CN112183859A - Method and device for updating routing configuration table, electronic equipment and storage medium - Google Patents
Method and device for updating routing configuration table, electronic equipment and storage medium Download PDFInfo
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- 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
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- 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
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- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
Abstract
The invention provides a method and a device for updating a routing configuration table, electronic equipment and a storage medium, wherein the method for updating the routing configuration table comprises the following steps: obtaining historical routing track information; deleting the abnormal transportation historical routing track information from the historical routing track information comprises the following steps: counting the number of the logistics nodes from the starting logistics node to the arriving logistics node in the historical routing track information; determining the logistics nodes with the number smaller than a preset abnormal value as abnormal logistics nodes; deleting historical routing track information passing through the abnormal logistics node; taking the starting logistics node to the arriving logistics node in the deleted historical routing track information as a learning item, and learning the routing information of the logistics track between the starting logistics node and the arriving logistics node by adopting a learning model; and updating the routing configuration table in real time according to the learned routing information. The method and the device provided by the invention integrally improve the timeliness 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 an apparatus for updating a routing configuration table, an electronic device, and a storage medium.
Background
Currently, the sorting of logistics packages generally relies on the sorting codes generated by a logistics platform (such as the logistics platform of an e-commerce), and the first segment code (destination distribution center code) of the sorting codes determines the general transportation trend of the packages from the origin to the destination, which is the most critical sorting code. However, the identification of the first segment of the sort code relies heavily on the routing configuration table of the logistics company. If the updating of the route configuration table is not timely, after the actual route changes, a sorting code identification error can be generated, package misdistribution is finally caused, the timeliness is seriously slowed down, and the timeliness and the transportation cost of package transportation are seriously influenced.
Therefore, how to update the routing configuration table in real time based on the historical routing track information and avoid the identification error of the sorting code, so that the timeliness of package transportation is integrally improved, the transportation cost is reduced, and the technical problem to be solved by the technical personnel in the field is urgently needed.
Disclosure of Invention
In order to overcome the defects of the related technologies, the invention provides a method and a device for updating a routing configuration table, electronic equipment and a storage medium, so that the routing configuration table can be updated in real time, and sorting code identification errors are avoided, thereby integrally improving the time efficiency of package transportation and reducing the transportation cost.
According to an aspect of the present invention, there is provided a routing configuration table updating method, including:
obtaining historical routing track information;
deleting the abnormal transportation historical routing track information from the historical routing track information, wherein the deleting comprises the following steps:
counting the number of the logistics nodes from the starting logistics node to the arrival logistics node in the historical routing track information;
determining the logistics nodes with the number of the logistics nodes smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical routing track information passing through the abnormal logistics node from the starting logistics node to the arriving logistics node from the historical routing track information;
taking the starting logistics node to the arriving logistics node in the deleted historical routing track information as a learning item, and learning the routing information of the logistics track between the starting logistics node and the arriving logistics node by adopting a learning model;
and updating the routing configuration table in real time according to the learned routing information.
In some embodiments of the present invention, the deleting the abnormal transportation historical routing track information from the historical routing track information further includes:
and deleting historical routing track information of which the transportation time from the departure logistics node to the arrival logistics node is greater than a first preset time threshold value from the historical routing track information, wherein 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 historical routing track information.
In some embodiments of the present invention, the preset abnormal value 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 abnormal transportation history routing track information from the history routing track information includes:
and deleting historical routing track information of which the dispatch nodes associated with the sign-off places are inconsistent with the dispatch nodes of the historical routing track information from the historical routing 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 routing configuration table contains routing information of the logistics track from the starting logistics node to the arriving logistics node;
and if not, adding the routing information of the logistics track from the starting logistics node to the arriving logistics node into the routing configuration table.
In some embodiments of the present invention, if it is determined that the routing configuration table includes routing information of a logistics trajectory from the departure logistics node to the arrival logistics node, it is determined whether the routing information of the logistics trajectory from the departure logistics node to the arrival logistics node included in the routing configuration table is consistent with the learned routing information of the logistics trajectory from the departure logistics node to the arrival logistics node;
if not, then:
replacing the learned routing information of the logistics track from the starting logistics node to the arriving logistics node with the routing information of the logistics track from the starting logistics node to the arriving logistics node contained in the routing configuration table; or
And storing the learned routing information of the logistics track from the starting logistics node to the arriving logistics node as alternative routing information into the routing configuration table.
In some embodiments of the present invention, the learning, with the starting logistics node to the arriving logistics node in the deleted historical routing track information as a learning item, using a learning model to learn the routing information of the logistics track between the starting logistics node and the arriving logistics node includes:
and determining a logistics track from the starting logistics node to the arriving logistics node for updating the routing configuration table.
In some embodiments of the present invention, the determining a logistics trajectory from the departure logistics node to the arrival logistics node for updating a routing configuration table comprises:
and determining the logistics track with the logistics parameters of the logistics track from the starting logistics node to the arriving logistics node in accordance with a 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 logistics parameter comprises frequency of occurrence of a logistics trajectory, and the predetermined parameter rule comprises any one of the following parameter rules:
the occurrence frequency of the logistics track is highest;
the stream trajectory has a frequency of occurrence greater than a first predetermined percentage of the highest frequency of occurrence.
In some embodiments of the present invention, the logistics parameter includes the number of occurrences of the logistics track and the transportation aging of the logistics track, and the predetermined parameter rule includes any one of the following parameter rules:
the occurrence frequency of the logistics track is greater than the set frequency, and the average transportation timeliness of the logistics track is shortest;
the proportion of the occurrence times of the logistics tracks to the total number of the logistics tracks is larger than a second preset percentage, and the average transportation timeliness of the logistics tracks is shortest.
In some embodiments of the present invention, the determining a logistics trajectory from the departure logistics node to the arrival logistics node for updating a routing configuration table comprises:
and determining the logistics track between the starting logistics node and the arriving logistics node marked by the user as the logistics track for updating the routing configuration table.
According to another aspect of the present invention, there is also provided a routing configuration table updating apparatus, including:
an acquisition module configured to acquire historical routing track information;
a deleting module configured to delete the abnormal transportation historical routing track information from the historical routing track information, including:
counting the number of the logistics nodes from the starting logistics node to the arrival logistics node in the historical routing track information;
determining the logistics nodes with the number of the logistics nodes smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical routing track information passing through the abnormal logistics node from the starting logistics node to the arriving logistics node from the historical routing track information;
the learning module is configured to use a learning model to learn the routing information of the logistics track from the starting logistics node to the arrival logistics node in the deleted historical routing track information as a learning item;
and the updating module is configured to update the routing configuration table in real time according to the learned routing 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 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 invention deletes the abnormal historical routing track information of the transportation through the judgment of the abnormal logistics node from the historical routing track information, and uses the screened historical routing track information as a learning item, thereby adopting a learning model to learn the routing information of the logistics track and updating the routing configuration table in real time, thereby realizing the real-time updating of the routing configuration table, avoiding the identification error of the sorting code, integrally improving the timeliness of the package transportation and reducing the transportation cost.
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The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 shows a flow chart of a routing configuration table updating method according to an embodiment of the invention.
Fig. 2 is a flow chart illustrating real-time updating of a routing configuration table based on learned routing information according to an embodiment of the present invention.
Fig. 3 is a flow chart illustrating real-time updating of a routing configuration table based on learned routing information according to another embodiment of the present invention.
Fig. 4 is a block diagram illustrating a routing configuration table updating apparatus according to an embodiment of the present invention.
Fig. 5 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the invention.
Fig. 6 schematically shows an electronic device in an exemplary embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the steps. For example, some steps may be decomposed, and some steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
In each embodiment of the present invention, the routing configuration table updating method provided by the present invention may be applied to updating and managing a routing configuration table by a logistics company, and may also be applied to updating and managing a routing configuration table by a different logistics company by an e-commerce platform. The foregoing merely provides various application scenarios of the present invention, and the application scenarios of the present invention are not limited thereto, and are not repeated herein.
Fig. 1 shows a flow chart of a routing configuration table updating method according to an embodiment of the invention. The method for updating the routing configuration table comprises the following steps:
step S110: and acquiring historical routing track information.
Specifically, each historical routing track information may include, but is not limited to, a delivery location, a receiving location, a collecting node, a distributing node, a sending node, a collecting courier information, a sending courier information (e.g., as a record of the last courier in the logistics track), an arrival time, a departure time, etc. of each node.
Step S120: and deleting the abnormal transportation historical routing track information from the historical routing track information.
Specifically, in some embodiments, historical routing trace information with abnormal logistics nodes may be culled from the historical routing trace information. The removing method may be executed as steps S121 to S123:
step S121: and in the historical routing track information, counting the number of the logistics nodes from the starting logistics node to the arriving logistics node.
Specifically, the departure logistics node and the arrival logistics node may be any logistics node in the routing track, for example, a collecting node, a distributing node, a transit node, a dispatching node, and the like, which is not limited in the present invention.
Step S122: and determining the logistics nodes with the number of the logistics nodes smaller than a preset abnormal value as abnormal logistics nodes.
Specifically, the preset abnormal value 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 difference between the number of normal logistics nodes and the number of abnormal logistics nodes is large based on normal distribution, and therefore the preset abnormal value can be determined based on the position where the number changes sharply. Further, the preset abnormal value can be obtained by calculating the mean value of the number of the determined abnormal logistics nodes between the same starting logistics node and the arriving logistics node. In some variations, the preset outlier may also be manually entered as an empirical value. The present invention is not limited to this, and it is within the scope of the present invention to predict the abnormal value of the abnormal logistics node by using models such as machine learning algorithm and deep learning algorithm.
Step S123: and deleting the historical routing track information passing through the abnormal logistics node from the starting logistics node to the arriving logistics node from the historical routing track information.
Therefore, the abnormal historical routing track information can be eliminated through data statistics and abnormal value comparison in the steps from the step S121 to the step S123, the method is simple to implement, 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 routing configuration table.
Further, the invention can further delete the abnormal transportation history routing track information in other modes. For example, historical routing track information in which 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 routing track information, and 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 historical routing track information. The first preset time threshold is calculated according to an average value/median of transit time from a departure logistics node to an arrival logistics node in each historical routing track information, which is not limited in the invention. Therefore, the historical routing track information of the transportation abnormity can be eliminated according to the abnormity of the transportation time.
For another example, historical routing trace information in which the dispatch node associated with the sign-off location does not match the dispatch node of the historical routing trace information may be deleted from the historical routing trace information. Therefore, the historical routing track information of the inconsistency between the actual delivery node and the delivery node associated with the sign-off place can be deleted, and the inconsistency between the delivery nodes can be sent only when the logistics track is abnormal (for example, the delivery node associated with the sign-off place is piled up with packages; the delivery direction of the packages associated with the historical routing track information is wrong, so that the packages are delivered in other modes, and the like).
Therefore, the invention can realize the elimination mode of various abnormal transportation historical routing track information in multiple aspects, so as to avoid the influence of wrong historical routing track information on subsequent learning and further influence the updating accuracy of the routing configuration table. The present invention can also realize more removing methods of the history route track information of the transportation abnormality, and the removing methods are all within the protection scope of the present invention and are not described herein.
Step S130: and taking the starting logistics node to the arriving logistics node in the deleted historical routing track information as a learning item, and learning the routing information of the logistics track between the starting logistics node and the arriving logistics node by adopting a learning model.
In some preferred embodiments of the present invention, the learning model employs Natural Language Processing (NLP), so that the historical routing trace information can be structurally learned, and the structured routing information can be obtained. Specifically, the historical routing trace information used in step S130 is subjected to the deletion processing in step S120, and the retained historical routing trace information has high 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: and determining a logistics track from the starting logistics node to the arriving logistics node for updating the routing configuration table. In some embodiments, only the logistics track from the starting logistics node to the arriving logistics node determined for updating the routing configuration table can be learned, so as to greatly improve the learning efficiency and the updating efficiency. In still other embodiments, the physical distribution trajectory from the departure physical distribution node to the arrival physical distribution node for updating the routing configuration table may be determined from data obtained after partial processing by the natural language processing technology, and the natural language processing technology in the latter half of learning may be performed only on the physical distribution trajectory from the departure physical distribution node to the arrival physical distribution node for updating the routing configuration table. The present invention can be implemented in many different ways, which are not described herein.
In some specific implementations, the determining the logistics trajectory from the departure logistics node to the arrival logistics node for updating the routing configuration table may be implemented by: and determining the logistics track with the logistics parameters of the logistics track from the starting logistics node to the arriving logistics node in accordance with a preset parameter rule as the logistics track for updating the routing configuration table in a preset time period. For example, the physical distribution parameter includes an occurrence frequency of a physical distribution trajectory, and the predetermined parameter rule includes any one of the following parameter rules: the occurrence frequency of the logistics track is highest; the stream trajectory has a frequency of occurrence greater than a first predetermined percentage of the highest frequency of occurrence. Therefore, the logistics track with the highest appearance frequency and/or the appearance frequency larger than the first preset percentage of the highest appearance frequency is learned through the appearance frequency of the logistics track, so that the learning efficiency is improved, the accuracy and the reference value of a learned sample are improved, the routing configuration table can be updated in real time, and the updating accuracy of the routing configuration table is further improved. For another example, the logistics parameter includes the number of occurrences of the logistics track and the transportation aging of the logistics track, and the predetermined parameter rule includes any one of the following parameter rules: the occurrence frequency of the logistics track is greater than the set frequency, and the average transportation timeliness of the logistics track is shortest; the proportion of the occurrence times of the logistics tracks to the total number of the logistics tracks is larger than a second preset percentage, and the average transportation timeliness of the logistics tracks is shortest. Therefore, the transportation efficiency of the logistics track can be evaluated in consideration of transportation timeliness, so that the routing track with higher transportation efficiency can be further learned by combining the occurrence times of the logistics track and the transportation timeliness, 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 optimal routing track to the user is further facilitated. The set number, the first predetermined percentage, and the second predetermined percentage may be calculated according to historical data, or determined by inputting manual experience values, which is not intended to limit the present invention. The aforementioned predetermined parameter rules may also make statistical and regular decisions on parameters in a time dimension as a period (e.g., one month/one quarter). For another example, the logistics track marked by the user from the departure logistics node to the arrival logistics node may be determined as the logistics track for updating the routing configuration table. Considering that the manually labeled information is generally the optimal information, it may have a higher learning priority than the foregoing parameter rule, so as to facilitate manual configuration of the learning process.
Step S140: and updating the routing configuration table in real time according to the learned routing information.
Specifically, the execution of step S140 can refer to fig. 2 and fig. 3, which are not described herein again.
In the route configuration table updating method provided by the invention, the history route track information with abnormal transportation is deleted through the judgment of the abnormal logistics node from the history route track information, the screened history route track information is taken as a learning item, so that a learning model is adopted to learn the route information of the logistics track, 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 efficiency of package transportation, and reducing the transportation cost.
Referring now to fig. 2, fig. 2 is a flow diagram illustrating real-time updating of a routing configuration table based on learned routing information, according to an embodiment of the present invention. Fig. 2 shows the following steps together:
step S141: and judging whether the routing configuration table contains the routing information of the logistics track from the starting logistics node to the arriving logistics node.
If it is determined that the routing configuration table does not include the routing information of the logistics trajectory from the departure logistics node to the arrival logistics node, step S142 is executed: and adding the routing information of the logistics track from the starting logistics node to the arriving logistics node into the routing configuration table.
If the routing configuration table is judged to contain the routing information of the logistics track from the starting logistics node to the arriving logistics node, the step S143 is executed: and judging whether the routing information of the logistics track from the starting logistics node to the arriving logistics node contained in the routing configuration table is consistent with the learned routing information of the logistics track from the starting logistics node to the arriving logistics node.
If not, go to step S144: and replacing the learned routing information of the logistics track from the starting logistics node to the arriving logistics node with the routing information of the logistics track from the starting logistics node to the arriving logistics node contained in the routing configuration table.
If they match, the routing configuration table may not be updated. Therefore, whether to execute updated replacement operation is determined by comparing the information in the routing configuration table with the learned information, so that the writing operation to the routing configuration table is greatly reduced, and the situation that the reading (for sorting code identification) process of the routing configuration table is contradictory to the writing process, and the identification is influenced is avoided.
Further, the present invention can also implement the step S140 by using 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. When it is determined in step S143 that the two are not coincident with each other, step S145 is executed in fig. 3: and storing the learned routing information of the logistics track from the starting logistics node to the arriving logistics node as alternative routing information into the routing configuration table. Therefore, original data of the routing configuration table can be kept, and meanwhile, the learned information is stored in the routing configuration table as alternative information so that various information can be compared and selected in the process of sorting code identification/routing identification of the routing configuration table, meanwhile, the routing information is prevented from being changed in a short time due to short-time reasons (route repair, weather and the like), and the routing configuration table is prevented from being frequently changed under the condition that the original routing information is restored after the reasons are eliminated.
The above are merely a plurality of specific implementations of the logistics recommendation method of the present invention, and each implementation may be implemented independently or in combination, and the present invention is not limited thereto.
Referring now to fig. 4, fig. 4 is a block diagram illustrating a routing configuration table updating apparatus according to an embodiment of the present invention. The routing configuration table updating apparatus 200 includes an obtaining module 210, a deleting module 220, a learning module 230, and an updating module 240.
The obtaining module 210 is configured to obtain historical routing trace information.
The deleting module 220 is configured to delete the abnormal transportation history routing track information from the history routing track information, including: counting the number of the logistics nodes from the starting logistics node to the arrival logistics node in the historical routing track information; determining the logistics nodes with the number of the logistics nodes smaller than a preset abnormal value as abnormal logistics nodes; and deleting the historical routing track information passing through the abnormal logistics node from the starting logistics node to the arriving logistics node from the historical routing track information.
The learning module 230 is configured to use a learning model to learn the routing information of the logistics track from the departure logistics node to the arrival logistics node in the deleted historical routing track information as a learning item.
The update module 240 is configured to update the routing configuration table in real-time based on the learned routing information.
In the route configuration table updating device of the exemplary embodiment of the invention, the history route track information with abnormal transportation is deleted through the judgment of the abnormal logistics node from the history route track information, the screened history route track information is taken as a learning item, so that a learning model is adopted to learn the route information of the logistics track, 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, improving the time efficiency of package transportation as a whole and reducing the transportation cost.
Fig. 4 is a schematic diagram illustrating the routing configuration table updating apparatus 200 provided by the present invention, respectively, and the splitting, merging and adding of modules are within the 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 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, which when executed by, for example, a processor, may implement the steps of the routing configuration table updating method described in any one of the above embodiments. In some possible embodiments, 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 above-mentioned routing configuration table update method section of this specification, when the program product is run on the terminal device.
Referring to fig. 5, a program product 700 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the tenant computing device, partly on the tenant device, as a stand-alone software package, partly on the tenant computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing devices may be connected to the tenant computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In an exemplary embodiment of the invention, there is also provided an electronic device that may include a processor and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the routing configuration table updating method in any of the above embodiments via execution of the executable instructions.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 500 according to this embodiment of the invention is described below with reference to fig. 6. The electronic device 500 shown in fig. 6 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one memory unit 520, a bus 530 that couples various system components including the memory unit 520 and the processing unit 510, a display unit 540, and the like.
Wherein the storage unit stores program code, 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 above-mentioned routing configuration table updating method section of the present 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 route configuration table updating method according to the embodiment of the present invention.
Compared with the prior art, the invention has the advantages that:
the invention deletes the abnormal historical routing track information of the transportation through the judgment of the abnormal logistics node from the historical routing track information, and uses the screened historical routing track information as a learning item, thereby adopting a learning model to learn the routing information of the logistics track and updating the routing configuration table in real time, thereby realizing the real-time updating of the routing configuration table, avoiding the identification error of the sorting code, integrally improving the timeliness of the package transportation 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:
obtaining historical routing track information;
deleting the abnormal transportation historical routing track information from the historical routing track information, wherein the deleting comprises the following steps:
counting the number of the logistics nodes from the starting logistics node to the arrival logistics node in the historical routing track information;
determining the logistics nodes with the number of the logistics nodes smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical routing track information passing through the abnormal logistics node from the starting logistics node to the arriving logistics node from the historical routing track information;
taking the starting logistics node to the arriving logistics node in the deleted historical routing track information as a learning item, and learning the routing information of the logistics track between the starting logistics node and the arriving logistics node by adopting a learning model;
and updating the routing configuration table in real time according to the learned routing information.
2. The method for updating a routing configuration table according to claim 1, wherein the deleting the abnormal transportation historical routing trace information from the historical routing trace information further comprises:
and deleting historical routing track information of which the transportation time from the departure logistics node to the arrival logistics node is greater than a first preset time threshold value from the historical routing track information, wherein 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 historical routing track information.
3. The routing configuration table updating method according to claim 1, wherein the preset abnormal value is calculated based on a distribution curve of the number of occurrences of the logistics node.
4. The method for updating a routing configuration table according to claim 1, wherein the deleting the abnormal transportation historical routing trace information from the historical routing trace information further comprises:
and deleting historical routing track information of which the dispatch nodes associated with the sign-off places are inconsistent with the dispatch nodes of the historical routing track information from the historical routing track information.
5. The routing configuration table update method of claim 1, wherein the learning model employs natural language processing techniques.
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 routing configuration table contains routing information of the logistics track from the starting logistics node to the arriving logistics node;
and if not, adding the routing information of the logistics track from the starting logistics node to the arriving logistics node into the routing configuration table.
7. The method according to claim 6, wherein if it is determined that the routing configuration table includes the routing information of the logistics track from the departure logistics node to the arrival logistics node, it is determined whether the routing information of the logistics track from the departure logistics node to the arrival logistics node included in the routing configuration table is consistent with the learned routing information of the logistics track from the departure logistics node to the arrival logistics node;
if not, then:
replacing the learned routing information of the logistics track from the starting logistics node to the arriving logistics node with the routing information of the logistics track from the starting logistics node to the arriving logistics node contained in the routing configuration table; or
And storing the learned routing information of the logistics track from the starting logistics node to the arriving logistics node as alternative routing information into the routing configuration table.
8. The method according to claim 1, wherein learning the routing information of the logistics trajectory from the departure logistics node to the arrival logistics node in the deleted historical routing trajectory information by using a learning model comprises:
and determining a logistics track from the starting logistics node to the arriving logistics node for updating the routing configuration table.
9. The routing configuration table updating method of claim 8, wherein said determining a logistics trajectory between said departure logistics node to said arrival logistics node for updating a routing configuration table comprises:
and determining the logistics track with the logistics parameters of the logistics track from the starting logistics node to the arriving logistics node in accordance with a 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 according to claim 9, wherein the logistics parameter includes an occurrence frequency of a logistics track, and the predetermined parameter rule includes any one of the following parameter rules:
the occurrence frequency of the logistics track is highest;
the stream trajectory has a frequency of occurrence greater than a first predetermined percentage of the highest frequency of occurrence.
11. The routing configuration table updating method according to claim 9, wherein the logistics parameters include the number of occurrences of a logistics track and the transportation age of the logistics track, and the predetermined parameter rule includes any one of the following parameter rules:
the occurrence frequency of the logistics track is greater than the set frequency, and the average transportation timeliness of the logistics track is shortest;
the proportion of the occurrence times of the logistics tracks to the total number of the logistics tracks is larger than a second preset percentage, and the average transportation timeliness of the logistics tracks is shortest.
12. The routing configuration table updating method of claim 9, wherein said determining a logistics trajectory between said departure logistics node to said arrival logistics node for updating a routing configuration table comprises:
and determining the logistics track between the starting logistics node and the arriving 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 routing track information;
a deleting module configured to delete the abnormal transportation historical routing track information from the historical routing track information, including:
counting the number of the logistics nodes from the starting logistics node to the arrival logistics node in the historical routing track information;
determining the logistics nodes with the number of the logistics nodes smaller than a preset abnormal value as abnormal logistics nodes;
deleting the historical routing track information passing through the abnormal logistics node from the starting logistics node to the arriving logistics node from the historical routing track information;
the learning module is configured to use a learning model to learn the routing information of the logistics track from the starting logistics node to the arrival logistics node in the deleted historical routing track information as a learning item;
and the updating module is configured to update the routing configuration table in real time according to the learned routing information.
14. 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:
a method of updating a routing configuration table according to any 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 method of updating a routing configuration table according to any of claims 1 to 12.
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