WO2018205760A1 - 一种配送站选址的方法和装置 - Google Patents

一种配送站选址的方法和装置 Download PDF

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Publication number
WO2018205760A1
WO2018205760A1 PCT/CN2018/080619 CN2018080619W WO2018205760A1 WO 2018205760 A1 WO2018205760 A1 WO 2018205760A1 CN 2018080619 W CN2018080619 W CN 2018080619W WO 2018205760 A1 WO2018205760 A1 WO 2018205760A1
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Prior art keywords
road area
data
site
station
distribution
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PCT/CN2018/080619
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English (en)
French (fr)
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高腾斌
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北京京东尚科信息技术有限公司
北京京东世纪贸易有限公司
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Publication of WO2018205760A1 publication Critical patent/WO2018205760A1/zh

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    • 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"
    • G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management

Definitions

  • the present invention relates to the field of computers, and in particular, to a method and apparatus for location selection of a distribution station.
  • the existing terminal distribution station location has the following options:
  • the location of the rough terminal distribution station is carried out.
  • the embodiments of the present invention provide a method and apparatus for location selection of a distribution station, which can solve the problem that a reliable location selection solution cannot be quickly provided.
  • a method of site selection for a distribution station is provided.
  • a method for selecting a location of a delivery station includes: normalizing the acquired existing road area information and existing site information according to a preset mode to obtain standardized data; and inputting the standardized data into a preset mathematics In the model, the output road area information is used as the location selection scheme.
  • the site corresponds to multiple road zones, wherein the road zone information includes distance class data and order quantity data, and the site information includes latitude and longitude data and financial class data.
  • the mathematical model is based on data from existing road areas and existing sites, specifically:
  • ⁇ is the transportation cost from the sorting center to the order unit of the station
  • d ij is the distance from the sorting center i to the road area j
  • q tij is required to be delivered from the sorting center i in the t month
  • the total order quantity of j It is the number of orders that need to be delivered within 5 kilograms from the sorting center i in the t-month.
  • Y tik is the indicator variable
  • X tj is the indicator variable
  • ⁇ l is within the weight of 5 kg
  • ⁇ h is the coefficient of the distribution distance in the distribution cost function of the order unit of 5 kg to 10 kg
  • d jk is the distance between the center point of the road area j and the center point of the road area k.
  • the embodiment of the present invention performs standardization processing on the acquired road area information and the site information according to a preset mode, to obtain standardized data, which specifically includes: storing the road area information and the site information into a database, and according to the attribute threshold The database is screened to filter the data into standardized data.
  • an apparatus for site selection of a delivery station is provided.
  • An apparatus for selecting a location of a delivery station includes: a first processing module, configured to perform normalization processing on the obtained existing road area information and the existing site information according to a preset mode to obtain standardized data; The module inputs the standardized data into a preset mathematical model, and uses the road area information to be the location selection scheme.
  • the site corresponds to multiple road zones, wherein the road zone information includes distance class data and order quantity data, and the site information includes latitude and longitude data and financial class data.
  • the mathematical model is based on data from existing road areas and existing sites, specifically:
  • ⁇ is the transportation cost from the sorting center to the order unit of the station
  • d ij is the distance from the sorting center i to the road area j
  • q tij is required to be delivered from the sorting center i in the t month
  • the total order quantity of j It is the number of orders that need to be delivered within 5 kilograms from the sorting center i in the t-month.
  • Y tjk is the indicator variable
  • Y tjk 1 means that the road area in the road area j covers the road area k
  • Y tjk 0
  • ⁇ is a constant term in the monthly cost equation of the site
  • ⁇ l is a constant term in the distribution cost function of the order unit within the weight of 5 kg
  • ⁇ h is a function of the distribution cost of the order unit of 5 kg to 10 kg.
  • X tj is the indicator variable
  • ⁇ l is within 5 kg
  • ⁇ h is the coefficient of the distribution distance in the distribution cost function of the order unit of 5 kg to 10 kg
  • d jk is the distance between the center point of the road area j and the center point of the road area k.
  • the first processing module of the embodiment of the present invention is specifically configured to: store the road area information and the site information into a database, and perform screening from the database according to the attribute threshold, so that the filtered data is standardized data.
  • an electronic device for implementing a method of location selection of a delivery station.
  • An electronic device includes: one or more processors; storage means for storing one or more programs, when one or more programs are executed by one or more processors, such that one or more The processor implements a method of location selection of a delivery station in an embodiment of the present invention.
  • a computer readable medium is provided.
  • a computer readable medium according to an embodiment of the present invention, wherein a computer program is stored thereon, and when the program is executed by the processor, the method for addressing the delivery station in the embodiment of the present invention is implemented.
  • One embodiment of the above invention has the following advantages or advantages: since the parameters of the route and the site and the preset mathematical model are used to carry out the location selection of the distribution station, it is impossible to quickly provide a reliable The technical problems of the location scheme, and then achieve the technical effect of selecting the location of the distribution station based on big data, which is beneficial to improve the work efficiency at the time of site selection; through the processing of big data, the terminal distribution station selection is effectively improved.
  • the effect of the work of the site while ensuring the quality of service, achieves the purpose of cost-effective layout of the distribution station; by continuously adding machine learning technology to the terminal distribution station location method, the mathematical model is continuously optimized, and a more rational and scientific terminal is provided.
  • the method of site selection for distribution stations since the parameters of the route and the site and the preset mathematical model are used to carry out the location selection of the distribution station, it is impossible to quickly provide a reliable The technical problems of the location scheme, and then achieve the technical effect of selecting the location of the distribution station based on big data, which is beneficial to improve the
  • FIG. 1 is a schematic diagram of main steps of a method for location selection of a delivery station in accordance with an embodiment of the present invention
  • FIG. 2 is a schematic diagram of implementation steps of a method for location selection of a delivery station according to an embodiment of the present invention
  • FIG. 3 is a schematic diagram of a location selection scheme of a relationship between an original station and a sorting center according to an embodiment of the present invention
  • FIG. 4 is a schematic diagram of a location scheme of a relationship between a site and a sorting center according to an embodiment of the present invention
  • FIG. 5 is a schematic diagram of main modules of an apparatus for addressing a delivery station according to an embodiment of the present invention
  • FIG. 6 is a schematic diagram showing the hardware structure of an electronic device for implementing a method for addressing a delivery station according to an embodiment of the present invention.
  • the technical solution of the embodiment of the present invention is to obtain all the parameters of each region and the site in the current region, and then input the parameters into the mathematical model that is fitted in advance, and finally calculate the calculated result, thereby achieving The use of big data to integrate all parameters while providing a reliable location solution that guarantees quality of service and operating costs.
  • FIG. 1 is a schematic diagram of the main steps of a method of location selection of a delivery station in accordance with an embodiment of the present invention.
  • a method for selecting a location of a distribution station mainly includes the following steps:
  • Step S11 normalizing the obtained existing road area information and the existing site information according to a preset mode to obtain standardized data. In this step, it is first necessary to obtain the big data of each road area and each station, so as to use the data for subsequent site selection, that is, the existing road area information and the existing site information in the present invention.
  • the site corresponds to a plurality of road areas, and the information of the road area includes distance class data and order quantity data, and the information of the site includes latitude and longitude data and financial class data, wherein:
  • the distance class data includes: distance data from the sorting center to the center point of the road area, distance data between the center points of the road area, and the distance from the sorting center to the station.
  • the order quantity data includes: the delivery staff distributes the far and near parts, the order quantity of different weight intervals, and the monthly order quantity of the station.
  • Financial data includes: site rent, vehicle unit price, monthly salary of site management personnel, delivery staff single amount, driver monthly salary, site operation cost, site equipment cost, urban average monthly rent.
  • the specific processing process includes: storing the road area information and the site information into the database, and filtering from the database according to the attribute threshold (storage in the database) All the data attributes of the road area information and all the data attributes of the site information, therefore, it is necessary to filter the data attributes in the database, that is, to filter out the data whose assignment matches the attribute threshold), so that the filtered data is standardized data.
  • the standardization process can also establish storage for the basic data warehouse, such as adding, deleting, changing, checking and other basic functions.
  • Step S12 The standardized data is input into a preset mathematical model, and the output road area information is an address selection scheme.
  • the location selection scheme of the model output may be a specific point or a specific area.
  • the output road area information is used as the location selection scheme, that is, the mathematical model.
  • a score for the road area is obtained. The purpose of the score is to select the recommended road area with the lowest cost within the preconditions (guaranteed the aging and service experience).
  • the road area information is fixed. And existing, according to the output road area information, and then choose to build a station within the road area to achieve the best location to build a station.
  • the mathematical model is based on the data of the existing road area and the existing site, specifically:
  • ⁇ is the transportation cost from the sorting center to the order unit of the station
  • d ij is the distance from the sorting center i to the road area j
  • q tij is required to be delivered from the sorting center i in the t month
  • the total order quantity of j It is the number of orders that need to be delivered within 5 kilograms from the sorting center i in the t-month.
  • Y tjk is the indicator variable
  • Y tjk 1 means that the road area in the road area j covers the road area k
  • Y tjk 0
  • ⁇ is a constant term in the monthly cost equation of the site
  • ⁇ l is a constant term in the distribution cost function of the order unit within the weight of 5 kg
  • ⁇ h is a function of the distribution cost of the order unit of 5 kg to 10 kg.
  • X tj is the indicator variable
  • ⁇ l is within 5 kg
  • ⁇ h is the coefficient of the distribution distance in the distribution cost function of the order unit of 5 kg to 10 kg
  • d jk is the distance between the center point of the road area j and the center point of the road area k.
  • the present invention mainly considers the case where there are a plurality of sorting centers in the current area. If there is only one sorting center in the current area, the above formula can be further simplified.
  • the simplified formula is:
  • d j is the distance from the sorting center to the road area j
  • q tj is the total number of orders that need to be delivered from the sorting center to the road area j in the t month. It is the number of orders within 5 kilograms that need to be delivered from the sorting center to the road area j within the weight of 5 kilograms. It is the order quantity of 5 kg to 10 kg that needs to be delivered from the sorting center to the road area j in the t month.
  • the technical solution of the embodiment of the present invention is that the standardized data of the station and the road area are sequentially input into the mathematical model, and the obtained output data is also the data of the corresponding station and the road area (in other words, the input is the road area). Information, the output is also the road area information), and then manual intervention, based on these data and the actual situation to determine the final location plan.
  • the smallest of all output data can be taken.
  • the location scheme determined in this step may deviate from the actual situation (for example, the location in the preliminary scheme may be on the road or in the riverbed), and therefore, the preliminary scheme needs to be further optimized.
  • the factors affecting the feasibility assessment include factors such as travel demand, land use, traffic conditions and service facilities.
  • FIG. 2 is a schematic diagram of implementation steps of a method for location of a delivery station in accordance with an embodiment of the present invention.
  • the technical solution of the embodiment of the present invention first acquires some big data, mainly including distance class data, operation financial class data, and order quantity data, and processes the data into table conversion;
  • the processed data is input into the mathematical model for program generation, resulting in a site selection scheme that saves both investment costs and covers all areas.
  • the distance class data includes: distance data from the sorting center to the center point of the road zone, distance data between the center points of the road zone, and the distance of the station from the sorting center to the site;
  • the order quantity data includes: delivery The staff distributes the quantity of the far and near parts, the order quantity of different weight intervals, and the monthly order quantity of the site;
  • the financial data includes: the site rent, the vehicle unit price, the monthly salary of the station management personnel, the delivery staff single amount, the driver monthly salary, the site operation cost, Site equipment cost, urban average monthly rent.
  • FIG. 3 it is a schematic diagram of a location scheme of the relationship between the original site and the sorting center according to the embodiment of the present invention. After the technical solution of the embodiment of the present invention is applied, the location scheme shown in FIG. 4 is obtained.
  • 1a indicates the location of the initial site; 1b indicates that the existing site will not be cancelled before the lease expires; 1c indicates that the number of sites will not be reduced; 1d indicates that each zone is required to be covered by one site; 1e indicates that the road area can only be covered by the road area with the station; 1f and 1j represent variables defining X tj and Y tjk as 0-1.
  • the method for selecting a location of a distribution station since the technical parameters of the location of the distribution station are adopted by using various parameters of the road area and the site and the preset mathematical model, the overcoming of one cannot be quickly provided.
  • the technical problem of reliable location scheme and then achieve the technical effect of selecting the location of the distribution station based on big data, which is beneficial to improve the work efficiency at the time of site selection; and effectively improve the distribution of terminals through the processing of big data.
  • the effect of the site selection work achieves the goal of cost-effective layout of the distribution station while ensuring the quality of service; continuously optimizes the mathematical model by adding machine learning technology to the terminal distribution station location method, and provides more rational science.
  • the method of location selection of terminal distribution stations since the technical parameters of the location of the distribution station are adopted by using various parameters of the road area and the site and the preset mathematical model, the overcoming of one cannot be quickly provided.
  • the technical problem of reliable location scheme and then achieve the technical effect of selecting the location of the distribution station based on big data, which is beneficial to improve the
  • Figure 5 is a schematic illustration of the main modules of a device for addressing a delivery station in accordance with an embodiment of the present invention.
  • the device 50 for the location of the delivery station in the embodiment of the present invention mainly includes: a first processing module 51 and a second processing module 52. among them:
  • the first processing module 51 is configured to perform normalization processing on the acquired road area information and the site information according to a preset mode to obtain standardized data.
  • the second processing module 52 inputs the normalized data into a preset mathematical model to output the result as an addressing scheme.
  • the site corresponds to multiple road zones, wherein the site information includes distance class data and order quantity data, and the site information includes latitude and longitude data and financial class data.
  • ⁇ is the transportation cost from the sorting center to the order unit of the station
  • d ij is the distance from the sorting center i to the road area j
  • q tij is required to be delivered from the sorting center i in the t month
  • the total order quantity of j It is the number of orders that need to be delivered within 5 kilograms from the sorting center i in the t-month.
  • Y tjk is the indicator variable
  • Y tjk 1 means that the road area in the road area j covers the road area k
  • Y tjk 0
  • ⁇ is a constant term in the monthly cost equation of the site
  • ⁇ l is a constant term in the distribution cost function of the order unit within the weight of 5 kg
  • ⁇ h is a function of the distribution cost of the order unit of 5 kg to 10 kg.
  • X tj is the indicator variable
  • ⁇ l is within 5 kg
  • ⁇ h is the coefficient of the distribution distance in the distribution cost function of the order unit of 5 kg to 10 kg
  • d jk is the distance between the center point of the road area j and the center point of the road area k.
  • the first processing module 51 of the embodiment of the present invention is specifically configured to: store the road area information and the site information into a database, and perform screening from the database according to the attribute threshold, so that the filtered data is standardized data.
  • the technical problem of not being able to quickly provide a reliable site selection solution is overcome.
  • the technical effect of selecting the location of the distribution station based on big data is achieved, which is beneficial to improving the work efficiency at the time of site selection; by processing the big data, the effect of the site selection work of the terminal is effectively improved.
  • the purpose of cost-effective layout of the distribution station is realized; the mathematical model is continuously optimized by adding the machine learning technology to the terminal distribution station location method, and a more rational and scientific method for selecting the terminal of the terminal is provided.
  • the invention also provides an electronic device and a readable storage medium.
  • An electronic device of an embodiment of the present invention includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by a processor, the instructions being executed by the at least one processor to enable At least one processor is capable of performing the method of dispatching a station of an embodiment of the present invention.
  • a computer readable storage medium of the present invention the computer readable storage medium storing computer instructions for causing the computer to perform a method of addressing a delivery station provided by the present invention.
  • FIG. 6 a block diagram of a computer system 600 suitable for use in implementing a terminal device in accordance with an embodiment of the present invention is shown.
  • the terminal device shown in FIG. 6 is merely an example, and should not impose any limitation on the function and scope of use of the embodiments of the present invention.
  • computer system 600 includes a central processing unit (CPU) 601 that can be loaded into a program in random access memory (RAM) 603 according to a program stored in read only memory (ROM) 602 or from storage portion 608. And perform various appropriate actions and processes.
  • RAM random access memory
  • ROM read only memory
  • RAM random access memory
  • various programs and data required for the operation of the system 600 are also stored.
  • the CPU 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604.
  • An input/output (I/O) interface 605 is also coupled to bus 604.
  • the following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a storage portion 608 including a hard disk or the like. And a communication portion 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the Internet.
  • Driver 610 is also coupled to I/O interface 605 as needed.
  • a removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like, is mounted on the drive 610 as needed so that a computer program read therefrom is installed into the storage portion 608 as needed.
  • embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method of the flowchart of the main steps.
  • the computer program can be downloaded and installed from the network via communication portion 609, and/or installed from removable media 611.
  • CPU central processing unit
  • the computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium or any combination of the two.
  • the computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, 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), optical storage device, magnetic storage device, or any suitable combination of the foregoing.
  • a computer readable storage medium may be any tangible medium that can contain or store a program, which can be used by or in connection with an instruction execution system, apparatus or device.
  • a computer readable signal medium may include a data signal that is propagated in the baseband or as part of a carrier, in which computer readable program code is carried. Such propagated data signals can take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • the computer readable signal medium can also be any computer readable medium other than a computer readable storage medium, which can transmit, propagate, or transport a program for use by or in connection with the instruction execution system, apparatus, or device.
  • Program code embodied on a computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
  • each block of the flowchart or block diagrams can represent a module, a program segment, or a portion of code that includes one or more Executable instructions.
  • the functions noted in the blocks may also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams or flowcharts, and combinations of blocks in the block diagrams or flowcharts can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be used A combination of dedicated hardware and computer instructions is implemented.
  • the modules involved in the embodiments of the present invention may be implemented by software or by hardware.
  • the described modules may also be provided in the processor, for example, as a processor comprising a first processing module and a second processing module.
  • the names of these units do not in any way constitute a limitation on the unit itself.
  • the present invention also provides a computer readable medium, which may be included in the apparatus described in the above embodiments, or may be separately present and not incorporated in the apparatus.
  • the computer readable medium carries one or more programs.
  • the device includes: acquiring the existing road area information and the existing site information according to a preset mode.
  • the standardization process is performed to obtain standardized data; the standardized data is input into a preset mathematical model, and the output road area information is selected as the location plan.
  • the technical parameters of the location of the distribution station are adopted by using various parameters of the road area and the site and the preset mathematical model, the technology that cannot provide a reliable location solution quickly is overcome.
  • the problem, and then achieve the technical effect of selecting the location of the distribution station based on big data is conducive to improving the work efficiency at the time of site selection; by processing the big data, effectively improving the effect on the location of the terminal distribution station
  • the purpose of cost-effective layout of the distribution station is realized; by continuously adding the machine learning technology to the terminal distribution station location method, the mathematical model is continuously optimized, and a more rational and scientific terminal distribution site is provided. method.

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Abstract

本发明提供一种配送站选址的方法、装置、电子设备和可读存储介质，能够解决无法快速提供一个可靠的选址方案的问题。该方法包括：将获取的已有路区信息以及已有站点信息按照预设的模式进行标准化处理，得到标准化数据；将标准化数据输入到预置的数学模型中，以输出的路区信息为选址方案。

Description

一种配送站选址的方法和装置 技术领域
本发明涉及计算机领域，尤其涉及一种配送站选址的方法和装置。
背景技术
目前，快递物流行业终端配送站的选址工作，没有通过技术支持进行实现，普遍情况是通过管理人员根据经验及友商的站点布局进行选择，大部分终端配送站选址没有科学依据，对于选定的配送站也经常出现修改，调整，搬迁，拆除等重复工作，造成资源的极大浪费。
现有的终端配送站选址有以下几种方案：
1、根据行政区划进行粗糙地终端配送站选址工作。
2、根据友商及行业内的布局情况进行照搬，在其他品牌终端配送站周边寻找位置进行选址建站。
3、参考一部分政治因素、商业中心圈因素进行终端配送站选址工作。
4、根据快递物流行业的管理层级划分，由下自上提报建站申请，逐级审批后进行终端配送站选址工作。
5、结合快递物流行业中的区域划分习惯，对大的区域进行逐步拆分，并配合进行终端配送站选址工作。
6、结合订单密度进行终端配送站选址工作。
在实现本发明过程中，发明人发现现有技术中至少存在如下问题：
1、无法根据站点的实时变化，提供出在兼顾服务质量的同时保证运营成本的终端配送站的可靠选址方案；
2、人工选址大大降低了工作效率。
发明内容
有鉴于此，本发明实施例提供一种配送站选址的方法和装置，能够解决无法快速提供一个可靠的选址方案的问题。
为实现上述目的，根据本发明的一个方面，提供了一种配送站选址的方法。
本发明实施例的一种配送站选址的方法包括：将获取的已有路区信息以及已有站点信息按照预设的模式进行标准化处理，得到标准化数据；将标准化数据输入到预置的数学模型中，以输出的路区信息为选址方案。
可选地，站点对应多个路区，其中，路区信息包括距离类数据以及订单量数据，站点信息包括经纬度数据以及财务类数据。
可选地，数学模型是根据已有路区和已有站点的数据拟合出的，具体为：
Figure PCTCN2018080619-appb-000001
其中，ω是从分拣中心到站点的订单单位距离运输成本，d ij是从分拣中心i到路区j的距离，q tij是在第t个月需要从分拣中心i送达路区j的总订单数量，
Figure PCTCN2018080619-appb-000002
是在第t个月需要从分拣中心i送达路区j的重量5千克以内的订单数量，
Figure PCTCN2018080619-appb-000003
是在第t个月需要从分拣中心i送达路区j的重量5千克到10千克的订单数量，Y tik是指示变量，Y tjk＝1代表在路区j的站点覆盖了路区k，否则Y tjk＝0，α是站点月成本方程中的常数项，α l是重量5千克以内订单单位配送成本函数中的常数项，α h是重量5千克到10千克订单单位配送成本函数中的常数项，X tj是指示变量，X tj＝1代表在第t个月路区j已建立站点，否则X tj＝0，t＝0代表前一年12月， β l是重量5千克以内订单单位配送成本函数中配送距离的系数，β h是重量5千克到10千克订单单位配送成本函数中配送距离的系数，d jk是路区j的中心点与路区k的中心点的距离。
可选地，本发明实施例将获取的路区信息以及站点信息按照预设的模式进行标准化处理，得到标准化数据，具体包括：将路区信息以及站点信息存储到数据库中，并根据属性阈值从数据库中进行筛选，以筛选出的数据为标准化数据。
为实现上述目的，根据本发明实施例的另一方面，提供了一种配送站选址的装置。
本发明实施例一种配送站选址的装置包括：第一处理模块，用于将获取的已有路区信息以及已有站点信息按照预设的模式进行标准化处理，得到标准化数据；第二处理模块，将标准化数据输入到预置的数学模型中，以输出的路区信息为选址方案。
可选地，站点对应多个路区，其中，路区信息包括距离类数据以及订单量数据，站点信息包括经纬度数据以及财务类数据。
可选地，数学模型是根据已有路区和已有站点的数据拟合出的，具体为：
Figure PCTCN2018080619-appb-000004
其中，ω是从分拣中心到站点的订单单位距离运输成本，d ij是从分拣中心i到路区j的距离，q tij是在第t个月需要从分拣中心i送达路区j的总订单数量，
Figure PCTCN2018080619-appb-000005
是在第t个月需要从分拣中心i送达路区j的重量5千克以内的订单数量，
Figure PCTCN2018080619-appb-000006
是在第t个月需要从分拣中心i送达路区j的重 量5千克到10千克的订单数量，Y tjk是指示变量，Y tjk＝1代表在路区j的站点覆盖了路区k，否则Y tjk＝0，α是站点月成本方程中的常数项，α l是重量5千克以内订单单位配送成本函数中的常数项，α h是重量5千克到10千克订单单位配送成本函数中的常数项，X tj是指示变量，X tj＝1代表在第t个月路区j已建立站点，否则X tj＝0，t＝0代表前一年12月，β l是重量5千克以内订单单位配送成本函数中配送距离的系数，β h是重量5千克到10千克订单单位配送成本函数中配送距离的系数，d jk是路区j的中心点与路区k的中心点的距离。
可选地，本发明实施例的第一处理模块具体用于：将路区信息以及站点信息存储到数据库中，并根据属性阈值从数据库中进行筛选，以筛选出的数据为标准化数据。
为实现上述目的，根据本发明实施例的再一方面，提供了一种实现配送站选址的方法的电子设备。
本发明实施例的一种电子设备包括：一个或多个处理器；存储装置，用于存储一个或多个程序，当一个或多个程序被一个或多个处理器执行，使得一个或多个处理器实现本发明实施例的配送站选址的方法。
为实现上述目的，根据本发明实施例的又一方面，提供了一种计算机可读介质。
本发明实施例的一种计算机可读介质，其上存储有计算机程序，程序被处理器执行时实现本发明实施例的配送站选址的方法。
上述发明中的一个实施例具有如下优点或有益效果：因为采用路区和站点的各项参数以及预置的数学模型，进行配送站的选址的技术手段，所以克服了无法快速提供一个可靠的选址方案的技术问题，进 而达到基于大数据来对配送站进行选址的技术效果，有利于提高在选址时的工作效率；通过对大数据的处理，有效地提高了对终端配送站选址的工作的效果，在保证服务质量的同时实现了节约成本合理布局配送站的目的；通过将机器学习技术增加到终端配送站选址方法中实现不断优化数学模型，并提供更合理科学的终端配送站选址的方法。
上述的非惯用的可选方式所具有的进一步效果将在下文中结合具体实施方式加以说明。
附图说明
附图用于更好地理解本发明，不构成对本发明的不当限定。其中：
图1是根据本发明实施例的配送站选址的方法的主要步骤的示意图；
图2是根据本发明实施例的配送站选址的方法的实现步骤的示意图；
图3是根据本发明实施例的原有站点与分拣中心关系的选址方案示意图；
图4是根据本发明实施例的通过站点与分拣中心关系的选址方案示意图；
图5是根据本发明实施例的配送站选址的装置的主要模块的示意图；
图6是用于实现本发明实施例的配送站选址的方法的电子设备的硬件结构示意图。
具体实施方式
以下结合附图对本发明的示范性实施例做出说明，其中包括本发明实施例的各种细节以助于理解，应当将它们认为仅仅是示范性的。因此，本领域普通技术人员应当认识到，可以对这里描述的实施例做出各种改变和修改，而不会背离本发明的范围和精神。同样，为了清楚和简明，以下的描述中省略了对公知功能和结构的描述。
本发明实施例的技术方案是通过获取当前区域内各区域以及站点的所有参数，然后把这些参数输入到提前拟合出的数学模型中进行计算，最后再对计算出的结果进行分析，从而达到利用大数据对所有参数进行整合的同时提供出了一个可以保证服务质量以及运营成本的可靠的选址方案。
图1是根据本发明实施例的配送站选址的方法的主要步骤的示意图。
如图1所示，本发明实施例的一种配送站选址的方法主要包括如下步骤：
步骤S11：将获取的已有路区信息以及已有站点信息按照预设的模式进行标准化处理，得到标准化数据。本步骤中首先需要获取各路区以及各站点的大数据，以便后续利用这些数据进行选址，也即本发明中的已有路区信息以及已有站点信息。
在一些实施例中，站点对应多个路区，路区的信息包括距离类数据以及订单量数据，站点的信息包括经纬度数据以及财务类数据，其中：
距离类数据包括：分拣中心到路区中心点的距离数据、路区中心点之间的距离数据、分拣中心到站点的传站距离。
订单量数据包括：配送员配送远近件单量，不同重量区间的订单量、站点月单量。
财务类数据包括：站点租金、车辆单位价格、站点管理人员月薪、配送员单量计提、司机月薪、站点运营成本、站点设备成本、城区平均月租金。
具体的，针对本发明中站点和路区所涉及到的名词做出解释，如下表1所示：
Figure PCTCN2018080619-appb-000007
表1站点和路区所涉及的名字
需要注意的是，获取的路区信息以及站点信息都是已经赋值的，具体的处理过程包括：将路区信息以及站点信息存储到数据库中，并根据属性阈值从数据库中进行筛选(数据库中存储的路区信息的所有数据属性以及站点信息的所有数据属性，因此，需要针对数据库中的数据属性进行筛选，也即筛选出赋值符合属性阈值的数据)，以筛选出的数据为标准化数据。在这里，标准化处理还可以为基本的数据仓库建立存储，如增、删、改、查等基本功能。
步骤S12：将标准化数据输入到预置的数学模型中，以输出的路区信息为选址方案。需要说明的是，该模型输出的选址方案可以是一个具体的点，也可以是一个特定的区域，本发明中，是以输出的路区信 息为选址方案的，也就是说，数学模型会根据限定条件及输入参数得出一个对于路区的评分，该评分目的是选择出满足限制条件(保证时效及服务体验的前提)内成本最低的建议建站的路区，该路区信息是固定且现有的，根据输出的路区信息，然后选择在该路区范围内建站便可实现最佳的位置进行建站。而数学模型是根据已有路区和已有站点的数据拟合出的，具体为：
Figure PCTCN2018080619-appb-000008
其中，ω是从分拣中心到站点的订单单位距离运输成本，d ij是从分拣中心i到路区j的距离，q tij是在第t个月需要从分拣中心i送达路区j的总订单数量，
Figure PCTCN2018080619-appb-000009
是在第t个月需要从分拣中心i送达路区j的重量5千克以内的订单数量，
Figure PCTCN2018080619-appb-000010
是在第t个月需要从分拣中心i送达路区j的重量5千克到10千克的订单数量，Y tjk是指示变量，Y tjk＝1代表在路区j的站点覆盖了路区k，否则Y tjk＝0，α是站点月成本方程中的常数项，α l是重量5千克以内订单单位配送成本函数中的常数项，α h是重量5千克到10千克订单单位配送成本函数中的常数项，X tj是指示变量，X tj＝1代表在第t个月路区j已建立站点，否则X tj＝0，t＝0代表前一年12月，β l是重量5千克以内订单单位配送成本函数中配送距离的系数，β h是重量5千克到10千克订单单位配送成本函数中配送距离的系数，d jk是路区j的中心点与路区k的中心点的距离。
另外，本发明主要考虑的是在当前区域内存在多个分拣中心的情况。若在当前区域内仅存在一个分拣中心，还可以对上述公式进行进一步的简化，简化后的公式为：
Figure PCTCN2018080619-appb-000011
其中，d j是从分拣中心到路区j的距离，q tj是在第t个月需要从分 拣中心送达路区j的总订单数量，
Figure PCTCN2018080619-appb-000012
是在第t个月需要从分拣中心送达路区j的重量5千克以内的订单数量，
Figure PCTCN2018080619-appb-000013
是在第t个月需要从分拣中心送达路区j的重量5千克到10千克的订单数量。
需要说明的是，本发明实施例的技术方案是将站点以及路区的的标准化数据依次输入到数学模型中，得到的输出数据也是相应的站点与路区的数据(换言之，输入的是路区信息，输出的也是路区信息)，然后再进行人工干预，根据这些数据与实际情况确定最终的选址方案。当然，为了得到更少的输出数据以便进行人工干预，可以取所有输出数据中的最小数据。
如上所述的情况，本步骤中确定出的选址方案可能与实际情况存在偏差(例如，初步方案中的位置可能在马路上或河床内)，因此，需要对该初步方案进一步优化。在这里，我们可以对选址方案进行可行性评价(即人工干预)，并确定最终选址方案，其中，可行性评价的影响因素包括出行需求、用地情况、交通情况和服务设施水平等因素。
此外，还可以将机器学习技术增加到配送站选址的方法中，实现不断优化数学模型，并提供更合理科学的终端配送站选址的方法。
图2是根据本发明实施例的配送站选址的方法的实现步骤的示意图。
由图2可以看出，本发明实施例的技术方案首先会获取一些大数据，主要包括距离类数据、运营财务类数据以及订单量数据，并将这些数据进行表转化的处理；然后再将这些处理后的数据输入到数学模型中进行方案生成，从而得出一个既节省了投资成本也覆盖了所有区域的选址方案。在具体的实施场景中，距离类数据包括：分拣中心到路区中心点的距离数据、路区中心点之间的距离数据、分拣中心到站 点的传站距离；订单量数据包括：配送员配送远近件单量，不同重量区间的订单量、站点月单量；财务类数据包括：站点租金、车辆单位价格、站点管理人员月薪、配送员单量计提、司机月薪、站点运营成本、站点设备成本、城区平均月租金。如图3所示，为本发明实施例的原有站点与分拣中心关系的选址方案示意图，通过运用本发明实施例的技术方案之后便得到图4所示的选址方案。
进一步的，本发明实施例中数学模型的限制条件如下表1所示：
Figure PCTCN2018080619-appb-000014
表1数学模型的限制条件
如表1所示，1a表示初始化站点的位置；1b表示确保现有站点租期未到之前不会被取消；1c表示确保站点数量不会减少；1d表示要求每个路区被一个站点覆盖；1e表示保证路区只能被有站点的路区覆盖；1f和1j表示定义X tj和Y tjk为0-1的变量。
根据本发明实施例的配送站选址的方法可以看出，因为采用路区和站点的各项参数以及预置的数学模型，进行配送站的选址的技术手段，所以克服了无法快速提供一个可靠的选址方案的技术问题，进而达到基于大数据来对配送站进行选址的技术效果，有利于提高在选址时的工作效率；通过对大数据的处理，有效地提高了对终端配送站选 址的工作的效果，在保证服务质量的同时实现了节约成本合理布局配送站的目的；通过将机器学习技术增加到终端配送站选址方法中实现不断优化数学模型，并提供更合理科学的终端配送站选址的方法。
图5是根据本发明实施例的配送站选址的装置的主要模块的示意图。
如图5所示，本发明实施例的配送站选址的装置50主要包括：第一处理模块51以及第二处理模块52。其中：
第一处理模块51，用于将获取的路区信息以及站点信息按照预设的模式进行标准化处理，得到标准化数据；
第二处理模块52，将标准化数据输入到预置的数学模型中，以输出结果为选址方案。
本发明实施例中，站点对应多个路区，其中，站点信息包括距离类数据以及订单量数据，站点信息包括经纬度数据以及财务类数据。
需要说明的是，数学模型是根据已有路区和站点的数据拟合出的，具体为：
Figure PCTCN2018080619-appb-000015
其中，ω是从分拣中心到站点的订单单位距离运输成本，d ij是从分拣中心i到路区j的距离，q tij是在第t个月需要从分拣中心i送达路区j的总订单数量，
Figure PCTCN2018080619-appb-000016
是在第t个月需要从分拣中心i送达路区j的重量5千克以内的订单数量，
Figure PCTCN2018080619-appb-000017
是在第t个月需要从分拣中心i送达路区j的重量5千克到10千克的订单数量，Y tjk是指示变量，Y tjk＝1代表在路区j的站点覆盖了路区k，否则Y tjk＝0，α是站点月成本方程中的常数项，α l是重量5千克以内订单单位配送成本函数中的常数项，α h是重量5千克 到10千克订单单位配送成本函数中的常数项，X tj是指示变量，X tj＝1代表在第t个月路区j已建立站点，否则X tj＝0，t＝0代表前一年12月，β l是重量5千克以内订单单位配送成本函数中配送距离的系数，β h是重量5千克到10千克订单单位配送成本函数中配送距离的系数，d jk是路区j的中心点与路区k的中心点的距离。
此外，本发明实施例的第一处理模块51具体用于：将路区信息以及站点信息存储到数据库中，并根据属性阈值从数据库中进行筛选，以筛选出的数据为标准化数据。
从以上描述可以看出，因为采用路区和站点的各项参数以及预置的数学模型，进行配送站的选址的技术手段，所以克服了无法快速提供一个可靠的选址方案的技术问题，进而达到基于大数据来对配送站进行选址的技术效果，有利于提高在选址时的工作效率；通过对大数据的处理，有效地提高了对终端配送站选址的工作的效果，在保证服务质量的同时实现了节约成本合理布局配送站的目的；通过将机器学习技术增加到终端配送站选址方法中实现不断优化数学模型，并提供更合理科学的终端配送站选址的方法。
根据本发明的实施例，本发明还提供了一种电子设备和一种可读存储介质。
本发明实施例的电子设备包括：至少一个处理器；以及，与至少一个处理器通信连接的存储器；其中，存储器存储有可被一个处理器执行的指令，指令被至少一个处理器执行，以使至少一个处理器能够执行本发明实施例的配送站选址的方法。
本发明的计算机可读存储介质，所述计算机可读存储介质存储计算机指令，所述计算机指令用于使所述计算机执行本发明所提供的配送站选址的方法。
下面参考图6，其示出了适于用来实现本发明实施例的终端设备的计算机***600的结构示意图。图6示出的终端设备仅仅是一个示例，不应对本发明实施例的功能和使用范围带来任何限制。
如图6所示，计算机***600包括中央处理单元(CPU)601，其可以根据存储在只读存储器(ROM)602中的程序或者从存储部分608加载到随机访问存储器(RAM)603中的程序而执行各种适当的动作和处理。在RAM 603中，还存储有***600操作所需的各种程序和数据。CPU 601、ROM 602以及RAM 603通过总线604彼此相连。输入/输出(I/O)接口605也连接至总线604。
以下部件连接至I/O接口605：包括键盘、鼠标等的输入部分606；包括诸如阴极射线管(CRT)、液晶显示器(LCD)等以及扬声器等的输出部分607；包括硬盘等的存储部分608；以及包括诸如LAN卡、调制解调器等的网络接口卡的通信部分609。通信部分609经由诸如因特网的网络执行通信处理。驱动器610也根据需要连接至I/O接口605。可拆卸介质611，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器610上，以便于从其上读出的计算机程序根据需要被安装入存储部分608。
特别地，根据本发明公开的实施例，上文主要步骤的流程图描述的过程可以被实现为计算机软件程序。例如，本发明公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行主要步骤的流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信部分609从网络上被下载和安装，和/或从可拆卸介质611被安装。在该计算机程序被中央处理单元(CPU)601执行时，执行本发明的***中限定的上述功能。
需要说明的是，本发明所示的计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质或者是上述两者的任意组合。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的***、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本发明中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行***、装置或者器件使用或者与其结合使用。而在本发明中，计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行***、装置或者器件使用或者与其结合使用的程序。计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、RF等等，或者上述的任意合适的组合。
附图中的流程图和框图，图示了按照本发明各种实施例的***、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，上述模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图或流程图中的每个方框、以及框图或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的***来实现，或者 可以用专用硬件与计算机指令的组合来实现。
描述于本发明实施例中所涉及到的模块可以通过软件的方式实现，也可以通过硬件的方式来实现。所描述的模块也可以设置在处理器中，例如，可以描述为：一种处理器包括第一处理模块以及第二处理模块。其中，这些单元的名称在某种情况下并不构成对该单元本身的限定。
作为另一方面，本发明还提供了一种计算机可读介质，该计算机可读介质可以是上述实施例中描述的设备中所包含的；也可以是单独存在，而未装配入该设备中。上述计算机可读介质承载有一个或者多个程序，当上述一个或者多个程序被一个该设备执行时，使得该设备包括：将获取的已有路区信息以及已有站点信息按照预设的模式进行标准化处理，得到标准化数据；将标准化数据输入到预置的数学模型中，以输出的路区信息为选址方案。
根据本发明实施例的技术方案，因为采用路区和站点的各项参数以及预置的数学模型，进行配送站的选址的技术手段，所以克服了无法快速提供一个可靠的选址方案的技术问题，进而达到基于大数据来对配送站进行选址的技术效果，有利于提高在选址时的工作效率；通过对大数据的处理，有效地提高了对终端配送站选址的工作的效果，在保证服务质量的同时实现了节约成本合理布局配送站的目的；通过将机器学习技术增加到终端配送站选址方法中实现不断优化数学模型，并提供更合理科学的终端配送站选址的方法。
上述具体实施方式，并不构成对本发明保护范围的限制。本领域技术人员应该明白的是，取决于设计要求和其他因素，可以发生各种各样的修改、组合、子组合和替代。任何在本发明的精神和原则之内所作的修改、等同替换和改进等，均应包含在本发明保护范围之内。

Claims (10)

  1. 一种配送站选址的方法，其特征在于，包括：
    将获取的已有路区信息以及已有站点信息按照预设的模式进行标准化处理，得到标准化数据；
    将所述标准化数据输入到预置的数学模型中，以输出的路区信息为选址方案。
  2. 根据权利要求1所述的方法，其特征在于，所述站点对应多个路区，其中，所述路区信息包括距离类数据以及订单量数据，所述站点信息包括经纬度数据以及财务类数据。
  3. 根据权利要求1所述的方法，其特征在于，所述数学模型是根据已有路区和已有站点的数据拟合出的，具体为：
    Figure PCTCN2018080619-appb-100001
    其中，ω是从分拣中心到站点的订单单位距离运输成本，d ij是从分拣中心i到路区j的距离，q tij是在第t个月需要从分拣中心i送达路区j的总订单数量，
    Figure PCTCN2018080619-appb-100002
    是在第t个月需要从分拣中心i送达路区j的重量5千克以内的订单数量，
    Figure PCTCN2018080619-appb-100003
    是在第t个月需要从分拣中心i送达路区j的重量5千克到10千克的订单数量，Y tjk是指示变量，Y tjk＝1代表在路区j的站点覆盖了路区k，否则Y tjk＝0，α是站点月成本方程中的常数项，α l是重量5千克以内订单单位配送成本函数中的常数项，α h是重量5千克到10千克订单单位配送成本函数中的常数项，X tj是指示变量，X tj＝1代表在第t个月路区j已建立站点，否则X tj＝0，t＝0代表前一年12月，β l是重量5千克以内订单单位配送成本函数中配送距离的系数，β h是重量5千克到10千克订单单位配送成本函数中配送距离的系数，d jk是路区j的中心点与路区k的中心点的距离。
  4. 根据权利要求1所述的方法，其特征在于，将获取的路区信息以及站点信息按照预设的模式进行标准化处理，得到标准化数据，具体包括：
    将所述路区信息以及所述站点信息存储到数据库中，并根据属性阈值从所述数据库中进行筛选，以筛选出的数据为标准化数据。
  5. 一种配送站选址的装置，其特征在于，包括：
    第一处理模块，用于将获取的已有路区信以及以已有及站点信息按照预设的模式进行标准化处理，得到标准化数据；
    第二处理模块，将所述标准化数据输入到预置的数学模型中，以输出的路区信息为选址方案。
  6. 根据权利要求5所述的装置，其特征在于，所述站点对应多个路区，其中，所述路区信息包括距离类数据以及订单量数据，所述站点信息包括经纬度数据以及财务类数据。
  7. 根据权利要求5所述的装置，其特征在于，所述数学模型是根据已有路区和已有站点的数据拟合出的，具体为：
    Figure PCTCN2018080619-appb-100004
    其中，ω是从分拣中心到站点的订单单位距离运输成本，d ij是从分拣中心i到路区j的距离，q tij是在第t个月需要从分拣中心i送达路区j的总订单数量，
    Figure PCTCN2018080619-appb-100005
    是在第t个月需要从分拣中心i送达路区j的重量5千克以内的订单数量，
    Figure PCTCN2018080619-appb-100006
    是在第t个月需要从分拣中心i送达路区j的重量5千克到10千克的订单数量，Y tjk是指示变量，Y tjk＝1代表在路区j的站点覆盖了路区k，否则Y tjk＝0，α是站点月成本方程中的常数项，α l是重量5千克以内订单单位配送成本函数中的常数项，α h是重量5千克 到10千克订单单位配送成本函数中的常数项，X tj是指示变量，X tj＝1代表在第t个月路区j已建立站点，否则X tj＝0，t＝0代表前一年12月，β l是重量5千克以内订单单位配送成本函数中配送距离的系数，β h是重量5千克到10千克订单单位配送成本函数中配送距离的系数，d jk是路区j的中心点与路区k的中心点的距离。
  8. 根据权利要求5所述的装置，其特征在于，所述第一处理模块具体用于：
    将所述路区信息以及所述站点信息存储到数据库中，并根据属性阈值从所述数据库中进行筛选，以筛选出的数据为标准化数据。
  9. 一种电子设备，其特征在于，包括：
    一个或多个处理器；
    存储装置，用于存储一个或多个程序，
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如权利要求1-4中任一所述的方法。
  10. 一种计算机可读介质，其上存储有计算机程序，其特征在于，所述程序被处理器执行时实现如权利要求1-4中任一所述的方法。
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