CN113011672A - Logistics timeliness prediction method and device, electronic equipment and storage medium - Google Patents
Logistics timeliness prediction method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113011672A CN113011672A CN202110344428.8A CN202110344428A CN113011672A CN 113011672 A CN113011672 A CN 113011672A CN 202110344428 A CN202110344428 A CN 202110344428A CN 113011672 A CN113011672 A CN 113011672A
- Authority
- CN
- China
- Prior art keywords
- predicted
- waybill
- prediction
- aging
- data
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000032683 aging Effects 0.000 claims abstract description 78
- 238000004590 computer program Methods 0.000 claims description 7
- 238000012549 training Methods 0.000 claims description 5
- 238000010801 machine learning Methods 0.000 claims description 4
- 238000012827 research and development Methods 0.000 abstract description 8
- 238000012545 processing Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 3
- 230000002431 foraging effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000004883 computer application Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
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
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Development Economics (AREA)
- Software Systems (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Operations Research (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Game Theory and Decision Science (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a logistics timeliness prediction method, a device, electronic equipment and a storage medium, wherein the logistics timeliness prediction method comprises the following steps: determining the track category of the waybill to be predicted based on the waybill information of the waybill to be predicted; determining a prediction model according to the track category of the waybill to be predicted; and predicting the time efficiency of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted by adopting the determined prediction model. The invention realizes the research and development of efficient aging prediction service and improves the reliability and the business coverage rate of the aging prediction service based on the complicated and changeable logistics data under different conditions.
Description
Technical Field
The invention relates to the field of computer application, in particular to a logistics aging prediction method and device, electronic equipment and a storage medium.
Background
In the logistics aging prediction scene, the quantity and quality of data sources of different logistics suppliers and different lines are complex and variable. For example, some logistics suppliers provide large amounts of logistics data and high quality of data; some logistics suppliers provide logistics data with small data volume and poor data quality (such as partial data missing and data error). The current stream aging prediction needs to be realized based on the complicated and diversified stream data. However, there are the following problems in the implementation: the research and development efficiency of the aging prediction service is low, the aging prediction reliability is poor, and the service coverage rate is insufficient.
Therefore, how to realize efficient research and development of the aging prediction service and improve the reliability and the business coverage rate of the aging prediction service based on complex and changeable logistics data under different conditions is a technical problem to be solved urgently in the field.
Disclosure of Invention
In order to overcome the defects of the related technologies, the invention provides a logistics aging prediction method, a logistics aging prediction device, electronic equipment and a storage medium, so that efficient research and development of aging prediction services are realized and the reliability and the service coverage rate of the aging prediction services are improved based on complex and changeable logistics data under different conditions.
According to one aspect of the invention, a logistics aging prediction method is provided, which comprises the following steps:
determining the track category of the waybill to be predicted based on the waybill information of the waybill to be predicted;
determining a prediction model according to the track category of the waybill to be predicted;
and predicting the time efficiency of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted by adopting the determined prediction model.
In some embodiments of the present application, the trajectory category of the waybill to be predicted is determined based on the quantity and/or quality of historical trajectory data having at least one same attribute as the waybill to be predicted.
In some embodiments of the present application, when the quantity and/or quality of the historical trajectory data having at least one same attribute as the waybill to be predicted belongs to a first threshold range, the waybill to be predicted belongs to a first trajectory category, the first trajectory category is associated with a first prediction model, and the first prediction model is trained at least from the historical trajectory data having at least one same attribute as the waybill to be predicted.
In some embodiments of the present application, the first predictive model is one or more machine learning models.
In some embodiments of the present application, when the quantity and/or quality of the historical trajectory data having at least one same attribute as the waybill to be predicted belongs to a second threshold range, the waybill to be predicted belongs to a second trajectory category, and the second trajectory category is associated with a second prediction model, and the second prediction model is obtained based on statistics of at least the historical trajectory data having at least one same attribute of the waybill to be predicted.
In some embodiments of the present application, the second predictive model is built according to the following steps:
acquiring aging data of historical track data with at least one same attribute as the waybill to be predicted;
calculating the median and/or average of the obtained aging data.
In some embodiments of the present application, when the quantity and/or quality of the historical trajectory data having at least one same attribute as the waybill to be predicted belongs to a third threshold range, the waybill to be predicted belongs to a third trajectory category, and the third trajectory category is associated with a third prediction model, and the third prediction model is an aging query configuration table.
In some embodiments of the present application, the first threshold range is greater than the second threshold range, which is greater than the third threshold range.
In some embodiments of the present application, the attributes include one or more of a combination of logistics service provider, distribution address, receiving address, pull node, dispatch node, and transit node.
In some embodiments of the present application, the predicting, by using the determined prediction model, the time period of the waybill to be predicted based on the waybill information and/or the trajectory data of the waybill to be predicted includes:
forecasting the time efficiency among the transportation nodes of the freight bill to be forecasted by adopting the determined forecasting model based on the freight bill information and/or the track data of the freight bill to be forecasted;
and calculating the time efficiency of the freight bill to be predicted based on the predicted time efficiency among the transport nodes.
According to another aspect of the present application, there is also provided a logistics aging prediction apparatus, including:
the first determination module is configured to determine the track category of the waybill to be predicted based on the waybill information of the waybill to be predicted;
the second determination module is configured to determine a prediction model according to the track category of the freight note to be predicted;
and the prediction module is configured to predict the time effectiveness of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted by adopting the determined prediction model.
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:
the method and the device determine the track type of the freight note to be predicted through the freight note information of the freight note to be predicted, determine a prediction model according to the track type of the freight note to be predicted, adopt the determined prediction model, and predict the timeliness of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted, so that different prediction models are used for timeliness prediction aiming at different track types, thereby being suitable for scenes of various different data, realizing the research and development of efficient timeliness prediction service and improving the reliability and the business coverage rate of the timeliness prediction service based on the complex and changeable logistics data of different conditions.
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 flow chart of a logistics aging prediction method according to an embodiment of the invention.
FIG. 2 shows a flow diagram for building a second predictive model according to an embodiment of the invention.
Fig. 3 is a flowchart illustrating a process of predicting the aging of the waybill to be predicted based on waybill information and/or trajectory data of the waybill to be predicted by using the determined prediction model according to an embodiment of the present invention.
Fig. 4 is a flow chart illustrating a logistics aging prediction method according to an embodiment of the invention.
Fig. 5 is a block diagram illustrating a device for predicting the aging of a compartment stream according to an embodiment of the present invention.
Fig. 6 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the invention.
Fig. 7 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 prediction method provided by the present invention may be applied to a logistics platform, an e-commerce platform, or any third party platform that needs to implement logistics aging prediction, 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 prediction method according to an embodiment of the invention. The logistics aging prediction method comprises the following steps:
step S110: and determining the track category of the freight note to be predicted based on the freight note information of the freight note to be predicted.
Specifically, the track category of the order to be predicted may be determined based on the quantity and/or quality of historical track data having at least one same attribute as the waybill to be predicted. The attributes comprise one or more combinations of logistics service providers, distribution addresses, receiving nodes, dispatching nodes and transit nodes. Therefore, the invention can realize the classification of different track categories according to the historical track data of various different attribute combinations. The desired attributes may be set as desired. For example, in some embodiments, the logistics service provider, the delivery address (delivery city) and the receiving address (receiving city) may be selected as attributes to ensure that the trajectory class of the order to be predicted is based on historical trajectory data belonging to the same logistics service provider, delivery address (delivery city) and receiving address (receiving city) as the order to be predicted fig. 3 illustrates a flow chart of determining the same according to an embodiment of the present invention.
Otherwise. In other embodiments, the starting transit node and the target transit node may be used as the selected attributes to ensure the trajectory category of the order to be predicted according to the historical trajectory data of the same starting transit node and target transit node as the order to be predicted. The present invention can also realize various attribute combinations, which are not described herein.
Specifically, in the present embodiment, the trajectory category is determined based on the number of historical trajectory data. In other embodiments, the trajectory category is determined based on the quality of the historical trajectory data. The quality of the historical track data may include, but is not limited to, the completeness of the historical track data, the accuracy of the historical track data, and the timeliness (whether closer to the current time) of the historical track data. The invention is not so limited. The quantity/quality of the historical track data is an important index influencing the aging prediction model. Other indicators that may affect the aging prediction model to be selected may also be within the scope of the present invention, and are not described herein.
Step S120: and determining a prediction model according to the track category of the waybill to be predicted.
Specifically, step S120 may determine different prediction models according to different trajectory categories. In the foregoing embodiment of the trajectory classification by the quantity and/or quality of the historical trajectory data, the greater the quantity and/or quality of the historical trajectory data, the more intelligent the determined prediction model is. The prediction model has high intelligence degree, such as deep learning model, machine learning model, statistical model and look-up table (mapping table) model. The foregoing is merely exemplary and the invention is not limited thereto.
In particular, the determined prediction model may be established in advance from different trajectory categories. In other embodiments, the predictive models may also be selected and built in real-time according to trajectory categories.
Step S130: and predicting the time efficiency of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted by adopting the determined prediction model.
Specifically, according to different prediction models, the invention can directly realize the sign-in time of the freight bill to be predicted or the time of arriving at a certain set node. In some variations, the present invention may also determine a total age forecast for a shipment by predicting the age between nodes.
According to the logistics aging prediction method provided by the invention, the track type of the freight note to be predicted is determined through the freight note information of the freight note to be predicted, so that a prediction model is determined according to the track type of the freight note to be predicted, the determined prediction model is adopted, and the aging of the freight note to be predicted is predicted based on the freight note information and/or the track data of the freight note to be predicted, so that different prediction models are used for aging prediction aiming at different track types, thus the method is suitable for scenes of various different data, and based on complex and changeable logistics data of different conditions, the research and development of efficient aging prediction service are realized, and the reliability and the service coverage rate of the aging prediction service are improved.
Different trajectory categories and correspondingly applied aging prediction models of the present invention will be described below with reference to a plurality of embodiments.
In some embodiments of the present application, the waybill to be predicted belongs to a first track category when the quantity and/or quality of the historical track data having at least one same attribute as the waybill to be predicted belongs to a first threshold range. Attributes may include, but are not limited to, one or more combinations of logistics facilitators, distribution addresses, recipient addresses, package nodes, dispatch nodes, transit nodes. Specifically, the first threshold range is a threshold range with the largest lower limit value in the value ranges of the track categories, so that the historical track data of the corresponding attribute of the waybill to be predicted has a larger quantity and/or a higher quality. Since the first trajectory category represents a greater amount and/or a higher quality of the corresponding historical trajectory data, it can also be suitable to use a more intelligent predictive model.
In particular, the first trajectory category may, for example, be associated with a first predictive model. The first prediction model is obtained by training at least historical track data of the waybill to be predicted, wherein the historical track data has at least one same attribute. In this embodiment, the historical trajectory data used for determining the trajectory category of the waybill to be predicted may be the same data as the historical trajectory data used for training the first prediction model. The invention is not so limited. In some variations, the historical trajectory data used for determining the trajectory category of the waybill to be predicted and the historical trajectory data used for training the first prediction model may also be historical trajectory data of different historical time periods, where the different historical time periods described include partial overlap and complete non-overlap of the historical time periods, and the present invention is not limited thereto.
In particular, the first predictive model may be one or more machine learning models. For example, the XGBoost algorithm may be used. The present invention is not limited thereto, and other neural network models, regression models, time series prediction models, etc. as the first prediction model are within the scope of the present invention.
In some embodiments of the present application, the waybill to be predicted belongs to a second trajectory category when the quantity and/or quality of the historical trajectory data having at least one same attribute as the waybill to be predicted belongs to a second threshold range. The meaning of the attribute is consistent with the attribute used in the first trajectory type judgment. Specifically, the upper limit of the second threshold range may be smaller than the lower limit of the first threshold range. The lower limit of the second threshold range may not be smaller than the upper limit of the lowest threshold range. Thus, the historical trajectory data representing the corresponding attributes of the waybill to be predicted is of moderate quantity and/or moderate quality. Since the second trajectory category represents a moderate amount and/or moderate quality of corresponding historical trajectory data, it can also be adapted to use a moderately intelligent predictive model.
In particular, the second trajectory category may be associated with a second predictive model, for example. The second prediction model is obtained based on the statistics of at least one historical track data with the same attribute of the waybill to be predicted. In this embodiment, the historical trajectory data used for determining the trajectory category of the waybill to be predicted may be the same data as the historical trajectory data for statistically constructing the second prediction model. In some variations, the historical trajectory data used for determining the trajectory category of the waybill to be predicted may also be historical trajectory data of a different historical time period from the historical trajectory data of the statistically-constructed second prediction model.
Referring now to FIG. 2, FIG. 2 illustrates a flow diagram for building a second predictive model, according to an embodiment of the invention. Fig. 2 shows the following steps together:
step S101: and acquiring aging data of historical track data with at least one same attribute as the waybill to be predicted.
Step S102: calculating the median and/or average of the obtained aging data.
Thus, the second predictive model may be built with a median and/or mean in statistics by the steps described above. Specifically, the second prediction model may include a median and/or average of aging data between different nodes, so as to serve as an aging prediction result between nodes with the freight note to be predicted in a query-like manner. Further, the second predictive model may be updated periodically or on demand, and although the second predictive model is statistically based and thus has a relatively low degree of intelligence relative to the first predictive model, it still has a self-learning and updating function, thus also enabling efficient time-based prediction.
In some embodiments of the present application, the waybill to be predicted belongs to a third track category when the quantity and/or quality of the historical track data having at least one same attribute as the waybill to be predicted belongs to a third threshold range. The meaning of the attribute is consistent with the attribute used in the first trajectory type judgment. Specifically, the upper limit of the third threshold range may be smaller than the lower limit of the second threshold range. Thus, the historical trajectory data representing the corresponding attributes of the waybill to be predicted is of a lower quantity and/or lower quality. Since the third trajectory class represents a lower amount and/or lower quality of corresponding historical trajectory data, it may be appropriate to use a less intelligent predictive model.
In particular, the third trajectory category may, for example, be associated with a third predictive model. The third prediction model is an aging query configuration table. The aging inquiry configuration table may be provided by the logistics provider, and the aging inquiry configuration table stores transit times between the nodes provided by the logistics provider. In one embodiment, the third predictive model may be obtained by synchronizing the average time-averaged variance between the secondary cities of the logistics provider. Therefore, for the third track category, the time-based prediction can be realized in the form of node table look-up.
The three trajectory categories and the corresponding prediction models provided by the present invention are only schematically illustrated, and the present invention is not limited thereto, and it is within the scope of the present invention to adopt any two trajectory categories and corresponding prediction models, or add other trajectory categories and prediction models as needed.
Referring now to fig. 3, fig. 3 is a flow chart illustrating a process of predicting the timeliness of the waybill to be predicted based on waybill information and/or trajectory data of the waybill to be predicted by using the determined prediction model according to the embodiment of the present invention. Fig. 3 shows the following steps in total:
step S131: forecasting the time efficiency among the transportation nodes of the freight bill to be forecasted by adopting the determined forecasting model based on the freight bill information and/or the track data of the freight bill to be forecasted;
step S132: and calculating the time efficiency of the freight bill to be predicted based on the predicted time efficiency among the transport nodes.
Therefore, the invention can realize the time efficiency prediction between any nodes according to the requirement and also realize the total time efficiency in the transportation process.
In each real-time example of the present invention, the aging may be a time period or a time point, and the present invention is not limited thereto.
Referring now to fig. 4, fig. 4 is a flow chart illustrating a method for logistics aging prediction according to an embodiment of the present invention. Fig. 4 shows the following steps in total:
firstly, acquiring waybill information (201); the trajectory category is then determined from the waybill information, and the prediction model used is determined from the trajectory category (202).
And carrying out aging prediction (207) on the waybill information of the first track type with large historical data amount and high quality by adopting a first prediction model. The first predictive model is trained (209) by inputting historical trajectory data (208). The first prediction model is implemented, for example, by an XGBoost algorithm, so that the training of the model, i.e., the construction and refinement of the feature tree, is performed. Inputting the waybill information into a first prediction model (210), calculating nodes (211) corresponding to the waybill information in the feature tree in the trained first prediction model, and adding the nodes according to the node values to obtain an aging prediction value (212). Thereby, the aging prediction of the first trajectory category is realized.
And carrying out aging prediction on waybill information of a second track category with moderate historical data quantity and moderate quality by adopting a second prediction model (203). The second prediction model establishes a second prediction model (206) by obtaining historical trajectory data (204), statistical age medians or averages (205). The second prediction model (206) may include node group and age statistics between node groups, such that age statistics between nodes in the transportation path are added (or obtained directly) as an age prediction result. Thereby, the aging prediction of the second trajectory category is realized.
And carrying out aging prediction (213) on the waybill information of the third track type with small historical data amount and low quality by adopting a third prediction model. The third predictive model receives a look-up table (214) from the logistics provider. The look-up table may be, for example, an average time-averaged value falling library among cities of the logistics provider; and provides a configuration platform for easy modification. Therefore, the time-based prediction query of the third track category can be realized based on the query table in a distributed cache or database query mode.
The above are merely a plurality of specific implementations of the logistics aging prediction method of the present invention, and each implementation 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. 5, fig. 5 is a block diagram illustrating a logistics aging prediction apparatus according to an embodiment of the present invention. The logistics aging prediction apparatus 300 includes a first determination module 310, a second determination module 320, and a prediction module 330.
The first determination module 310 is configured to determine a trajectory category of the waybill to be predicted based on the waybill information of the waybill to be predicted.
The second determining module 320 is configured to determine a prediction model according to the track category of the waybill to be predicted.
The prediction module 330 is configured to predict the aging of the waybill to be predicted based on the waybill information and/or the trajectory data of the waybill to be predicted, using the determined prediction model.
In the logistics aging prediction device of the exemplary embodiment of the invention, the track type of the waybill to be predicted is determined according to the waybill information of the waybill to be predicted, so that the prediction model is determined according to the track type of the waybill to be predicted, the determined prediction model is adopted, and the aging of the waybill to be predicted is predicted based on the waybill information and/or the track data of the waybill to be predicted, so that different prediction models are used for aging prediction aiming at different track types, thereby being suitable for scenes of various different data, and realizing the research and development of efficient aging prediction service and improving the reliability and the service coverage rate of the aging prediction service based on the complex and changeable logistics data of different conditions.
Fig. 5 is a schematic diagram illustrating the logistics aging prediction apparatus 300 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 inventive concept. The logistics time efficiency prediction apparatus 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 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 age prediction method section above of this specification, when the program product is run on the terminal device.
Referring to fig. 6, 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 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. 7. The electronic device 500 shown in fig. 7 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. 7, 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, to cause the processing unit 510 to perform the steps according to various exemplary embodiments of the present invention described in the logistics aging prediction 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. 1-4.
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 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 prediction method according to the embodiment of the present invention.
Compared with the prior art, the invention has the advantages that:
the method and the device determine the track type of the freight note to be predicted through the freight note information of the freight note to be predicted, determine a prediction model according to the track type of the freight note to be predicted, adopt the determined prediction model, and predict the timeliness of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted, so that different prediction models are used for timeliness prediction aiming at different track types, thereby being suitable for scenes of various different data, realizing the research and development of efficient timeliness prediction service and improving the reliability and the business coverage rate of the timeliness prediction service based on the complex and changeable logistics data of different conditions.
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 logistics aging prediction method is characterized by comprising the following steps:
determining the track category of the waybill to be predicted based on the waybill information of the waybill to be predicted;
determining a prediction model according to the track category of the waybill to be predicted;
and predicting the time efficiency of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted by adopting the determined prediction model.
2. The logistics aging prediction method of claim 1, wherein the trajectory category of the waybill to be predicted is determined based on the quantity and/or quality of historical trajectory data having at least one same attribute as the waybill to be predicted.
3. The logistics aging prediction method of claim 2, wherein when the quantity and/or quality of the historical trajectory data having at least one same attribute as the waybill to be predicted belongs to a first threshold range, the waybill to be predicted belongs to a first trajectory category, the first trajectory category is associated with a first prediction model, and the first prediction model is obtained by training at least the historical trajectory data having at least one same attribute as the waybill to be predicted.
4. The logistics aging prediction method of claim 3, wherein the first prediction model is one or more machine learning models.
5. The logistics aging prediction method of claim 2, wherein when the quantity and/or quality of the historical trajectory data having at least one same attribute as the waybill to be predicted belongs to a second threshold range, the waybill to be predicted belongs to a second trajectory category, the second trajectory category is associated with a second prediction model, and the second prediction model is obtained based on statistics of at least the historical trajectory data having at least one same attribute as the waybill to be predicted.
6. The logistics aging prediction method of claim 5, wherein the second prediction model is built according to the following steps:
acquiring aging data of historical track data with at least one same attribute as the waybill to be predicted;
calculating the median and/or average of the obtained aging data.
7. The logistics aging prediction method of claim 2, wherein when the quantity and/or quality of the historical track data having at least one same attribute with the waybill to be predicted belongs to a third threshold range, the waybill to be predicted belongs to a third track category, the third track category is associated with a third prediction model, and the third prediction model is an aging query configuration table.
8. The logistics aging prediction method of any one of claims 3 to 7, wherein the first threshold range is greater than the second threshold range, and the second threshold range is greater than the third threshold range.
9. The logistics aging prediction method of any one of claims 3 to 7, wherein the attributes comprise one or more combinations of logistics service providers, distribution addresses, receiving addresses, collecting nodes, dispatching nodes and transit nodes.
10. The logistics aging prediction method of any one of claims 1 to 7, wherein the predicting the aging of the waybill to be predicted based on waybill information and/or trajectory data of the waybill to be predicted by using the determined prediction model comprises:
forecasting the time efficiency among the transportation nodes of the freight bill to be forecasted by adopting the determined forecasting model based on the freight bill information and/or the track data of the freight bill to be forecasted;
and calculating the time efficiency of the freight bill to be predicted based on the predicted time efficiency among the transport nodes.
11. A logistics aging prediction device is characterized by comprising:
the first determination module is configured to determine the track category of the waybill to be predicted based on the waybill information of the waybill to be predicted;
the second determination module is configured to determine a prediction model according to the track category of the freight note to be predicted;
and the prediction module is configured to predict the time effectiveness of the freight note to be predicted based on the freight note information and/or the track data of the freight note to be predicted by adopting the determined prediction model.
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 method for predicting the aging of the material flow as claimed in 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 method for predicting the aging of the material flow as claimed in any one of claims 1 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110344428.8A CN113011672B (en) | 2021-03-29 | 2021-03-29 | Logistics aging prediction method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110344428.8A CN113011672B (en) | 2021-03-29 | 2021-03-29 | Logistics aging prediction method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113011672A true CN113011672A (en) | 2021-06-22 |
| CN113011672B CN113011672B (en) | 2024-04-19 |
Family
ID=76409483
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110344428.8A Active CN113011672B (en) | 2021-03-29 | 2021-03-29 | Logistics aging prediction method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113011672B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114240279A (en) * | 2021-12-06 | 2022-03-25 | 北京京东振世信息技术有限公司 | Logistics timeliness monitoring method, device, equipment and storage medium |
| CN114418236A (en) * | 2022-02-09 | 2022-04-29 | 北京京东振世信息技术有限公司 | Information prediction method, information prediction device, storage medium and electronic equipment |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109313742A (en) * | 2017-05-16 | 2019-02-05 | 北京嘀嘀无限科技发展有限公司 | Determine the method and system for estimating arrival time |
| CN109784820A (en) * | 2019-03-28 | 2019-05-21 | 上海中通吉网络技术有限公司 | A kind of express delivery routing intelligence solution, device, terminal device and storage medium |
| US20200011692A1 (en) * | 2017-06-13 | 2020-01-09 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for recommending an estimated time of arrival |
| CN111008805A (en) * | 2019-12-06 | 2020-04-14 | 京东数字城市(成都)科技有限公司 | Method and device for determining appropriate time, storage medium and electronic equipment |
| CN111080007A (en) * | 2019-12-13 | 2020-04-28 | 上海中通吉网络技术有限公司 | Express arrival time prediction method, device, equipment and storage medium |
| 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 |
| CN111476588A (en) * | 2019-01-24 | 2020-07-31 | 北京嘀嘀无限科技发展有限公司 | Order demand prediction method and device, electronic equipment and readable storage medium |
| CN111861302A (en) * | 2020-05-21 | 2020-10-30 | 上海寻梦信息技术有限公司 | Logistics order changing processing method and device, electronic equipment and storage medium |
| CN111861328A (en) * | 2020-07-21 | 2020-10-30 | 上海寻梦信息技术有限公司 | Method for establishing logistics identification library, and logistics track query updating method and equipment |
| CN111950803A (en) * | 2020-08-24 | 2020-11-17 | 上海寻梦信息技术有限公司 | Logistics object delivery time prediction method and device, electronic equipment and storage medium |
| CN111967793A (en) * | 2020-08-28 | 2020-11-20 | 上海寻梦信息技术有限公司 | Logistics timeliness evaluation method and device, electronic equipment and storage medium |
| CN112101839A (en) * | 2020-07-10 | 2020-12-18 | 上海寻梦信息技术有限公司 | Method for establishing express delivery time prediction model, prediction method and related equipment |
| 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 |
| 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 |
| 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 |
-
2021
- 2021-03-29 CN CN202110344428.8A patent/CN113011672B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109313742A (en) * | 2017-05-16 | 2019-02-05 | 北京嘀嘀无限科技发展有限公司 | Determine the method and system for estimating arrival time |
| US20200011692A1 (en) * | 2017-06-13 | 2020-01-09 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for recommending an estimated time of arrival |
| CN111476588A (en) * | 2019-01-24 | 2020-07-31 | 北京嘀嘀无限科技发展有限公司 | Order demand prediction method and device, electronic equipment and readable storage medium |
| CN109784820A (en) * | 2019-03-28 | 2019-05-21 | 上海中通吉网络技术有限公司 | A kind of express delivery routing intelligence solution, device, terminal device and storage medium |
| CN111008805A (en) * | 2019-12-06 | 2020-04-14 | 京东数字城市(成都)科技有限公司 | Method and device for determining appropriate time, storage medium and electronic equipment |
| CN111080007A (en) * | 2019-12-13 | 2020-04-28 | 上海中通吉网络技术有限公司 | Express arrival time prediction method, device, equipment and storage medium |
| 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 |
| CN111861302A (en) * | 2020-05-21 | 2020-10-30 | 上海寻梦信息技术有限公司 | Logistics order changing processing method and device, electronic equipment and storage medium |
| CN112101839A (en) * | 2020-07-10 | 2020-12-18 | 上海寻梦信息技术有限公司 | Method for establishing express delivery time prediction model, prediction method and related equipment |
| CN111861328A (en) * | 2020-07-21 | 2020-10-30 | 上海寻梦信息技术有限公司 | Method for establishing logistics identification library, and logistics track query updating method and equipment |
| CN111950803A (en) * | 2020-08-24 | 2020-11-17 | 上海寻梦信息技术有限公司 | Logistics object delivery time prediction method and device, electronic equipment and storage medium |
| CN111967793A (en) * | 2020-08-28 | 2020-11-20 | 上海寻梦信息技术有限公司 | Logistics timeliness evaluation method and device, electronic equipment and storage medium |
| 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 |
| 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 |
| 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 |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114240279A (en) * | 2021-12-06 | 2022-03-25 | 北京京东振世信息技术有限公司 | Logistics timeliness monitoring method, device, equipment and storage medium |
| CN114418236A (en) * | 2022-02-09 | 2022-04-29 | 北京京东振世信息技术有限公司 | Information prediction method, information prediction device, storage medium and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113011672B (en) | 2024-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109360012B (en) | Advertisement delivery channel selection method and device, storage medium and electronic equipment | |
| CN106875066B (en) | Vehicle travel behavior prediction method, device, server and storage medium | |
| CN107133645B (en) | Method, equipment and storage medium for predicting order cancelling behavior of passenger | |
| CN111950803A (en) | Logistics object delivery time prediction method and device, electronic equipment and storage medium | |
| WO2020168851A1 (en) | Behavior recognition | |
| CN111179055B (en) | Credit line adjusting method and device and electronic equipment | |
| CN113011672A (en) | Logistics timeliness prediction method and device, electronic equipment and storage medium | |
| US20190066137A1 (en) | Systems and methods for modeling impact of commercial development on a geographic area | |
| CN109978465A (en) | Source of goods recommended method, device, electronic equipment, storage medium | |
| CN111967808B (en) | Method, device, electronic equipment and storage medium for determining commodity circulation object receiving mode | |
| CN110363583B (en) | Method and device for creating user consumption behavior label based on position information and electronic equipment | |
| CN112347316A (en) | GraphSAGE-based bad preference behavior detection method and device and electronic equipment | |
| CN110598989B (en) | Goods source quality evaluation method, device, equipment and storage medium | |
| CN111353642A (en) | Telephone traffic scheduling prediction method, device, electronic equipment and storage medium | |
| La Rocca et al. | Heuristics for electric taxi fleet management at Teo Taxi | |
| CN113011664A (en) | Logistics timeliness monitoring method and device, electronic equipment and storage medium | |
| CN113011665A (en) | Logistics timeliness prediction method, device, equipment and storage medium | |
| CN113537671B (en) | Sorting aging prediction method and device, storage medium and electronic equipment | |
| CN116797112A (en) | Vehicle scheduling method, device, electronic equipment and medium | |
| CN114547106A (en) | Data query method and device, storage medium and computer system | |
| CN110874737B (en) | Payment mode recommendation method and device, electronic equipment and storage medium | |
| CN113176873A (en) | Method and apparatus for information management, electronic device, and medium | |
| CN112785234A (en) | Goods recommendation method, device, equipment and storage medium | |
| CN114549032A (en) | Stock processing method and device, computer storage medium and electronic equipment | |
| CN114169944B (en) | User demand determination method and device, storage medium and electronic equipment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |