CN113011664A - Logistics timeliness monitoring method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a logistics aging monitoring method, a logistics aging monitoring device, electronic equipment and a storage medium, wherein the logistics aging monitoring method comprises the following steps: forecasting the aging parameters of the logistics waybills in each transportation based on the logistics data; aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension; and judging whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not based on the comparison of the aggregated timeliness parameters of the line dimension and the timeliness confidence interval, wherein the timeliness confidence interval is obtained by calculation based on the timeliness parameters of historical logistics data. The invention realizes the timeliness monitoring of the line dimension, thereby realizing the advanced prediction of the timeliness monitoring of each logistics package, intervening in advance to ensure the service quality and improving the logistics experience of the logistics user.
Description
Technical Field
The invention relates to the field of computer application, in particular to a logistics aging monitoring method and device, electronic equipment and a storage medium.
Background
At present, no matter on an e-commerce platform or a logistics platform, aging monitoring of logistics tracks is only conducted through existing logistics tracks and logistics information of logistics packages, and accordingly data monitoring is conducted.
Therefore, how to realize the aging monitoring of the line dimension, so that the advanced prediction of the aging monitoring of each logistics package can be realized, the intervention can be performed in advance to ensure the service quality, the logistics experience of the logistics user is improved, and the technical problem to be solved in the field is urgently solved.
Disclosure of Invention
In order to overcome the defects of the related technologies, the invention provides a logistics aging monitoring method, a logistics aging monitoring device, electronic equipment and a storage medium, so that aging monitoring of line dimensions is realized, thereby realizing advanced prediction of aging monitoring of each logistics package, intervening in advance to guarantee service quality and improving logistics experience of logistics users.
According to one aspect of the invention, a logistics aging monitoring method is provided, which comprises the following steps:
forecasting the aging parameters of the logistics waybills in each transportation based on the logistics data;
aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension;
and judging whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not based on the comparison of the aggregated timeliness parameters of the line dimension and the timeliness confidence interval, wherein the timeliness confidence interval is obtained by calculation based on the timeliness parameters of historical logistics data.
In some embodiments of the present application, the predicting the aging parameter of the logistics list in each transportation based on the logistics data includes:
and predicting the aging parameters of the logistics waybills in each transportation by adopting the trained regression model based on the logistics data.
In some embodiments of the present application, predicting the aging parameter of the logistics list in each transportation based on the logistics data comprises:
judging whether the predicted aging parameters of the logistics waybills in each transportation are located in the aging confidence interval or not;
if not, judging that the transportation timeliness of the logistics freight bill is abnormal.
In some embodiments of the present application, the determining whether the transportation aging of the logistics waybill of the line dimension is abnormal based on the comparison of the aging parameter of the aggregated line dimension with the aging confidence interval includes:
determining whether aging parameters based on the aggregated line dimensions are all within the aging confidence interval;
if not, judging that the transportation timeliness of the logistics waybill of the line dimension is abnormal.
In some embodiments of the present application, the aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension includes:
and determining the number of the logistics waybills in the transportation of each aging parameter according to the line dimension.
In some embodiments of the present application, the determining whether the transportation aging of the logistics waybill of the line dimension is abnormal based on the comparison of the aging parameter of the aggregated line dimension with the aging confidence interval includes:
determining whether the number of in-transit logistics waybills, for which the aggregated route dimension-based aging parameter is within the aging confidence interval, is greater than a first number threshold;
if not, judging that the transportation timeliness of the logistics waybill of the line dimension is abnormal.
In some embodiments of the present application, determining the number of logistics waybills in transit for each aging parameter according to the line dimension further includes:
determining the aging parameters of which the number of the logistics waybills in transportation is larger than a second set number threshold value as high-quantity aging parameters;
determining the aging parameters of which the number of the logistics waybills in transportation is not more than a second set number threshold value as low-level aging parameters,
and carrying out abnormity judgment on the high-quantity aging parameters and the low-quantity aging parameters at different error tolerance rates, wherein the error tolerance rate of the high-quantity aging parameters is less than that of the low-quantity aging parameters.
In some embodiments of the present application, the step of predicting the aging parameters of the logistics waybills in each transportation based on the logistics data, and the step of aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension is performed periodically at a set time period.
In some embodiments of the present application, the logistics aging monitoring method further comprises a high aging parameter abnormity judging step and/or a low aging parameter abnormity judging step,
the high quantity aging parameter abnormity judging step comprises the following steps:
judging whether the change rate of the high quantity aging parameter of the current period and the high quantity aging parameter of the previous period is greater than a first change rate or not;
if yes, judging that the transportation timeliness of the logistics waybill of the line dimension in the current period is abnormal;
the low aging parameter abnormity judgment step comprises the following steps:
judging whether the change rate of the low aging parameter of the current period and the low aging parameter of the previous period is greater than a second change rate or not;
if yes, judging that the transportation timeliness of the logistics waybill of the line dimension in the current period is abnormal;
wherein the first rate of change is less than the second rate of change.
In some embodiments of the present application, the age confidence interval is calculated based on the following steps:
acquiring the maximum aging parameter of historical logistics data;
calculating an aging confidence interval range based on the maximum aging parameter of the historical logistics data;
calculating a historical aging baseline based on aging parameters of historical logistics data;
and calculating the aging confidence interval according to the aging confidence interval range and the historical aging baseline.
In some embodiments of the present application, the aging confidence interval is updated periodically.
In some embodiments of the present application, the line dimensions include one or more of a full route, a trunk route, and a branch route.
In some embodiments of the present application, the aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension further includes:
and aggregating the predicted aging parameters of the logistics waybills in each transportation according to the logistics suppliers.
In some embodiments of the present application, when it is determined that the transportation time efficiency of the logistics waybill of the line dimension is abnormal based on the comparison between the time efficiency parameter of the aggregated line dimension and the time efficiency confidence interval, abnormality notification information is sent to the logistics management terminal.
In some embodiments of the present application, the determining whether the transportation aging of the logistics waybill of the line dimension is abnormal based on the comparison of the aging parameter of the aggregated line dimension with the aging confidence interval includes:
based on the comparison of the aggregated aging parameters of the line dimension with the total aging confidence interval, judging whether the transportation aging of the logistics waybill of the line dimension is abnormal again, wherein,
the aging confidence interval is obtained by calculation based on aging parameters of historical logistics data in a first time period;
the total aging confidence interval is calculated and obtained based on the aging parameters of the logistics data in the second time period,
the first time period is a historical time period before the current time, and the duration of the second time period is greater than the first time period.
According to another aspect of the present application, there is also provided a logistics aging monitoring apparatus, including:
the forecasting module is configured to forecast the aging parameters of the logistics waybills in each transportation based on the logistics data;
a polymerization module configured to polymerize the predicted aging parameters of the logistics waybills in each transport according to the line dimensions;
and the judging module is configured to judge whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not based on the comparison of the aggregated timeliness parameters of the line dimension and a timeliness confidence interval, wherein the timeliness confidence interval is obtained based on the calculation of the timeliness parameters of historical logistics data.
According to still another aspect of the present invention, there is also provided an electronic apparatus, including: a processor; a storage medium having stored thereon a computer program which, when executed by the processor, performs the steps as described above.
According to yet another aspect of the present invention, there is also provided a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps as described above.
Compared with the prior art, the invention has the advantages that:
according to the method, the aging parameters of the logistics waybill in transportation are predicted, aggregation is carried out according to the line dimensions, so that the aging parameters of the aggregated line dimensions can be compared with the aging confidence interval obtained through calculation of historical logistics data, whether the transportation aging of the logistics waybill of the line dimensions is abnormal or not is judged, and therefore aging monitoring of the line dimensions is achieved, early prediction of aging monitoring of each logistics package can be achieved, intervention can be carried out in advance to guarantee service quality, and logistics experience of logistics users is improved.
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The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 shows a flow chart of a logistics aging monitoring method according to an embodiment of the invention.
Fig. 2 shows a flowchart for determining transportation aging abnormality of the logistics freight bill according to the embodiment of the invention.
Fig. 3 shows a flowchart for determining transportation aging abnormality of the logistics freight notes of the line dimension according to an embodiment of the invention.
Fig. 4 shows a flowchart for determining transportation aging abnormality of the logistics freight notes of the line dimension according to another embodiment of the invention.
Fig. 5 shows a flow chart for dividing the high amount aging parameter and the low amount aging parameter according to an embodiment of the present invention.
Fig. 6 shows a flowchart of the high quantity aging parameter abnormality determination step according to an embodiment of the present invention.
Fig. 7 is a flowchart showing the low aging parameter abnormality determination step according to the embodiment of the present invention.
FIG. 8 illustrates a flow chart for calculating an age confidence interval in accordance with an embodiment of the present invention.
Fig. 9 is a block diagram of a logistics aging monitoring apparatus according to an embodiment of the invention.
Fig. 10 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the invention.
Fig. 11 schematically illustrates an electronic device in an exemplary embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the steps. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
In each embodiment of the present invention, the logistics aging monitoring method provided by the present invention can be applied to a logistics platform, an e-commerce platform, or any third party platform that needs to implement logistics aging monitoring, but the application scenario of the present invention is not limited thereto, and is not described herein again.
Fig. 1 shows a flow chart of a logistics aging monitoring method according to an embodiment of the invention. The logistics aging monitoring method comprises the following steps:
step S110: and predicting the aging parameters of the logistics waybills in each transportation based on the logistics data.
In one specific implementation, step S110 may use a trained regression model to predict aging parameters of the logistics waybill in each transportation based on the logistics data. Specifically, the regression model may be a regression model based on an XGBoost algorithm (machine learning system with extensible lifting trees) to predict the express delivery timeliness. The invention is not limited thereto, and the aging prediction algorithm in step S110 may adopt any machine learning model or model combination. The invention is not limited by the age prediction algorithm implemented. Different logistics data can be agreed according to different prediction algorithms. For example, some age prediction models only require a delivery city and a recipient city; some aging prediction models require delivery cities, receiving cities and current logistics trajectory information. The present invention can be implemented in many different ways, which are not described herein.
Specifically, the aging parameters described in step S110 include, but are not limited to, the time length or the arrival time from the cable to a certain node; the length of time or arrival time from one node to another; the time length or the arrival time from the article receiving to the sign-in can realize the meaning of more aging parameters, and is not described herein.
Step S120: and aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension.
Specifically, the route dimensions in step S120 include, but are not limited to, one or more of a full route, a trunk route, and a branch route. The full-line route is a full-line route from a collecting node (network point) to a signing node (network point). A trunk route is a line from an originating distribution node (mesh point) to a destination distribution node (mesh point). The branch routes include a route from the source node to the originating distribution node and/or a route from the destination distribution node to the sign-on node. Thereby, aggregation of aging parameters for line dimensions can be achieved.
Further, in some implementations, the step S120 may also aggregate the predicted aging parameters of the logistics waybills in each transportation according to the logistics provider. Therefore, the aggregation of the aging parameters of the line dimension can be realized according to the logistics suppliers and the logistics freight notes of different logistics suppliers at the aggregation part, and then according to the lines of the logistics suppliers. Therefore, the invention can be suitable for the aging monitoring of not only a single logistics platform (logistics provider) but also a plurality of logistics platforms (logistics providers).
Step S130: and judging whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not based on the comparison of the aggregated timeliness parameters of the line dimension and the timeliness confidence interval, wherein the timeliness confidence interval is obtained by calculation based on the timeliness parameters of historical logistics data.
Specifically, since the aging confidence interval is calculated based on the aging parameter of the historical logistics data, it is possible to represent a normal, non-abnormal aging interval indicated in the historical logistics data. And judging the abnormity of the line according to the comparison of the aging parameters of the aggregated line dimensions and the aging confidence interval.
Furthermore, the situation that the logistics transportation order passing through the line is likely to have abnormal aging can be predicted through abnormal judgment of the line of the logistics order in transportation, so that the line is replaced or a logistics company is replaced in advance, the logistics transportation aging is guaranteed to be normal, the logistics transportation efficiency is improved, and the logistics experience of a user is improved.
Further, in some specific implementations, when the aging parameter of the aggregated line dimension is compared with the aging confidence interval in step S130, and the transportation aging of the logistics waybill of the line dimension is judged to be abnormal, an abnormal notification message may be sent to the logistics management terminal. The logistics management terminal comprises but is not limited to terminal equipment of logistics suppliers' aging managers, terminal equipment of logistics aging managers of e-commerce platforms, terminal equipment of senders and terminal equipment of receivers. The sent abnormal notification message may be a telephone message, a short message, a push message, a message of a working system developed by each platform, and the like, and the present invention is not limited thereto.
According to the logistics aging monitoring method, the aging parameters of the logistics waybills in transportation are predicted, and aggregation is carried out according to the line dimensions, so that the aging parameters of the aggregated line dimensions can be compared with the aging confidence interval obtained through calculation of historical logistics data, whether the transportation aging of the logistics waybills of the line dimensions is abnormal or not is judged, and therefore aging monitoring of the line dimensions is achieved, early prediction of aging monitoring of each logistics package can be achieved, intervention can be carried out in advance to guarantee service quality, and logistics experience of logistics users is improved.
Referring now to fig. 2, fig. 2 is a flow chart illustrating the determination of transportation timeliness anomaly of the logistics list according to an embodiment of the present invention. Specifically, steps in the flowchart shown in fig. 2 are executed after step S110 in fig. 1. The steps shown in fig. 2 include:
step S111: and judging whether the predicted aging parameters of the logistics waybills in each transportation are positioned in the aging confidence interval.
If the determination in step S111 is no, step S112 is executed to determine that the transportation timeliness of the logistics freight bill is abnormal.
Therefore, the aging of a single logistics transportation order can be monitored and judged abnormally through the steps shown in fig. 2, so that in the embodiment, the aging execution interval can be reused, and the aging abnormal judgment of a single logistics transportation order and the aging abnormal judgment of a line can be realized. Further, in this embodiment, the determination of the transportation aging abnormality of the single logistics waybill may be performed before, after, or synchronously with the determination of the line abnormality, which is not limited in this invention.
Referring now to fig. 3, fig. 3 illustrates a flow chart for determining transportation timeliness anomalies for a logistics freight manifest for the line dimension, according to an embodiment of the present invention. Fig. 3 shows the following steps in total:
step S131: determining whether aging parameters based on the aggregated line dimensions are all within the aging confidence interval.
If the determination in step S131 is no, step S132 is executed: and judging that the transportation timeliness of the logistics waybill of the line dimension is abnormal.
Thus, the embodiment determines whether the transportation aging of the line dimension is abnormal based on that the aging parameters of the aggregated line dimension are all located within the aging confidence interval. Therefore, in the embodiment, the time efficiency abnormity judgment of the line dimension is strict, so that the line can be exchanged to improve the line transportation efficiency based on the line abnormity.
In another embodiment, step S120 in fig. 1 may aggregate the predicted aging parameters of the logistics waybills in each transportation according to the line dimension, which may include: and determining the number of the logistics waybills in the transportation of each aging parameter according to the line dimension. In other words, in this embodiment, the corresponding relationship between the aging parameter and the number of waybills can be obtained according to the aggregation of the line dimensions.
Based on the above embodiment, a flowchart for determining transportation timeliness abnormality of the logistics waybill of the line dimension according to another embodiment of the present invention is described with reference to fig. 4. Fig. 4 shows the following steps in total:
step S132: determining whether the number of in-transit logistics waybills, for which the aggregated route dimension-based aging parameter is within the aging confidence interval, is greater than a first number threshold;
if the determination in step S132 is no, step S133 is executed: and judging that the transportation timeliness of the logistics waybill of the line dimension is abnormal.
Specifically, the first number threshold may be set as needed, so that all aging parameters are not required to be located in the aging confidence interval, thereby providing a certain tolerance for the line anomaly and reducing false alarm of the line anomaly. Further, the tolerance can be set in a proper range through relative adjustment of the first quantity threshold and the aging confidence interval, and the accuracy of aging abnormity error reporting is improved.
Referring now to fig. 5, fig. 5 illustrates a flow chart for partitioning the high amount aging parameter and the low amount aging parameter according to an embodiment of the present invention. Fig. 5 shows the steps performed after step S120 in fig. 1 determines the number of in-transit logistics statements for each aging parameter according to the line dimension:
step S121: and determining the aging parameters of which the number of the logistics waybills in the transportation is larger than a second set number threshold value as high-quantity aging parameters.
Step S122: and determining the aging parameters of which the number of the logistics waybills in the transportation is not more than a second set number threshold value as low-level aging parameters.
And carrying out abnormity judgment on the high-quantity aging parameters and the low-quantity aging parameters at different error tolerance rates, wherein the error tolerance rate of the high-quantity aging parameters is less than that of the low-quantity aging parameters.
Specifically, considering that the tolerance of the aging parameters of different numbers of logistics orders is different during actual transportation, the aging parameters can be divided into high-quantity aging parameters and low-quantity aging parameters through the steps shown in fig. 5. Because the number of logistics waybills with high amount of aging parameters is greater than the number of logistics waybills with low amount of aging parameters, the error tolerance of the high amount of aging parameters is less than that of the low amount of aging parameters. In other words, the proportion of the logistics waybill quantity with the high aging parameter not in the aging confidence interval to the total logistics waybill quantity with the high aging parameter can be used as the error of the high aging parameter, and the proportion of the logistics waybill quantity with the high aging parameter not in the aging confidence interval to the total logistics waybill quantity with the high aging parameter is limited to be smaller than the set proportion threshold by setting the error tolerance. The error tolerance of the low-magnitude aging parameter is achieved in a similar manner and is not described in detail herein. Further, the second set number threshold in this embodiment may be set as needed, and the invention is not limited thereto.
In some embodiments of the present application, the step of predicting the aging parameters of the logistics waybills in each transportation based on the logistics data, and the step of aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension is performed periodically at a set time period.
Specifically, for example, the step of predicting the aging parameter of each in-transit logistics list based on the logistics data, and the step of aggregating the predicted aging parameter of each in-transit logistics list according to the line dimension may be performed in a period of one hour, 3 hours, 6 hours, or the like. Further, in this embodiment, the historical logistics data for the calculated aging confidence interval may be historical logistics data of the previous day, the previous two days, the previous fifteen days, etc., thereby improving the accuracy of the calculation of the aging confidence interval through a wider range of data and data closer to the current time on the one hand.
In a specific implementation of the foregoing embodiment, in order to implement the anomaly detection of the variation degree of the aging parameter, the logistics aging monitoring method further includes a high-quantity aging parameter anomaly determination step and/or a low-quantity aging parameter anomaly determination step.
Referring now to fig. 6, fig. 6 shows a flowchart of the high quantity aging parameter abnormality determining step according to an embodiment of the present invention. Fig. 6 shows the following steps in total:
step S135: judging whether the change rate of the high quantity aging parameter of the current period and the high quantity aging parameter of the previous period is greater than a first change rate or not;
if the determination in step S135 is yes, step S136 is executed: and judging that the transportation timeliness of the logistics waybill of the line dimension in the current period is abnormal.
In particular, according to the findings of the inventors in the research and analysis of the data of the logistics transportation, the rate of change of the high quantity aging parameter of the adjacent period of the normal logistics transportation is small. Therefore, the first change rate can be set as required, so that whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not is judged according to the first change rate. Specifically, the change rate of the high quantity aging parameter of the current period and the high quantity aging parameter of the previous period may be calculated as the ratio of the absolute value of the difference between the high quantity aging parameter of the current period and the high quantity aging parameter of the previous period to the high quantity aging parameter of the previous period. The invention is not so limited.
Further, the low-level aging parameter has a higher tolerance for its change rate due to the smaller data size. Referring to fig. 7, fig. 7 is a flowchart illustrating a low aging parameter abnormality determination step according to an embodiment of the present invention. Fig. 7 shows the following steps in total:
step S137: judging whether the change rate of the low aging parameter of the current period and the low aging parameter of the previous period is greater than a second change rate or not;
if the determination in step S137 is yes, step S138 is executed: and judging that the transportation timeliness of the logistics waybill of the line dimension in the current period is abnormal. Wherein the first rate of change is less than the second rate of change.
Therefore, in the embodiment, the second change rate larger than the first change rate can be set as required, so that whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not is judged according to the second change rate. Specifically, the change rate of the low aging parameter of the current period and the low aging parameter of the previous period may be calculated as the ratio of the absolute value of the difference between the low aging parameter of the current period and the low aging parameter of the previous period to the low aging parameter of the previous period. The invention is not so limited.
Referring now to fig. 8, fig. 8 illustrates a flow chart for calculating an age confidence interval in accordance with an embodiment of the present invention. Fig. 8 shows the following steps in total:
step S101: and acquiring the maximum aging parameters of the historical logistics data.
Step S102: and calculating an aging confidence interval range based on the maximum aging parameter of the historical logistics data.
Specifically, in some embodiments, step S102 may calculate a minimum age confidence interval range (in the form of a scale). The invention does not limit the way in which the minimum time-dependent confidence interval range is calculated. In some embodiments, the calculation of step S102 may be implemented in a mapping table, a trained learning model, or the like.
Step S103: and calculating a historical aging baseline based on the aging parameters of the historical logistics data.
Specifically, the historical aging baseline may be, for example, a statistical historical aging mean or median. In other embodiments, the calculation of step S103 may also be implemented by a trained machine learning model.
Step S104: and calculating the aging confidence interval according to the aging confidence interval range and the historical aging baseline.
Specifically, the historical aging baseline may be multiplied by the upper and lower limits of the aging confidence interval range to obtain the upper and lower limits of the aging confidence interval.
Thus, the calculation of the time-dependent confidence interval can be achieved by the above steps. In some implementations of the above embodiments, the time-based confidence interval is updated periodically. Further, a step of artificially compounding the aging confidence interval may be further provided, and the present invention is not limited thereto.
Specifically, in some embodiments of the present invention, a step of secondary abnormality determination may be further included. For example, after step S130 in fig. 1, it may further include: and judging whether the transportation timeliness of the logistics waybill of the line dimension is abnormal again based on the comparison of the aggregated timeliness parameters of the line dimension and the total timeliness confidence interval. Specifically, the aging confidence interval is obtained by calculation based on an aging parameter of historical logistics data in a first time period. The first time period is a historical time period before the current time. For example, the first time period may be the previous day, the previous two days, the previous fifteen days, etc. The total aging confidence interval is obtained by calculation based on the aging parameters of the logistics data in the second time period. The duration of the second time period may be longer than that of the first time period (the second time period may be, for example, 100 hours, 200 hours, and the like, but the invention is not limited thereto), so that a larger amount of data reference is included in the calculation of the time-based confidence interval, and the accuracy of the secondary abnormality judgment is improved. The second time period may be any one or combination of historical time periods, current time periods, and future time periods.
Specifically, in a specific implementation of the present invention, step S110 may select a regression model based on the XGBoost algorithm to predict the express delivery timeliness, where the start of prediction is a pickup node, and the end of prediction is a sign-in node of the user. In other words, the predicted aging is the time period from the item collecting node to the sign-in node. The aging confidence interval can be obtained by a big data Hive (a data warehouse tool based on Hadoop) task by dividing the logistics suppliers, the trunk lines and the branch lines into statistics on an aging baseline and the aging confidence interval. And storing the calculated aging confidence interval into a database, and performing manual compounding periodically. During monitoring, real-time monitoring can be achieved by adopting a Flink (distributed stream data flow engine) task, the logistics providers are distributed every hour, and the trunk line and the branch line are compared based on the aging baseline and the aging confidence interval, so that the aging abnormity can be judged. Further, the monitoring strategy may employ a mode in which the variation tolerance of the high-volume region is relatively low and the variation tolerance of the low-valley region is relatively high. When the abnormity is identified, a mobile phone, a short message or pushing can be selected for notification through a self-developed working system.
The above are merely a plurality of specific implementation manners of the logistics aging monitoring method of the present invention, and each implementation manner may be implemented independently or in combination, and the present invention is not limited thereto. Furthermore, the flow charts of the present invention are merely schematic, the execution sequence between the steps is not limited thereto, and the steps can be split, combined, exchanged sequentially, or executed synchronously or asynchronously in other ways within the protection scope of the present invention.
Referring now to fig. 9, fig. 9 is a block diagram illustrating a logistics aging monitoring apparatus according to an embodiment of the present invention. The logistics aging monitoring apparatus 200 includes an acquisition module 210, an aggregation module 220, and a judgment module 230.
The obtaining module 210 is configured as a prediction module configured to predict aging parameters of the logistics waybill in each transportation based on the logistics data.
The aggregation module 220 is configured to aggregate the predicted aging parameters for each in-transit logistics manifest according to the line dimensions.
The determining module 230 is configured to determine whether the transportation aging of the logistics waybill of the line dimension is abnormal based on the comparison between the aging parameter of the aggregated line dimension and an aging confidence interval, wherein the aging confidence interval is calculated based on the aging parameter of the historical logistics data.
In the logistics aging monitoring device in the exemplary embodiment of the invention, the aging parameters of the logistics waybills in transportation are predicted and aggregated according to the line dimensions, so that the aggregated aging parameters of the line dimensions can be compared with the aging confidence interval obtained by calculating through historical logistics data, and whether the transportation aging of the logistics waybills in the line dimensions is abnormal or not is judged, thereby realizing the aging monitoring of the line dimensions, realizing the advanced prediction of the aging monitoring of each logistics package, intervening in advance to ensure the service quality and improving the logistics experience of logistics users.
Fig. 9 is a schematic diagram illustrating the logistics aging monitoring apparatus 200 provided by the present invention, respectively, and the splitting, combining, and adding of modules are within the protection scope of the present invention without departing from the concept of the present invention. The logistics aging monitor 200 provided by the present invention can be implemented by software, hardware, firmware, plug-in and any combination thereof, which is not limited by the present invention.
In an exemplary embodiment of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored, and the computer program, when executed by a processor for example, may implement the steps of the logistics aging monitoring method in any one of the above embodiments. In some possible embodiments, the aspects of the present invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the present invention described in the logistics aging monitoring method section above of this specification, when the program product is run on the terminal device.
Referring to fig. 10, a program product 700 for implementing the above method according to an embodiment of the present invention is described, which 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 in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use 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.
The 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 is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an 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" programming language or similar programming languages. The program code may execute entirely on the tenant computing device, partly on the tenant device, as a stand-alone software package, partly on the tenant computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing devices may be connected to the tenant computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In an exemplary embodiment of the invention, there is also provided an electronic device that may include a processor and a memory for storing executable instructions of the processor. Wherein the processor is configured to execute the steps of the logistics aging monitoring method in any one of the above embodiments via executing the executable instructions.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 500 according to this embodiment of the invention is described below with reference to fig. 11. The electronic device 500 shown in fig. 11 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 11, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one memory unit 520, a bus 530 that couples various system components including the memory unit 520 and the processing unit 510, a display unit 540, and the like.
Wherein the storage unit stores program code, which can be executed by the processing unit 510, so that the processing unit 510 executes the steps according to various exemplary embodiments of the present invention described in the logistics aging monitoring method section described above in this specification. For example, the processing unit 510 may perform the steps as shown in any one or more of fig. 1-6.
The memory unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The memory unit 520 may also include a program/utility 5204 having a set (at least one) of program modules 5205, such program modules 5205 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.
The electronic device 500 may also communicate with one or more external devices 600 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a tenant to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may be through input/output (I/O) interfaces 550. Also, the electronic device 500 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 560. The network adapter 560 may communicate with other modules of the electronic device 500 via the bus 530. 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 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the above-mentioned logistics aging monitoring method according to the embodiment of the present invention.
Compared with the prior art, the invention has the advantages that:
according to the method, the aging parameters of the logistics waybill in transportation are predicted, aggregation is carried out according to the line dimensions, so that the aging parameters of the aggregated line dimensions can be compared with the aging confidence interval obtained through calculation of historical logistics data, whether the transportation aging of the logistics waybill of the line dimensions is abnormal or not is judged, and therefore aging monitoring of the line dimensions is achieved, early prediction of aging monitoring of each logistics package can be achieved, intervention can be carried out in advance to guarantee service quality, and logistics experience of logistics users is improved.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
Claims (18)
1. A logistics aging monitoring method is characterized by comprising the following steps:
forecasting the aging parameters of the logistics waybills in each transportation based on the logistics data;
aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension;
and judging whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not based on the comparison of the aggregated timeliness parameters of the line dimension and the timeliness confidence interval, wherein the timeliness confidence interval is obtained by calculation based on the timeliness parameters of historical logistics data.
2. The method for monitoring logistics aging according to claim 1, wherein predicting aging parameters of each in-transit logistics list based on logistics data comprises:
and predicting the aging parameters of the logistics waybills in each transportation by adopting the trained regression model based on the logistics data.
3. The method for monitoring logistics aging according to claim 1, wherein predicting aging parameters of each in-transit logistics list based on logistics data comprises:
judging whether the predicted aging parameters of the logistics waybills in each transportation are located in the aging confidence interval or not;
if not, judging that the transportation timeliness of the logistics freight bill is abnormal.
4. The logistics aging monitoring method of claim 1, wherein the determining whether the transportation aging of the logistics waybill of the line dimension is abnormal based on the comparison of the aggregated aging parameter of the line dimension with the aging confidence interval comprises:
determining whether aging parameters based on the aggregated line dimensions are all within the aging confidence interval;
if not, judging that the transportation timeliness of the logistics waybill of the line dimension is abnormal.
5. The method for monitoring logistics aging according to claim 1, wherein the aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension comprises:
and determining the number of the logistics waybills in the transportation of each aging parameter according to the line dimension.
6. The logistics aging monitoring method of claim 5, wherein the determining whether the transportation aging of the logistics waybill of the line dimension is abnormal based on the comparison of the aggregated aging parameter of the line dimension with the aging confidence interval comprises:
determining whether the number of in-transit logistics waybills, for which the aggregated route dimension-based aging parameter is within the aging confidence interval, is greater than a first number threshold;
if not, judging that the transportation timeliness of the logistics waybill of the line dimension is abnormal.
7. The method for monitoring logistics aging according to claim 5, wherein the determining the number of logistics waybills in transit for each aging parameter according to the line dimension further comprises:
determining the aging parameters of which the number of the logistics waybills in transportation is larger than a second set number threshold value as high-quantity aging parameters;
determining the aging parameters of which the number of the logistics waybills in transportation is not more than a second set number threshold value as low-level aging parameters,
and carrying out abnormity judgment on the high-quantity aging parameters and the low-quantity aging parameters at different error tolerance rates, wherein the error tolerance rate of the high-quantity aging parameters is less than that of the low-quantity aging parameters.
8. The method for monitoring logistics aging according to claim 7, wherein the step of predicting aging parameters of each in-transit logistics list based on logistics data, and the step of aggregating the predicted aging parameters of each in-transit logistics list according to line dimensions is performed at set time period cycles.
9. The logistics aging monitoring method of claim 8, further comprising a high amount aging parameter abnormality judgment step and/or a low amount aging parameter abnormality judgment step,
the high quantity aging parameter abnormity judging step comprises the following steps:
judging whether the change rate of the high quantity aging parameter of the current period and the high quantity aging parameter of the previous period is greater than a first change rate or not;
if yes, judging that the transportation timeliness of the logistics waybill of the line dimension in the current period is abnormal;
the low aging parameter abnormity judgment step comprises the following steps:
judging whether the change rate of the low aging parameter of the current period and the low aging parameter of the previous period is greater than a second change rate or not;
if yes, judging that the transportation timeliness of the logistics waybill of the line dimension in the current period is abnormal;
wherein the first rate of change is less than the second rate of change.
10. The logistics aging monitoring method of claim 1, wherein the aging confidence interval is calculated based on the following steps:
acquiring the maximum aging parameter of historical logistics data;
calculating an aging confidence interval range based on the maximum aging parameter of the historical logistics data;
calculating a historical aging baseline based on aging parameters of historical logistics data;
and calculating the aging confidence interval according to the aging confidence interval range and the historical aging baseline.
11. The logistics aging monitoring method of any one of claims 1 to 10, wherein the aging confidence interval is updated periodically.
12. The logistics ageing monitoring method of any one of claims 1 to 10, wherein the line dimensions include one or more of a full line route, a trunk route and a branch route.
13. The method for monitoring logistics aging according to any one of claims 1 to 10, wherein the aggregating the predicted aging parameters of the logistics waybills in each transportation according to the line dimension further comprises:
and aggregating the predicted aging parameters of the logistics waybills in each transportation according to the logistics suppliers.
14. The logistics aging monitoring method of any one of claims 1 to 10, wherein when the transportation aging of the logistics waybill of the line dimension is judged to be abnormal based on the comparison of the aging parameter of the aggregated line dimension and the aging confidence interval, an abnormality notification message is sent to the logistics management terminal.
15. The method for monitoring logistics aging according to any one of claims 1 to 10, wherein the determining whether the transportation aging of the logistics waybill of the line dimension is abnormal or not based on the comparison of the aging parameter of the aggregated line dimension with the aging confidence interval comprises:
based on the comparison of the aggregated aging parameters of the line dimension with the total aging confidence interval, judging whether the transportation aging of the logistics waybill of the line dimension is abnormal again, wherein,
the aging confidence interval is obtained by calculation based on aging parameters of historical logistics data in a first time period;
the total aging confidence interval is calculated and obtained based on the aging parameters of the logistics data in the second time period,
the first time period is a historical time period before the current time, and the duration of the second time period is greater than the first time period.
16. A logistics aging monitoring device is characterized by comprising:
the forecasting module is configured to forecast the aging parameters of the logistics waybills in each transportation based on the logistics data;
a polymerization module configured to polymerize the predicted aging parameters of the logistics waybills in each transport according to the line dimensions;
and the judging module is configured to judge whether the transportation timeliness of the logistics waybill of the line dimension is abnormal or not based on the comparison of the aggregated timeliness parameters of the line dimension and a timeliness confidence interval, wherein the timeliness confidence interval is obtained based on the calculation of the timeliness parameters of historical logistics data.
17. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory having stored thereon a computer program that, when executed by the processor, performs:
the logistics ageing monitoring method of any one of claims 1 to 15.
18. A storage medium having a computer program stored thereon, the computer program when executed by a processor performing:
the logistics ageing monitoring method of any one of claims 1 to 15.
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