CN112183859B - Route configuration table updating method, device, electronic equipment and storage medium - Google Patents
Route configuration table updating method, device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN112183859B CN112183859B CN202011040526.4A CN202011040526A CN112183859B CN 112183859 B CN112183859 B CN 112183859B CN 202011040526 A CN202011040526 A CN 202011040526A CN 112183859 B CN112183859 B CN 112183859B
- Authority
- CN
- China
- Prior art keywords
- logistics
- node
- track
- information
- route
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 230000002159 abnormal effect Effects 0.000 claims abstract description 36
- 230000005856 abnormality Effects 0.000 claims abstract description 12
- 238000003058 natural language processing Methods 0.000 claims description 7
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012217 deletion Methods 0.000 claims description 6
- 230000037430 deletion Effects 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 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
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
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"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman 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
Abstract
The invention provides a routing configuration table updating method, a device, electronic equipment and a storage medium, wherein the routing configuration table updating method comprises the following steps: acquiring historical route track information; deleting the historical route track information of the transportation abnormality from the historical route track information comprises: counting the occurrence number of each logistics node from a departure logistics node to an arrival logistics node in the historical route track information; determining the logistics nodes with the occurrence number smaller than a preset abnormal value as abnormal logistics nodes; deleting the historical route track information passing through the abnormal logistics node; taking the departure logistics nodes to the arrival logistics nodes in the deleted historical routing track information as learning items, and adopting a learning model to learn the routing information of the logistics track between the departure logistics nodes and the arrival logistics nodes; and updating the route configuration table in real time according to the learned route information. The method and the device provided by the invention integrally improve the time efficiency of package transportation and reduce the transportation cost.
Description
Technical Field
The present invention relates to the field of computer applications, and in particular, to a method and apparatus for updating a routing configuration table, an electronic device, and a storage medium.
Background
Currently, sorting of logistic packages typically relies on sorting codes generated by a logistic platform (such as an e-commerce logistic platform), the first segment of which (the destination dispatch center code) determines the approximate transportation trend of the package from origin to destination, the most critical sorting code. However, the identification of the first segment of the sort code is heavily dependent on the routing configuration table of the logistics company. If the update of the routing configuration table is not timely, after the actual route is changed, a sorting code identification error can be generated, finally, the package is wrongly sent, the ageing is seriously slowed, and the ageing and the transportation cost of package transportation are seriously affected.
Therefore, how to update the route configuration table in real time based on the historical route track information, and avoid the identification error of the sorting codes, so that the package transportation timeliness is integrally improved, the transportation cost is reduced, and the technical problem to be solved by the technicians in the field is urgent.
Disclosure of Invention
In order to overcome the defects of the related art, the invention provides a method, a device, electronic equipment and a storage medium for updating a routing configuration table, so that the real-time updating of the routing configuration table is realized, the identification error of a sorting code is avoided, the package transportation timeliness is improved as a whole, and the transportation cost is reduced.
According to one aspect of the present invention, there is provided a routing configuration table updating method, including:
acquiring historical route track information;
deleting the historical route track information of the transportation abnormality from the historical route track information, comprising:
counting the occurrence number of each logistics node from a departure logistics node to an arrival logistics node in the historical route track information;
determining the logistics nodes with the occurrence number smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical route track information from the departure logistics node to the arrival logistics node passing through the abnormal logistics node from the historical route track information;
taking the departure logistics node to the arrival logistics node in the deleted historical routing track information as a learning item, and adopting a learning model to learn the routing information of the logistics track between the departure logistics node and the arrival logistics node;
and updating the route configuration table in real time according to the learned route information.
In some embodiments of the present invention, the deleting the historical route trace information of the transportation anomaly from the historical route trace information further includes:
and deleting the historical route track information from the historical route track information, wherein the transportation time from the departure logistics node to the arrival logistics node is longer than a first preset time threshold value, and the first preset time threshold value is calculated according to the transportation time from the departure logistics node to the arrival logistics node in each piece of historical route track information.
In some embodiments of the invention, the preset outlier is calculated based on a distribution curve of the number of occurrences of the logistics node.
In some embodiments of the present invention, the deleting the historical route trace information of the transportation anomaly from the historical route trace information includes:
and deleting the historical route track information, in which the dispatch nodes associated with the signing place are inconsistent with the dispatch nodes of the historical route track information, from the historical route track information.
In some embodiments of the invention, the learning model employs natural language processing techniques.
In some embodiments of the present invention, the updating the routing configuration table in real time according to the learned routing information includes:
judging whether the route configuration table contains the route information of the logistics track from the departure logistics node to the arrival logistics node or not;
if not, the route information of the logistics track from the departure logistics node to the arrival logistics node is added into the route configuration table.
In some embodiments of the present invention, if the routing configuration table includes routing information of a physical distribution track from the departure physical distribution node to the arrival physical distribution node, determining whether the routing information of the physical distribution track from the departure physical distribution node to the arrival physical distribution node included in the routing configuration table is consistent with the learned routing information of the physical distribution track from the departure physical distribution node to the arrival physical distribution node;
if not, then:
replacing the route information of the physical distribution track from the departure physical distribution node to the arrival physical distribution node, which is contained in the route configuration table, with the route information of the physical distribution track from the departure physical distribution node to the arrival physical distribution node; or alternatively
And storing the learned route information of the logistics track from the departure logistics node to the arrival logistics node into the route configuration table as alternative route information.
In some embodiments of the present invention, the learning, using the start logistics node to the arrival logistics node in the deleted historical route track information as a learning item, uses a learning model to learn the route information of the logistics track between the start logistics node and the arrival logistics node, includes:
determining a logistics track between the departure logistics node and the arrival logistics node for updating a routing configuration table.
In some embodiments of the invention, the determining a logistics trajectory between the departure logistics node to the arrival logistics node for updating a routing configuration table comprises:
and determining the logistics track of which the logistics parameters of the logistics track between the departure logistics node and the arrival logistics node accord with the preset parameter rule as the logistics track for updating the routing configuration table in a preset time period.
In some embodiments of the invention, the logistic parameter comprises a frequency of occurrence of a logistic trajectory, and the predetermined parameter rules comprise any one of the following parameter rules:
the occurrence frequency of the logistics track is highest;
the frequency of occurrence of the flow trace is greater than a first predetermined percentage of the highest frequency of occurrence.
In some embodiments of the present invention, the logistic parameter includes a number of occurrences of a logistic track and a transportation age of the logistic track, and the predetermined parameter rule includes any one of the following parameter rules:
the occurrence times of the logistics track are larger than the set times, and the average transportation timeliness of the logistics track is shortest;
the number of occurrences of the physical distribution track is greater than a second predetermined percentage of the total number of physical distribution tracks, and the average shipping timeliness of the physical distribution track is minimized.
In some embodiments of the invention, the determining a logistics trajectory between the departure logistics node to the arrival logistics node for updating a routing configuration table comprises:
and determining the logistics track from the departure logistics node to the arrival logistics node marked by the user as the logistics track for updating the routing configuration table.
According to still another aspect of the present invention, there is also provided a routing configuration table updating apparatus, including:
an acquisition module configured to acquire historical route trace information;
a deletion module configured to delete historical route track information of a transportation anomaly from the historical route track information, comprising:
counting the occurrence number of each logistics node from a departure logistics node to an arrival logistics node in the historical route track information;
determining the logistics nodes with the occurrence number smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical route track information from the departure logistics node to the arrival logistics node passing through the abnormal logistics node from the historical route track information;
the learning module is configured to learn the route information of the logistics track between the departure logistics node and the arrival logistics node by taking the departure logistics node and the arrival logistics node in the deleted historical route track information as learning items and adopting a learning model;
and the updating module is configured to update the route configuration table in real time according to the learned route information.
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 a further 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 route track information of the transportation abnormality is deleted from the historical route track information through the judgment of the abnormal logistics node, and the filtered historical route track information is taken as a learning item, so that the route information of the logistics track is learned by adopting a learning model, and the route configuration table is updated in real time, thereby realizing the real-time updating of the route configuration table, avoiding the identification error of the sorting code, integrally improving the package transportation timeliness and reducing the transportation cost.
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 routing configuration table updating method according to an embodiment of the present invention.
Fig. 2 is a flow chart illustrating updating a routing configuration table in real-time based on learned routing information, in accordance with a specific embodiment of the present invention.
Fig. 3 is a flow chart illustrating updating a routing configuration table in real-time based on learned routing information, according to another embodiment of the present invention.
Fig. 4 shows a block diagram of a routing configuration table updating apparatus according to an embodiment of the present invention.
Fig. 5 schematically illustrates a computer-readable storage medium according to an exemplary embodiment of the present invention.
Fig. 6 schematically illustrates a schematic diagram of 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. However, the exemplary embodiments may be embodied in many 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 the 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 present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof 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 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 diagrams depicted in the figures are exemplary only and not necessarily all steps are included. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
In various embodiments of the present invention, the routing configuration table updating method provided by the present invention may be applied to updating and managing the routing configuration table by a logistics company, and may also be applied to updating and managing the routing configuration table by different logistics companies by an e-commerce platform. The above is merely illustrative of various application scenarios of the present invention, and the application scenarios of the present invention are not limited thereto, and are not repeated here.
Fig. 1 shows a flowchart of a routing configuration table updating method according to an embodiment of the present invention. The route configuration table updating method comprises the following steps:
step S110: historical route track information is obtained.
Specifically, each historical route track information may include, but is not limited to, a delivery location, a receiving collecting node, a distributing node, a sending node, receiving courier information, sending courier information (e.g., with a record of the last courier in the logistic track), arrival time of each node, departure time, etc.
Step S120: and deleting the historical route track information of the abnormal transportation from the historical route track information.
Specifically, in some embodiments, historical route trace information with outlier logistics nodes may be culled from the historical route trace information. The rejection method may be performed in steps S121 to S123:
step S121: and counting the occurrence number of each logistics node from the departure logistics node to the arrival logistics node in the historical route track information.
Specifically, the departure logistics node and the arrival logistics node may be any logistics nodes in the route track, for example, a collecting node, a distributing node, a transit node, a sending node, and the like, and the present invention is not limited thereto.
Step S122: and determining the logistics nodes with the occurrence number smaller than a preset abnormal value as abnormal logistics nodes.
In particular, the preset outliers may be calculated based on a distribution curve of the number of occurrences of the logistics node. The distribution curve is generally normal distribution, so that the number of occurrence of normal logistics nodes and the number of occurrence of abnormal logistics nodes can be greatly different based on the normal distribution, and the preset abnormal value can be determined based on the position where the number is suddenly changed. Further, the preset abnormal value can be obtained by calculating the average value of the number of the determined abnormal logistics nodes from the same departure logistics node to the arrival logistics node. In some variations, the preset outliers may also be empirical values that are manually entered. The invention is not limited by this, and prediction of abnormal values of abnormal logistics nodes is performed by using models such as a machine learning algorithm, a deep learning algorithm and the like, and the invention is also within the protection scope of the invention.
Step S123: and deleting the historical route track information from the departure logistics node to the arrival logistics node and passing through the abnormal logistics node from the historical route track information.
Therefore, the historical route track information of the abnormality can be removed through data statistics and abnormal value comparison in the steps S121 to S123, the method is simple to realize, high in calculation efficiency and more suitable for real-time calculation, and therefore the method can be effectively used for real-time updating of the route configuration table.
Furthermore, the invention can further realize the deletion of the historical route track information of the abnormal transportation in other modes. For example, the historical route track information that the transportation time from the departure logistics node to the arrival logistics node is greater than a first preset time threshold may be deleted from the historical route track information, where the first preset time threshold is calculated according to the transportation time from the departure logistics node to the arrival logistics node in each piece of the historical route track information. The first preset time threshold is calculated according to the average value/median of the transportation time from the departure logistics node to the arrival logistics node in each of the historical route track information, which is not limited by the present invention. Therefore, the historical route track information of the transportation abnormality can be removed according to the abnormality of the transportation time.
For another example, the historical route track information that the dispatch node associated with the sign-up is inconsistent with the dispatch node of the historical route track information may be deleted from the historical route track information. Thus, the history route track information of the inconsistency of the actual dispatch node and the dispatch node associated with the signing place can be deleted, and the fact that the inconsistency of the dispatch node is sent only when the flow track is abnormal (for example, package accumulation occurs on the dispatch node associated with the signing place; the package dispatch direction associated with the history route track information is wrong, thus causing dispatch by other modes and the like) is considered.
Therefore, the method and the device can realize the rejection mode of the historical route track information of various transportation anomalies through various aspects so as to avoid the influence of the incorrect historical route track information on subsequent learning, thereby influencing the updating accuracy of the route configuration table. The invention can also realize more types of removing modes of the historical route track information of the abnormal transportation, and the removing modes are all within the protection scope of the invention and are not repeated here.
Step S130: and taking the departure logistics node to the arrival logistics node in the deleted historical routing track information as a learning item, and adopting a learning model to learn the routing information of the logistics track between the departure logistics node and the arrival logistics node.
In some preferred embodiments of the invention, the learning model employs natural language processing techniques (Natural Language Processing, NLP), whereby historical routing trace information may be learned in a structured manner, such that structured routing information may be obtained. Specifically, the historical route track information used in step S130 is subjected to the deletion process in step S120, and the retained historical route track information has higher accuracy and reference value.
Further, since the present invention updates the routing configuration table in real time, in order to further improve the data processing efficiency of the system, the following steps may be performed in step S130: determining a logistics track between the departure logistics node and the arrival logistics node for updating a routing configuration table. In some embodiments, only the logistics track between the departure logistics node and the arrival logistics node, which are determined for updating the routing configuration table, can be learned, so that learning efficiency and updating efficiency are greatly improved. In still other embodiments, the data obtained after the processing of the natural language processing technology part may be further used to determine a flow path between the departure flow node and the arrival flow node for updating the routing configuration table, and the learning of the natural language processing technology of the second half may be performed only on the flow path between the departure flow node and the arrival flow node for updating the routing configuration table. The present invention may implement more variations, and will not be described in detail herein.
In some specific implementations, the determining the flow trajectory between the departure flow node and the arrival flow node for updating the routing configuration table may be implemented by: and determining the logistics track of which the logistics parameters of the logistics track between the departure logistics node and the arrival logistics node accord with the preset parameter rule as the logistics track for updating the routing configuration table in a preset time period. For example, the logistic parameter includes a frequency of occurrence of a logistic track, and the predetermined parameter rule includes any one of the following parameter rules: the occurrence frequency of the logistics track is highest; the frequency of occurrence of the flow trace is greater than a first predetermined percentage of the highest frequency of occurrence. Therefore, the physical distribution track with the highest frequency of occurrence and/or the first preset percentage of the physical distribution track with the frequency of occurrence larger than the highest frequency of occurrence is learned through the frequency of occurrence of the physical distribution track, so that the learning efficiency is improved, the accuracy and the reference value of a learned sample are improved, and the updating accuracy of the routing configuration table is further improved while the routing configuration table can be updated in real time. For another example, the logistic parameter includes the occurrence number of logistic tracks and the transportation timeliness of the logistic tracks, and the predetermined parameter rule includes any one of the following parameter rules: the occurrence times of the logistics track are larger than the set times, and the average transportation timeliness of the logistics track is shortest; the number of occurrences of the physical distribution track is greater than a second predetermined percentage of the total number of physical distribution tracks, and the average shipping timeliness of the physical distribution track is minimized. Therefore, considering the transportation efficiency of the logistics track during transportation, the routing track with higher transportation efficiency can be further learned by combining the occurrence times and transportation timeliness of the logistics track, so that the routing configuration table is updated, the routing track obtained according to the data in the routing configuration table has higher transportation timeliness, and the recommendation of the better routing track to a user is further facilitated. The above-mentioned set times, the first predetermined percentage, and the second predetermined percentage may be calculated according to historical data or determined by manual experience value input, which is not a limitation of the present invention. The foregoing predetermined parameter rules may also be used to make statistics and rule decisions on parameters in a time dimension period (e.g., a month/quarter). For another example, a physical distribution trajectory between the departure physical distribution node and the arrival physical distribution node marked by the user may be determined as a physical distribution trajectory for updating the routing configuration table. Considering that the manually-marked information is generally optimal, the method can have higher learning priority compared with the parameter rule so as to be convenient for manually configuring the learning process.
Step S140: and updating the route configuration table in real time according to the learned route information.
Specifically, the execution of step S140 may refer to fig. 2 and 3, and will not be described herein.
In the route configuration table updating method provided by the invention, the history route track information of the transportation abnormality is deleted through the judgment of the abnormal logistics node from the history route track information, and the filtered history route track information is taken as a learning item, so that the route information of the logistics track is learned by adopting a learning model, and the route configuration table is updated in real time, thereby realizing the real-time updating of the route configuration table, avoiding the identification error of the sorting code, integrally improving the package transportation timeliness and reducing the transportation cost.
Referring now to fig. 2, fig. 2 is a flow chart illustrating updating of routing configuration tables in real-time based on learned routing information, according to an embodiment of the present invention. Fig. 2 shows the following steps in total:
step S141: and judging whether the route configuration table contains the route information of the logistics track from the departure logistics node to the arrival logistics node.
If it is determined that the route configuration table does not include the route information of the route track from the departure logistics node to the arrival logistics node, step S142 is performed: and adding the route information of the logistics track from the departure logistics node to the arrival logistics node into the route configuration table.
If the route configuration table is judged to contain the route information of the logistics track from the departure logistics node to the arrival logistics node, executing step S143: and judging whether the route information of the logistics track from the departure logistics node to the arrival logistics node contained in the route configuration table is consistent with the learned route information of the logistics track from the departure logistics node to the arrival logistics node.
If not, step S144 is executed: and replacing the route information of the physical distribution track between the departure physical distribution node and the arrival physical distribution node, which is contained in the route configuration table, with the learned route information of the physical distribution track between the departure physical distribution node and the arrival physical distribution node.
If so, the routing configuration table may not be updated. Therefore, the information in the routing configuration table is compared with the learned information, so that whether to execute the updated replacement operation is determined, the writing operation on the routing configuration table is greatly reduced, and the contradiction between the writing process and the reading process (for sorting code identification) of the routing configuration table is avoided, so that the identification is influenced.
Further, the present invention may also implement the above step S140 by the embodiment shown in fig. 3. Specifically, step S143 of step S141 of fig. 3 coincides with step S143 of step S141 of fig. 2. In fig. 3, when step S143 determines that the images do not match, step S145 is executed: and storing the learned route information of the logistics track from the departure logistics node to the arrival logistics node into the route configuration table as alternative route information. Therefore, the original data of the route configuration table can be reserved, the learning obtained information is stored as the alternative information in the route configuration table, so that various information can be compared and selected in the process of sorting code identification/route identification of the route configuration table, meanwhile, the route information is prevented from being changed in a short time due to short-time reasons (repairing roads, weather and the like), and after the reasons are eliminated, the route configuration table is changed frequently under the condition that the original route information is restored.
The above are merely a plurality of specific implementations of the method for recommending a stream according to 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 routing configuration table updating apparatus according to an embodiment of the present invention. The routing configuration table updating apparatus 200 includes an acquisition module 210, a deletion module 220, a learning module 230, and an updating module 240.
The acquisition module 210 is configured to acquire historical routing trace information.
The deletion module 220 is configured to delete the historical route trace information of the transportation anomaly from the historical route trace information, including: counting the occurrence number of each logistics node from a departure logistics node to an arrival logistics node in the historical route track information; determining the logistics nodes with the occurrence number smaller than a preset abnormal value as abnormal logistics nodes; and deleting the historical route track information from the departure logistics node to the arrival logistics node and passing through the abnormal logistics node from the historical route track information.
The learning module 230 is configured to learn route information of a logistics track between a departure logistics node and an arrival logistics node by using the deleted departure logistics node to the arrival logistics node in the historical route track information as learning items and using a learning model.
The updating module 240 is configured to update the routing configuration table in real time according to the learned routing information.
In the route configuration table updating device of the exemplary embodiment of the invention, the history route track information of the transportation abnormality is deleted from the history route track information through the judgment of the abnormal logistics node, the filtered history route track information is taken as a learning item, so that the route information of the logistics track is learned by adopting a learning model, and the route configuration table is updated in real time, thereby realizing the real-time updating of the route configuration table, avoiding the identification error of the sorting code, integrally improving the time of package transportation and reducing the transportation cost.
Fig. 4 is a schematic diagram only, and shows the route configuration table updating device 200 provided by the present invention, and the splitting, merging and adding of the modules are all within the protection scope of the present invention without departing from the concept of the present invention. The routing configuration table updating apparatus 200 provided by the present invention may be implemented by software, hardware, firmware, plug-in and any combination thereof, which is not limited to this embodiment.
In an exemplary embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by, for example, a processor, can implement the steps of the routing configuration table updating method described 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 carry out the steps according to the various exemplary embodiments of the invention as described in the above-mentioned route configuration table updating method section of this specification, when said program product is run on the terminal device.
Referring to fig. 5, a program product 700 for implementing the above-described 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 thereto, and in this 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. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk 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 data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium 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 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, partially on the tenant device, as a stand-alone software package, partially on the tenant computing device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of remote computing devices, the remote computing device 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., connected through the internet using an internet service provider).
In an exemplary embodiment of the invention, an electronic device is also provided, which may include a processor, and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the routing configuration table updating method in any of the above embodiments via execution of the executable instructions.
Those skilled in the art will appreciate that the various aspects of the invention may be implemented as a system, method, or program product. Accordingly, aspects of the invention may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may 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 merely an example, and should not be construed as limiting the functionality and scope of use of 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 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 connecting the different system components (including the memory unit 520 and the processing unit 510), a display unit 540, etc.
Wherein the storage unit stores program code executable by the processing unit 510 such that the processing unit 510 performs the steps according to various exemplary embodiments of the present invention described in the above-described route configuration table updating method section of the present specification. For example, the processing unit 510 may perform the steps shown in any one or more of fig. 1-4.
The memory unit 520 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 5201 and/or cache memory unit 5202, and may further include Read Only Memory (ROM) 5203.
The storage 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 or some combination of which may include an implementation of a network environment.
Bus 530 may be one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 500 may also communicate with one or more external devices 600 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a tenant to interact with the electronic device 500, and/or any device (e.g., router, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 550. Also, electronic device 500 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through 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, other hardware and/or software modules may be used in connection with electronic device 500, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, 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 (may be a CD-ROM, a usb disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the above-mentioned routing configuration table updating 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 route track information of the transportation abnormality is deleted from the historical route track information through the judgment of the abnormal logistics node, and the filtered historical route track information is taken as a learning item, so that the route information of the logistics track is learned by adopting a learning model, and the route configuration table is updated in real time, thereby realizing the real-time updating of the route configuration table, avoiding the identification error of the sorting code, integrally improving the package transportation timeliness and reducing the transportation cost.
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 (15)
1. A method for updating a routing configuration table, comprising:
acquiring historical route track information;
deleting the historical route track information of the transportation abnormality from the historical route track information, comprising:
counting the occurrence number of each logistics node from a departure logistics node to an arrival logistics node in the historical route track information;
determining the logistics nodes with the occurrence number smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical route track information from the departure logistics node to the arrival logistics node passing through the abnormal logistics node from the historical route track information;
taking the departure logistics node to the arrival logistics node in the deleted historical routing track information as a learning item, and adopting a learning model to learn the routing information of the logistics track between the departure logistics node and the arrival logistics node;
and updating the route configuration table in real time according to the learned route information.
2. The routing configuration table updating method according to claim 1, wherein said deleting the history routing track information of the transportation abnormality from the history routing track information further comprises:
and deleting the historical route track information from the historical route track information, wherein the transportation time from the departure logistics node to the arrival logistics node is longer than a first preset time threshold value, and the first preset time threshold value is calculated according to the transportation time from the departure logistics node to the arrival logistics node in each piece of historical route track information.
3. The routing configuration table updating method according to claim 1, wherein the preset anomaly value is calculated based on a distribution curve of the number of occurrences of the logistics node.
4. The routing configuration table updating method according to claim 1, wherein said deleting the history routing track information of the transportation abnormality from the history routing track information further comprises:
and deleting the historical route track information, in which the dispatch nodes associated with the signing place are inconsistent with the dispatch nodes of the historical route track information, from the historical route track information.
5. The routing table updating method according to claim 1, wherein the learning model adopts a natural language processing technique.
6. The routing configuration table updating method of claim 1, wherein the updating the routing configuration table in real time according to the learned routing information comprises:
judging whether the route configuration table contains the route information of the logistics track from the departure logistics node to the arrival logistics node or not;
if not, the route information of the logistics track from the departure logistics node to the arrival logistics node is added into the route configuration table.
7. The method according to claim 6, wherein if it is determined that the routing configuration table contains routing information of a physical distribution path from the departure physical distribution node to the arrival physical distribution node, it is determined whether the routing information of the physical distribution path from the departure physical distribution node to the arrival physical distribution node contained in the routing configuration table is identical to the learned routing information of the physical distribution path from the departure physical distribution node to the arrival physical distribution node;
if not, then:
replacing the route information of the physical distribution track from the departure physical distribution node to the arrival physical distribution node, which is contained in the route configuration table, with the route information of the physical distribution track from the departure physical distribution node to the arrival physical distribution node; or alternatively
And storing the learned route information of the logistics track from the departure logistics node to the arrival logistics node into the route configuration table as alternative route information.
8. The method of claim 1, wherein learning the route information of the logistics track from the departure logistics node to the arrival logistics node by using the deleted history route track information as a learning term includes:
determining a logistics track between the departure logistics node and the arrival logistics node for updating a routing configuration table.
9. The routing table updating method according to claim 8, wherein said determining a flow trace between said departure flow node to said arrival flow node for updating a routing table comprises:
and determining the logistics track of which the logistics parameters of the logistics track between the departure logistics node and the arrival logistics node accord with the preset parameter rule as the logistics track for updating the routing configuration table in a preset time period.
10. The routing configuration table updating method of claim 9, wherein the logistic parameter includes an occurrence frequency of a logistic track, and the predetermined parameter rule includes any one of the following parameter rules:
the occurrence frequency of the logistics track is highest;
the frequency of occurrence of the flow trace is greater than a first predetermined percentage of the highest frequency of occurrence.
11. The routing table updating method according to claim 9, wherein the logistic parameter includes the number of occurrences of the logistic track and the transportation timeliness of the logistic track, and the predetermined parameter rule includes any one of the following parameter rules:
the occurrence times of the logistics track are larger than the set times, and the average transportation timeliness of the logistics track is shortest;
the number of occurrences of the physical distribution track is greater than a second predetermined percentage of the total number of physical distribution tracks, and the average shipping timeliness of the physical distribution track is minimized.
12. The routing table updating method according to claim 9, wherein said determining a flow trace between said departure flow node to said arrival flow node for updating a routing table comprises:
and determining the logistics track from the departure logistics node to the arrival logistics node marked by the user as the logistics track for updating the routing configuration table.
13. A routing configuration table updating apparatus, comprising:
an acquisition module configured to acquire historical route trace information;
a deletion module configured to delete historical route track information of a transportation anomaly from the historical route track information, comprising:
counting the occurrence number of each logistics node from a departure logistics node to an arrival logistics node in the historical route track information;
determining the logistics nodes with the occurrence number smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical route track information from the departure logistics node to the arrival logistics node passing through the abnormal logistics node from the historical route track information;
the learning module is configured to learn the route information of the logistics track between the departure logistics node and the arrival logistics node by taking the departure logistics node and the arrival logistics node in the deleted historical route track information as learning items and adopting a learning model;
and the updating module is configured to update the route configuration table in real time according to the learned route information.
14. An electronic device, the electronic device comprising:
a processor;
a memory having stored thereon a computer program which, when executed by the processor, performs:
a routing configuration table updating method according to any one of claims 1 to 12.
15. A storage medium having a computer program stored thereon, the computer program when executed by a processor performing:
a routing configuration table updating method according to any one of claims 1 to 12.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011040526.4A CN112183859B (en) | 2020-09-28 | 2020-09-28 | Route configuration table updating method, device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011040526.4A CN112183859B (en) | 2020-09-28 | 2020-09-28 | Route configuration table updating method, device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112183859A CN112183859A (en) | 2021-01-05 |
| CN112183859B true CN112183859B (en) | 2024-04-05 |
Family
ID=73944734
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011040526.4A Active CN112183859B (en) | 2020-09-28 | 2020-09-28 | Route configuration table updating method, device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112183859B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107682108A (en) * | 2017-11-27 | 2018-02-09 | 国网浙江省电力有限公司 | A kind of optical transfer network OTN route selection method |
| WO2018227389A1 (en) * | 2017-06-13 | 2018-12-20 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining estimated time of arrival |
| CN111415048A (en) * | 2020-04-10 | 2020-07-14 | 大连海事大学 | Vehicle path planning method based on reinforcement learning |
| CN111612199A (en) * | 2019-02-26 | 2020-09-01 | 北京京东尚科信息技术有限公司 | Logistics network optimization method and device, computer equipment and storage medium |
| CN111695847A (en) * | 2019-05-17 | 2020-09-22 | 上海寻梦信息技术有限公司 | Number section management method, system, equipment and storage medium for logistics electronic bill |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100541434B1 (en) * | 2003-02-10 | 2006-01-10 | 삼성전자주식회사 | Layout modeling system for transport system |
| WO2008109363A1 (en) * | 2007-03-02 | 2008-09-12 | United States Postal Service | System and method of identifying shipping anomalies |
| JP2012008659A (en) * | 2010-06-22 | 2012-01-12 | Sony Corp | Data processing device, data processing method, and program |
| KR102502235B1 (en) * | 2015-07-16 | 2023-02-21 | 삼성전자주식회사 | Logistic monitoring system and the method |
| KR20170049204A (en) * | 2015-10-28 | 2017-05-10 | 삼성에스디에스 주식회사 | Method for risk scoring in logistics system |
| CN107957268A (en) * | 2016-10-18 | 2018-04-24 | 菜鸟智能物流控股有限公司 | Navigation data generation method and related equipment, object conveying method and related equipment |
| US10769920B2 (en) * | 2018-09-22 | 2020-09-08 | Fedex Corporate Services, Inc. | Systems, apparatus, and methods for detecting an environmental anomaly and initiating an enhanced automatic response using elements of a wireless node network and using sensor data from ID nodes associated with packages and environmental threshold conditions per package |
| US11131554B2 (en) * | 2018-12-26 | 2021-09-28 | Beijing Voyager Technology Co., Ltd. | Systems and methods for vehicle telemetry |
| US11287270B2 (en) * | 2018-12-26 | 2022-03-29 | Beijing Voyager Technology Co., Ltd. | Systems and methods for safe route planning for a vehicle |
-
2020
- 2020-09-28 CN CN202011040526.4A patent/CN112183859B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018227389A1 (en) * | 2017-06-13 | 2018-12-20 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining estimated time of arrival |
| CN107682108A (en) * | 2017-11-27 | 2018-02-09 | 国网浙江省电力有限公司 | A kind of optical transfer network OTN route selection method |
| CN111612199A (en) * | 2019-02-26 | 2020-09-01 | 北京京东尚科信息技术有限公司 | Logistics network optimization method and device, computer equipment and storage medium |
| CN111695847A (en) * | 2019-05-17 | 2020-09-22 | 上海寻梦信息技术有限公司 | Number section management method, system, equipment and storage medium for logistics electronic bill |
| CN111415048A (en) * | 2020-04-10 | 2020-07-14 | 大连海事大学 | Vehicle path planning method based on reinforcement learning |
Non-Patent Citations (2)
| Title |
|---|
| 基于物联网的物流信息管理系统可靠性设计;韦彬贵;李江洪;;大众科技(02);36-38 * |
| 集装箱物流在途状态智能感知与异常识别研究;李青夏;工程科技二辑;1-105 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112183859A (en) | 2021-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108920135B (en) | User-defined service generation method and device, computer equipment and storage medium | |
| US11222296B2 (en) | Cognitive user interface for technical issue detection by process behavior analysis for information technology service workloads | |
| CN112184115B (en) | Logistics early warning method, logistics recommendation method, device and related equipment | |
| CN111950803A (en) | Logistics object delivery time prediction method and device, electronic equipment and storage medium | |
| US10176437B2 (en) | Method and apparatus to analytically support parts provision for hardware maintenance service | |
| CN114356859A (en) | Data import method and device, equipment and computer readable storage medium | |
| CN112183859B (en) | Route configuration table updating method, device, electronic equipment and storage medium | |
| CN113868368A (en) | Method, electronic device and computer program product for information processing | |
| CN114792089A (en) | Method, apparatus and program product for managing computer system | |
| CN113222205A (en) | Path planning method and device | |
| CN111861328B (en) | Method for establishing logistics identification library, and logistics track query updating method and equipment | |
| CN112104687B (en) | Logistics routing service monitoring method and device, electronic equipment and storage medium | |
| CN114418236A (en) | Information prediction method, information prediction device, storage medium and electronic equipment | |
| CN113011664A (en) | Logistics timeliness monitoring method and device, electronic equipment and storage medium | |
| CN113449062A (en) | Track processing method and device, electronic equipment and storage medium | |
| CN112419025A (en) | User data processing method and device, storage medium and electronic equipment | |
| CN112488625A (en) | Returned piece identification method, returned piece identification device, returned piece identification equipment and storage medium | |
| CN115230775B (en) | Train running state data processing method and device and electronic equipment | |
| CN117350745B (en) | After-sales processing method, device, equipment and medium for e-commerce platform | |
| CN110766501A (en) | Data nesting storage method and device, electronic equipment and computer readable medium | |
| CN113723890B (en) | Information processing method, device, equipment and storage medium | |
| CN114091625B (en) | Vehicle part failure prediction method and system based on fault code sequence | |
| US20180018145A1 (en) | Aggregated multi-objective optimization | |
| US11461007B2 (en) | Method, device, and computer program product for determining failures and causes of storage devices in a storage system | |
| CN113011665A (en) | Logistics timeliness prediction method, device, 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |