CN111563630A - Logistics service network node layout method and system based on address longitude and latitude clustering - Google Patents
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Abstract
The invention discloses a method and a system for arranging logistics service network points based on address longitude and latitude clustering, which have good fault tolerance and accuracy, are rapid and reliable in arrangement prediction and are suitable for arranging logistics express service network points. The technical scheme is as follows: firstly, big data of a mail receiving and sending address of a client in the logistics industry are uniformly collected, and dirty data are removed through data cleaning; then, using an address longitude and latitude resolution API of the map service, and transmitting the cleaned address to obtain the longitude and latitude corresponding to the address; and then clustering the collected large data of the addressees of the clients in the logistics industry by adopting a DBSCAN algorithm to obtain N clusters, setting a minimum sum of Euclidean distances from a target function minimum point (x, y) to all points in each cluster, and recommending and selecting the addresses of the logistics express service network points when the (x, y) is converged.
Description
Technical Field
The invention relates to a layout method of a service network of logistics express delivery, in particular to a layout method and a system of a service network of logistics express delivery for carrying out DBSCAN clustering based on longitude and latitude of a customer address.
Background
The logistics express delivery service network is a network for providing logistics express receiving and sending services for customers, and the receiving and sending addresses of the customers are service sites required by the customers, but because the construction of the service network needs to invest corresponding resources and cost, the service network cannot completely correspond to the receiving and sending addresses of all the customers, but is distributed in an area with high density of receiving and sending service requirements, and the sum of radiation distances distributed by actual service networks is minimum for achieving convenience of receiving and sending services of the customers.
DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise) is a relatively representative Density-Based Clustering algorithm. Unlike the partitioning and hierarchical clustering method, which defines clusters as the largest set of density-connected points, it is possible to partition areas with sufficiently high density into clusters and find clusters of arbitrary shape in a spatial database of noise.
Disclosure of Invention
The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.
The invention aims to solve the problems and provides a method and a system for arranging logistics service network points based on address longitude and latitude clustering, which have good fault tolerance and accuracy, are quick and reliable in arrangement prediction and are suitable for arranging logistics express service network points.
The technical scheme of the invention is as follows: the invention discloses a logistics service network layout method based on address longitude and latitude clustering, which comprises the following steps:
step 1: uniformly extracting address information in the user information database, wherein the address information comprises a receiving and sending address;
step 2: carrying out data cleaning on the extracted address data of the receiving and sending articles according to a given range, and removing redundant data and error data;
and step 3: according to the longitude and latitude data of each receiving and sending address, marking the longitude and latitude points of all the receiving and sending addresses as core points, boundary points or noise points respectively;
and 4, step 4: deleting the noise points marked in the step 3;
and 5: performing clustering processing on all acquired receiving and sending addresses after the noise points are deleted, performing clustering analysis based on the density of longitude and latitude data mapping of the receiving and sending addresses to obtain a plurality of clusters, and setting a target function minimization point (x, y) for each cluster, wherein the sum of Euclidean distances from the target function minimization point (x, y) in each cluster to all points in the cluster is minimum;
step 6: keeping all the class clusters except the maximum class cluster in the cluster analysis;
and 7: acquiring a maximum cluster in the clustering analysis, and modifying clustering processing parameters according to the maximum cluster;
and 8: repeating the clustering processing of the steps 5 to 7 until the set target requirement is met;
and step 9: and setting the logistics service network points based on all the clusters which meet the target requirement and the target function minimizing point (x, y) corresponding to each cluster.
According to an embodiment of the method for arranging the logistics service network points based on address longitude and latitude clustering, a user information database stores database data of service users and historical data of sediments.
According to an embodiment of the logistics service network layout method based on address longitude and latitude clustering, the longitude and latitude data of the mail receiving and sending address in the step 3 is obtained through calling of an application program interface of a map service program.
According to an embodiment of the method for arranging the logistics service network points based on address longitude and latitude clustering, the clustering processing in the steps 5 to 7 is DBSCAN clustering.
According to an embodiment of the method for laying out logistics service network points based on address longitude and latitude clustering, in step 9, an objective function minimization point (x, y) in each cluster is used as a logistics service network point, and the coverage area of the logistics service network point is a cluster corresponding to the objective function minimization point.
The invention also discloses a logistics service network layout system based on address longitude and latitude clustering, which comprises the following steps:
the address information extraction module is used for uniformly extracting the address information in the user information database, wherein the address information comprises a receiving and sending address;
the data cleaning module is used for cleaning the extracted address data of the receiving and sending articles according to a given range and removing redundant data and error data;
the marking module is used for marking all the longitude and latitude points of the receiving and sending addresses as core points, boundary points or noise points respectively according to the longitude and latitude data of each receiving and sending address;
the noise point deleting module deletes the noise points marked by the marking module;
the cluster analysis module is used for carrying out cluster processing on all the acquired receiving and sending addresses after the noise points are deleted, carrying out cluster analysis based on the density of longitude and latitude data mapping of the receiving and sending addresses to obtain a plurality of clusters, and setting a target function minimum point (x, y) for each cluster, wherein the sum of Euclidean distances from the target function minimum point (x, y) in each cluster to all the points in the cluster is minimum; keeping all the class clusters except the maximum class cluster in the cluster analysis; acquiring a maximum cluster in the clustering analysis, and modifying clustering processing parameters according to the maximum cluster; repeating the processing process of the clustering analysis module until the set target requirement is met;
and the logistics service network point setting module is used for setting the logistics service network points based on all the clusters which meet the target requirements and the target function minimum points (x, y) corresponding to each cluster.
According to an embodiment of the logistics service network layout system based on address longitude and latitude clustering, a user information database stores database data of service users and historical data of sediments.
According to an embodiment of the logistics service network layout system based on address longitude and latitude clustering, longitude and latitude data of the mail receiving and sending address in the marking module are obtained through calling of an application program interface of a map service program.
According to an embodiment of the logistics service network layout system based on address longitude and latitude clustering, the clustering process of the clustering analysis module is DBSCAN clustering.
According to an embodiment of the logistics service network node layout system based on address longitude and latitude clustering, a logistics service network node setting module takes an objective function minimum point (x, y) in each class of clusters as a logistics service network node, and the coverage area of the logistics service network node is the class cluster corresponding to the objective function minimum point.
Compared with the prior art, the invention has the following beneficial effects: according to the scheme, big data of a client receiving and sending address in the logistics industry are uniformly collected, and dirty data are removed through data cleaning; then, using an address longitude and latitude resolution API of the map service, and transmitting the cleaned address to obtain the longitude and latitude corresponding to the address; and then clustering the collected large data of the addressees of the clients in the logistics industry by adopting a DBSCAN algorithm to obtain N clusters, setting a minimum sum of Euclidean distances from a target function minimum point (x, y) to all points in each cluster, and recommending and selecting the addresses of the logistics express service network points when the (x, y) is converged. Compared with the prior art, the method has the advantages of good fault tolerance and accuracy, quick and reliable layout prediction, and suitability for the layout of the logistics express service network.
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The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings. In the drawings, components are not necessarily drawn to scale, and components having similar relative characteristics or features may have the same or similar reference numerals.
Fig. 1 shows a flowchart of an embodiment of the method for arranging logistics service network points based on address latitude and longitude clustering according to the present invention.
Fig. 2 is a schematic diagram illustrating an embodiment of the system for arranging logistics service network points based on longitude and latitude clustering of addresses according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are only exemplary and should not be construed as imposing any limitation on the scope of the present invention.
Fig. 1 shows a flow of an embodiment of the method for laying out a logistics service network based on address latitude and longitude clustering according to the invention. Referring to fig. 1, the following is a detailed description of the implementation steps of the method of the present implementation.
Step 1: the address information (such as the address of the mail receiving and sending) in the service user database is uniformly extracted by using the database data of the service user and the historical data of the sediment stored in the user information database.
In order to ensure that the extracted address data of the receiving and sending mail does not influence the density difference reflecting the actual address distribution, the area of the actual extracted data aims at the whole geographical range of the specific service network node layout.
Step 2: and carrying out data cleaning on the extracted address data of the receiving and sending articles according to a given range, and removing redundant data and error data.
The data needs to truly reflect the business requirements of the service network, so the same receiving and sending address repeated at different time is the repeated business service of the same address, the address counting is needed when the data is cleaned, but the data cannot be cleaned into single data, the same receiving and sending address repeated at the same time but with different receiving and sending names is the business service of different clients in the same address, the address counting is needed when the data is cleaned, and the data cannot be cleaned into single data. These address counts need to be calculated into the address distribution density.
And step 3: and calling the longitude and latitude data of each receiving and sending address by using an address resolution API (such as a hundredth map API) of the map service, and marking the points of the longitude and latitude of all the receiving and sending addresses as core points, boundary points or noise points.
Core points refer to points that contain more than the number MinPts within the neighborhood of radius Eps. Boundary points refer to points within the radius Eps that are less than MinPts but fall within the neighborhood of the core point. A noise point refers to a point that is neither a core point nor a boundary point.
And 4, step 4: and deleting the noise points marked in the step 3.
And 5: performing DBSCAN clustering on all the acquired receiving and sending address after the noise points are deleted, performing clustering analysis according to the mapping density of the longitude and latitude data of the receiving and sending address to obtain N clusters, and setting a target function minimizing point (x, y) for each cluster, wherein the sum of Euclidean distances from the target function minimizing point (x, y) in each cluster to all the points in the cluster is the minimum.
DBSCAN (sensitivity-Based Spatial Clustering of Applications with Noise) is a relatively representative Density-Based Clustering algorithm. The specific implementation manner of the DBSCAN cluster analysis is as follows: firstly, an edge is endowed to all core points with the distance within the neighborhood of the radius Eps, then a cluster is formed based on each group of connected core points, each boundary point is assigned to a cluster of the core points associated with the boundary point, N clusters are obtained, and finally, the Euclidean distance sum of the objective function minimization points (x, y), (x, y) to all the points in each cluster is set to be minimum.
Step 6: all class clusters except the largest class cluster are retained in the DBSCAN cluster analysis.
And 7: and acquiring a maximum class cluster in the DBSCAN clustering analysis, and modifying parameters of the DBSCAN clustering algorithm according to the maximum class cluster.
And 8: and repeating the steps 5 to 7 until the set target requirement is met.
For example, when the objective function minimizing point (x, y) converges to the set condition, the objective function minimizing point (x, y) meets the set target requirement.
And step 9: and outputting all the clusters which meet the target requirement and the target function minimization point (x, y) corresponding to each cluster.
The objective function minimization point (x, y) in each cluster can be used as a service mesh point, and the coverage area of the service mesh point is the cluster corresponding to the objective function minimization point (x, y).
Fig. 2 shows the principle of an embodiment of the logistics service network layout system based on address latitude and longitude clustering according to the invention. Referring to fig. 2, the system of the present embodiment includes: the system comprises an address information extraction module, a data cleaning module, a marking module, a noise point deleting module, a cluster analysis module and a logistics service network point setting module.
The address information extraction module is used for uniformly extracting the address information in the user information database, and the address information comprises a receiving and sending address. Wherein the user information database stores database data of service users and history data of the deposits. In order to ensure that the extracted address data of the receiving and sending mail does not influence the density difference reflecting the actual address distribution, the area of the actual extracted data aims at the whole geographical range of the specific service network node layout.
The data cleaning module is used for cleaning the extracted address data of the receiving and sending articles according to a given range and removing redundant data and error data.
The data needs to truly reflect the business requirements of the service network, so the same receiving and sending address repeated at different time is the repeated business service of the same address, the address counting is needed when the data is cleaned, but the data cannot be cleaned into single data, the same receiving and sending address repeated at the same time but with different receiving and sending names is the business service of different clients in the same address, the address counting is needed when the data is cleaned, and the data cannot be cleaned into single data. These address counts need to be calculated into the address distribution density.
The marking module is used for marking the longitude and latitude points of all the receiving and sending addresses as core points, boundary points or noise points respectively according to the longitude and latitude data of each receiving and sending address. The longitude and latitude data of the receiving and sending address in the marking module is obtained by calling an application program interface of a map service program.
Core points refer to points that contain more than the number MinPts within the neighborhood of radius Eps. Boundary points refer to points within the radius Eps that are less than MinPts but fall within the neighborhood of the core point. A noise point refers to a point that is neither a core point nor a boundary point.
And the noise point deleting module is used for deleting the noise points marked by the marking module.
The cluster analysis module is used for carrying out cluster processing on all the acquired receiving and sending addresses after the noise points are deleted, carrying out cluster analysis based on the density of longitude and latitude data mapping of the receiving and sending addresses to obtain a plurality of clusters, and setting a target function minimum point (x, y) for each cluster, wherein the sum of Euclidean distances from the target function minimum point (x, y) in each cluster to all the points in the cluster is minimum; keeping all the class clusters except the maximum class cluster in the cluster analysis; acquiring a maximum cluster in the clustering analysis, and modifying clustering processing parameters according to the maximum cluster; the above-described processing procedure of the cluster analysis module is repeated until the set target requirement is met (for example, when the target function minimizing point (x, y) converges to the set condition, the set target requirement is met).
The clustering process in this embodiment is DBSCAN clustering. The specific implementation manner of the DBSCAN cluster analysis is as follows: firstly, an edge is endowed to all core points with the distance within the neighborhood of the radius Eps, then a cluster is formed based on each group of connected core points, each boundary point is assigned to a cluster of the core points associated with the boundary point, N clusters are obtained, and finally, the Euclidean distance sum of the objective function minimization points (x, y), (x, y) to all the points in each cluster is set to be minimum.
The logistics service network point setting module is used for setting the logistics service network points based on all the clusters meeting the target requirements and the target function minimum point (x, y) corresponding to each cluster. In this embodiment, the objective function minimization point (x, y) in each cluster is used as a logistics service node, and the coverage area of the logistics service node is the cluster corresponding to the objective function minimization point.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A logistics service network layout method based on address longitude and latitude clustering is characterized by comprising the following steps:
step 1: uniformly extracting address information in the user information database, wherein the address information comprises a receiving and sending address;
step 2: carrying out data cleaning on the extracted address data of the receiving and sending articles according to a given range, and removing redundant data and error data;
and step 3: according to the longitude and latitude data of each receiving and sending address, marking the longitude and latitude points of all the receiving and sending addresses as core points, boundary points or noise points respectively;
and 4, step 4: deleting the noise points marked in the step 3;
and 5: performing clustering processing on all acquired receiving and sending addresses after the noise points are deleted, performing clustering analysis based on the density of longitude and latitude data mapping of the receiving and sending addresses to obtain a plurality of clusters, and setting a target function minimization point (x, y) for each cluster, wherein the sum of Euclidean distances from the target function minimization point (x, y) in each cluster to all points in the cluster is minimum;
step 6: keeping all the class clusters except the maximum class cluster in the cluster analysis;
and 7: acquiring a maximum cluster in the clustering analysis, and modifying clustering processing parameters according to the maximum cluster;
and 8: repeating the clustering processing of the steps 5 to 7 until the set target requirement is met;
and step 9: and setting the logistics service network points based on all the clusters which meet the target requirement and the target function minimizing point (x, y) corresponding to each cluster.
2. The method as claimed in claim 1, wherein the user information database stores database data of service users and historical data of deposits.
3. The method as claimed in claim 1, wherein the latitude and longitude data of the mail receiving address in step 3 is obtained by calling application program interface of map service program.
4. The method as claimed in claim 1, wherein the clustering process in steps 5 to 7 is DBSCAN clustering.
5. The method as claimed in claim 1, wherein in step 9, the objective function minimization point (x, y) in each cluster is used as a logistics service node, and the coverage area of the logistics service node is the cluster corresponding to the objective function minimization point.
6. A logistics service network layout system based on address longitude and latitude clustering is characterized by comprising:
the address information extraction module is used for uniformly extracting the address information in the user information database, wherein the address information comprises a receiving and sending address;
the data cleaning module is used for cleaning the extracted address data of the receiving and sending articles according to a given range and removing redundant data and error data;
the marking module is used for marking all the longitude and latitude points of the receiving and sending addresses as core points, boundary points or noise points respectively according to the longitude and latitude data of each receiving and sending address;
the noise point deleting module deletes the noise points marked by the marking module;
the cluster analysis module is used for carrying out cluster processing on all the acquired receiving and sending addresses after the noise points are deleted, carrying out cluster analysis based on the density of longitude and latitude data mapping of the receiving and sending addresses to obtain a plurality of clusters, and setting a target function minimum point (x, y) for each cluster, wherein the sum of Euclidean distances from the target function minimum point (x, y) in each cluster to all the points in the cluster is minimum; keeping all the class clusters except the maximum class cluster in the cluster analysis; acquiring a maximum cluster in the clustering analysis, and modifying clustering processing parameters according to the maximum cluster; repeating the processing process of the clustering analysis module until the set target requirement is met;
and the logistics service network point setting module is used for setting the logistics service network points based on all the clusters which meet the target requirements and the target function minimum points (x, y) corresponding to each cluster.
7. The logistics service site layout system based on latitude and longitude clustering of addresses of claim 6, wherein the user information database stores database data of service users and historical data of sediments.
8. The logistics service site layout system based on latitude and longitude clustering of addresses as claimed in claim 6, wherein the latitude and longitude data of the mail receiving address in the marking module is obtained by calling through an application program interface of a map service program.
9. The logistics service site layout system based on latitude and longitude clustering of addresses of claim 6, wherein the clustering process of the cluster analysis module is DBSCAN clustering.
10. The system of claim 6, wherein the logistics service network node setting module takes the objective function minimization point (x, y) in each cluster as a logistics service network node, and the coverage area of the logistics service network node is the cluster corresponding to the objective function minimization point.
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| CN112801189A (en) * | 2021-01-29 | 2021-05-14 | 上海寻梦信息技术有限公司 | Method and device for detecting longitude and latitude abnormity, electronic equipment and storage medium |
| CN113537828A (en) * | 2021-08-04 | 2021-10-22 | 拉扎斯网络科技（上海）有限公司 | Virtual site mining method and device |
| CN113762864A (en) * | 2021-01-06 | 2021-12-07 | 北京京东振世信息技术有限公司 | Logistics site location method and device |
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