CN108171452B - Express delivery point addressing method and device - Google Patents
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Abstract
The embodiment of the invention discloses an express point site selection method and device, relates to the technical field of intelligent logistics, and can automatically calculate the site selection result of an express point and relieve the problem of restriction of working experience of personnel. The invention comprises the following steps: according to the extracted order data, coordinate points are obtained through electronic map analysis; clustering the order data according to the coordinate points, and determining the single amount information corresponding to each coordinate point; reading logistics data, and extracting express delivery points from the obtained coordinate points by using the logistics data and the single quantity information, wherein the logistics data at least comprises: the method comprises the steps of presetting distribution radius information of express delivery points and distribution distance information corresponding to each coordinate point; and outputting the extracted express delivery point as an address selection result. The invention is suitable for intelligent address selection of express delivery points.
Description
Technical Field
The invention relates to the technical field of intelligent logistics, in particular to an express delivery point addressing method and device.
Background
Along with the high-speed development of the logistics industry, express delivery points are more and more densely arranged, and the difficulty in arranging the express delivery points is more and more large. At present, the express point site selection in the logistics industry is mainly to determine the starting point position through simple manual scoring or combining the working experience of personnel according to the operation data of the existing express points, and needs that regional responsible persons have rich operation experience and have sensitive perception on the local market condition.
However, the method of manually determining the express delivery points is still not accurate enough, and due to different goods quantity change conditions of each distribution point and the influence of large-scale sales promotion activities, the situation of warehouse burst often occurs, and the current processing method still separates one distribution point into two distribution points nearby through a responsible person in a delegation region so as to meet the demand of delivery quantity. However, the extensive network establishment mode is restricted by the working experience of the personnel, which often causes a great waste of logistics resources, thereby increasing the operating cost.
Disclosure of Invention
The embodiment of the invention provides an express point address selection method and device, which can automatically calculate the address selection result of an express point and relieve the problem of restriction of working experience of personnel.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
and obtaining order distribution information through map analysis, counting the order distribution according to a period, and predicting the future development of the growth rate of a period of time in the future. Through address analysis of order addresses, single quantity distribution information of each coordinate point is gathered, a site selection model for site selection is maintained by a large data platform, and detailed address information and network point related data suitable for setting the express delivery point are calculated through the site selection model, so that the optimal position supporting service development requirement for setting the express delivery point is calculated. Therefore, the order data in a period of time (in specific prediction, non-daily data in special promotion periods such as twenty-one can be eliminated), and the future cargo volume is predicted, such as: express delivery points of about half a year in the future can be determined through single quantity prediction.
At present, the express delivery point is manually determined in a way of weight calculation mainly by manually setting the weight and collecting corresponding information for calculation. Especially, most franchised express companies and third-party express companies cannot invest too many resources to plan express points due to practical requirements such as operating cost and timeliness of setting, and often set the express points by means of personal experience.
In the invention, the method for acquiring the order coordinate information and the big data mathematical model by using the map analysis technology mainly solves the problem of site selection which is more judged by subjectivity and has no accurate data support in the selection process, calculates the reasonable position of the express delivery point and the distribution range of the express delivery point by using big data operation, and reduces the influence of artificial subjective judgment to the maximum extent.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a possible system architecture according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method provided by an embodiment of the present invention;
FIGS. 3, 4 and 5 are schematic diagrams of specific examples provided by the embodiments of the present invention;
fig. 6 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments. Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention. As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The method flow in this embodiment may be specifically executed on a system as shown in fig. 1, where the system includes: the system comprises a logistics strategy platform, a big data platform, a BI, an address resolution server and a user terminal, wherein all end devices of the system can establish channels through the Internet and perform data interaction through respective data transmission ports. Wherein:
the BI and address resolution server disclosed in this embodiment may be specifically a workstation, a super computer, or a server cluster for data processing, which is composed of multiple servers. BI (Business Intelligence), which is a technology used for integrating existing data in an enterprise, establishing an enterprise-level data warehouse and data mining products, helping the enterprise realize the application value of the data, and providing decision-making data support for the enterprise. For example, some enterprises in the industry implement full-flow monitoring of logistics transportation through BI systems, and can clearly know the transportation link of each parcel of a customer. And the address resolution server is mainly used for converting the address of the client into an accurate coordinate which can be identified by a map and establishing a binding relationship between the order and the coordinate, so that the specific position information of the client can be accurately mastered.
The big data platform disclosed in the embodiment is used for training screening modeling. On the hardware level, the device may be specifically a workstation, a super computer, or the like, or a server cluster for data processing, which is composed of a plurality of servers.
The logistics policy platform may be a server cluster for data processing, which is formed by a plurality of servers, on a hardware level, and is configured to run a corresponding execution program and execute the process in this embodiment.
The user terminal may be implemented as a single device, or integrated into various media data playing systems, such as a smart phone, a Tablet Personal Computer (Tablet Personal Computer), a Laptop Computer (Laptop Computer), a Personal Digital Assistant (PDA), and so on. The user terminal is generally used for ordering operations by a user, for example, the user operates the user terminal to log in an online shopping platform, selects a commodity to join in a shopping cart on the online shopping platform, then fills in a receiving address and submits an order, and the formed order data can be collected by an address resolution server.
It should be noted that, in practical application, the order submitted by the user terminal may be directly sent to the address resolution server and form order data, or sent to the front-end server of the online shopping platform first, and the order data formed by the online shopping platform is forwarded to the address resolution server. Further, the address resolution server may be one or a set of separate server systems, or the address resolution server may also be integrated in a server cluster of the online shopping platform and undertake the address resolution function of the online shopping platform.
An embodiment of the present invention provides an express delivery point addressing method, as shown in fig. 2, including:
and S1, analyzing the order data according to the extracted order data through an electronic map to obtain coordinate points.
The address resolution server extracts the order placing address of the user from the order data, resolves the address by contrasting an electronic map, resolves the order placing address into longitude and latitude, and obtains a coordinate point through map identification and analysis.
And S2, clustering the order data according to the coordinate points, and determining the single amount information corresponding to each coordinate point.
Wherein the single amount information includes: and the order quantity change condition of the corresponding coordinate point. The address resolution server may cluster order data according to the latitude and longitude of the order placing address of the user, for example: and clustering the order placement addresses with the longitude and the latitude in the same geographic range, and then clustering corresponding order data according to the clustering result of the order placement addresses. And according to the clustered order data, counting the single quantity information of each coordinate point, such as: and summarizing the unit quantity information according to the longitude and the latitude to obtain the daily average unit quantity information of each coordinate point.
And S3, reading logistics data, and extracting express delivery points from the acquired coordinate points by using the logistics data and the single quantity information.
Wherein the logistics data at least comprises: the distribution radius information of the preset express delivery points and the distribution distance information corresponding to the coordinate points. Specifically, an express delivery point which accords with the site selection model is extracted from the obtained coordinate points through the site selection model trained by the big data platform and by using the relevant parameters. The relevant parameters required to be used by the addressing model may include: the single quantity information, the preset delivery radius information of the express delivery points, the delivery distance information corresponding to each coordinate point, and the like can also comprise parameters such as delivery route information.
And S4, outputting the extracted express delivery points as address selection results.
Specifically, the process of extracting the express delivery point from the obtained coordinate point can be specifically executed on an address resolution server, for example, the address resolution server requests a big data platform to obtain an address selection model, and then screening calculation is performed by using related parameters; or directly executed by a big data platform, and outputting the address selection result to equipment such as a logistics strategy platform, an address resolution server and the like.
At present, the express delivery point is manually determined in a way of weight calculation mainly by manually setting the weight and collecting corresponding information for calculation. Especially, most franchised express companies and third-party express companies cannot invest too many resources to plan express points due to practical requirements such as operating cost and timeliness of setting, and often set the express points by means of personal experience.
Although the working mode of establishing the express delivery point is originally, fast and convenient, the working mode is also limited by personal experience, and is inaccurate and easy to make mistakes. Moreover, the front-end personnel in the logistics system are difficult to effectively predict the future cargo volume, so that the future expansion and relocation of the express delivery point are difficult to accurately plan. This has led to the fact that the existing manual site selection of the network site mainly depends on stores and manual selection of express delivery sites, and the manual site selection can only be judged according to the current single quantity: the development degree of the network points in a certain time in the future cannot be predicted, and the increase rate of the goods quantity of each network point is different due to regional reasons; due to the fact that the goods quantity of part of the net points is rapidly increased, large explosion-promoting bins (such as 418/618 and the like) are caused (the goods quantity is increased by 3-10 times), and then one net point is manually split into two net points nearby, and waste and repetition of resources exist; the delivery radius of partial network points is too long due to inaccurate manual site selection, so that the delivery speed of a courier is low, and the user experience is influenced.
In the embodiment, order distribution information is obtained through map analysis, order distribution is counted according to a period, and the future development prediction of the enhancement rate of a period of time in the future is predicted. Through address analysis of order addresses, single quantity distribution information of each coordinate point is gathered, a site selection model for site selection is maintained by a large data platform, and detailed address information and network point related data suitable for setting the express delivery point are calculated through the site selection model, so that the optimal position supporting service development requirement for setting the express delivery point is calculated. . Therefore, the order data in a period of time (in specific prediction, non-daily data in special promotion periods such as twenty-one can be eliminated), and the future cargo volume is predicted, such as: express delivery points of about half a year in the future can be determined through single quantity prediction. Specifically, the method for acquiring the order coordinate information and the big data mathematical model through the map analysis technology mainly solves the problem that more sites are selected by subjective judgment and no accurate data support exists in the selection process, calculates the reasonable position of the express delivery point and the distribution range of the express delivery point through big data operation, and reduces the influence of artificial subjective judgment to the maximum extent.
It should be noted that the site selection calculation task is usually initiated by the logistics policy platform, and the process of this embodiment is completed through the interactive data between the end devices in the system. The specific computation load of the site selection computation task may be performed by a device with a relatively high computation capability in the system shown in fig. 1, for example, the site selection result may be output by the logistics policy platform. If the computing resources of the address resolution server or the big data platform have enough margin, the address selection result can be output by the address resolution server or the big data platform. For example: as shown in fig. 3, after the address selection condition is input, a calculation task is initiated by the logistics strategy platform, so that the previous work of manually collecting a large amount of statistical data is changed, and the difficulty that manual large amount of data calculation software cannot support is reduced; as shown in fig. 3, the site selection result shows the difference between the existing site selection and the site selection from multiple dimensions such as the number of network points, the single volume, the cost of starting points, the cost of allocating, the number of distribution kilometers, and the like, taking a basalt area as an example, the existing 13 express points are optimized to cover a single volume area with 98.6% of network points set as 7 express points by using an express point site selection model.
The present embodiment further provides a specific scheme of step S1, that is, obtaining the coordinate point by analyzing the electronic map according to the extracted order data, including:
and S1-1, extracting the package information corresponding to each order number from the order data.
Wherein the package information includes: the recipient address. For example: the message granularity of the addressee can be the maximum street, and the minimum district house number, the sales point and the like. And S1-2, determining coordinate points pointed by the addressees corresponding to the order numbers from the electronic map.
And S2, clustering the order data according to the coordinate points, and determining the single amount information corresponding to each coordinate point. For example: as shown in fig. 4, for the delivery address placed by the user, the address coordinate in the package information of each order is obtained by using electronic map analysis, and the address coordinate is synchronized to the BI system for calculating the single amount information of the coordinate point. The method comprises the steps that address coordinates and order data are received through a BI, package information corresponding to each order number is obtained according to the order number, the package information in the interval is counted according to the dimensionality of nearly 3 months, nearly 6 months and nearly one year, the daily average single quantity information of each coordinate point in the time period is counted, and the same-ratio growth rate information of the area to which each coordinate point belongs is calculated. Specifically, the data can be processed and sorted by using BI, the daily average unit quantity information of each interest point in the area is counted according to the hierarchical dimensions of provinces, cities, counties and interest points, and the data is summarized according to daily increment (nearly 3 months, nearly 6 months and nearly one year).
And S3, reading logistics data, and extracting express delivery points from the acquired coordinate points by using the logistics data and the single quantity information.
And S4, outputting the extracted express delivery points as address selection results.
For example: the big data platform can regularly obtain the latest data of BI statistics every day, and synchronize to the database of the big data platform for storage, and the user uses model operation and express delivery point addressing results after initiating operation requirements. The site selection model trained by the large data platform can be specifically designed and operated by referring to a plurality of dimensions such as distribution distance, growth rate, coverage radius, single quantity upper and lower limits, maximum coverage interest points, lowest cost and the like, and is used for calculating site selection results. The logistics strategy platform initiates a calculation request task according to the dimensionality of a provincial region or a logistics center, and the big data platform refers to the single quantity, the distribution radius, the distribution distance and the coverage point number through a big data model according to relevant site selection conditions (such as the single quantity, the distribution radius, the site selection type and the growth rate) of the logistics request and calculates the site opening positions meeting the conditions.
Optionally, this embodiment further provides a specific scheme of step S2, that is, the clustering the order data according to the coordinate points in step S2 includes:
and S2-1, clustering the order data with the same addressee to a coordinate point corresponding to the same addressee.
Alternatively, it comprises: s2-1', the order data with the same addressee and the contact information of the same addressee are clustered to the same addressee.
Wherein, the package information corresponding to each order number further comprises: contact information of the recipient, such as the recipient's telephone number. Therefore, order data are clustered from multiple dimensions, and clustering accuracy is improved.
Further, the specific scheme of determining the single quantity information corresponding to each coordinate point in step S2 includes:
s2-2, for one coordinate point: and according to the order data clustered to the one coordinate point, counting to obtain the daily average order number corresponding to the one coordinate point.
And S2-3, calculating the order increase rate/decrease rate information of the one coordinate point in each time period according to the daily average order number of the one coordinate point in each time period.
For example: and receiving coordinate and order data through a BI (business intelligence) module, processing and sorting the data, counting daily average single quantity information of each interest point in the area according to province, city, district and county and hierarchical dimensions of the interest points, and summarizing the data according to daily increment (nearly 3 months, nearly 6 months and nearly one year). Acquiring the package information corresponding to each order number according to the order number, counting the package information in the interval according to the dimensionality of nearly 3 months, nearly 6 months and nearly one year, further counting the daily average unit quantity information of each coordinate point in the time period, and calculating the same-ratio growth rate information or the same-ratio reduction rate information of the region to which each coordinate point belongs.
The present embodiment further provides a specific solution of step S3, that is, the determining the single quantity information corresponding to each coordinate point includes:
s3-1, reading preset express delivery points, and determining coordinate points within the distribution radius coverage range of each express delivery point.
And S3-2, counting according to the single quantity information of the coordinate points in the distribution radius coverage range to obtain the sum of the single quantities in the distribution radius coverage range of each express delivery point.
S3-3, removing express delivery points with the sum of single quantities lower than the lower limit of the single quantities.
For example: as a possibility, a rectangle formed by the lower left-hand coordinates and the upper right-hand coordinates of all the aggregated coordinate points is used as a boundary, preset express delivery points are uniformly arranged in the rectangle, and the distance between adjacent points in the horizontal or vertical direction is a fixed value. As shown in fig. 5 (the unit of the horizontal and vertical coordinates in fig. 5 is an equal-scale distance unit of the map, for example, 1: 10000m, that is, 1 unit in the coordinates is equal to 1 ten thousand meters), the points arranged in a matrix are used as the preset express delivery points, and the irregular distribution other than the preset express delivery points is the coordinate points. The coordinate points in one matrix are connected with other coordinate points to determine whether all the positions can be covered, that is, whether the single quantity is enough, whether the distribution radius is within the range, whether the distribution distance of the points covers the most points, and other determination factors. And checking each preset express point, judging whether the sum of all coordinate point single quantities in the coverage radius range of the express point exceeds the lower limit of the starting point single quantity, and rejecting the preset express points which do not meet the conditions. And then checking whether each coordinate point is within the coverage radius of a certain express delivery point, wherein the coordinate points which do not meet the conditions are uncovered coordinate points and are placed into a coordinate point set which cannot be covered. Usually, the number of coordinate points in a coordinate point set which cannot be covered is limited, and the delivery problem can be solved by carrying out long-distance transportation on nearby express delivery points. And initiating a calculation request task according to the dimensionality of the provincial region or the logistics center, and calculating the website opening positions which meet the conditions by the big data through a big data model according to relevant site selection conditions (single quantity, distribution radius, site selection type and growth rate) of the logistics request by referring to the single quantity, the distribution radius, the distribution distance and the coverage points.
Further, the method can also comprise the following steps:
and S3-4, identifying to obtain the covered coordinate points.
Wherein the covered coordinate points include: and the coordinate points fall within the distribution radius coverage range of the express delivery points which are not rejected.
And S3-5, obtaining express delivery points output as the address selection result through an address selection model according to the covered coordinate points and the express delivery points which are not removed.
For example: after receiving data from the logistics strategy platform and analyzing the site selection condition, determining whether the planning task is express site selection or self-picking site selection, and extracting the interest points of the area to be planned, according to the consensus in the industry at present, the interest points can be understood as: a certain range of landmark points, and aggregating various points in a certain range of areas around the landmark points onto the landmark points, such as: a business center is a point of interest, but the locations contained in the business center include: coordinates of a series of locations of office building No. 1, office building No. 2 harbor No. 1, office building No. 4 harbor No. 2, office building No. 2, and the like.
Aggregating and adding single quantities of interest points with the straight line distance not more than the aggregation radius (such as 0.5KM) in the original interest points;
and taking a rectangle formed by the coordinates of the lower left corner and the upper right corner of all the aggregated interest points as a boundary, uniformly distributing the alternative express delivery points in the rectangle, and taking the distance between the adjacent points in the horizontal direction or the vertical direction as a fixed value.
And if the address is calculated for the first time, considering that the calculation of the express delivery point which is not locked is arranged at a reasonable position. Otherwise, the position of the proposed express point calculated in the previous round can be modified in a moving mode, a decision maker can move part of the proposed express points in the interface according to actual conditions, and after the express points are determined to be opened in the next model calculation, position data of other express points to be opened are calculated.
And checking each alternative express point, judging whether the sum of the single quantities of all interest points in the coverage radius range of the alternative express point exceeds the lower limit of the single quantity of the opening point, and rejecting the alternative express points which do not meet the conditions. And for the locked express delivery points, if the locked express delivery points do not meet the conditions, rejecting the locked express delivery points.
And checking whether each interest point is within the coverage radius of a certain express delivery point, wherein the interest points which do not meet the condition are uncovered interest points and are put into the interest point set which cannot be covered.
And establishing a site selection model by using the filtered alternative express delivery points and the interest points, and solving through data operation to obtain coordinates for opening the express delivery points and an interest point set radiated by the express delivery.
The big data platform can calculate address selection result information such as express point opening positions, single quantity, coverage interest points, line number, line coverage coordinates, maximum distribution distance and minimum distribution distance which meet conditions according to address selection conditions of users. After the big data returns the calculation result to the site selection system, the logistics strategy platform can display specific opening position information and related data of the network points.
The present embodiment further provides a specific scheme of step S4, that is, the outputting the extracted express delivery point as an address selection result includes:
s4-1, displaying the express delivery points output as the address selection results on the electronic map, and sending the electronic map to a strategy platform.
And the strategy platform is used for issuing the address selection result to the logistics service provider.
S4-2, generating an information list according to the express delivery points output as the address selection result, and sending the information list to the strategy platform.
Therefore, franchised express companies, third-party express companies and the like can conveniently obtain the site selection result from the logistics strategy platform and lay express points, and the traditional manual laying mode is replaced.
This embodiment still provides an express delivery point addressing device, includes:
the analysis module is used for analyzing the extracted order data through an electronic map to obtain a coordinate point;
the statistical module is used for clustering the order data according to the coordinate points and determining the single amount information corresponding to each coordinate point;
the screening module is used for reading logistics data, extracting express delivery points from the obtained coordinate points by using the logistics data and the single amount information, wherein the logistics data at least comprises: the method comprises the steps of presetting distribution radius information of express delivery points and distribution distance information corresponding to each coordinate point;
and the output module is used for outputting the extracted express points as the address selection results.
The analysis module is specifically configured to extract package information corresponding to each order number from order data, where the package information includes: a recipient address; determining coordinate points pointed by the addressees corresponding to the order numbers from the electronic map;
the statistical module is specifically configured to cluster order data with the same recipient address to coordinate points corresponding to the same recipient address. Or, the clustering the order data according to the coordinate points includes: clustering order data which have the same addressee and contact information of the same addressee to the same addressee, wherein the package information corresponding to each order number further comprises: contact information of the recipient, wherein for one coordinate point: according to the order data clustered to the coordinate point, counting to obtain the daily average order number corresponding to the coordinate point; and calculating the order increase rate/decrease rate information of the one coordinate point in each time period according to the daily average order quantity of the one coordinate point in each time period.
Further, the screening module is specifically configured to read preset express delivery points and determine coordinate points within a distribution radius coverage range of each express delivery point; counting to obtain the sum of the single quantities within the distribution radius coverage range of each express delivery point according to the single quantity information of the coordinate points within the distribution radius coverage range; then eliminating express points with the sum of the single quantity lower than the lower limit of the single quantity;
when the covered coordinate point is obtained through identification, obtaining an express delivery point output as an address selection result through an address selection model according to the covered coordinate point and the express delivery point which is not removed, wherein the covered coordinate point comprises: and the coordinate points fall within the distribution radius coverage range of the express delivery points which are not rejected.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points. The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An express delivery point addressing method is characterized by comprising the following steps:
according to the extracted order data, coordinate points are obtained through electronic map analysis;
clustering the order data according to the coordinate points, and determining the single amount information corresponding to each coordinate point, wherein the single amount information comprises: the order quantity change condition of the corresponding coordinate point;
reading logistics data, and extracting express delivery points from the obtained coordinate points by using the logistics data and the single quantity information, wherein the logistics data at least comprises: the method comprises the steps of presetting distribution radius information of express delivery points and distribution distance information corresponding to each coordinate point;
outputting the extracted express delivery points as address selection results;
after receiving data from a logistics strategy platform and analyzing site selection conditions, extracting interest points of an area to be planned, wherein the interest points comprise: a range of landmark locations; and carrying out polymerization and single quantity addition on the interest points of which the linear distance does not exceed the polymerization radius;
taking a rectangle formed by the coordinates of the lower left corner and the upper right corner of all the aggregated interest points as a boundary, uniformly distributing alternative express delivery points in the rectangle, and taking the distance between adjacent points in the horizontal or vertical direction as a fixed value;
checking each alternative express point, and eliminating alternative express points which do not meet the conditions; each interest point is checked, the interest points which do not meet the conditions are uncovered interest points, and the interest points are placed in the interest point set which cannot be covered;
and establishing a site selection model by using the filtered alternative express delivery points and the interest points, and solving through data operation to obtain coordinates for setting the express delivery points and an interest point set radiated by the express delivery.
2. The method of claim 1, wherein the obtaining coordinate points by electronic map parsing according to the extracted order data comprises:
extracting package information corresponding to each order number from order data, wherein the package information comprises: a recipient address;
and determining coordinate points pointed by the addressees corresponding to the order numbers from the electronic map.
3. The method of claim 2, wherein said clustering said order data according to said coordinate points comprises: clustering order data with the same receiving address to a coordinate point corresponding to the same receiving address;
or, the clustering the order data according to the coordinate points includes: clustering order data which have the same addressee and contact information of the same addressee to the same addressee, wherein the package information corresponding to each order number further comprises: contact information of the recipient.
4. The method of claim 1 or 3, wherein determining the single amount of information corresponding to each coordinate point comprises:
for one coordinate point: according to the order data clustered to the coordinate point, counting to obtain the daily average order number corresponding to the coordinate point;
and calculating the order increase rate/decrease rate information of the one coordinate point in each time period according to the daily average order number of the one coordinate point in each time period.
5. The method of claim 4, wherein the extracting, from the obtained coordinate points, the express delivery points using the logistics data and the single amount information comprises:
reading preset express delivery points, and determining coordinate points within the distribution radius coverage range of each express delivery point;
according to the single quantity information of the coordinate points within the distribution radius coverage range, counting to obtain the single quantity sum within the distribution radius coverage range of each express delivery point;
and eliminating express delivery points with the sum of the single amount lower than the lower limit of the single amount.
6. The method of claim 5, further comprising:
identifying and obtaining covered coordinate points, wherein the covered coordinate points comprise: coordinate points falling within the distribution radius coverage range of express delivery points which are not removed;
and obtaining express delivery points output as the address selection result through an address selection model according to the covered coordinate points and the express delivery points which are not removed.
7. The method of claim 1, wherein outputting the extracted express delivery point as an addressing result comprises:
displaying the express delivery points output as site selection results on the electronic map, and sending the electronic map to a logistics strategy platform, wherein the logistics strategy platform is used for issuing the site selection results to a logistics service provider;
and generating an information list according to the express delivery points output as the address selection result, and sending the information list to the logistics strategy platform.
8. An express delivery point addressing device, comprising:
the analysis module is used for analyzing the extracted order data through an electronic map to obtain a coordinate point;
the statistical module is used for clustering the order data according to the coordinate points and determining the single amount information corresponding to each coordinate point;
the screening module is used for reading logistics data, extracting express delivery points from the obtained coordinate points by using the logistics data and the single amount information, wherein the logistics data at least comprises: the method comprises the steps of presetting distribution radius information of express delivery points and distribution distance information corresponding to each coordinate point;
the output module is used for outputting the extracted express points as address selection results;
after receiving data from a logistics strategy platform and analyzing site selection conditions, extracting interest points of an area to be planned, wherein the interest points comprise: a range of landmark locations; and carrying out polymerization and single quantity addition on the interest points of which the linear distance does not exceed the polymerization radius;
taking a rectangle formed by the coordinates of the lower left corner and the upper right corner of all the aggregated interest points as a boundary, uniformly distributing alternative express delivery points in the rectangle, and taking the distance between adjacent points in the horizontal or vertical direction as a fixed value;
checking each alternative express point, and eliminating alternative express points which do not meet the conditions; each interest point is checked, the interest points which do not meet the conditions are uncovered interest points, and the interest points are placed in the interest point set which cannot be covered;
and establishing a site selection model by using the filtered alternative express delivery points and the interest points, and solving through data operation to obtain coordinates for setting the express delivery points and an interest point set radiated by the express delivery.
9. The apparatus according to claim 8, wherein the analysis module is specifically configured to extract package information corresponding to each order number from order data, and the package information includes: a recipient address; determining coordinate points pointed by the addressees corresponding to the order numbers from the electronic map;
the statistical module is specifically configured to cluster order data having the same recipient address to coordinate points corresponding to the same recipient address, or cluster the order data according to the coordinate points, and includes: clustering order data which have the same addressee and contact information of the same addressee to the same addressee, wherein the package information corresponding to each order number further comprises: contact information of the recipient, wherein for one coordinate point: according to the order data clustered to the coordinate point, counting to obtain the daily average order number corresponding to the coordinate point; and calculating the order increase rate/decrease rate information of the one coordinate point in each time period according to the daily average order quantity of the one coordinate point in each time period.
10. The device according to claim 9, wherein the screening module is specifically configured to read preset express delivery points and determine coordinate points within a distribution radius coverage range of each express delivery point; counting to obtain the sum of the single quantities within the distribution radius coverage range of each express delivery point according to the single quantity information of the coordinate points within the distribution radius coverage range; then eliminating express points with the sum of the single quantity lower than the lower limit of the single quantity;
when the covered coordinate point is obtained through identification, according to the covered coordinate point and the express delivery point which is not removed, the express delivery point which is output as the address selection result is obtained through an address selection model, wherein the covered coordinate point comprises: and the coordinate points fall within the distribution radius coverage range of the express delivery points which are not rejected.
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