module, a transaction order application module, a logistics detail application module, etc. may be included, so that prompt information about a particular product that may delay delivery, etc. may be provided in a product detail page for the relevant product, an order detail page for the relevant transaction order, a logistics detail page, etc. In addition, a query interface can be provided for the complaint application module, the consultation application module and the like, so that when complaints or consultations of a consumer user for a certain commodity or trade order are received, whether the corresponding commodity or trade order is subjected to disclaimer processing or not can be queried through the query interface, and the like. Through the mode, the information of the information which is processed in a disclaimer-free mode among the plurality of application modules at the downstream can be aligned, and the problems that the user experience is influenced due to inconsistent information and the like are avoided.

The following describes in detail specific implementations provided in embodiments of the present application.

Example one

First, in an embodiment of the present application, from the perspective of a server, a data processing method is provided, and referring to fig. 2, the method may include:

s201: and identifying at least one target geographical area with logistics abnormity according to the logistics state characteristics of the plurality of logistics orders in the target time period.

Since specific weather or control events generally affect logistics in a certain area, perception of such events has a hysteresis for the merchandise information service system, and particularly, the control events generally need to be noticed after being announced to the whole society by related departments, but the influence on logistics may exist before. For example, for a regulatory class event, the system may not be able to perceive it 72 hours after the start time of the event, but actually, the logistics in the corresponding area have been affected since the start time of the particular event.

Therefore, in the embodiment of the present application, at least one target geographic area where logistics abnormality exists may be first identified according to the logistics state characteristics of a plurality of logistics orders in a target time period. Specifically, since a specific event may occur at any time, the specific identification of the abnormal region may be performed periodically, for example, the identification process may be performed once per day, and the like. When the identification is performed each time, the logistics abnormal probability of the logistics order can be predicted according to the logistics state characteristics of the plurality of logistics orders in the target time period. Then, aggregation can be performed according to the upstream and downstream logistics node relation of the logistics line corresponding to the logistics order with the logistics abnormality, and at least one target geographical area with the logistics abnormality is determined.

The logistics order may be a logistics order generated according to a trade order in the commodity information service system, and specifically, one trade order may correspond to one logistics order, or one trade order may be split into a plurality of sub-orders, which respectively correspond to one logistics order, and so on. And during each identification, the abnormal area identification can be carried out according to the logistics state characteristics of the logistics orders which are in an unfinished state in the previous day.

In specific implementation, an abnormal logistics order may be determined according to the logistics state characteristics of the logistics order (for example, the current logistics node, the stay time at the logistics node, the collection waiting time, the time from the previous node to the current logistics node, and the like). The determination can be specifically performed by comparing with daily or historical contemporaneous logistics state characteristics of the corresponding region and the like. For example, a certain logistics order is currently located at a certain site waiting for the next node and has been stopped for 3 days, but for historical contemporaneous data finding that the average stop time of the site is 1 day, the logistics order may belong to an order with a logistics anomaly, and so on.

After a plurality of logistics orders with logistics abnormity are determined, because a specific logistics order corresponds to a certain logistics line which is composed of a plurality of logistics nodes, such as a collecting station, a distribution center, a transfer station, a dispatching station and the like, the abnormal logistics orders can be aggregated according to the upstream and downstream relation of the logistics nodes, and whether regional logistics abnormity exists or not is judged. For example, if multiple abnormal logistics orders are associated with a site, the site may have an abnormality; in addition, if there is an abnormality in a plurality of sites corresponding to the same wavelength division center, there may be an abnormality in the wavelength division center, and so on. Otherwise, if there is only an individual station with an anomaly in the same wave-splitting center, the wave-splitting center can be considered to have no anomaly, and the anomaly can be located only within the station range, and so on. In this way, logistics nodes with anomalies can be determined, which can be associated with geographical areas such as cities, counties, streets and the like, and therefore the anomalies can be converted into descriptions through the geographical areas. For example, assuming that a plurality of sites within a city have logistical anomalies and that a plurality of counties are involved, the city may be identified as the geographic region where the logistical anomalies exist. And if a plurality of sites with abnormalities only relate to a certain county or partial county, only the partial county can be identified as a geographical area with logistics abnormalities, and the like. In this way, the specific identified geographic area where the logistics anomaly exists may have a plurality of different granularities, for example, some secondary areas (city level), tertiary areas (county level), etc., and the difference of the granularities may be used to determine the difference of the influence surface of different geographic areas.

It should be noted that there is no inclusion-contained relationship between different target geographic areas, for example, if a city is identified as an abnormal geographic area, each county under the city is no longer identified as an abnormal geographic area, and if a county is identified as an abnormal geographic area, it is proved that only some counties in the city in which the county is located have logistic abnormalities, and therefore, the city is no longer identified as an abnormal geographic area. Therefore, when orders are hit subsequently, the same order only hits one abnormal geographical area, and further lists of orders respectively influenced by the abnormal areas can be determined in the area dimension.

It should be noted that, specifically, in the process of identifying abnormal regions through an algorithm, the specific output may be the abnormal probability of each geographic region. In an optional implementation manner, in the process of identifying the abnormal area according to the logistics state characteristics of the logistics order, weather related data, special event data and the like of the corresponding area may be further acquired to roughly determine the reason for generating the abnormal flow in the specific geographic area. Of course, as mentioned above, since the acquisition of the special event information may have hysteresis, the information about the reason of the logistics abnormality may be empty and may be supplemented by the platform staff.

In addition, in addition to identifying the probability of anomaly for a particular geographic area, the probability of delivery for a particular merchant for delivering a particular item within the geographic area may also be identified. This information is identified because, in subsequent steps, a hit for a good and/or trade order (i.e., determining which goods and/or trade orders are affected by a particular geographic anomaly) is required, including trade orders that have been shipped, and also including trade orders that are not currently shipped, and even goods that have not been traded. Since the shipment has not yet been generated, the destination needs to be predicted. However, since the same merchant user may not only have only one shipment room, even in practical applications, most merchant users may correspond to multiple different shipment rooms, and therefore, it is necessary to determine from which shipment room a specific trade order or commodity will be shipped, and further determine whether the trade order will be affected by an abnormal area.

The method includes the steps that when the delivery probability that a merchant delivers specific commodities in a geographical area is identified, historical delivery records of a plurality of merchants are obtained, and the merchants can deliver commodities in different bins, namely different commodities are delivered from different delivery bins, so that the delivery probability that the specific merchant delivers the specific commodities in the geographical area can be determined according to the historical delivery records and the geographical area where the specific delivery bin is located.

That is, in the above manner, the algorithm may output two data tables, namely, a first data table, which is a correspondence between the geographic area and the anomaly probability, and a second data table, which is a correspondence between the geographic area, the merchant, the commodity, and the shipping probability.

S202: determining at least one commodity and/or trade order affected by the logistics abnormal situation of the target geographic area.

After determining a plurality of target geographic areas with logistics anomalies, at least one commodity and/or trade order influenced by the logistics anomalies of the target geographic areas can be determined. Specifically, at least one commodity and/or trade order affected by the logistics abnormal condition of the target geographic area can be screened from all commodities and unfinished trade orders in the commodity information service system. Which may include shipped trade orders, undelivered trade orders, and even commodities for which a trade order has not yet been generated.

For a trading order in a shipped state, whether the trading order is affected by the logistics abnormal condition of a corresponding target geographical area can be determined according to whether the logistics node corresponding to the trading order, where the trading order is located currently, and/or the logistics node which will arrive in the future hits the target geographical area with abnormality. For example, assuming that a certain trade order is currently collected and is waiting to be sent to the next station at a certain site, but a logistics abnormality exists in the county where the site is located, the trade order can be determined as the trade order affected by the logistics abnormality in the county. Or, if a certain trade order is currently in transit, but a city where the receiving place of the trade order is located has a logistics abnormality, the trade order may be determined as a trade order affected by the logistics abnormality of the city, and so on.

For the transaction order in the to-be-delivered state, a delivery place can be predicted first, then a plurality of logistics nodes of a corresponding logistics line can be predicted according to the predicted delivery place and the receiving place information corresponding to the transaction order, and then whether the transaction order in the to-be-delivered state is the transaction order influenced by the logistics abnormal condition of the corresponding target geographical area is determined according to whether the predicted logistics nodes hit the target geographical area or not.

Specifically, when a delivery place is predicted, if a corresponding merchant user only relates to a single delivery bin, the geographic area where the single delivery bin is located can be determined as the delivery place; if the corresponding merchant user is associated with a plurality of delivery warehouses, the delivery location can be predicted according to the commodity corresponding to the transaction order and the historical delivery record of the merchant user for the commodity. That is, if the merchant user is shipping from a certain warehouse for the current goods in the historical shipping record, it can be determined that the transaction order has a higher probability of shipping from the warehouse, and so on.

Specifically, as described above, in an optional implementation manner, in the process of identifying at least one target geographic area where logistics abnormality exists, according to historical shipping records of multiple merchant users, merchant users who take the target geographic area as a shipping place may be determined, and according to a condition of a shipping bin associated with the merchant users, a shipping probability of a specific commodity shipped in the target geographic area by the merchant user is determined to be predicted and stored. Therefore, for the transaction order in the to-be-delivered state, the delivery place of the transaction order can be predicted by inquiring the stored delivery probability according to the merchant user and commodity information related to the transaction order.

In addition, when the delivery probability of the merchant user for delivering the specific commodity in the target geographic area is predicted in advance, at least one commodity affected by the logistics abnormal condition of the target geographic area can be determined according to the stored delivery probability information, and the like. That is to say, before a consumer user purchases a certain commodity, it can be determined whether the commodity is affected by the logistics abnormal area, and if so, prompt information about logistics aging can be provided in advance in a page such as a commodity detail page.

S203: and performing exception processing on the at least one affected commodity and/or transaction order according to an exception processing strategy corresponding to the target geographic area.

After determining at least one commodity and/or trade order respectively affected by a plurality of geographical areas with logistics abnormality, performing abnormality processing on the at least one commodity and/or trade order affected according to an abnormality processing strategy corresponding to a target geographical area.

As described above, in the embodiment of the present application, a specifically identified target geographic area may correspond to multiple different area-level granularities, so that different exception handling policies may be used to perform exception handling on the specific target geographic area according to different granularities and/or different receiving/transmitting duty ratios.

Specifically, the exception handling policy may include a first exception handling policy, where the first exception handling policy includes: the commodities and/or trade orders influenced by the logistics abnormal situation in the same geographic area are all used as the commodities and/or trade orders which are suitable for performing duty-free processing on merchant users when the logistics delay situation is generated. For example, the first abnormality processing policy may be applied to a geographical area of the aforementioned city or above, or a geographical area of a prefecture level where the cargo area is relatively high. Of course, in a specific implementation, a list of such identified target geographic areas, including information such as corresponding anomaly probabilities, may be provided to the staff of the platform. For example, as shown in FIG. 3, the identified target geographic region may be included, and as can be seen, some city-level geographic regions may be included, and some county-level geographic regions may be included. This is because, although the geographic area of the prefecture level is not at a high regional level, the ratio of the received goods to the received goods may be high, and therefore, the automatic disclaimer processing and the display can be performed at that level. And for cities with low goods receiving and dispatching occupation ratio, automatic exemption processing can be carried out only at the grade of the city.

After receiving the specific target geographic area identification result, the worker may further confirm whether to adopt the first exception handling policy. For example, some cities, although having secondary granularity, may not need to be handled by the first exception handling policy because the receiving-transmitting duty ratio is low (e.g., few or no merchants have shipped from the city), and so on. In addition, as shown in fig. 3, an "edit" option may be provided in the interface, and the staff may also supplement information about the cause of the abnormality, etc. In addition, due time information about the policy may be determined only at a future time, and the latest delivery time is affected by the disclaimer due time, so that the information can be dynamically maintained through the interface. That is, for a certain target geographic area, after being identified as an abnormal area, there may be no deadline information, and a specific deadline may be reconfigured when a subsequent sensing is finished with a related event, or when the target geographic area is identified by an algorithm that there is no longer an abnormality in the target geographic area, and so on. It should be noted that, in the embodiment of the present application, identification about an abnormal area may be performed every day, while an abnormal logistics situation in the same geographic area may last for multiple days, and such an abnormal logistics situation may also be changing continuously, which may require a worker to edit or maintain his deadline, latest shipment time, and the like. Therefore, the geographical region list provided to the staff member every day may include historical stock identification results and also may include newly identified increment identification results in the day.

After the target geographic area suitable for the first exception handling policy is determined, a list of the target geographic area suitable for the first exception handling policy may also be disclosed. In this way, both merchant users and consumer users can learn the list.

In addition, the information of the transaction order for the exemption processing of the merchant user according to the first exception handling strategy can be provided for the corresponding merchant user client. That is to say, the merchant user can not only know which areas are automatically exempted from responsibility by the platform, but also know which specific transaction orders are automatically exempted from responsibility by the platform.

Or, the specific exception handling policy may further include a second exception handling policy, where the second exception handling policy may be: and prompting the corresponding merchant user to perform exemption registration according to the commodities and/or transaction orders influenced by the logistics abnormal conditions in the same geographic area, and determining the commodities and/or transaction orders suitable for performing exemption processing on the merchant user when the logistics time delay condition is generated after receiving a registration request of the merchant user. This second exception handling policy may be applicable, for example, to target geographic areas of smaller area-level granularity, or to target geographic areas of lower percentage of received traffic, although area-level granularity is higher. This second exception handling strategy may be employed because such smaller area-level granularity target geographic areas may only affect fewer merchant users, but such smaller area-level granularity target geographic areas may be larger in number and not amenable to automated disclaimer handling by way of a bulletin.

That is, for such a region with a smaller influence range, a plurality of goods and transaction orders affected by the abnormal logistics situation of the region can be identified, and then the goods and transaction orders can be provided to the merchant user, and the merchant user is prompted to register the goods and/or transaction orders, and after the merchant registers the goods and/or transaction orders, the goods and/or transaction orders are added to the list of exempt-from-handling goods and/or transaction orders.

If the same merchant user has a plurality of commodities and/or trade orders which are applicable to the second exception handling strategy, operation options for batch preparation of the commodities and/or the trade orders can be provided through the client corresponding to the merchant user. That is, the merchant user may prepare for a plurality of goods and/or trade orders in bulk, without requiring preparation for orders one by one, thereby improving efficiency. For the platform side, after receiving the preparation application, the specific commodity and/or transaction order is determined after the system performs the identification of the abnormal area and hits the commodity or order, so that manual review is not required, and the verification can be automatically passed, so that the labor and time cost of the platform side can be saved.

That is, in the embodiment of the present application, it can be demonstrated in the order dimension for the merchant end which orders are automatically exempted from responsibility by the platform. For example, as shown in FIG. 4, it is a client interface of a merchant user in which order details affected by a logistic anomaly are exposed, wherein, as shown at 41 in FIG. 4, for orders that have been automatically exempt from liability by the platform, the exempt status may be exposed in the interface as "exempt" status. Of course, the merchant user may also register and manually review the passed transaction order to show the "exempted" status (in this case, if the manual review has not been passed, the "in-review" status may also be shown). Additionally, the platform may identify trade orders that may be affected by the fractional regional logistics anomaly but are not automatically disclaimed by the platform, which may be shown as a "backlog" status, as shown at 42 in FIG. 4. In addition, for the pending transaction orders identified by such a system, a batch preparation operation option may also be provided, for example, as shown at 43 in fig. 4, by which the user may effect a "one-click" batch preparation of multiple transaction orders.

Of course, in a specific implementation, the specific exception handling policy is not limited to the above two, and for example, in a specific implementation, the exception handling policy may further include: and informing the corresponding merchant users of the commodities and/or trade orders affected by the specific abnormal areas, so that the merchant users can generate psychological expectation on possible logistics aging delay situations, and the like.

In a word, no matter whether the platform is automatically exempted from responsibility, or the affected trade order list is provided for the merchant user, and after the merchant user reports the responsibility, the merchant user can conduct exemption processing, and the commodity and/or the trade order list which is suitable for conducting exemption processing on the merchant user when the logistics time delay condition occurs can be obtained. The manifest may provide information to the associated downstream application modules to enable alignment of disclaimer information among the plurality of downstream application modules.

For example, in particular, a list of goods and/or transaction orders that can be processed for the merchant user without liability may be provided to the auto-reimbursement application module, the merchant assessment application module, and the like, so that when the auto-reimbursement application module finds a problem with a transaction order of a merchant, such as delayed shipment, before triggering a particular auto-reimbursement process, it may first determine whether the transaction order is in the list, and if so, may not trigger the auto-reimbursement process. For the merchant assessment application module, if it is found that a certain transaction order of a certain merchant has a problem of delayed delivery and the like, it may also be determined whether the transaction order is located in the aforementioned list, and if so, the transaction order may be removed from the assessment data, that is, the problem of delayed delivery and the like of the transaction order does not affect the assessment of the merchant user, and the like.

In addition, the system may also comprise some application modules related to logistics aging expression for the consumer user, for example, a commodity detail application module, a transaction order application module and/or a logistics detail application module, and the like. In specific implementation, the list may also be provided to the application module, so that when a commodity detail page of a related commodity, an order detail/list page of a related transaction order, and/or a logistics detail information page of a related transaction order is displayed to a consumer user, corresponding prompt information may be provided. For example, as shown at 51 in fig. 5 (a), in the product detail page, the original displayed document "48-hour delivery" or the like may be modified to "influenced by the star, and the delivery of the product in the current area may be influenced". As another example, as shown at 52 in fig. 5 (B), in the trade order page, "influenced by x, the current regional delivery may be influenced" may also be displayed. Further, as shown at 53 in fig. 5 (C), in the logistics detail page corresponding to the specific transaction order, it can also be shown that "affected by, your order may be delayed from being delivered, please patience wait", etc. Or, in an optional mode, for an abnormal area with partial flow (capable of supporting the receiving and sending of a small amount of logistics packages), the predicted delivery time can be predicted and displayed through a specific page.

Furthermore, the commodity information service system may further provide a complaint application module or a consultation application module, and the consumer user may initiate complaints or consultation with respect to a certain commodity or a transaction order, and at this time, a query interface may also be provided to the application module, so that the complaint application module or the consultation application module may initiate a query with respect to a specified commodity or a transaction order through the query result after receiving the complaints or consultation of the user, and at this time, it may determine whether the specified commodity or the transaction order is in a disclaimed list according to the commodity and/or the transaction order list, and return the query result. In this way, the service personnel of the complaint or the consultation can obtain the support on objective data when replying to the complaint or the consultation, and can keep consistent with the disclaimer information disclosed by other channels.

In summary, according to the embodiment of the application, at least one target geographical area with logistics abnormality can be identified according to the logistics state characteristics of the plurality of logistics orders in the target time period, so that the logistics abnormality condition of the specific area can be sensed in advance. And then, at least one commodity and/or trade order influenced by the logistics abnormal condition of the target geographic area can be determined, and then, the at least one commodity and/or trade order influenced by the logistics abnormal condition can be subjected to abnormal processing according to an abnormal processing strategy corresponding to the target geographic area. Therefore, the target geographical area with logistics abnormality can be automatically determined, so that the system can more timely and quickly acquire which commodities and/or transaction orders are possibly required to be subjected to disclaimer processing, the occurrence probability of the capital loss caused by automatic claims and the like of a merchant user is reduced, and the occupation of manpower and time cost of the merchant and a platform side is reduced.

In a preferred embodiment, the specifically identified target geographic area may include a plurality of different area-level granularities, and the exception handling policy is layered based on the difference of the granularities or the difference of the ratio of the received goods to the sent goods, that is, the goods and/or the trade orders hit in different target geographic areas may be handled by using different exception handling policies. Wherein, for some target geographic areas with larger granularity (for example, some secondary areas at the city level), or those with higher ratio of receiving and dispatching goods (for example, tertiary areas at the county level, etc.) although the granularity is smaller, the goods and/or trade orders hitting the target geographic areas can be automatically determined as disclainable, and a list of the target geographic areas can be also publicly shown. For a target geographic area with smaller granularity or a target geographic area with a lower receiving and dispatching duty ratio although the granularity is larger, the hit commodities and/or transaction orders can be provided for the merchant user and prompted to be prepared, and after the merchant user initiates preparation, the commodities and/or the transaction orders can be automatically checked to pass. By the aid of the hierarchical strategy mechanism, the completeness of exception identification and application can be guaranteed.

Example two

The second embodiment provides an order information processing method mainly from the perspective of the merchant, and referring to fig. 6, the method may include:

s601: receiving provisioning notification information provided by a server, wherein the provisioning notification information comprises: at least one commodity and/or trade order associated with a current merchant user that is affected by a logistical anomaly in the target geographic area; the target geographic area is determined after identification is carried out according to logistics state characteristics of a plurality of logistics orders in a target time period;

s602: displaying the information of the at least one commodity and/or the trade order and providing a preparation operation option;

s603: and after receiving a preparation operation request of the merchant user for the at least one commodity and/or trade order, submitting the request to the server so as to add the at least one commodity and/or trade order into a trade order list capable of performing disclaimer-free processing on the merchant user when a logistics time delay condition is generated.

Wherein, the preparation operation options include: an operational option for batch backtracking of the at least one item and/or trade order.

During specific implementation, the automatic disclaimer-free commodity and/or transaction order information which is provided by the server and is related to the current merchant user can be received and displayed; wherein the automatically disclaimed goods and/or trade orders comprise: automatically processing the disclaimed merchandise and/or trade orders based on the identified target geographic area of higher area-level granularity or higher pick-and-place ratio.

For the parts of the second embodiment that are not described in detail, reference may be made to the description of the first embodiment, and details are not repeated here.

It should be noted that, in the embodiments of the present application, the user data may be used, and in practical applications, the user-specific personal data may be used in the scheme described herein within the scope permitted by the applicable law, under the condition of meeting the requirements of the applicable law and regulations in the country (for example, the user explicitly agrees, the user is informed, etc.).

Corresponding to the first embodiment, the embodiment of the present application further provides a data processing apparatus, referring to fig. 7, the apparatus may include:

the area identification unit 701 is configured to identify at least one target geographic area where logistics are abnormal according to logistics state characteristics of a plurality of logistics orders in a target time period;

a commodity/order hit unit 702, configured to determine at least one commodity and/or trade order affected by the logistics abnormal situation in the target geographic area;

and a policy executing unit 703, configured to perform exception handling on the at least one affected commodity and/or trade order according to an exception handling policy corresponding to the target geographic area.

Specifically, the area identification unit may be specifically configured to:

the abnormal logistics order identification subunit is used for predicting the logistics abnormal probability of the logistics orders according to the logistics state characteristics of the logistics orders in the target time period;

the abnormal logistics site identification subunit is used for performing step-by-step upward aggregation according to the upstream and downstream logistics node relation of the logistics line corresponding to the logistics order with the logistics abnormality, and determining the logistics node with the logistics abnormality;

and the abnormal area identification subunit is used for determining at least one target geographical area with the logistics abnormality according to the geographical area where the logistics node with the logistics abnormality is located.

The commodity/order hit unit may specifically be configured to:

Alternatively, the goods/order hit unit may specifically include:

a delivery place forecasting subunit, which is used for forecasting the delivery place of the transaction orders in the to-be-delivered state;

the logistics node prediction subunit is used for predicting a plurality of logistics nodes of the corresponding logistics line according to the predicted delivery location and the receiving location information corresponding to the transaction order;

and the hit subunit is configured to determine, according to whether the predicted multiple logistics nodes hit the target geographic area, whether the transaction order in the to-be-delivered state is a transaction order affected by a logistics abnormal condition of the corresponding target geographic area.

Wherein the ship-to-ship predictor unit is specifically configured to:

Alternatively, the apparatus may further include:

the merchant user determining unit is used for determining merchant users taking the target geographic area as a delivery place according to historical delivery records of a plurality of merchant users after identifying at least one target geographic area with logistics abnormality;

the delivery probability predicting and storing unit is used for determining the delivery probability of the merchant user for delivering the specific commodity in the target geographic area according to the condition of the delivery warehouse associated with the merchant user, predicting and storing the delivery probability;

in this case, the delivery location predictor unit may be specifically configured to:

and for the transaction order in the to-be-delivered state, predicting the delivery place of the transaction order by inquiring the stored delivery probability according to the merchant user and commodity information related to the transaction order.

Additionally, the goods/order hit unit may be further configured to:

and predicting the delivery places of the plurality of commodities according to the stored delivery probability information, and determining at least one commodity influenced by the logistics abnormal situation of the target geographical area according to the prediction result of the delivery places of the commodities.

In an optional embodiment, the target geographic area may correspond to a plurality of different area-level granularities, where different target geographic areas correspond to different exception handling policies according to different area-level granularities and/or different cargo receiving and dispatching ratios.

The exception handling policy comprises a first exception handling policy, and the first exception handling policy comprises: the commodities and/or trade orders influenced by the logistics abnormal conditions in the same geographic area are all used as commodities and/or trade orders which are suitable for carrying out disclaimer-free processing on merchant users when logistics delay conditions occur.

At this time, the apparatus may further include:

and the display unit is used for displaying the list of the target geographic area which is suitable for the first exception handling strategy.

In addition, the apparatus may further include:

and the exempted order information providing unit is used for providing the transaction order information for exempting responsibility processing of the merchant user according to the first exception handling strategy to the corresponding merchant user client.

In addition, the exception handling policy includes a second exception handling policy, where the second exception handling policy is: aiming at commodities and/or transaction orders influenced by logistics abnormal conditions in the same geographic area, providing reporting notification information for corresponding merchant users, and after receiving a reporting request of the merchant users for the commodities and/or the transaction orders, determining the commodities and/or the transaction orders as the commodities and/or the transaction orders which can be processed without liability when logistics delay is generated.

At this time, the apparatus may further include:

and the batch preparation option providing unit is used for providing operation options for batch preparation of a plurality of commodities and/or trade orders through the client corresponding to the merchant user if the same merchant user has a plurality of commodities and/or trade orders applicable to the second exception handling policy.

In addition, the apparatus may further include:

and the responsibility-free information providing unit is used for providing the information of the commodity and/or the trade order list to the related downstream application modules so as to realize the synchronization of the responsibility-free information among the plurality of downstream application modules.

Wherein the disclaimer information providing unit may be specifically configured to:

and providing the information of the commodity and/or the trade order list to an automatic reimbursement application module and/or a merchant auditing application module so as to cancel the trigger of an automatic reimbursement process aiming at the trade order and/or eliminate the influence of the trade order on merchant auditing results when the trade order generation flow in the list is delayed.

Alternatively, the disclaimer information providing unit may be specifically configured to:

Corresponding to the second embodiment, the embodiment of the present application further provides an order information processing apparatus, referring to fig. 8, the apparatus may include:

a provisioning prompt receiving unit 801, configured to receive provisioning notification information provided by a server, where the provisioning notification information includes: at least one commodity and/or trade order associated with a current merchant user that is affected by a logistical anomaly in a target geographic area; the target geographic area is determined after identification is carried out according to logistics state characteristics of a plurality of logistics orders in a target time period;

a display unit 802, configured to display information of the at least one commodity and/or the transaction order, and provide a preparation operation option;

the preparation request submitting unit 803 is configured to submit the preparation operation request of the merchant user for the at least one commodity and/or the transaction order to the server, so as to add the at least one commodity and/or the transaction order into a transaction order list capable of performing exemption processing on the merchant user when a logistics time delay condition occurs.

In a specific implementation, the display unit may be further configured to:

receiving and displaying automatically exempted commodity and/or transaction order information which is provided by a server and related to the current merchant user; wherein the automatically disclaimed goods and/or trade orders comprise: automatically processing the disclaimed merchandise and/or trade orders based on the identified target geographic area of higher area-level granularity or higher pick-and-place ratio.

In addition, the present application also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method described in any of the preceding method embodiments.

And an electronic device comprising:

one or more processors; and

a memory associated with the one or more processors for storing program instructions that, when read and executed by the one or more processors, perform the steps of the method of any of the preceding method embodiments.

Fig. 9 schematically shows an architecture of an electronic device, which may specifically include a processor 910, a video display adapter 911, a disk drive 912, an input/output interface 913, a network interface 914, and a memory 920. The processor 910, the video display adapter 911, the disk drive 912, the input/output interface 913, the network interface 914, and the memory 920 can be communicatively connected by a communication bus 930.

The processor 910 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solution provided in the present Application.

The Memory 920 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 920 may store an operating system 921 for controlling operation of the electronic device 900, a Basic Input Output System (BIOS) for controlling low-level operation of the electronic device 900. In addition, web browser 923, data storage management system 924, information handling system 925, and the like may also be stored. The information processing system 925 can be an application program that implements the operations of the foregoing steps in this embodiment. In summary, when the technical solution provided in the present application is implemented by software or firmware, the relevant program code is stored in the memory 920 and invoked by the processor 910 for execution.

The input/output interface 913 is used to connect the input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and the output devices may include a display, speaker, vibrator, indicator light, etc.

The network interface 914 is used for connecting a communication module (not shown in the figure) to implement communication interaction between the present device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, bluetooth and the like).

The bus 930 includes a path to transfer information between various components of the device, such as the processor 910, the video display adapter 911, the disk drive 912, the input/output interface 913, the network interface 914, and the memory 920.

It should be noted that although the above-mentioned devices only show the processor 910, the video display adapter 911, the disk drive 912, the input/output interface 913, the network interface 914, the memory 920, the bus 930 and so on, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above-described apparatus may also include only the components necessary to implement the embodiments of the present application, and need not include all of the components shown in the figures.

From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.

All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement without inventive effort.

The data processing method and the electronic device provided by the present application are introduced in detail, and specific examples are applied in the text to explain the principles and embodiments of the present application, and the descriptions of the above embodiments are only used to help understand the method and the core idea of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, the specific implementation and the application range may be changed. In view of the above, the description should not be taken as limiting the application.

Claims

1. A method of data processing, comprising:

2. The method of claim 1,

the determining at least one commodity and/or trade order affected by the logistics abnormal situation of the target geographic area comprises:

3. The method of claim 1,

predicting a plurality of logistics nodes of the corresponding logistics line according to the predicted delivery place and the information of the delivery place corresponding to the transaction order;

4. The method of claim 3,

the forecasting of the delivery place for the transaction order in the delivery state comprises the following steps:

if the corresponding merchant user only associates a single delivery warehouse, determining the geographic area where the single delivery warehouse is located as the delivery place;

5. The method of claim 1,

the target geographic area corresponds to a plurality of different area-level granularities, wherein different target geographic areas correspond to different exception handling strategies according to different area-level granularities and/or different cargo receiving and dispatching ratios.

6. The method of claim 5,

the disclaimer processing strategy comprises a first exception processing strategy, and the first exception processing strategy comprises the following steps: the commodities and/or trade orders influenced by the logistics abnormal situation in the same geographic area are all used as the commodities and/or trade orders which are suitable for performing duty-free processing on merchant users when the logistics delay situation is generated.

7. The method of claim 6, further comprising:

8. The method of claim 6, further comprising:

9. The method of claim 8,

10. The method of claim 9, further comprising:

11. The method of any one of claims 1 to 10, further comprising:

12. An order information processing method is characterized by comprising the following steps:

13. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 12.

14. An electronic device, comprising:

one or more processors; and

memory associated with the one or more processors for storing program instructions which, when read and executed by the one or more processors, perform the steps of the method of any one of claims 1 to 12.