CN116109219A - Logistics abnormal event processing method, device and equipment - Google Patents
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Abstract
The invention relates to a method, a device and equipment for processing a logistics abnormal event, and belongs to the technical field of logistics. According to the method, an abnormal event is acquired, a target analysis rule aiming at the abnormal event is generated according to basic element information of the abnormal event and a preset sample analysis rule, data analysis is carried out on associated logistics data, and first analysis data and second analysis data are generated; and after triggering a preset condition, pushing the abnormal event to the internal examination application end and the client management application end. By establishing a target analysis rule of the abnormal event, analyzing the logistics data in real time, generating first analysis data and second analysis data related to the abnormal event, and rapidly pushing the abnormal event to an examination avoidance application end and a client management application end, the processing efficiency of the abnormal event is improved, the processing timeliness is reduced, and the processing effect of the abnormal event is improved.
Description
Technical Field
The invention belongs to the technical field of logistics, and particularly relates to a method, a device and equipment for processing a logistics abnormal event.
Background
In the logistics transportation process, the express mail is transferred from collection and sorting to a link of dispatch, and various internal and external emergency accidents, such as epidemic situation control road sealing control, government requirement shutdown and the like, possibly occur in any link. These anomalies often result in an extended operational cycle of the express, affecting the aging of the express. For the outside, greatly influence the experience of receiving customers, produce user complaints, lead to the loss of merchant customers. For the occurrence of abnormal events in the interior, the workload of customer service is greatly increased, and the labor cost of customer service is greatly increased; meanwhile, the examination data of the internal examination roles are influenced, so that the examination results are inaccurate.
In this regard, when an off-line website, a transfer part, a salesman or a driver finds an abnormal event, the event is reported to an upper level in time through an OA flow or a driver system and the like, then the upper level prolongs the examination class time in time, the affected express item number is counted manually and fed back to a merchant or a sender, and the express item is returned or reissued to perform abnormal timely treatment through communication with a client. However, when the customer's express mail is delayed for a large amount for a long time and the logistics stagnates, and the customer finds the merchant or the delivery network point to consult or complain, the delivery network point can find out that some abnormality occurs in the transit link through repeated confirmation, and then report the abnormality post-discovery, at this time, a large amount of negative influence is caused to the customer and the superior, the customer experience is poor, and the customer is basically irrecoverable.
Therefore, the processing mode after the abnormal event is found often has no standard and perfect reporting form and channel, the information of abnormal reporting lacks unified management, the overall reporting timeliness is influenced, and the influence on the related service is not evaluated; and most network points and areas are manually subjected to exception handling, so that the processing efficiency is low, the analysis accuracy is low, and the aging and the processing effect of exception handling are further affected.
Disclosure of Invention
Therefore, the invention provides a method, a device and equipment for processing a logistic abnormal event, which are helpful for solving the problems of prolonged processing time and poor processing effect in the abnormal event processing process.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in a first aspect, a method for processing a logistic anomaly event includes:
acquiring an abnormal event;
generating a target analysis rule for the abnormal event according to the basic element information of the abnormal event and a preset sample analysis rule;
carrying out data analysis on the logistics data associated with the abnormal event based on the target analysis rule to generate first analysis data and second analysis data associated with the abnormal event; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data;
if the first analysis data trigger a first preset condition, pushing the abnormal event to an assessment exemption application end;
and if the second analysis data trigger a second preset condition, pushing the abnormal event to a client management application terminal.
Further, the basic element information includes at least one of: roles, time ranges, types of abnormal events, theoretical signing time of express delivery, theoretical transportation time of express delivery and theoretical collecting time of express delivery.
Further, the target parsing rule includes at least one of: target infrastructure rules, target business association rules, and target scope of influence rules.
Further, the step of performing data analysis on the logistics data associated with the abnormal event based on the target analysis rule to generate first analysis data and second analysis data associated with the abnormal event includes:
carrying out data analysis on the logistics data associated with the abnormal event to generate first analysis data associated with the abnormal event;
and carrying out single-number dimension data analysis on the logistics data related to the abnormal event based on the first analysis data to generate the second analysis data.
Further, the preset sample analysis rule is determined based on the following steps:
acquiring various types of sample abnormal events;
integrating basic element information of various types of sample abnormal events to obtain a sample basic structure rule;
differentiating service influence matters and exemption contents of various types of sample abnormal events to obtain a sample service relevance rule and a sample influence range rule;
and determining the sample infrastructure rule, the sample business association rule and the sample influence range rule as the sample analysis rule.
Further, the method further comprises:
and generating abnormal marking data by inquiring the pushing waybill configuration rule based on the second analysis data.
Further, before pushing the exception event to the customer management application, the method further comprises:
acquiring a business association rule and client management application side configuration;
filtering the second analysis data through a pushing de-duplication mechanism to generate de-duplication analysis data;
and sending the de-duplication analysis data to the client management application terminal.
Further, after pushing the exception event to the customer management application, the method further comprises:
calculating the influence level of the abnormal event;
and if the influence level of the abnormal event reaches a preset influence level, carrying out early warning on the associated service of the abnormal event.
In a second aspect, the present invention provides a device for handling a logistic anomaly event, including:
the acquisition module is used for acquiring the abnormal event;
the target analysis rule generation module is used for generating a target analysis rule aiming at the abnormal event according to the basic element information of the abnormal event and a preset sample analysis rule;
the data analysis module is used for carrying out data analysis on the logistics data related to the abnormal event based on the target analysis rule, and generating first analysis data and second analysis data related to the abnormal event; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data;
the first pushing module is used for pushing the abnormal event to the examination avoidance application end if the first analysis data trigger a first preset condition;
and the second pushing module is used for pushing the abnormal event to the client management application terminal if the second analysis data trigger a second preset condition.
In a third aspect, the present invention provides a processing apparatus for a logistic anomaly event, including:
one or more memories having executable programs stored thereon;
one or more processors configured to execute the executable program in the memory to implement the steps of any of the methods described above.
The invention adopts the technical proposal and has at least the following beneficial effects:
according to the method, an abnormal event is obtained, a target analysis rule aiming at the abnormal event is generated according to basic element information of the abnormal event and a preset sample analysis rule, and data analysis is carried out on logistics data related to the abnormal event based on the target analysis rule, so that first analysis data and second analysis data related to the abnormal event are generated; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data; if the first analysis data triggers a first preset condition, pushing the abnormal event to an examination avoidance application end; and if the second analysis data triggers a second preset condition, pushing the abnormal event to the client management application terminal. The abnormal events are integrated and managed uniformly based on the sample analysis rules, the target analysis rules are generated, the associated logistics data are subjected to real-time data analysis, the first analysis data and the second analysis data associated with the abnormal events are generated, the abnormal events are rapidly pushed to the examination avoidance application end and the client management application end, the processing efficiency of the abnormal events is improved, the processing timeliness is reduced, and therefore the processing effect of the abnormal events is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for handling a logistic anomaly event according to an embodiment of the present invention;
FIG. 2 is a block diagram schematically illustrating a device for handling a logistic anomaly event according to an embodiment of the present invention;
FIG. 3 is a block diagram of a device for handling logistic anomalies according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
Referring to fig. 1, fig. 1 is a flowchart of a method for handling a logistic anomaly event according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
s11, acquiring an abnormal event;
step S12, generating a target analysis rule for the abnormal event according to the basic element information of the abnormal event and a preset sample analysis rule;
step S13, carrying out data analysis on the logistics data associated with the abnormal event based on the target analysis rule, and generating first analysis data and second analysis data associated with the abnormal event; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data;
step S14, if the first analysis data trigger a first preset condition, pushing the abnormal event to an examination avoidance application end;
and step S15, if the second analysis data trigger a second preset condition, pushing the abnormal event to a client management application end.
Further, the basic element information includes at least one of: roles, time ranges, types of abnormal events, theoretical signing time of express delivery, theoretical transportation time of express delivery and theoretical collecting time of express delivery.
Further, the target parsing rule includes at least one of: target infrastructure rules, target business association rules, and target scope of influence rules.
It should be noted that, the technical scheme provided by the embodiment is mainly applied to data processing associated with abnormal events in the logistics distribution process.
In this embodiment, the execution body may be an electronic device such as a terminal device or a server.
It should be noted that, the logistics express is transferred from collection and sorting to dispatch, and various abnormal events may occur in any link, mainly including various types such as natural disasters, site operation, transportation lines, equipment, sudden accidents, epidemic situation control road sealing and control, government requirement for shutdown, etc. The basic element information of the abnormal event is information for describing the specific condition of the abnormal event, and mainly comprises the following contents: roles (stations, buses, lines) in abnormal events, time ranges, abnormal event types (such as disasters, equipment faults and the like), theoretical signing time of express delivery, theoretical delivery time of express delivery and theoretical collecting time of express delivery.
The preset sample analysis rule is to integrate and manage the reporting form, channel, information and other standards of the full scene abnormal event in the abnormal system in advance to form a unified sample analysis rule. And each type of abnormal event establishes a target analysis rule according to the sample analysis rule, and carries out big data analysis.
It should be noted that the target parsing rule at least includes one of the following: target infrastructure rules, target business association rules, and target scope of influence rules. The target basic structure rule is to integrate the dimensions of the abnormal event into an abnormal event structure comprising basic dimensions of net points, areas, three-section codes, express mail, bus lines, transfer parts and the like. The target business association rule is to divide the business application dimension into abnormal event structures associated with business, including delivery delay, work order type, missing abnormality, stop delivery, error delivery and the like. The target influence range rule is to subdivide the influence data of the abnormal event into abnormal event structures containing dimensions of influence objects, influence time ranges, influence operations and the like.
And carrying out multidimensional data analysis on the logistics data associated with the abnormal event according to the target analysis rule, and generating first analysis data and second analysis data. And the data granularity of the obtained second analysis data is smaller than that of the first analysis data due to different analysis dimensions and granularity.
It should be noted that the application end of the examination avoidance is an application for providing data support for the avoidance of the examination inside the express item aging, such as the examination of dispatching aging of examination roles of website, site, courier and the like. When a large number of express items are delayed due to an abnormal event, if the checking roles are checked according to the normal checking rules, the checking of the checking roles is unreasonable, and according to the situation, checking data of the checking roles such as a website corresponding to the abnormal event are reduced by pushing the abnormal event to an application checking reduction terminal, so that the checking data becomes more reasonable. The assessment relief is mainly applied to relief scenes such as blanking-delay compliance, customer complaint work order non-penalties, order work order non-penalties, net work order non-penalties and the like.
It should be noted that, the client management application end is mainly aimed at the management of client services, including internal client service scenes such as manual customer service, online customer service, express mail tracking, and the like, and external client service scenes including business ends such as multiple pieces, birds, bytes, and the like, and client ends such as official networks, voice robots, and the like. When an abnormal event occurs, a large amount of customer consultation and complaints exist, the workload of the internal customer service can be greatly increased, the labor cost of the customer service is greatly increased, and the abnormal event is pushed to the customer management application end, so that the work of the internal customer service is more targeted, the workload is correspondingly reduced, and the cost is reduced. Meanwhile, when an abnormal event occurs, the issuing merchant also faces the loss caused by customer returning, complaining and the like, the customer experience is affected, and the abnormal event is pushed to the customer management application end, so that the merchant end and the application end can timely receive the abnormal event information and associated express data affected by the abnormal event, the merchant can quickly react, the loss is reduced, and the loss of the customer is reduced.
Note that, the first preset condition and the second preset condition are preset one trigger condition, and in this embodiment, specific contents of the first preset condition and the second preset condition are not specifically limited.
In the process of pushing the analysis data, the external data distribution service is built, the service positioning is carried out by using only processing without storage as a basic principle, and the logic is executed and the data is pushed outwards immediately after the condition triggering is designated. In the pushing process, by means of technologies and tools such as message queues, distributed caches, bloom filter technology families, database sub-library sub-tables, mapstruct high-efficiency object mapping and the like, message classification and distribution in different machines are completed through deployment of a set of codes, and clear sub-work and mutual isolation among clusters are completed by combining a Group mechanism based on a Dubbo component, so that high service availability is realized; the high timeliness of data pushing is realized through the distributed cache, the cuckoo filter and the asynchronous component; node current limiting, multiple retry mechanism and distributed storage of distributed records are realized through the Redis Lua script, so that high reliability of data flow is realized, and the receiving and the distribution of hundred million-level traffic are carried.
It can be understood that according to the method, the device and the system, the abnormal event is obtained, the target analysis rule for the abnormal event is generated according to the basic element information of the abnormal event and the preset sample analysis rule, the logistic data associated with the abnormal event is subjected to data analysis based on the target analysis rule, and the first analysis data and the second analysis data associated with the abnormal event are generated; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data; if the first analysis data triggers a first preset condition, pushing the abnormal event to an examination avoidance application end; and if the second analysis data triggers a second preset condition, pushing the abnormal event to the client management application terminal. The abnormal events are integrated and managed uniformly based on the sample analysis rules, the target analysis rules are generated, the associated logistics data are subjected to real-time data analysis, the first analysis data and the second analysis data associated with the abnormal events are generated, the abnormal events are rapidly pushed to the examination avoidance application end and the client management application end, the processing efficiency of the abnormal events is improved, the processing timeliness is reduced, and therefore the processing effect of the abnormal events is improved.
Further, the step of performing data analysis on the logistics data associated with the abnormal event based on the target analysis rule to generate first analysis data and second analysis data associated with the abnormal event includes:
carrying out data analysis on the logistics data associated with the abnormal event to generate first analysis data associated with the abnormal event;
and carrying out single-number dimension data analysis on the logistics data related to the abnormal event based on the first analysis data to generate the second analysis data.
The first analysis data is a result of analysis data associated with the abnormal event, which is obtained by performing preliminary data analysis on the logistics data associated with the abnormal event according to the target analysis rule. The first parsing data includes at least one of a target infrastructure rule, a target business association rule, and a target scope of influence rule. The content dimensions included in the data results of the different types of abnormal event analysis are different, and in this embodiment, the method is not limited.
The second analysis data is a single-number-dimension second data analysis of the logistics data related to the abnormal event after the initial data analysis of the logistics data related to the abnormal event, and an analysis data result related to the abnormal event is obtained. The single number dimension analysis is to further process big data of the analyzed first analysis data to analyze the single number of the related logistics data. The data granularity of the second analysis data is smaller than that of the first analysis data, and the data information on the waybill number level is further contained on the basis of three aspects of a target infrastructure rule, a target business association rule and a target influence range rule.
Further, the preset sample analysis rule is determined based on the following steps:
acquiring various types of sample abnormal events;
integrating basic element information of various types of sample abnormal events to obtain a sample basic structure rule;
differentiating service influence matters and exemption contents of various types of sample abnormal events to obtain a sample service relevance rule and a sample influence range rule;
and determining the sample infrastructure rule, the sample business association rule and the sample influence range rule as the sample analysis rule.
It can be understood that the preset sample analysis rule is to integrate and manage the reporting form, channel, information and other standards of the whole scene abnormal event in advance in the abnormal system, so as to form a unified sample analysis rule. And each type of abnormal event establishes a target analysis rule according to the sample analysis rule, and carries out big data analysis. The preset sample analysis rule comprises three aspects of a sample basic structure rule, a sample service association rule and a sample influence range rule. The sample basic structure rule integrates the dimensions of various abnormal events into an abnormal event structure containing basic dimensions of net points, areas, three-section codes, express mail, bus lines, transfer parts and the like. The sample business association rule is to divide the business application dimension associated with various abnormal events into abnormal event structures associated with business, including delivery delay, work order type, missing abnormality, stop and error. The sample influence range rule is to subdivide the influence data of the abnormal event into abnormal event structures containing dimensions of influence objects, influence time ranges, influence operations and the like. Firstly, acquiring sample abnormal events of all types, and acquiring a sample basic structure rule by integrating basic element information of the sample abnormal events of all types; differentiating service influence matters and exemption contents of various types of sample abnormal events to obtain a sample service relevance rule and a sample influence range rule; and finally, determining the sample infrastructure rule, the sample service association rule and the sample influence range rule as sample analysis rules.
It can be understood that the standard of the full scene abnormal events such as the net point, the transit, the line, the vehicle and the like in the express link is unified, the sample analysis rule is established, the standard is provided for the post expansion and the other abnormal connection, and the processing speed is increased; the method has the advantages that various types of abnormal events such as natural disasters, site operation, transportation lines, equipment, sudden accidents and the like are integrated into main abnormal event base structures of network points, areas, three-section codes, express items and bus line dimensions, and the abnormal event base structures which are related to business and related to business influence ranges are subdivided on the basis of influence objects, influence time periods, influence operations and the like, so that an application end can directly receive information of the abnormal events, abnormal data of business scenes are obtained, and the cost and timeliness of frequent multiple butt joints of a large number of internal and external applications are reduced.
Further, the method further comprises:
and generating abnormal marking data by inquiring the pushing waybill configuration rule based on the second analysis data.
It should be noted that the pushing waybill configuration rule is a pushing rule set when pushing waybill number data. The anomaly marking data is data for marking data in which anomalies exist. After the logistics data associated with the abnormal event is subjected to the second data analysis of the single number dimension, the data is subjected to relevant matching according to the configuration rule by inquiring the pushing waybill configuration rule, and the express data with the single number abnormal is marked to generate corresponding abnormal marking data.
Further, before pushing the exception event to the customer management application, the method further comprises:
acquiring a business association rule and client management application side configuration;
filtering the second analysis data through a pushing de-duplication mechanism to generate de-duplication analysis data;
and sending the de-duplication analysis data to the client management application terminal.
It should be noted that, the push deduplication mechanism performs repeated data filtering processing operation on the pushed data, and mainly ensures that the data pushed to the client management application end is the latest data. Before pushing the abnormal event to the client management application end, acquiring a business association rule and client management application end configuration, performing repeated data filtering and removing processing on the second analysis data through a pushing duplication removing mechanism according to a business scene, generating the latest analysis data, and pushing the latest analysis data to the client management application end. And simultaneously, writing the push record into a push log.
Further, after pushing the exception event to the customer management application, the method further comprises:
calculating the influence level of the abnormal event;
and if the influence level of the abnormal event reaches a preset influence level, carrying out early warning on the associated service of the abnormal event.
It is understood that the preset influence level is a level of influence degree set in advance, and in this embodiment, the specific content of the influence level is not limited. After the abnormal event is pushed to the client management application end, whether the abnormal influence degree reaches the degree of early warning of the associated service is judged by intelligently calculating the influence level of the abnormal event, such as whether the express mail delay reaches the hour level or the day level and judging whether the influence level reaches the preset influence level.
Referring to fig. 2, fig. 2 is a schematic block diagram of a processing device for a logistic anomaly event according to an embodiment of the present invention, where the processing device 2 for a logistic anomaly event includes:
an acquisition module 21 for acquiring an abnormal event;
the target analysis rule generation module 22 is configured to generate a target analysis rule for the abnormal event according to the basic element information of the abnormal event and a preset sample analysis rule;
the data analysis module 23 is configured to perform data analysis on the logistic data associated with the abnormal event based on the target analysis rule, and generate first analysis data and second analysis data associated with the abnormal event; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data;
the first pushing module 24 is configured to push the abnormal event to the assessment exemption application end if the first analysis data triggers a first preset condition;
and the second pushing module 25 is configured to push the abnormal event to the client management application if the second analysis data triggers a second preset condition.
The specific manner in which the respective modules of the processing apparatus 2 for a logistic anomaly event in the above embodiment perform operations has been described in detail in the above-described embodiments of the related methods, and will not be described in detail herein.
Referring to fig. 3, fig. 3 is a schematic block diagram of an apparatus for handling a logistic anomaly event according to an embodiment of the present invention, where the apparatus 3 for handling a logistic anomaly event includes:
one or more memories 31 on which executable programs are stored;
one or more processors 32 for executing the executable programs in the memory 31 to implement the steps of the method of any one of the above.
The specific manner in which the processor 32 executes the program in the memory 31 of the processing apparatus 3 for a logistic abnormality event in the above-described embodiment has been described in detail in the embodiment concerning the method, and will not be described in detail here.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality", "multiple" means at least two.
It will be understood that when an element is referred to as being "mounted" or "disposed" on another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present, and further, as used herein, connection may comprise a wireless connection; the use of the term "and/or" includes any and all combinations of one or more of the associated listed items.
Any process or method description in a flowchart or otherwise described herein may be understood as: means, segments, or portions of code representing executable instructions including one or more steps for implementing specific logical functions or processes are included in the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including in a substantially simultaneous manner or in an inverse order, depending upon the function involved, as would be understood by those skilled in the art of embodiments of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. A method for handling a logistic anomaly event, comprising:
acquiring an abnormal event;
generating a target analysis rule for the abnormal event according to the basic element information of the abnormal event and a preset sample analysis rule;
carrying out data analysis on the logistics data associated with the abnormal event based on the target analysis rule to generate first analysis data and second analysis data associated with the abnormal event; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data;
if the first analysis data trigger a first preset condition, pushing the abnormal event to an assessment exemption application end;
and if the second analysis data trigger a second preset condition, pushing the abnormal event to a client management application terminal.
2. The method of claim 1, wherein the base element information comprises at least one of: roles, time ranges, types of abnormal events, theoretical signing time of express delivery, theoretical transportation time of express delivery and theoretical collecting time of express delivery.
3. The method of claim 1, wherein the target parsing rule includes at least one of: target infrastructure rules, target business association rules, and target scope of influence rules.
4. The method of claim 1, wherein the performing data parsing on the logistics data associated with the abnormal event based on the target parsing rule to generate the first parsed data and the second parsed data associated with the abnormal event comprises:
carrying out data analysis on the logistics data associated with the abnormal event to generate first analysis data associated with the abnormal event;
and carrying out single-number dimension data analysis on the logistics data related to the abnormal event based on the first analysis data to generate the second analysis data.
5. The method of claim 1, wherein the predetermined sample parsing rule is determined based on:
acquiring various types of sample abnormal events;
integrating basic element information of various types of sample abnormal events to obtain a sample basic structure rule;
differentiating service influence matters and exemption contents of various types of sample abnormal events to obtain a sample service relevance rule and a sample influence range rule;
and determining the sample infrastructure rule, the sample business association rule and the sample influence range rule as the sample analysis rule.
6. The method according to claim 1, wherein the method further comprises:
and generating abnormal marking data by inquiring the pushing waybill configuration rule based on the second analysis data.
7. The method of claim 1, wherein prior to pushing the exception to a customer management application, the method further comprises:
acquiring a business association rule and client management application side configuration;
filtering the second analysis data through a pushing de-duplication mechanism to generate de-duplication analysis data;
and sending the de-duplication analysis data to the client management application terminal.
8. The method of claim 1, wherein after pushing the exception to a customer management application, the method further comprises:
calculating the influence level of the abnormal event;
and if the influence level of the abnormal event reaches a preset influence level, carrying out early warning on the associated service of the abnormal event.
9. A device for handling logistic anomaly events, comprising:
the acquisition module is used for acquiring the abnormal event;
the target analysis rule generation module is used for generating a target analysis rule aiming at the abnormal event according to the basic element information of the abnormal event and a preset sample analysis rule;
the data analysis module is used for carrying out data analysis on the logistics data related to the abnormal event based on the target analysis rule, and generating first analysis data and second analysis data related to the abnormal event; wherein the data granularity of the second analytic data is smaller than the data granularity of the first analytic data;
the first pushing module is used for pushing the abnormal event to the examination avoidance application end if the first analysis data trigger a first preset condition;
and the second pushing module is used for pushing the abnormal event to the client management application terminal if the second analysis data trigger a second preset condition.
10. A logistic anomaly event processing apparatus, comprising:
one or more memories having executable programs stored thereon;
one or more processors configured to execute the executable program in the memory to implement the steps of the method of any one of claims 1-8.
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| CN202310003273.0A CN116109219A (en) | 2023-01-03 | 2023-01-03 | Logistics abnormal event processing method, device and equipment |
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| CN202310003273.0A CN116109219A (en) | 2023-01-03 | 2023-01-03 | Logistics abnormal event processing method, device and equipment |
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