CN111950803A - Logistics object delivery time prediction method and device, electronic equipment and storage medium - Google Patents
Logistics object delivery time prediction method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN111950803A CN111950803A CN202010858193.XA CN202010858193A CN111950803A CN 111950803 A CN111950803 A CN 111950803A CN 202010858193 A CN202010858193 A CN 202010858193A CN 111950803 A CN111950803 A CN 111950803A
- Authority
- CN
- China
- Prior art keywords
- logistics
- predicted
- historical
- logistics object
- delivery time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a logistics object delivery time prediction method, a device, electronic equipment and a storage medium, wherein the logistics object delivery time prediction method comprises the following steps: acquiring a starting site and a target site of a logistics object to be predicted; acquiring historical logistics tracks and historical aging information of the starting point to the destination point according to historical logistics data; selecting a delivery path according to the acquired historical logistics track; determining a predicted time effect according to the historical time effect information of the dispatch path; and predicting the delivery time of the logistics object to be predicted according to the predicted time effectiveness. The method and the device provided by the invention improve the accuracy of the forecast of the delivery time of the logistics object.
Description
Technical Field
The present invention relates to the field of computer applications, and in particular, to a method and an apparatus for predicting delivery time of a logistics object, an electronic device, and a storage medium.
Background
In the existing scenario of e-commerce platform, the work of predicting the delivery time of express packages becomes the key content concerned by e-commerce platform, express companies and terminal customers, and directly concerns the operation quality and customer experience of logistics and e-commerce platform.
At present, most express companies finish package transportation based on a hub and spoke type network, and the final package delivery time is directly determined by a path scheme from a collection network point to a delivery network point on the network.
In daily operation, each express company configures a complete path scheme (including a collection site, a plurality of distribution centers and a delivery site) between any two sites in the country through a Transportation Management System (TMS), and subsequent express packages are circulated strictly according to the path scheme configured on the TMS.
However, since the actual line goods volume may fluctuate periodically with the date change, the partial sub-paths in the original path scheme may have a scenario that the vehicle loading rate is not up to the standard and transportation with the vehicle on other lines is required. This situation may prompt the route planner to adjust the route scheme from the same package collecting point to the package dispatching point in the TMS, and further cause the package delivery time to change.
The path scheme changes scenes, and brings great difficulty to the conventional parcel delivery time prediction work. Therefore, how to improve the accuracy of predicting the delivery time of the logistics object is a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
In order to overcome the defects of the related technologies, the invention provides a method and a device for predicting the delivery time of a logistics object, an electronic device and a storage medium, so as to improve the accuracy of predicting the delivery time of the logistics object.
According to an aspect of the present invention, there is provided a method for predicting delivery time of a physical distribution object, including:
acquiring a starting site and a target site of a logistics object to be predicted;
acquiring historical logistics tracks and historical aging information of the starting point to the destination point according to historical logistics data;
selecting a delivery path according to the acquired historical logistics track;
determining a predicted time effect according to the historical time effect information of the dispatch path;
and predicting the delivery time of the logistics object to be predicted according to the predicted time effectiveness.
In some embodiments of the present invention, the selecting a dispatch path according to the obtained historical logistics trajectory includes:
taking the path scheme with the second highest frequency in the acquired historical logistics track as the dispatch path; and/or
And taking the path scheme with the fastest time effect in the acquired historical logistics tracks as the dispatch path.
In some embodiments of the present invention, the selecting a dispatch path according to the obtained historical logistics trajectory includes:
a dispatch path is selected from the acquired historical logistics trajectory according to a trained selection model.
In some embodiments of the invention, the determining a predicted age from historical age information for the dispatch path comprises:
and taking the mean value or the median of the historical aging information of the dispatch path as the predicted aging.
In some embodiments of the present invention, the predicting the delivery time of the logistics object to be predicted according to the predicted aging further comprises:
acquiring an initial network point and a middle network point between target network points of a logistics object to be predicted;
acquiring historical logistics tracks and historical aging information from the intermediate network point to a destination network point according to historical logistics data;
selecting an intermediate delivery path according to the acquired historical logistics track;
determining an intermediate prediction aging according to the historical aging information of the intermediate dispatch path;
and updating the delivery time of the logistics object to be predicted according to the intermediate prediction aging.
In some embodiments of the present invention, after acquiring a starting point of a logistics object to be predicted and an intermediate point between destination points, and before acquiring a historical logistics trajectory from the intermediate point to the destination point and historical aging information according to historical logistics data, the method further includes:
judging whether the middle mesh point belongs to the dispatch path or not;
and if so, not updating the delivery time of the logistics object to be predicted.
In some embodiments of the present invention, the step of acquiring an intermediate site between a starting site and a destination site of the logistics object to be predicted is performed after the logistics object leaves the intermediate site and before reaching a next site of the intermediate site.
In some embodiments of the present invention, the updating the delivery time of the logistics object to be predicted according to the intermediate prediction aging includes:
and combining the time when the logistics object to be predicted leaves the intermediate network point and the intermediate prediction aging as the delivery time of the logistics object to be predicted.
In some embodiments of the present invention, the step of updating the delivery time of the logistics object to be predicted according to the intermediate prediction aging is performed on each intermediate node between the starting node and the destination node in sequence according to the logistics trajectory of the logistics object to be predicted.
In some embodiments of the invention, the initiating site is a collecting site and the destination site is a delivery site.
According to another aspect of the present invention, there is provided a logistics object arrival time prediction apparatus, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire a starting site and a destination site of a logistics object to be predicted;
the second acquisition module is configured to acquire historical logistics tracks from the starting point to the destination point and historical aging information according to historical logistics data;
the selecting module is configured to select a delivery path according to the acquired historical logistics track;
the determining module is configured to determine a predicted aging according to the historical aging information of the dispatch path;
and the prediction module is configured to predict the delivery time of the logistics object to be predicted according to the predicted time effectiveness.
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 invention, the historical logistics track from the starting point to the destination point and the historical timeliness information are obtained through the historical logistics data, so that the forecasting timeliness is determined according to the historical timeliness information of the selected delivery path, therefore, the delivery time is forecasted through the delivery path selected by the historical data instead of the delivery path obtained by the transportation management system, and the accuracy of forecasting the delivery time of the logistics object is improved.
In addition, the invention also obtains the starting point of the logistics object to be predicted and the intermediate point between the target points after the arrival time prediction is carried out for the first time, thereby carrying out the prediction update of the arrival time based on the intermediate point, realizing the prediction of the arrival time in real time, and further improving the accuracy of the prediction of the arrival time of the logistics object based on the update of the arrival time prediction.
Drawings
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 flowchart of a logistics object delivery time prediction method according to an embodiment of the invention.
Fig. 2 shows a flow chart for updating a logistics object delivery time prediction according to a specific embodiment of the present invention.
Fig. 3 shows a flow chart for updating the logistics object delivery time prediction according to another embodiment of the invention.
Fig. 4 is a block diagram showing a logistics object arrival time prediction apparatus according to an embodiment of the present invention.
Fig. 5 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the invention.
Fig. 6 schematically shows 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.
Fig. 1 shows a flowchart of a logistics object delivery time prediction method according to an embodiment of the invention. The logistics object delivery time prediction method comprises the following steps:
step S110: and acquiring a starting point and a target point of the logistics object to be predicted.
Specifically, most of the current express companies finish package transportation by adopting a hub and spoke type network, and the logistics track of the hub and spoke type network can be roughly divided into five track segments, namely front-end collecting, departure branch, trunk transportation, arrival branch and tail-end delivery. The front-end collecting means that collecting personnel are excluded from collecting net points, and the front-end collecting means rush to the position of a sender within a specified time range to complete the process of package collecting operation. The departure branch line indicates that the branch line office sends out a branch line van at a specified time point, and the collection package is transported to the collection process of the originating distribution center. The trunk transportation represents the process that the packages are sorted and loaded in the originating distribution center, then transferred for zero or multiple times and finally reach the destination distribution center. And the arrival of the branch line indicates the process that after the packages arrive at the target distribution center, the distribution network points are determined through sorting operation, and the branch line regular bus transports the distribution network points. The end delivery represents the process that the packages are delivered to a delivery site, and finally delivered to a signoff and signed by a consignee through a delivery member.
The corresponding path scheme is formed by sequentially connecting a collecting network point, an originating distribution center, a plurality of transfer distribution centers (usually 0-3), a target distribution center and a distributing network point. Therefore, the starting network point and the destination network point can be selected from a collecting network point, a starting distribution center, a plurality of transfer distribution centers (usually 0-3), a destination distribution center and a delivery network point according to needs.
Step S120: and acquiring historical logistics tracks and historical aging information from the starting point to the destination point according to historical logistics data.
Specifically, the selection time of the historical logistics data can be set as required. For example, the required information may be acquired from historical logistics data in the week before the current time, historical logistics data in the two weeks before, historical logistics data in the month before, and the like. In the present embodiment, it is considered that the distribution line generally changes in units of weeks, and therefore, the acquisition time range of the historical distribution data may be set to one week or one natural week before the current time.
Step S130: and selecting a delivery path according to the acquired historical logistics track.
Specifically, step S130 is equivalent to predicting a dispatch route from the starting point to the destination point of the logistics object to be tested in the transportation according to the historical logistics track.
In some embodiments, the path scheme with the second highest frequency in the obtained historical logistics trajectory may be used as the dispatch path, and thus, the path scheme with the second highest frequency in the obtained historical logistics trajectory is predicted as the dispatch path from the start point to the destination point of the to-be-tested logistics object in the transportation.
In other embodiments, the most aged path scheme in the obtained historical logistics tracks may be used as the dispatch path, and thus, the most aged path scheme in the obtained historical logistics tracks is predicted to be the dispatch path from the start point to the destination point in the transportation of the logistics object to be tested.
In still other embodiments, the dispatch path may be determined in combination with the frequency and the time effectiveness (for example, when the frequency is consistent, the most time effective path scheme is used as the dispatch path, the comprehensive value is calculated according to the preset weight according to the time effectiveness and the frequency, and the optimal path scheme is used as the dispatch path, etc.), so that the dispatch path from the starting point to the destination point of the logistics object to be tested in the transportation may be predicted in combination with the time effectiveness and the frequency.
In some variations, a dispatch path may also be selected from the acquired historical logistics trajectory based on a trained selection model. The trained selection model is used for predicting a delivery path from the starting point to the destination point according to the historical logistics track. The selected model may be any machine learning model or deep learning model, and the invention is not limited thereto.
The above is merely a schematic illustration of various dispatch paths of the present invention, and the present invention is not limited thereto.
Step S140: and determining a predicted aging according to the historical aging information of the dispatch path.
Specifically, step S140 may implement, as the predicted aging, a mean value or a median of the historical aging information of the dispatch path as follows. For example, if the number of routes from the starting point to the destination point in the historical logistics track is N, the average or median is calculated according to the historical aging information of the route from the starting point to the destination point in the N historical logistics tracks (i.e. the time of the route from the starting point to the destination point according to the route from the starting point to the destination point), so as to predict the aging from the starting point to the destination point. The invention is not limited thereto, and other mathematical calculation methods are also within the scope of the invention.
Step S150: and predicting the delivery time of the logistics object to be predicted according to the predicted time effectiveness.
Therefore, the delivery time of the logistics object to be predicted can be predicted according to the predicted timeliness according to the positions of the starting point and the destination point in the logistics track of the logistics object to be predicted.
According to the logistics object delivery time prediction method, the historical logistics track from the starting point to the destination point and the historical timeliness information are obtained through historical logistics data, so that the prediction timeliness is determined according to the historical timeliness information of the selected delivery path, therefore, the delivery time prediction is carried out through the delivery path selected through the historical data instead of the delivery path obtained through a transportation management system, and the accuracy of the logistics object delivery time prediction is improved.
In some embodiments of the present invention, the initiating site may be a collecting site and the destination site may be a serving site. Therefore, when the logistics object to be predicted arrives at the collecting point, the steps S110 to S150 can be executed to predict the arrival time of the logistics object.
In the above embodiment, steps S110 to S150 may be implemented as follows:
step 1: and selecting all path schemes and corresponding timeliness of the express companies associated with the logistics objects to be predicted from the collecting network point to the delivery network point within the past week. The express company can take the form of express company codes as input, and the express company codes can be of a character string type and are used for uniquely marking the express companies corresponding to the logistics objects to be predicted currently. The collecting network points can be input in the form of collecting network point codes, and the collecting network point codes can be of a character string type and are used for uniquely marking the collecting network points corresponding to the current logistics object to be predicted. The dispatch network point can take a dispatch network point code form as input, and the dispatch network point code can be a character string type and is used for uniquely marking the dispatch network point corresponding to the current logistics object to be predicted.
Step 2: and sequencing the path schemes from the collecting network point to the dispatching network point in the past week according to the sequence of the frequency of the path schemes from high to low, and taking the first sequenced path scheme as a dispatching path. If the dispatch path is empty, the predicted age is set to empty and the jump is made to Step 4.
Step 3: and (4) counting the actual aging of each historical track corresponding to the dispatch path in Step2, and selecting a counting median as a predicted aging.
Step 4: if the prediction aging is empty, the prediction cannot be made; and if the prediction aging is not empty, predicting that the delivery time of the logistics object to be predicted is the sum of the collecting time and the prediction aging of the logistics object to be predicted. The delivery time of the logistics object to be predicted and the collecting time of the logistics object to be predicted are predicted to be date type data.
Step 5: and at least outputting the predicted delivery time of the logistics object to be predicted.
Thus, the delivery time of the logistics object to be predicted is predicted.
In some embodiments of the present invention, the foregoing steps S110 to S150 implement initial prediction of the delivery time of the logistics object to be predicted, and after the initial prediction, the present invention can also implement real-time delivery time prediction during transportation of the logistics object to be predicted, so as to update the delivery time prediction. Referring now to fig. 2, fig. 2 illustrates a flow diagram for updating a logistic object delivery time prediction in accordance with an embodiment of the present invention. After the step S150 of predicting the delivery time of the logistics object to be predicted according to the predicted aging, the method may further include the following steps:
step S210: and acquiring a starting point and a middle point between destination points of the logistics object to be predicted.
In particular, an intermediate mesh point may be any mesh point between the originating mesh point and the destination mesh point.
In some embodiments of the present invention, the real-time prediction of the delivery time may be performed at an intermediate site, whereby the step of acquiring an intermediate site between a starting site and a destination site of the logistics object to be predicted is performed after the logistics object leaves the intermediate site and before reaching a next site of the intermediate site.
In some embodiments of the present invention, since the logistics trajectory of the logistics object to be predicted is updated and changed in real time, the logistics object to be predicted can be predicted in real time by performing steps S210 to S250 to predict and update the delivery time in real time when reaching an intermediate point from the starting point to the destination point.
Step S220: and acquiring historical logistics tracks and historical aging information from the intermediate network point to the destination network point according to historical logistics data.
Specifically, the time period for predicting the required historical logistics data in real time may be the same as step S120. In some embodiments, the time period of the historical logistics data required for real-time prediction may also be the same as step S120, and the invention is not limited thereto.
Step S230: and selecting an intermediate delivery path according to the acquired historical logistics track.
Specifically, the selection manner of the intermediate dispatch path may be the same as the selection manner of the dispatch path in step S130, thereby facilitating the nested implementation of the algorithm. In other embodiments, the selection manner of the intermediate dispatch path may be different from the selection manner of the dispatch path in step S130, which is not intended to limit the present invention.
Step S240: and determining an intermediate predicted aging according to the historical aging information of the intermediate dispatch path.
Specifically, the intermediate predicted age may be determined in the same manner as the predicted age determination in step S140, thereby facilitating nested implementation of the algorithm. In other embodiments, the determining manner of the intermediate predicted aging may also be different from the determining manner of the intermediate predicted aging in step S140, and the present invention is not limited thereto.
Step S250: and updating the delivery time of the logistics object to be predicted according to the intermediate prediction aging.
In an embodiment where the step of acquiring the intermediate node between the starting node and the destination node of the to-be-predicted logistics object is performed after the logistics object leaves the intermediate node and before the logistics object reaches the next node of the intermediate node, the time when the to-be-predicted logistics object leaves the intermediate node and the intermediate prediction aging may be combined into the delivery time of the to-be-predicted logistics object, so as to calculate the delivery time.
Therefore, the invention also obtains the starting point of the logistics object to be predicted and the intermediate point between the target points after the arrival time prediction is carried out for the first time, thereby carrying out the prediction update of the arrival time based on the intermediate point, realizing the prediction of the arrival time in real time, and further improving the accuracy of the prediction of the arrival time of the logistics object based on the update of the arrival time prediction.
Referring now to fig. 3, fig. 3 illustrates a flow diagram for updating a logistic object delivery time prediction in accordance with another embodiment of the present invention. Steps S210 to S250 in the flowchart shown in fig. 3 are the same as those shown in fig. 2, and different from fig. 2, after step S210 and before step S220, a step S211 is further included: and judging whether the middle mesh point belongs to the dispatch path. If the result is no, that is, the logistics object to be predicted is not transported according to the dispatch route selected in step S120, the arrival time needs to be predicted and updated, and step S220 is continuously executed. If yes, that is, the logistics object to be predicted is dispatched to transport according to the dispatch route selected in step S120, so that the arrival time prediction does not need to be updated, and thus the arrival time of the logistics object to be predicted is not updated to end the process.
In the embodiment shown in fig. 3, by determining whether the intermediate node belongs to the dispatch path, the number of execution times of real-time prediction (prediction update) is reduced, the overall algorithm execution efficiency is improved, and the load consumption of the algorithm on the system is reduced.
Specifically, the steps S210 to S250 described above may be implemented as follows:
step 1: if the intermediate mesh point belongs to the dispatch path, the current predicted delivery time is equal to the previous predicted delivery time, and the Step7 is skipped; if the intermediate mesh point does not belong to the dispatch path, Step2 is executed. The intermediate network point can be input in the form of intermediate network point code, and the intermediate network point code can be a character string type and is used for uniquely marking the intermediate network point corresponding to the current logistics object to be predicted.
Step 2: and selecting all intermediate path schemes and corresponding time efficiency of the express companies associated with the logistics objects to be predicted from the intermediate network points to the delivery network points in the past month. The express company can take the form of express company codes as input, and the express company codes can be of a character string type and are used for uniquely marking the express companies corresponding to the logistics objects to be predicted currently. The dispatch network point can take a dispatch network point code form as input, and the dispatch network point code can be a character string type and is used for uniquely marking the dispatch network point corresponding to the current logistics object to be predicted.
Step 3: and sequencing all the intermediate path schemes from the intermediate network points to the dispatch network points in the past month according to the sequence of the frequency of the intermediate path schemes from high to low, and taking the intermediate path scheme sequenced as the first intermediate dispatch path. If the intermediate dispatch path is empty, then the current predicted delivery time is made equal to the previous predicted delivery time and Step7 is skipped.
Step 4: and (4) counting the actual aging of each historical track corresponding to the middle dispatch path in Step3, and selecting a counting median as the middle predicted aging.
Step 5: if the intermediate prediction aging is empty, the current predicted delivery time is equal to the previous predicted delivery time, and the Step7 is skipped; if the intermediate predicted age is not empty, Step6 is executed.
Step 6: if the sum of the time (date type) when the logistics object to be predicted leaves the middle point and the middle prediction aging is equal to the previous prediction delivery time, enabling the current prediction delivery time to be equal to the previous prediction delivery time, and jumping to Step 7; and if the sum of the time (date type) when the logistics object to be predicted leaves the intermediate site and the intermediate prediction aging is not equal to the previous prediction arrival time, updating the current prediction arrival time to the sum of the time when the logistics object to be predicted leaves the intermediate site and the intermediate prediction aging.
Step 7: the currently predicted delivery time is output.
The above are merely a plurality of specific implementations of the present invention, and each implementation may be implemented independently or in combination, and the present invention is not limited thereto.
Referring now to fig. 4, fig. 4 is a block diagram illustrating a logistics object arrival time prediction apparatus according to an embodiment of the present invention. The logistics object delivery time prediction apparatus 300 includes a first obtaining module 310, a second obtaining module 320, a selecting module 330, a determining module 340, and a predicting module 350.
The first obtaining module 310 is configured to obtain a starting point and a destination point of the logistics object to be predicted.
The second obtaining module 320 is configured to obtain historical logistics tracks from the starting point to the destination point and historical aging information according to historical logistics data.
The selecting module 330 is configured to select a dispatch path according to the acquired historical logistics trajectory.
The determination module 340 is configured to determine a predicted age based on historical age information for the dispatch path.
The prediction module 350 is configured to predict the delivery time of the logistics object to be predicted according to the predicted aging.
In the logistics object delivery time prediction device according to the exemplary embodiment of the invention, the historical logistics track from the starting point to the destination point and the historical aging information are obtained through historical logistics data, so that the prediction aging is determined according to the historical aging information of the selected delivery path, and therefore, the delivery time prediction is performed through the delivery path selected through the historical data instead of the delivery path obtained through the transportation management system, and the accuracy of the logistics object delivery time prediction is improved.
Fig. 4 is a schematic diagram illustrating the logistics object arrival time prediction apparatus 300 provided by the present invention, and the splitting, merging and adding of modules are within the protection scope of the present invention without departing from the concept of the present invention. The logistics object delivery time prediction apparatus 300 provided by the present invention can be implemented by software, hardware, firmware, plug-in, and any combination thereof, and the present invention is not limited thereto.
In an exemplary embodiment of the present invention, there is also provided a computer-readable storage medium, on which a computer program is stored, which when executed by, for example, a processor, can implement the steps of the logistics object delivery time prediction 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 object delivery time prediction method section above of this specification, when the program product is run on the terminal device.
Referring to fig. 5, 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 object delivery time prediction method in any one of the above embodiments via execution of 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. 6. The electronic device 500 shown in fig. 6 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. 6, 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 executable by the processing unit 510 to cause the processing unit 510 to perform the steps according to various exemplary embodiments of the present invention described in the logistics object delivery time prediction method section above in this specification. For example, the processing unit 510 may perform the steps as shown in any one or more of fig. 1-4.
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 may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may 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 may be a personal computer, a server, or a network device, etc.) to execute the logistics object delivery time prediction method according to the embodiment of the present invention.
Compared with the prior art, the invention has the advantages that:
according to the invention, the historical logistics track from the starting point to the destination point and the historical timeliness information are obtained through the historical logistics data, so that the forecasting timeliness is determined according to the historical timeliness information of the selected delivery path, therefore, the delivery time is forecasted through the delivery path selected by the historical data instead of the delivery path obtained by the transportation management system, and the accuracy of forecasting the delivery time of the logistics object is improved.
In addition, the invention also obtains the starting point of the logistics object to be predicted and the intermediate point between the target points after the arrival time prediction is carried out for the first time, thereby carrying out the prediction update of the arrival time based on the intermediate point, realizing the prediction of the arrival time in real time, and further improving the accuracy of the prediction of the arrival time of the logistics object based on the update of the arrival time prediction.
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 (13)
1. A method for predicting delivery time of a physical distribution object, comprising:
acquiring a starting site and a target site of a logistics object to be predicted;
acquiring historical logistics tracks and historical aging information of the starting point to the destination point according to historical logistics data;
selecting a delivery path according to the acquired historical logistics track;
determining a predicted time effect according to the historical time effect information of the dispatch path;
and predicting the delivery time of the logistics object to be predicted according to the predicted time effectiveness.
2. The method for predicting logistics object arrival time according to claim 1, wherein said selecting a dispatch path according to the obtained historical logistics trajectory comprises:
taking the path scheme with the second highest frequency in the acquired historical logistics track as the dispatch path; and/or
And taking the path scheme with the fastest time effect in the acquired historical logistics tracks as the dispatch path.
3. The method for predicting logistics object arrival time according to claim 1, wherein said selecting a dispatch path according to the obtained historical logistics trajectory comprises:
a dispatch path is selected from the acquired historical logistics trajectory according to a trained selection model.
4. The method of predicting logistics object arrival time of claim 1 wherein said determining a predicted age based on historical age information of the dispatch path comprises:
and taking the mean value or the median of the historical aging information of the dispatch path as the predicted aging.
5. The method for predicting delivery time of a logistics object according to claim 1, wherein the predicting the delivery time of the logistics object to be predicted according to the predicted aging further comprises:
acquiring an initial network point and a middle network point between target network points of a logistics object to be predicted;
acquiring historical logistics tracks and historical aging information from the intermediate network point to a destination network point according to historical logistics data;
selecting an intermediate delivery path according to the acquired historical logistics track;
determining an intermediate prediction aging according to the historical aging information of the intermediate dispatch path;
and updating the delivery time of the logistics object to be predicted according to the intermediate prediction aging.
6. The method for predicting delivery time of a logistics object according to claim 5, wherein after acquiring the intermediate point between the starting point and the destination point of the logistics object to be predicted, and before acquiring the historical logistics trajectory and the historical aging information of the intermediate point to the destination point according to the historical logistics data, the method further comprises:
judging whether the middle mesh point belongs to the dispatch path or not;
and if so, not updating the delivery time of the logistics object to be predicted.
7. The method for predicting delivery time of a logistics object as recited in claim 5, wherein the step of acquiring an intermediate site between a starting site and a destination site of the logistics object to be predicted is performed after the logistics object leaves the intermediate site and before the logistics object reaches a next site of the intermediate site.
8. The method according to claim 7, wherein the updating the delivery time of the logistics object to be predicted according to the intermediate prediction aging includes:
and combining the time when the logistics object to be predicted leaves the intermediate network point and the intermediate prediction aging as the delivery time of the logistics object to be predicted.
9. The method according to claim 7, wherein the step of updating the delivery time of the logistics object to be predicted according to the intermediate prediction aging is performed for each intermediate node between the starting node and the destination node in turn according to the logistics trajectory of the logistics object to be predicted.
10. The method for predicting delivery time of a physical distribution object according to any one of claims 1 to 9, wherein the starting point is a collecting point and the destination point is a delivering point.
11. A logistics object delivery time prediction device is characterized by comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is configured to acquire a starting site and a destination site of a logistics object to be predicted;
the second acquisition module is configured to acquire historical logistics tracks from the starting point to the destination point and historical aging information according to historical logistics data;
the selecting module is configured to select a delivery path according to the acquired historical logistics track;
the determining module is configured to determine a predicted aging according to the historical aging information of the dispatch path;
and the prediction module is configured to predict the delivery time of the logistics object to be predicted according to the predicted time effectiveness.
12. 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 object delivery time prediction method of any one of claims 1 to 10.
13. A storage medium having a computer program stored thereon, the computer program when executed by a processor performing:
the logistics object delivery time prediction method of any one of claims 1 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010858193.XA CN111950803A (en) | 2020-08-24 | 2020-08-24 | Logistics object delivery time prediction method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010858193.XA CN111950803A (en) | 2020-08-24 | 2020-08-24 | Logistics object delivery time prediction method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111950803A true CN111950803A (en) | 2020-11-17 |
Family
ID=73360084
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010858193.XA Pending CN111950803A (en) | 2020-08-24 | 2020-08-24 | Logistics object delivery time prediction method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111950803A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112465149A (en) * | 2020-11-30 | 2021-03-09 | 上海寻梦信息技术有限公司 | Same-city part identification method and device, electronic equipment and storage medium |
| CN112488195A (en) * | 2020-11-30 | 2021-03-12 | 上海寻梦信息技术有限公司 | Party delivery identification method, device, equipment and storage medium |
| CN112488623A (en) * | 2020-11-30 | 2021-03-12 | 上海寻梦信息技术有限公司 | Method, device, equipment and storage medium for displaying package pre-distribution route |
| CN112529305A (en) * | 2020-12-16 | 2021-03-19 | 北京交通大学 | Express item acquisition sequence prediction method based on deep learning |
| CN112580884A (en) * | 2020-12-24 | 2021-03-30 | 上海寻梦信息技术有限公司 | Cold chain transportation method, cold chain transportation network establishment method and related equipment |
| CN112598357A (en) * | 2020-12-30 | 2021-04-02 | 平潭综合实验区百汉国际贸易有限公司 | Order tracking processing system and working method thereof |
| CN112801354A (en) * | 2021-01-19 | 2021-05-14 | 上海德启信息科技有限公司 | Route planning method and device |
| CN113011672A (en) * | 2021-03-29 | 2021-06-22 | 上海寻梦信息技术有限公司 | Logistics timeliness prediction method and device, electronic equipment and storage medium |
| CN113011666A (en) * | 2021-03-29 | 2021-06-22 | 上海寻梦信息技术有限公司 | Aging prediction model training method, aging prediction method and related equipment |
| CN113011665A (en) * | 2021-03-29 | 2021-06-22 | 上海寻梦信息技术有限公司 | Logistics timeliness prediction method, device, equipment and storage medium |
| CN113034075A (en) * | 2021-03-29 | 2021-06-25 | 上海寻梦信息技术有限公司 | Logistics waybill timeliness pushing method, system, equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103794053A (en) * | 2014-03-05 | 2014-05-14 | 中商商业发展规划院有限公司 | Vague predicting method and system for city short-distance logistics simple target delivering time |
| CN108681860A (en) * | 2018-05-31 | 2018-10-19 | 广州同康科技发展有限公司 | A kind of commodity distribution control method based on item data analysis |
| CN109658037A (en) * | 2019-01-10 | 2019-04-19 | 跨越速运集团有限公司 | A kind of logistics route Intelligent planning method and system |
| CN109871991A (en) * | 2019-01-30 | 2019-06-11 | 深圳市快金数据技术服务有限公司 | A kind of recommended method, device, equipment and the medium of logistics route |
| US20190205828A1 (en) * | 2017-12-28 | 2019-07-04 | Business Objects Software Limited | Delivery prediction with degree of delivery reliability |
| CN110874666A (en) * | 2018-08-31 | 2020-03-10 | 北京京东尚科信息技术有限公司 | Method, system, equipment and storage medium for distributing articles |
| CN111260151A (en) * | 2020-02-12 | 2020-06-09 | 上海东普信息科技有限公司 | Multi-frequency dispatch duration prediction method, device, equipment and storage medium |
-
2020
- 2020-08-24 CN CN202010858193.XA patent/CN111950803A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103794053A (en) * | 2014-03-05 | 2014-05-14 | 中商商业发展规划院有限公司 | Vague predicting method and system for city short-distance logistics simple target delivering time |
| US20190205828A1 (en) * | 2017-12-28 | 2019-07-04 | Business Objects Software Limited | Delivery prediction with degree of delivery reliability |
| CN108681860A (en) * | 2018-05-31 | 2018-10-19 | 广州同康科技发展有限公司 | A kind of commodity distribution control method based on item data analysis |
| CN110874666A (en) * | 2018-08-31 | 2020-03-10 | 北京京东尚科信息技术有限公司 | Method, system, equipment and storage medium for distributing articles |
| CN109658037A (en) * | 2019-01-10 | 2019-04-19 | 跨越速运集团有限公司 | A kind of logistics route Intelligent planning method and system |
| CN109871991A (en) * | 2019-01-30 | 2019-06-11 | 深圳市快金数据技术服务有限公司 | A kind of recommended method, device, equipment and the medium of logistics route |
| CN111260151A (en) * | 2020-02-12 | 2020-06-09 | 上海东普信息科技有限公司 | Multi-frequency dispatch duration prediction method, device, equipment and storage medium |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112488195B (en) * | 2020-11-30 | 2024-04-19 | 上海寻梦信息技术有限公司 | Method, device, equipment and storage medium for identifying dispatch of dispatcher |
| CN112488195A (en) * | 2020-11-30 | 2021-03-12 | 上海寻梦信息技术有限公司 | Party delivery identification method, device, equipment and storage medium |
| CN112488623A (en) * | 2020-11-30 | 2021-03-12 | 上海寻梦信息技术有限公司 | Method, device, equipment and storage medium for displaying package pre-distribution route |
| CN112465149A (en) * | 2020-11-30 | 2021-03-09 | 上海寻梦信息技术有限公司 | Same-city part identification method and device, electronic equipment and storage medium |
| CN112529305A (en) * | 2020-12-16 | 2021-03-19 | 北京交通大学 | Express item acquisition sequence prediction method based on deep learning |
| CN112580884A (en) * | 2020-12-24 | 2021-03-30 | 上海寻梦信息技术有限公司 | Cold chain transportation method, cold chain transportation network establishment method and related equipment |
| CN112598357A (en) * | 2020-12-30 | 2021-04-02 | 平潭综合实验区百汉国际贸易有限公司 | Order tracking processing system and working method thereof |
| CN112801354A (en) * | 2021-01-19 | 2021-05-14 | 上海德启信息科技有限公司 | Route planning method and device |
| CN113011666A (en) * | 2021-03-29 | 2021-06-22 | 上海寻梦信息技术有限公司 | Aging prediction model training method, aging prediction method and related equipment |
| CN113011665A (en) * | 2021-03-29 | 2021-06-22 | 上海寻梦信息技术有限公司 | Logistics timeliness prediction method, device, equipment and storage medium |
| CN113034075A (en) * | 2021-03-29 | 2021-06-25 | 上海寻梦信息技术有限公司 | Logistics waybill timeliness pushing method, system, equipment and storage medium |
| CN113011672B (en) * | 2021-03-29 | 2024-04-19 | 上海寻梦信息技术有限公司 | Logistics aging prediction method and device, electronic equipment and storage medium |
| CN113011672A (en) * | 2021-03-29 | 2021-06-22 | 上海寻梦信息技术有限公司 | Logistics timeliness prediction method and device, electronic equipment and storage medium |
| CN113011666B (en) * | 2021-03-29 | 2024-04-26 | 上海寻梦信息技术有限公司 | Training method of aging prediction model, aging prediction method and related equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111950803A (en) | Logistics object delivery time prediction method and device, electronic equipment and storage medium | |
| CN112101839A (en) | Method for establishing express delivery time prediction model, prediction method and related equipment | |
| CN109840729B (en) | Method and system for positioning bin, storage medium and electronic equipment | |
| CN101620694A (en) | Logistics transportation decision-making support system and method, and transportation management system | |
| CN111947676B (en) | Express delivery path planning method and device, electronic equipment and storage medium | |
| CN111667085B (en) | Logistics routing network determining method, device, medium and electronic equipment | |
| CN110853349A (en) | Vehicle scheduling method, device and equipment | |
| CN111428902B (en) | Method and device for determining transport route | |
| CN111967793B (en) | Logistics timeliness evaluation method and device, electronic equipment and storage medium | |
| CN113222205B (en) | Path planning method and device | |
| Jiang et al. | A scheme for determining vehicle routes based on Arc-based service network design | |
| CN113592282A (en) | Article distribution method and device | |
| CN111882117A (en) | Express trunk route line flow prediction method, device and equipment | |
| CN116029637A (en) | Cross-border electronic commerce logistics channel intelligent recommendation method and device, equipment and storage medium | |
| CN111967808A (en) | Method and device for determining logistics object receiving mode, electronic equipment and storage medium | |
| CN112308312B (en) | Warehouse-leaving package transfer method, model training method and related equipment | |
| CN113011672B (en) | Logistics aging prediction method and device, electronic equipment and storage medium | |
| CN113011664A (en) | Logistics timeliness monitoring method and device, electronic equipment and storage medium | |
| CN112488624A (en) | Logistics routing information calculation method, device, equipment and storage medium | |
| CN113011666A (en) | Aging prediction model training method, aging prediction method and related equipment | |
| CN112819394B (en) | Waybill processing method and device, computer-readable storage medium and electronic equipment | |
| CN112288362A (en) | Parcel re-delivery method, parcel delivery method and related equipment | |
| CN111860918B (en) | Distribution method and device, electronic equipment and computer readable medium | |
| JP2018088069A (en) | Transportation service information providing apparatus and transportation service information providing method | |
| CN115759307A (en) | Order dispatching method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |