CN115293682A

CN115293682A - Abnormal logistics order monitoring method and related device

Info

Publication number: CN115293682A
Application number: CN202210831228.XA
Authority: CN
Inventors: 余刚; 陈伟; 杨周龙
Original assignee: Dongpu Software Co Ltd
Current assignee: Dongpu Software Co Ltd
Priority date: 2022-07-14
Filing date: 2022-07-14
Publication date: 2022-11-04

Abstract

The application provides an abnormal logistics order monitoring method and a related device, wherein the method comprises the following steps: acquiring statistical parameter values of various abnormal parameters of various distribution centers in a preset time period based on the state information of the logistics orders of the various distribution centers in the preset time period; generating database visualization information of graph types and/or table types based on the statistical parameter values of the abnormal parameters of the distribution centers in the preset time period, and displaying the database visualization information on display equipment; receiving the value range setting operation of a user aiming at each abnormal parameter of each distribution center by utilizing interactive equipment, and determining the preset value range corresponding to each abnormal parameter of each distribution center; and when detecting that the real-time parameter value of at least one abnormal parameter of the target distribution center is not in the corresponding preset numerical range, generating early warning information and displaying the early warning information on the display equipment. An online closed loop is formed according to the information processing flow of the abnormal logistics orders of the distribution center, and the processing efficiency is improved.

Description

Abnormal logistics order monitoring method and related device

Technical Field

The application relates to the technical field of logistics transportation and order monitoring and early warning, in particular to an abnormal logistics order monitoring method and a related device.

Background

With the rapid development of internet technology, online shopping is more and more popular. In the logistics field, when a logistics order is sorted through a distribution center, due to the reasons of misoperation of sorting workers, abnormal operation of a sorting robot and the like, the logistics order is subjected to abnormal conditions of wrong distribution, wrong packet collection, backflow, abnormal weighing and the like in the sorting process of the distribution center. When abnormal information occurs to the logistics orders of the distribution center, normally, the distribution workers report the abnormal orders independently or accumulate the abnormal orders for a long time for reporting, due to the fact that personnel flow of the distribution workers is large, timely response cannot be made to the reporting of the abnormal orders, the mode cannot sense the state information of the logistics orders in the distribution sorting process in real time, an online closed loop is not formed in the information processing flow of the abnormal logistics orders of the distribution center, and the processing efficiency of the abnormal logistics orders is low.

Based on this, the application provides an abnormal logistics order monitoring method and a related device, so as to solve the problems existing in the prior art.

Disclosure of Invention

The application aims to provide an abnormal logistics order monitoring method and a related device, which can sense the state information of a logistics order in the distribution and sorting process in real time, form an online closed loop aiming at the information processing flow of the abnormal logistics order of a distribution center, and improve the processing efficiency of the abnormal logistics order.

The purpose of the application is realized by adopting the following technical scheme:

in a first aspect, the present application provides a method for monitoring abnormal logistics orders, which is used for monitoring abnormal logistics orders of each allocation center, and the method includes:

acquiring statistical parameter values of various abnormal parameters of various distribution centers in a preset time period based on the state information of the logistics orders of the various distribution centers in the preset time period;

generating database visualization information of graph types and/or table types based on the statistical parameter values of the abnormal parameters of the distribution centers in the preset time period, and displaying the database visualization information on display equipment;

receiving the value range setting operation of each abnormal parameter of each distribution center by using interactive equipment, and determining the preset value range corresponding to each abnormal parameter of each distribution center in response to the value range setting operation;

when detecting that the real-time parameter value of at least one abnormal parameter of a target distribution center is not in a corresponding preset numerical range, generating early warning information and displaying the early warning information on the display equipment, wherein the target distribution center is one of the plurality of distribution centers.

The technical scheme has the beneficial effects that: firstly, according to historical data in a preset time period (namely state information of a logistics order of each distribution center in the preset time period, wherein the state information of the logistics order refers to the state information of the logistics order in the wave-separating sorting process), obtaining statistical parameter values of each abnormal parameter of each distribution center, wherein the statistical parameter values can reflect the performance quality of each distribution center in each abnormal parameter dimension; the statistical parameter values are utilized to generate diversified database visual information in a chart form, and a display function is provided through display equipment, so that monitoring personnel can visually, clearly and efficiently know the performance quality of each allocation center in each abnormal parameter aspect through the database visual information; then, a monitoring person or a management person can make differentiated assessment indexes (namely preset value ranges corresponding to the various abnormal parameters) of the various abnormal parameters for the various distribution centers according to historical performance differences of the various distribution centers, judge that the abnormal parameters reach the standard and do not need to be warned when the real-time parameter values of the abnormal parameters are in the corresponding preset value ranges for each distribution center, judge that the abnormal parameters do not reach the standard and possibly need to be warned when the real-time parameter values of the abnormal parameters are not in the corresponding preset value ranges (whether the distribution centers are the target distribution centers which are focused or not needs to be considered), and when making the assessment indexes, for example, can make a stricter assessment index for the distribution centers which are excellent in performance, and require that the daily number of the abnormal logistics orders is not more than 3, and make an assessment index which is suitable for the distribution centers which are poor in performance, and require that the daily number of the abnormal logistics orders is not more than 10; if a poor-performance allocation center or an allocation center serving as a key node is used as a target allocation center which focuses attention, when the real-time parameter values of one or more abnormal parameters of the target allocation center are not in the corresponding preset value range, early warning information is generated and displayed on display equipment, and the early warning information can be sent to preset user equipment (for example, intelligent terminal equipment such as a mobile phone, a tablet computer and intelligent wearable equipment).

The method and the system sense the state information of the logistics orders of the target distribution center in the distribution sorting process in real time, and can perform early warning in time when the fact that the real-time parameter value of at least one abnormal parameter of the target distribution center is not in the corresponding preset value range is detected, so that monitoring personnel can conveniently remind the personnel of the target distribution center to process the abnormal logistics orders as soon as possible after looking up the early warning information, namely, an online closed loop is formed for the information processing flow of the abnormal logistics orders of the target distribution center, and the processing efficiency of the abnormal logistics orders is improved.

The assessment analysis is carried out aiming at the abnormal logistics orders of the distribution centers, specifically, the state information of all the logistics orders of each distribution center is obtained, the visual information (chart format) of a database corresponding to the abnormal logistics orders is established, different assessment indexes are set for each distribution center, and the abnormal logistics orders of the target distribution center are early warned in time; the abnormal logistics orders can be intelligently and timely reported, a processing method for the abnormal logistics orders can be conveniently given in a follow-up intelligent mode or manual mode, or rewards are given for a better-performance distribution center, punishment is given to a distribution center with a poorer performance, and therefore the transportation efficiency of the logistics orders is integrally improved, and the decision-making efficiency and the management level of logistics merchants are improved.

In some optional embodiments, the starting time of the preset time period is before the current time and is separated from the current time by a first preset time interval, the ending time of the preset time period is before the current time and is separated from the current time by a second preset time interval, and the first preset time interval is greater than the second preset time interval.

The technical scheme has the beneficial effects that: the method comprises the steps that a preset time period is set in a specific time period before the current time, specifically, a first preset time interval from the current time is set as a starting time, a second preset time interval from the current time is set as an ending time, and the first preset time interval is set to be larger than the second preset time interval, so that a proper preset time period interval can be manually set, analysis processing can be conveniently carried out on historical data of the specific time period, and a proper abnormal parameter assessment index is obtained and is used for assessing each allocation center.

In some alternative embodiments, each anomaly parameter includes a quantity of anomalous logistics orders.

In some optional embodiments, each anomaly parameter further comprises a proportion of anomalous logistics orders. The proportion of the abnormal logistics orders refers to the ratio of the number of the abnormal logistics orders to the total number of the logistics orders, and the total number of the logistics orders is the sum of the number of the normal logistics orders and the number of the abnormal logistics orders.

In some alternative embodiments, each anomaly parameter includes an anomaly type and a cause type;

the acquiring statistical parameter values of the abnormal parameters of the distribution centers in the preset time period based on the state information of the logistics orders of the distribution centers in the preset time period comprises the following steps:

performing primary classification on each logistics order based on the state information of each logistics order to obtain a primary classification result of each logistics order, wherein the primary classification result is used for indicating whether each logistics order belongs to an abnormal logistics order;

performing exception classification on each abnormal logistics order based on the state information of each abnormal logistics order to obtain an exception type and a reason type of each abnormal logistics order, wherein the exception type of each abnormal logistics order is one of misdistribution, miscollection package, backflow and weighing exception, and the reason type of each abnormal logistics order is one of man-made reason, equipment reason, misdistribution reason and information reason;

and acquiring statistical parameter values of the abnormal type and the reason type of each distribution center in the preset time period based on the abnormal type and the reason type of the abnormal logistics order of each distribution center in the preset time period.

The technical scheme has the beneficial effects that: the process of obtaining the statistical parameter values of the abnormal parameters is divided into three stages: the method comprises the following steps that in the first stage, each logistics order is subjected to preliminary classification so as to judge whether each logistics order belongs to an abnormal logistics order or not; in the second stage, each abnormal logistics order classified (based on the result of the preliminary classification in the first stage) is subjected to abnormal classification (subdivision) to obtain an abnormal type and a cause type (the cause type here refers to a formation cause type or a formation cause type) of each abnormal logistics order, wherein the abnormal type of each abnormal logistics order is one of specific abnormal types, that is, all abnormal logistics orders are classified into the abnormal types, and similarly, the (formation) cause type of each abnormal logistics order is one of specific cause types, and the formation causes of all abnormal logistics orders are classified into the cause types; in the third stage, after obtaining the abnormal type and the cause type of the abnormal logistics order of each distribution center within the preset time period, the statistical parameter values (of the abnormal logistics order) of the abnormal type and the cause type corresponding to each distribution center can be obtained, that is, for each distribution center, after obtaining the abnormal type and the cause type of all the abnormal logistics orders of the distribution center, the number of the abnormal logistics order corresponding to a single abnormal type or cause type and the order information corresponding to the abnormal logistics order can be counted. In the second stage, the status information of each abnormal logistics order may include, for example, one or more of superior station information, inbound time, return time, responsible person, inbound line body number, return line body number, and outbound line body number, and the specific formation reason of the abnormal logistics order may be intelligently and accurately classified according to the above information.

In some alternative embodiments, the database visualization information includes a cause pie chart for a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of generating the reason pie chart comprises the following steps:

generating a reason pie chart corresponding to the target abnormal type based on the number of the abnormal logistics orders of the target abnormal type and the number of each reason type corresponding to the abnormal logistics orders of the target abnormal type in the preset time period, and displaying the name, the number and the proportion of the reason type corresponding to each part on the reason pie chart, wherein each part of the reason pie chart corresponds to one reason type;

the method further comprises the following steps:

when a selection operation for one of the parts of the reason pie chart is received, the selected part and the other parts of the reason pie chart are displayed by using different display parameters.

The technical scheme has the beneficial effects that: and a reason pie chart is generated, so that monitoring personnel can conveniently and quickly know the number and the proportion of each reason type. When a user selects one part, the reason type corresponding to the selected part can show a display effect different from that of other parts, and a difference is formed visually, so that monitoring personnel can focus attention on the selected reason type, the information receiving efficiency of the monitoring personnel is improved, the monitoring efficiency is improved integrally, and the logistics transportation efficiency is improved.

In some optional embodiments, the database visualization information comprises a ranked graph of a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of generating the ranking graph comprises:

ranking the distribution centers according to a first sequence from large to small or a second sequence from small to large on the basis of the quantity of the abnormal logistics orders of the distribution centers of the target abnormal type in the preset time period;

generating the ranking graph based on the number of abnormal logistics orders of the distribution centers with N positions before ranking, and displaying the name, the number and the proportion corresponding to each distribution center on the ranking graph, wherein N is a positive integer;

the method further comprises the following steps:

and when the switching operation aiming at the ranking graph is received, updating the ranking graph based on the number of abnormal logistics orders of the N-bit after-ranking distribution center.

The technical scheme has the beneficial effects that: when ranking the branch centers, the first few names with poor performance or the first few names with better performance can be clearly and intuitively displayed from large to small and from small to large; when the user switches the ranking graph, the ranking graph can be automatically switched between the first few names with poor performance and the first few names with good performance, and therefore the follow-up punishment on the first few names with poor performance, the rewarding on the first few names with good performance and the like are facilitated.

In some optional embodiments, the database visualization information comprises a trend graph of a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of generating the trend graph comprises:

and generating the trend graph based on the quantity of the abnormal logistics orders of the target abnormal type in M adjacent first time periods in the preset time period, wherein M is a positive integer.

The technical scheme has the beneficial effects that: the trend graph may be, for example, a line graph, an area graph, a stacked area graph, a funnel graph, etc., which can show a trend of the number of abnormal logistics orders of the target abnormal type over time. The first time period can be 1 day, 3 days, 1 week, 1 month and the like, and is convenient for checking the trend of the quantity of the abnormal logistics orders with the target abnormal type along with the change of time in adjacent days, weeks and months in the preset time period.

In some alternative embodiments, the database visualization information includes an overall trend graph and a data table;

the process of generating the overall trend graph comprises the following steps:

generating the overall trend graph based on the number of the abnormal logistics orders of K second adjacent time periods in the preset time period, wherein K is a positive integer;

the process of generating the data table includes:

when receiving a selection operation for one of the second time periods, generating the data table based on the number of each abnormal type corresponding to the abnormal logistics order in the selected second time period.

The technical scheme has the beneficial effects that: the second time period may be, for example, 1 day, 3 days, 1 week, 1 month, etc., and is convenient for viewing the overall trend of the number of all abnormal logistics orders of the abnormal type in the adjacent days, weeks, months in the preset time period, along with the time change. And when the monitoring personnel selects one of the second time periods, the data table can be generated based on the quantity of each abnormal type corresponding to the abnormal logistics orders of all the distribution centers in the selected second time period, the data table can embody the scale of each abnormal type in the second time period, and the monitoring personnel can conveniently know the specific quantity of each abnormal type in each specific second time period.

In a second aspect, the present application provides an abnormal logistics order monitoring apparatus for monitoring abnormal logistics orders of distribution centers, the apparatus includes:

the statistical module is used for acquiring statistical parameter values of various abnormal parameters of various distribution centers in a preset time period based on the state information of the logistics orders of various distribution centers in the preset time period;

the visualization module is used for generating database visualization information of graph types and/or table types based on the statistical parameter values of the abnormal parameters of the distribution centers in the preset time period and displaying the database visualization information on display equipment;

the setting module is used for receiving the value range setting operation of the user aiming at each abnormal parameter of each distribution center by utilizing the interactive equipment, responding to the value range setting operation and determining the preset value range corresponding to each abnormal parameter of each distribution center;

and the early warning module is used for generating early warning information and displaying the early warning information on the display equipment when detecting that the real-time parameter value of at least one abnormal parameter of the target distribution center is not in the corresponding preset numerical range, wherein the target distribution center is one of the plurality of distribution centers.

In some alternative embodiments, each exception parameter includes an exception type and a cause type;

the statistics module is configured to:

performing primary classification on each logistics order based on the state information of each logistics order to obtain a primary classification result of each logistics order, wherein the primary classification result is used for indicating whether each logistics order belongs to an abnormal logistics order or not;

and acquiring statistical parameter values of the abnormal type and the reason type of each allocation center in the preset time period based on the abnormal type and the reason type of the abnormal logistics order of each allocation center in the preset time period.

In some optional embodiments, the database visualization information comprises a cause pie chart of a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of the visualization module generating the reason pie chart comprises the following steps:

the visualization module is further to:

the process of the visualization module generating the ranking graph comprises:

the visualization module is further to:

when receiving a switching operation for the ranking graph, updating the ranking graph based on the number of abnormal logistics orders of the N ranked distribution centers.

the process of generating the trend graph by the visualization module comprises the following steps:

the process of the visualization module generating the overall trend graph comprises the following steps:

the process of the visualization module generating the data table comprises the following steps:

In a third aspect, the present application provides an electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of any of the above methods when executing the computer program.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of any of the methods described above.

Drawings

The present application is further described below with reference to the accompanying drawings and embodiments.

Fig. 1 shows a flow chart of an abnormal logistics order monitoring method provided by an embodiment of the present application.

Fig. 2 shows a schematic flowchart of obtaining a statistical parameter value according to an embodiment of the present application.

Fig. 3 shows a schematic structural diagram of an abnormal logistics order monitoring device according to an embodiment of the present application.

Fig. 4 shows a block diagram of an electronic device according to an embodiment of the present application.

Fig. 5 shows a schematic structural diagram of a program product provided in an embodiment of the present application.

Detailed Description

The technical solutions in the present application will be described below with reference to the drawings and the detailed description of the present application, and it should be noted that, in the case of no conflict, any combination between the embodiments or technical features described below may form a new embodiment.

In this application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a and b, a and c, b and c, a and b and c, wherein a, b and c can be single or multiple. It is to be noted that "at least one item" may also be interpreted as "one or more item(s)".

It is also noted that the terms "exemplary" or "such as" and the like are used herein to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.

Method embodiment

Referring to fig. 1, fig. 1 shows a schematic flowchart of an abnormal logistics order monitoring method provided in an embodiment of the present application.

The embodiment of the application provides an abnormal logistics order monitoring method, which is used for monitoring abnormal logistics orders of all distribution centers, and comprises the following steps:

step S101: acquiring statistical parameter values of various abnormal parameters of various distribution centers in a preset time period based on the state information of the logistics orders of the various distribution centers in the preset time period;

step S102: generating database visualization information of graph types and/or table types based on the statistical parameter values of the abnormal parameters of the distribution centers in the preset time period, and displaying the database visualization information on display equipment;

step S103: receiving the value range setting operation of each abnormal parameter of each distribution center by a user through interactive equipment, and determining the preset value range corresponding to each abnormal parameter of each distribution center in response to the value range setting operation;

step S104: when detecting that the real-time parameter value of at least one abnormal parameter of a target distribution center is not in a corresponding preset numerical range, generating early warning information and displaying the early warning information on the display equipment, wherein the target distribution center is one of the plurality of distribution centers.

Therefore, firstly, according to historical data in a preset time period (namely state information of a logistics order of each distribution center in the preset time period, wherein the state information of the logistics order refers to the state information of the logistics order in the wave-separating sorting process), statistical parameter values of various abnormal parameters of each distribution center are obtained, and the statistical parameter values can reflect the good and bad conditions of each distribution center in the aspect of each abnormal parameter dimension;

the statistical parameter values are utilized to generate diversified database visual information in a chart form, and a display function is provided through display equipment, so that monitoring personnel can visually, clearly and efficiently know the performance quality of each allocation center in each abnormal parameter aspect through the database visual information;

then, a monitoring person or a management person can make differentiated assessment indexes (namely preset value ranges corresponding to the various abnormal parameters) of the various abnormal parameters for the various distribution centers according to historical performance differences of the various distribution centers, judge that the abnormal parameters reach the standard and do not need to be warned when the real-time parameter values of the abnormal parameters are in the corresponding preset value ranges for each distribution center, judge that the abnormal parameters do not reach the standard and possibly need to be warned when the real-time parameter values of the abnormal parameters are not in the corresponding preset value ranges (whether the distribution centers are the target distribution centers which are focused or not needs to be considered), and when making the assessment indexes, for example, can make a stricter assessment index for the distribution centers which are excellent in performance, and require that the daily number of the abnormal logistics orders is not more than 3, and make an assessment index which is suitable for the distribution centers which are poor in performance, and require that the daily number of the abnormal logistics orders is not more than 10;

if a poor-performance allocation center or an allocation center serving as a key node is used as a target allocation center which focuses attention, when the real-time parameter values of one or more abnormal parameters of the target allocation center are not in the corresponding preset value range, early warning information is generated and displayed on display equipment, and the early warning information can be sent to preset user equipment (for example, intelligent terminal equipment such as a mobile phone, a tablet computer and intelligent wearable equipment).

The method and the system have the advantages that the state information of the logistics orders of the target distribution center in the distribution and sorting process is sensed in real time, when the fact that the real-time parameter value of at least one abnormal parameter of the target distribution center is not in the corresponding preset value range is detected, early warning can be timely conducted, monitoring personnel can conveniently process or remind the personnel of the target distribution center to process the abnormal logistics orders as soon as possible after checking the early warning information, namely, an on-line closed loop is formed in an information processing flow of the abnormal logistics orders of the target distribution center, and the processing efficiency of the abnormal logistics orders is improved.

The method and the device have the advantages that the abnormal logistics order condition of the whole network distribution center can be checked, the abnormal logistics order can be searched, and the distribution center with more abnormal logistics orders can be located; the abnormal logistics orders can be intelligently and timely reported, a processing method for the abnormal logistics orders can be conveniently given in a follow-up intelligent mode or manual mode, or rewards are given for a better-performance distribution center, punishment is given to a distribution center with a poorer performance, and therefore the transportation efficiency of the logistics orders is integrally improved, and the decision-making efficiency and the management level of logistics merchants are improved.

The method for obtaining the real-time parameter value of the at least one abnormal parameter of the target allocation center is not limited in the embodiment of the application, and the method may be, for example, receiving a real-time parameter value input by a user, obtaining a real-time parameter value by querying a preset storage address, or obtaining a real-time parameter value by real-time calculation. The real-time calculation process may include, for example: and acquiring real-time parameter values of various abnormal parameters of various distribution centers based on the state information of the logistics orders of the various distribution centers within a preset time length before the current time. The preset time period may be, for example, 5 minutes, 10 minutes, 30 minutes, 1 hour, 3 hours, 6 hours, 12 hours, 18 hours, 1 day, 2 days, etc., that is, the calculation process of the real-time parameter value is similar to the statistical parameter value, except that the selected time period is different, and the time period corresponding to the real-time parameter value is a period from a certain previous time to a current time. The previous time here refers to a time before the current time.

The interactive device is not limited in the embodiment of the application, and may be, for example, an intelligent terminal device such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, and an intelligent wearable device, or the interactive device may be a workstation or a console.

The embodiment of the present application does not limit the manner in which the interactive device receives various manual operations (or user operations). The operations are divided according to input modes, and for example, text input operation, audio input operation, video input operation, key operation, mouse operation, keyboard operation, intelligent touch pen operation and the like can be included. These operations include, but are not limited to, value range setting operations, selection operations, switching operations, query operations, and the like.

The length of the preset time period may be, for example, 1 day, 3 days, 1 week, 1 month, 3 months, half year, 1 year, or the like.

In the embodiment of the present application, the number of the distribution centers in the whole network may be, for example, 3, 5, 100, 1000, 5000, 10000, 20000, and the like. The mass data of the whole network distribution center are targeted for data mining and analysis, so that the follow-up differentiated effective measures can be effectively guided to be taken for each distribution center and each abnormal type, and the current logistics transportation efficiency and the user experience of the logistics transportation service are improved.

In an embodiment of the present application, the graph-type database visualization information may include, for example, one or more of the following:

bar graph: the bar graph compares various values using vertical data markers;

line drawing: the line graph is useful for displaying trends over time and comparing multiple data sequences;

a pie chart: pie charts are very useful for highlighting the scale;

bar graph: bar graphs are useful for displaying trends over time and for plotting multiple data sequences;

area diagram: the area map is useful for emphasizing the amount of change over time; the stacking area graph is also used for displaying the relation between the part and the whole;

dot diagram: the dot plot is very useful for displaying quantitative data in a non-clustered fashion;

combination diagram: the combination graph plots a plurality of data sequences by using a combination of a bar graph, an area graph, and a line graph within one graph; the composition graph is useful for highlighting the relationships between various data sequences;

a scatter diagram: scatter plots use data points to plot two metrics anywhere along the scale (not just at the conventional tick marks);

bubble diagram: bubble maps use data points and arbitrary positions of bubbles along a scale to plot a metric, as for astigmatism maps; the size of the bubble represents a third metric;

bulleted chart: the bullet chart is a variation of the bar chart; they can either compare a characteristic metric (bullets) with a target metric (target) or associate the comparison metric with colored regions in the background that provide other qualitative metrics (e.g., "very satisfied", "satisfied", and "not satisfied");

an instrument panel diagram: dashboard charts (also known as dial charts or speedometer charts) use pointers to display information as a reading on the dial;

arrangement diagram: the arrangement chart helps you improve these processes by identifying the main cause of the event; ranking the categories in order from most frequent to least frequent by the ranking graph; these charts are often used for quality control data so that you determine and reduce the main source of problems;

progressive histogram: progressive bar graphs (also called waterfall graphs) are similar to the stack graph, with each segment of a single stack vertically offset from the next;

quadrant graph: the quadrant graph is a bubble graph which divides the background into four equal areas; the quadrant graph is useful for plotting data containing three metrics (using the X-axis, Y-axis, and bubble size representing the third metric value);

marimekko diagram: the Marimekko plot is a percent-stacked plot, where the width of a column is proportional to the sum of the column values; each segment height is a percentage of the total value of the respective column;

radar chart: the radar chart combines a plurality of axes into a radial pattern; for each number, data is plotted along a separate axis from the center of the graph;

polar coordinate diagram: the polar diagram is a circular diagram that uses values and angles to display information as polar coordinates.

In an embodiment of the present application, the table-type database visualization information may include, for example, one or more of the following: a base table; a temporary table; materialized look-up tables (MQTs).

Basic table: these types of tables will hold persistent data. There are different types of base tables, including one or more of the following: a conventional table; a multi-dimensional cluster (MDC) table; an Insertion Time Cluster (ITC) table; a Range Cluster Table (RCT); a partition table; a basic temporary table.

Conventional table: the regular table with index is the "regular use" table option.

Multidimensional clustering (MDC) table: these types of tables are implemented as tables that are physically clustered over multiple keys or dimensions at the same time. MDC tables are used in data warehousing and large database environments. The cluster index of the conventional table supports single-dimensional clustering of data. MDC tables have the advantage that clustering of data in multiple dimensions is possible. The MDC table provides reliable clustering in the combined dimension. In contrast, while a cluster index may be built to a conventional table, and the database manager will attempt to cluster in this case, this clustering is unreliable, and the degree of clustering may decline over time. The MDC tables may co-exist with the partition tables, which may themselves be partition tables.

Insertion Time Clustering (ITC) table: these types of tables are conceptual and actually resemble MDC tables, but the rows are clustered by the time they are inserted into the table, rather than by one or more user-specified dimensions. The ITC table may be a partition table.

Range Cluster Table (RCT): these types of tables are implemented as sequential clusters of data that provide fast direct access. Each record in the table has a predetermined Record Identification (RID), which is an internal identification used to look up the record in the table. RCT tables are used where data is tightly clustered in one or more columns in a table. The maximum and minimum values in these columns define the range of possible values. You use these columns to access the records in the table; this is the best method to utilize the predetermined Record Identification (RID) of the RCT table.

And (4) dividing a table: these types of tables use a data organization scheme, i.e., table data is distributed into multiple storage objects (referred to as data partitions or ranges) according to values in one or more table partition key columns in the table. Data partitions may be added to the partition table, connected to the partition table, and disconnected from the partition table, and multiple data partition ranges of one table may be stored in one table space. The partition table may contain a large amount of data and simplify the roll-in and roll-out of table data.

Basic temporary table: these types of tables are used to associate time-based state information with data. The data in the tables that are not using temporary support are now represented, and the data in the temporary tables are valid for a period of time defined by the database system and/or the client application. For example, the database may store a history of tables (original values of deleted rows or updated rows) so past states of data may be queried. A date range may also be specified for a row of data to indicate when the data is considered valid by an application or business rule.

Temporary table: these types of tables are used as temporary worksheets for various database operations. The declared temporary table (DGTT) does not appear in the system directory and therefore is not retained for use by other applications or is not shared by other applications. When an application using the table terminates or disconnects from the database, the data in the table will be deleted, as will the table. Instead, created temporary tables (CGTT) will appear in the system directory without the need to define in every session that uses these tables. Thus, they are persistent and can be shared across different connections by other applications.

Materialized look-up table (MQT): MQTs are defined by queries, which also define the data of the MQTs. The use of materialized look-up tables improves the performance of the query. According to the query-optimized database configuration settings, the database manager determines that some portion of the query can be resolved using MQTs. MQTs are classified according to their data maintenance.

Shadow table: the shadow table is a column-organized MQT copy of the row organization table. It may contain all or part of the columns of the source row organization table. The shadow table is maintained by replication.

In this embodiment of the application, the display device may be, for example, an individual display device or an interactive device, a workstation, a console, and the like, which have a display function, the individual display device may be, for example, a single-screen display, a multi-screen display, a large-screen display, a giant-screen display, and the like, and the interactive device having a display function may be, for example, an intelligent terminal device, such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, and an intelligent wearable device.

In a practical application, in 2022, 7 months and 8 days, the statistical parameter values of the abnormal parameters of the allocation center a are as follows: the method comprises the following steps of (1) wrong distribution/day, 0 wrong collection/day, 2 backflow/day, abnormal weighing 1/day, artificial reason 1/day, equipment reason 0/day, wrong distribution reason 0/day and information reason 3/day;

the statistical parameter values of the abnormal parameters of the distribution center B are as follows: 3 errors/day, 2 errors/day, 1 error collection package/day, 5 reflux/day, 10 abnormal weighing/day, 13 artificial reasons/day, 0 equipment reason/day, 3 error exchange reasons/day and 5 information reasons/day.

It can be seen that the statistical parameter values can intuitively reflect the personnel capacity level of each distribution center. Moreover, a plurality of abnormal logistics orders from information reasons also attract attention of managers of logistics merchants, and the data are given to developers, so that the developers can research how to improve the informatization level and reduce the abnormal logistics orders caused by the information reasons.

In a practical application, for the allocation center a, the preset value ranges corresponding to the various abnormal parameters are determined as follows: the wrong distribution is not more than 1/day, the wrong collection bag is not more than 1/day, the backflow is not more than 1/day, the weighing abnormality is not more than 1/day, the artificial reason is not more than 10/month, the equipment reason is not more than 100/month, the wrong distribution reason is not more than 1/day, and the information reason is not more than 100/month;

aiming at the distribution center B, determining the preset value ranges corresponding to the various abnormal parameters as follows: the wrong distribution is not more than 3/day, the wrong collection bag is not more than 3/day, the backflow is not more than 3/day, the weighing abnormality is not more than 6/day, the artificial reason is not more than 10/day, the equipment reason is not more than 100/month, the wrong distribution reason is not more than 2/day, and the information reason is not more than 100/month.

When the number of the target allocation centers focused on is more than 1, step S104 may be performed separately for each target allocation center.

In another practical application, an assessment index (corresponding preset numerical range) can be set for each reason type corresponding to each abnormal type of each wavelength division center. For example, the assessment indexes are set as follows for each cause type corresponding to the abnormal type of weighing abnormality of the distribution center C; human reasons are not more than 5/day, equipment reasons are not more than 30/month, the wrong reasons are not more than 1/day, and the information reasons are not more than 50/month.

Therefore, the preset time period is set in the specific time period before the current time, specifically, a first preset time interval from the current time is a starting time, a second preset time interval from the current time is an ending time, and the first preset time interval is set to be larger than the second preset time interval, so that a proper preset time period interval can be manually set, analysis processing can be conveniently carried out on historical data of the specific time period, and a proper abnormal parameter assessment index is obtained and used for assessing each allocation center.

In one practical application, the starting time of the preset time period is 0 minutes 0 seconds at 7 months and 8 days 2022, and the starting time of the preset time period is 59 minutes 59 seconds at 23 months and 8 days 2022. The duration of the preset time period is 1 day.

In another practical application, the starting time of the preset time period is 2022 years, 0 minutes and 0 seconds at 6 months, 8 days and 0 minutes and 0 seconds at 0 days at 7 months, 8 days. The duration of the preset time period is 1 month.

Referring to fig. 2, fig. 2 is a schematic diagram illustrating a flow chart for obtaining a statistical parameter value according to an embodiment of the present application.

the step S101 may include:

step S201: performing primary classification on each logistics order based on the state information of each logistics order to obtain a primary classification result of each logistics order, wherein the primary classification result is used for indicating whether each logistics order belongs to an abnormal logistics order;

step S202: performing exception classification on each abnormal logistics order based on the state information of each abnormal logistics order to obtain an exception type and a reason type of each abnormal logistics order, wherein the exception type of each abnormal logistics order is one of wrong distribution, wrong collection package, backflow and weighing exception, and the reason type of each abnormal logistics order is one of man-made reasons, equipment reasons, wrong distribution reasons and information reasons;

step S203: and acquiring statistical parameter values of the abnormal type and the reason type of each allocation center in the preset time period based on the abnormal type and the reason type of the abnormal logistics order of each allocation center in the preset time period.

Therefore, the process of acquiring the statistical parameter values of the abnormal parameters is divided into three stages:

the method comprises the following steps that in the first stage, each logistics order is subjected to preliminary classification so as to judge whether each logistics order belongs to an abnormal logistics order or not;

in the second stage, each abnormal logistics order classified preliminarily (based on the preliminary classification result in the first stage) is subjected to abnormal classification (subdivision) to obtain an abnormal type and a reason type (the reason type here refers to a formation reason type or a formation reason type) of each abnormal logistics order, wherein the abnormal type of each abnormal logistics order is one of specific abnormal types, that is, all abnormal logistics orders are classified into the abnormal types, and similarly, the (formation) reason type of each abnormal logistics order is one of specific reason types, and the formation reasons of all abnormal logistics orders are classified into the abnormal types;

in the third stage, after obtaining the abnormal type and the cause type of the abnormal logistics order of each distribution center within the preset time period, the statistical parameter values (of the abnormal logistics order) of the abnormal type and the cause type corresponding to each distribution center can be obtained, that is, for each distribution center, after obtaining the abnormal type and the cause type of all the abnormal logistics orders of the distribution center, the number of the abnormal logistics order corresponding to a single abnormal type or cause type and the order information corresponding to the abnormal logistics order can be counted.

In the second stage, the status information of each abnormal logistics order may include, for example, one or more of superior station information, inbound time, return time, responsible person, inbound line body number, return line body number, and outbound line body number, and the specific formation reason of the abnormal logistics order may be intelligently and accurately classified according to the above information.

The wrong sorting means that when the express companies sort, scanning is wrong, and the area is wrongly sent.

The misdistribution means that in the express delivery process of the express delivery industry, due to computer technical problems, errors occur or problems are encountered when distribution areas are divided. If the address is wrong during delivery, abnormal information such as no answer of the receiver telephone and the like is generated, secondary delivery or return operation is generally carried out.

The package collection means that the express items at the same destination are loaded into a big bag, so that the transfer and loading and unloading of the express items are facilitated, scanning of one small express item is not needed during transfer, and the transfer efficiency is improved. The error collection package means that errors occur in the collection and packaging process as the name implies.

The return flow refers to the express delivery error distribution, needs to be sent back and is temporarily accumulated. The return flow piece is goods which are wrongly distributed by express and need to be sent back and temporarily accumulated.

Weighing anomalies may for example refer to a mismatch between the weighed weight and the actual weight, which differs considerably.

The delivery is the delivery of the express delivery to the consignee.

The information reasons may include, for example, system crash, system stuck, system not updated, system update error, hacking, etc. The system is an information system used by the distribution center.

In the embodiment of the present application, the preliminary classification result may be represented by one or more of chinese, letters, numbers, and symbols. For example, "yes", "abnormal", "Y", "1", "v", "A1" may be used to indicate that one physical distribution order belongs to an abnormal physical distribution order, and "no", "normal", "N", "0", "x", and "B2" may be used to indicate that one physical distribution order belongs to a normal physical distribution order.

In a practical application, the preliminary classification result of the logistics order 001 is used for indicating that the logistics order 001 belongs to an abnormal logistics order; performing abnormal classification on the logistics order 001 to obtain that the abnormal type of the logistics order 001 is reflux, and the reason type is artificial reason;

the primary classification result of the logistics order 002 is used for indicating that the logistics order 002 does not belong to an abnormal logistics order; the logistics order 001 is not classified abnormally next.

Only after one logistics order is preliminarily classified into an abnormal logistics order, the subsequent abnormal classification step is carried out, so that the data volume in the abnormal classification process can be reduced, and the waste of computing resources and useless work for the normal logistics order are avoided.

The embodiment of the application does not limit the primary classification process and the abnormal classification process, and both can adopt an intelligent mode or a manual mode. The intelligent mode may be, for example, a keyword extraction and intelligent classification mode, and the manual mode may be, for example, a mode of manually marking (or labeling) each logistics order.

In some optional embodiments, the step S201 may include: and based on the state information of each logistics order, performing primary classification on each logistics order by using a primary classification model to obtain a primary classification result of each logistics order, wherein the primary classification result is used for indicating whether each logistics order belongs to an abnormal logistics order.

Wherein, the training process of the preliminary classification model may include:

acquiring a first training set, wherein the first training set comprises a plurality of first training data, and each first training data comprises state information of a first sample logistics order and labeling data of a preliminary classification result of the first sample logistics order;

for each first training data in the first training set, performing the following:

inputting the state information of the first sample logistics order in the first training data into a preset first deep learning model to obtain prediction data of a preliminary classification result of the first sample logistics order;

updating model parameters of the first deep learning model based on the prediction data and the labeling data of the primary classification result of the first sample logistics order;

detecting whether a preset first training end condition is met or not; if yes, taking the trained first deep learning model as the preliminary classification model; and if not, continuing to train the first deep learning model by utilizing the next first training data.

In some optional embodiments, the step S202 may include: and performing exception classification on each abnormal logistics order by using an exception classification model based on the state information of each abnormal logistics order to obtain an exception type and a reason type of each abnormal logistics order, wherein the exception type of each abnormal logistics order is one of misdistribution, miscollection, backflow and weighing exceptions, and the reason type of each abnormal logistics order is one of man-made reasons, equipment reasons, misdistribution reasons and information reasons.

The training process of the abnormal classification model may include:

acquiring a second training set, wherein the second training set comprises a plurality of second training data, and each second training data comprises state information of a second sample logistics order and marking data of an abnormal type and a reason type of the second sample logistics order;

for each second training data in the second training set, performing the following:

inputting state information of a second sample logistics order in the second training data into a preset second deep learning model to obtain prediction data of the abnormal type and the reason type of the second sample logistics order;

updating model parameters of the second deep learning model based on the prediction data and the labeling data of the abnormal type and the reason type of the second sample logistics order;

detecting whether a preset second training end condition is met; if yes, taking the trained second deep learning model as the abnormal classification model; if not, continuously training the second deep learning model by using the next second training data.

Therefore, through design, a proper amount of neuron calculation nodes and a multilayer operation hierarchical structure are established, a proper input layer and a proper output layer are selected, a preset first deep learning model and a preset second deep learning model can be obtained, a functional relation from input to output is established through learning and tuning of the first deep learning model and the second deep learning model, although the functional relation between input and output cannot be found 100%, the functional relation can be close to a real incidence relation as much as possible, a primary classification model and an abnormal classification model obtained through training can be predicted and obtained based on any input data, and the accuracy and the reliability of calculation results are high.

In some optional implementations, the embodiment of the present application may train to obtain a preliminary classification model and an anomaly classification model, and in other optional implementations, the embodiment of the present application may adopt a preliminary classification model and an anomaly classification model that are trained in advance.

In some alternative embodiments, for example, data mining may be performed on the historical data to obtain relevant data (status information, preliminary classification result, anomaly type, cause type) of the sample logistics orders (including the first sample logistics order and the second sample logistics order) in the training set (including the first training set and the second training set), and the like. That is, these sample logistics orders may be real logistics orders. Of course, the data related to the sample logistics order may also be automatically generated by using a GAN model generation network.

The GAN model is a Generative adaptive Network (generic adaptive Network) that consists of a Generative Network and a discriminant Network. The generation network takes random sampling from the latent space (lattice space) as input, and the output result needs to imitate the real sample in the training set as much as possible. The input of the discrimination network is the real sample or the output of the generation network, and the purpose is to distinguish the output of the generation network from the real sample as much as possible. The generation network should cheat the discrimination network as much as possible. The two networks resist each other and continuously adjust parameters, and the final purpose is to make the judgment network unable to judge whether the output result of the generated network is real or not. The GAN model can be used for generating related data of a plurality of sample logistics orders for the training process of each model, so that the data volume of the original data acquisition can be effectively reduced, and the data acquisition and labeling cost is greatly reduced.

The method for acquiring the annotation data in the embodiment of the present application is not limited, and for example, a manual annotation method may be adopted, and an automatic annotation method or a semi-automatic annotation method may also be adopted. When the sample logistics order is a real logistics order, the real data can be obtained from the historical data in a keyword extraction mode to serve as the labeling data.

The training process of the preliminary classification model and the abnormal classification model is not limited in the embodiment of the present application, and for example, the above-mentioned supervised learning training mode may be adopted, or a semi-supervised learning training mode may be adopted, or an unsupervised learning training mode may be adopted.

In the embodiment of the present application, the preset training end condition (including the first training end condition and the second training end condition) is not limited, and it may be, for example, that the number of times of training reaches the preset number of times (the preset number of times is, for example, 1 time, 3 times, 10 times, 100 times, 1000 times, 10000 times, and the like), or the training data in the training set all completes one or multiple times of training, or the total loss value obtained by this training is not greater than the preset loss value.

In other alternative embodiments, each anomaly parameter includes an anomaly type and a cause type;

the step S101 may include:

performing keyword extraction on the state information of each logistics order to obtain an abnormal classification result of each logistics order, wherein the abnormal classification result is used for indicating whether each logistics order belongs to an abnormal logistics order and a corresponding abnormal type and a corresponding reason type when the logistics order belongs to the abnormal logistics order;

In some optional embodiments, the database visualization information may include a cause pie chart for a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of generating the reason pie chart may include:

the method may further comprise:

when a selection operation for one of the sections of the reason pie chart is received, the selected section and the other sections of the reason pie chart are displayed with different display parameters.

Therefore, a reason pie chart is generated, and monitoring personnel can conveniently and quickly know the number and the proportion of each reason type. When a user selects one part, the reason type corresponding to the selected part can show a display effect different from that of other parts, and a difference is formed visually, so that monitoring personnel can focus attention on the selected reason type, the information receiving efficiency of the monitoring personnel is improved, the monitoring efficiency is improved integrally, and the logistics transportation efficiency is improved.

In one practical application, the target abnormal type is reflow, the reason type corresponding to the selected part is an artificial reason, the selected part (i.e. the artificial reason part) is highlighted in yellow, and the other parts (i.e. the equipment reason, the cross reason and the information reason part) are displayed in gray.

In some optional embodiments, the database visualization information may include a ranked graph of a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of generating the ranking graph may include:

the method may further comprise:

Therefore, when ranking is carried out on each branch center, the first names with poor performance or the first names with better performance can be clearly and intuitively displayed from large to small and also from small to large; when the user switches the ranking graph, the ranking graph can be automatically switched between the first few names with poor performance and the first few names with good performance, and therefore the follow-up punishment on the first few names with poor performance, the rewarding on the first few names with good performance and the like are facilitated.

In the embodiment of the present application, N may be, for example, 2, 3, 5, 8, 10, 15, 30, 50, 100, or the like.

In a practical application, on 8/7/2022, the total number of abnormal logistics orders which are abnormally weighed in the distribution centers of the whole network is 100, the distribution centers are sorted in the descending order of the number, a ranking graph is generated based on the number of the abnormal logistics orders of the distribution centers which are 5-bit before ranking, and the names (distribution A to distribution E), the numbers (30, 25, 22, 14 and 12) and the proportion (3%, 2.5%, 2.2%, 1.4% and 1.2%) corresponding to each distribution center are displayed on the ranking graph.

In some alternative embodiments, the database visualization information may include a trend graph of a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of generating the trend graph may include:

Thus, the trend graph may be, for example, a line graph, an area graph, a stacked area graph, a funnel graph, etc., which are capable of showing a trend in the number of abnormal logistics orders of the target abnormal type over time. The first time period can be 1 day, 3 days, 1 week, 1 month and the like, and is convenient for checking the trend of the quantity of the abnormal logistics orders with the target abnormal type along with the change of time in adjacent days, weeks and months in the preset time period.

In the embodiment of the present application, M may be, for example, 2, 3, 5, 7, 8, 10, 15, 30, 50, 100, or the like.

In one practical application, a backflow trend graph of the whole network distribution center is generated based on the number of abnormal logistics orders of the abnormal type of backflow in the last 7 days.

the process of generating the data table includes:

when a selection operation for one of the second time periods is received, the data table is generated based on the number of each abnormal type corresponding to the abnormal logistics order in the selected second time period.

Thus, the second time period may be, for example, 1 day, 3 days, 1 week, 1 month, etc., which facilitates to view the overall trend of the number of all abnormal logistics orders of the abnormal type in the adjacent days, weeks, months in the preset time period, changing with time. And when the monitoring personnel selects one of the second time periods, the data table can be generated based on the quantity of each abnormal type corresponding to the abnormal logistics orders of all the distribution centers in the selected second time period, the data table can embody the scale of each abnormal type in the second time period, and the monitoring personnel can conveniently know the specific quantity of each abnormal type in each specific second time period.

In a practical application, based on the number of the abnormal logistics orders in the last 1 month, with 1 week as the second time period, a trend graph of the whole network distribution center is generated, and when the monitoring personnel selects the 2 nd week, based on the number of each abnormal type (wrong distribution 56, wrong distribution 37, wrong collection package 65, backflow 66, weighing abnormality 45) corresponding to the abnormal logistics orders in the 2 nd week, 1 data table is generated.

In a specific application scenario, an embodiment of the present application provides a method for monitoring an abnormal logistics order, where the method includes:

acquiring whole-network logistics order data, and determining various abnormal parameters of abnormal logistics orders;

acquiring the state information of the logistics orders of the whole network distribution center, and collecting the abnormal types of the abnormal logistics orders comprises the following steps: misdistribution, miscollection of packages, backflow pieces, weighing abnormality and the like; the formation reasons of the abnormal logistics orders can be divided into artificial reasons, equipment reasons, wrong reasons, information reasons and the like.

Selecting abnormal logistics orders within a specific time range, wherein the default starting and stopping time is T-1 day (the day of query operation is assumed to be T day), daily (not across days), weekly, monthly and self-defined are supported, and selection is performed according to the ending date except for self-definition; the custom start time (date X) must be less than or equal to the expiration time (date T) and the start-stop time can span 31 days at maximum.

Integrating data corresponding to various abnormal parameters of the abnormal logistics order, and establishing a database chart by utilizing the integrated data;

analyzing and processing the obtained data of the abnormal logistics orders of different types to generate the following chart:

1. aiming at the selected target abnormal type, generating a reason pie chart based on the quantity proportion of the abnormal logistics orders of all reason types, and specifically, displaying the total quantity of the target abnormal types and the quantity and proportion of all reason types according to the selected time period; in the corresponding pie chart, each reason type supports clicking, after clicking, the corresponding part of the clicked reason type in the pie chart is highlighted, and the corresponding parts of other reason types in the pie chart are darkened or grayed;

2. generating an abnormal logistics order ranking graph of a whole network distribution center aiming at the selected target abnormal type, specifically, displaying the whole network distribution center ranking (including short names, quantity and proportion of distribution centers) corresponding to the target abnormal type according to the selected time period, and supporting the mutual switching of the displayed objects between the first 5 names and the last 5 names;

3. generating a 7-day trend chart of the abnormal logistics order according to the selected target abnormal type, specifically, displaying a histogram of the ticket quantity trend of the day and the previous 7 days according to the selected time (as a cutoff date);

4. and generating a trend chart and a list of the whole abnormal logistics orders (corresponding to the whole network distribution center and all abnormal types), specifically, supporting national or regional totalization, keeping regional and time query conditions unchanged, and displaying the quantity corresponding to each abnormal type.

Step three, setting assessment indexes (namely preset value ranges corresponding to various abnormal parameters of various distribution centers) for abnormal logistics orders of various distribution centers of the whole network;

the establishment of a database chart is completed, different assessment indexes are established for each allocation center, for example, the reason types of each abnormal type of the backflow piece, specifically, the specific formation reason of the abnormal logistics order can be classified by combining information such as superior station information, station-entering time, backflow time, responsible persons, station-entering line body numbers, backflow line body numbers, station-exiting line body numbers and the like.

And step four, monitoring and early warning in time when abnormal information appears in the logistics order of the key node.

After setting the assessment indexes, checking the state information of the logistics orders of the whole network distribution center, monitoring and early warning abnormal logistics orders of the whole network distribution center, responding to the server in time when the information appears in the more critical logistics distribution center, transmitting the related information of the abnormal logistics orders to user equipment of a manager, and making an optimal judgment and solution method for the abnormal logistics orders by using a big data analysis method. The manager can process the corresponding abnormal logistics order through the client (namely the user equipment). Therefore, the method is beneficial for personnel to check the abnormal situation of the logistics order distributed in the whole network and search the abnormal information of the logistics order; and reporting abnormal logistics orders in time and giving an optimal processing method, so that the logistics transportation efficiency is improved, and the decision-making efficiency of logistics merchants is improved.

Apparatus embodiment

Referring to fig. 3, fig. 3 is a schematic structural diagram illustrating an abnormal logistics order monitoring apparatus according to an embodiment of the present application.

The embodiment of the application provides an abnormal logistics order monitoring device, and the specific implementation manner of the abnormal logistics order monitoring device is consistent with the implementation manner and the achieved technical effect recorded in the method embodiment, and part of the detailed contents are not repeated.

The device is used for monitoring abnormal logistics orders of each distribution center, and comprises:

the statistical module 101 is configured to obtain statistical parameter values of different parameters of each distribution center within a preset time period based on state information of a logistics order of each distribution center within the preset time period;

the visualization module 102 is configured to generate database visualization information of a graph type and/or a table type based on statistical parameter values of different abnormal parameters of each allocation center within the preset time period, and display the database visualization information on display equipment;

the setting module 103 is configured to receive, by using the interaction device, a value range setting operation of a user for each abnormal parameter of each allocation center, and determine, in response to the value range setting operation, a preset value range corresponding to each abnormal parameter of each allocation center;

the early warning module 104 is configured to generate early warning information and display the early warning information on the display device when it is detected that a real-time parameter value of at least one abnormal parameter of a target distribution center is not within a corresponding preset value range, where the target distribution center is one of the plurality of distribution centers.

In some alternative embodiments, each anomaly parameter may include an anomaly type and a cause type;

the statistics module 101 may be configured to:

the process of the visualization module 102 generating the reason pie chart may include:

generating a reason pie chart corresponding to the target abnormal type based on the number of the abnormal logistics orders of the target abnormal type in the preset time period and the number of each reason type corresponding to the abnormal logistics orders of the target abnormal type, and displaying the name, the number and the proportion of the reason type corresponding to each part on the reason pie chart, wherein each part of the reason pie chart corresponds to one reason type;

the visualization module 102 may also be configured to:

the process of the visualization module 102 generating the ranking map may include:

ranking the distribution centers according to a first sequence from large to small or according to a second sequence from small to large on the basis of the quantity of the abnormal logistics orders of the distribution centers of the target abnormal type in the preset time period;

the visualization module 102 may be further operable to:

In some optional embodiments, the database visualization information may include a trend graph of a target anomaly type, the target anomaly type being one of a plurality of anomaly types;

the process of the visualization module 102 generating the trend graph may include:

In some alternative embodiments, the database visualization information may include an overall trend graph and a data table;

the process of the visualization module 102 generating the overall trend graph may include:

the process of the visualization module 102 generating the data table may include:

Apparatus embodiment

An embodiment of the present application further provides an electronic device, where the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of any one of the methods when executing the computer program.

Referring to fig. 4, fig. 4 shows a block diagram of an electronic device 200 according to an embodiment of the present disclosure.

The electronic device 200 may include, for example, at least one memory 210, at least one processor 220, and a bus 230 connecting different platform systems.

The memory 210 may include readable media in the form of volatile memory, such as Random Access Memory (RAM) 211 and/or cache memory 212, and may further include Read Only Memory (ROM) 213.

Wherein the memory 210 further stores a computer program that can be executed by the processor 220 such that the processor 220 implements the steps of any of the methods described above.

Memory 210 may also include a utility 214 having at least one program module 215, such program modules 215 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Accordingly, the processor 220 can execute the computer programs described above, and can execute the utility 214.

The processor 220 may employ one or more Application Specific Integrated Circuits (ASICs), DSPs, programmable Logic Devices (PLDs), complex Programmable Logic Devices (CPLDs), field-Programmable Gate arrays (FPGAs), or other electronic components.

Bus 230 may be one or more of any of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a local bus using any of a variety of bus architectures.

The electronic device 200 may also communicate with one or more external devices 240, such as a keyboard, pointing device, bluetooth device, etc., as well as one or more devices capable of interacting with the electronic device 200, and/or any device (e.g., router, modem, etc.) that enables the electronic device 200 to communicate with one or more other computing devices. Such communication may be through input-output interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.

Media embodiments

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the methods are implemented, and a specific implementation manner of the method is consistent with the implementation manner and the achieved technical effect described in the foregoing method embodiment, and some details are not repeated.

Referring to fig. 5, fig. 5 shows a schematic structural diagram of a program product provided in an embodiment of the present application.

The program product is for implementing any of the methods described above. The program product may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in the embodiments of the present application, the readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in connection with an instruction execution system, apparatus, or device. The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that can communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).

While the present application is described in terms of various aspects, including exemplary embodiments, the principles of the invention should not be limited to the disclosed embodiments, but are also intended to cover various modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

Claims

1. An abnormal logistics order monitoring method is used for monitoring abnormal logistics orders of distribution centers, and comprises the following steps:

receiving the value range setting operation of each abnormal parameter of each distribution center by a user through interactive equipment, and determining the preset value range corresponding to each abnormal parameter of each distribution center in response to the value range setting operation;

2. The abnormal logistics order monitoring method of claim 1, wherein the starting time of the preset time period is before the current time and is separated from the current time by a first preset time interval, the ending time of the preset time period is before the current time and is separated from the current time by a second preset time interval, and the first preset time interval is greater than the second preset time interval.

3. The abnormal logistics order monitoring method of claim 2, wherein each abnormal parameter comprises an abnormal type and a reason type;

4. The abnormal logistics order monitoring method of claim 3, wherein the database visualization information comprises a cause pie chart of a target abnormal type, wherein the target abnormal type is one of a plurality of abnormal types;

the process of generating the reason pie chart comprises the following steps:

the method further comprises the following steps:

5. The abnormal logistics order monitoring method of claim 3, wherein the database visualization information comprises a ranking map of a target anomaly type, wherein the target anomaly type is one of a plurality of anomaly types;

the process of generating the ranking graph comprises:

the method further comprises the following steps:

6. The abnormal logistics order monitoring method of claim 3, wherein the database visualization information comprises a trend graph of a target anomaly type, wherein the target anomaly type is one of a plurality of anomaly types;

the process of generating the trend graph comprises:

7. The abnormal logistics order monitoring method of claim 3, wherein the database visualization information comprises an overall trend graph and a data table;

the process of generating the data table includes:

8. An abnormal logistics order monitoring device is used for monitoring abnormal logistics orders of distribution centers, and comprises:

the statistical module is used for acquiring statistical parameter values of different abnormal parameters of each distribution center in a preset time period based on the state information of the logistics orders of each distribution center in the preset time period;

9. An electronic device, characterized in that the electronic device comprises a memory storing a computer program and a processor implementing the steps of the method according to any of claims 1-7 when the processor executes the computer program.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.