CN112541624B - Site selection method, device, medium and electronic equipment for collecting throwing net points - Google Patents
Site selection method, device, medium and electronic equipment for collecting throwing net points Download PDFInfo
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Abstract
The embodiment of the application discloses a method, a device, a medium and electronic equipment for selecting a cable-drop network point. Comprising the following steps: determining a primary point set of the net points by adopting a set coverage model according to the client distribution points in the arrangement area; screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets; adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining the optimal solution of the number of each candidate point; adopting a balance algorithm to redistribute the service range by using each number of candidate points in the constraint range; and determining the serial routes from the candidate points to the client distribution points by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial routes to obtain the final selection result of the candidate points. The method for solving the address selection problem on the basis of large data volume is implemented, the candidate model is optimized, the cost is low, and the actual requirement can be met.
Description
Technical Field
The embodiment of the application relates to the technical field of logistics, in particular to a site selection method, a device, a medium and electronic equipment for collecting a throwing site.
Background
With the rapid development of logistics industry, express delivery is used as a pioneer of third party logistics and increasingly permeates various social and economic fields. The cable throwing point is taken as an important end node of the logistics network and is an infrastructure in the logistics system. In the working process of enhancing the optimization construction of the cable and the throwing network points and improving the cable and throwing capability and the service quality, the site selection layout, the service range, the resource allocation and the like of the cable and throwing network points are required to be planned scientifically and reasonably, so that the effective distribution and utilization of resources such as manpower, material resources, financial resources and the like of the cable and throwing network points are realized, and the delivery operation efficiency and the service quality are improved.
Common site-selection models generally assume that the distribution of the site to the customer sites is radial, i.e., vehicles from the site return to the site after each customer visit.
The traditional method needs manual on-site investigation, has small data acquisition quantity, only considers the distance from the net point to the client point, omits the consideration of the vehicle tour route, and causes the problems of high cost, difficult actual distribution route after site selection and the like.
Disclosure of Invention
The embodiment of the application provides a method, a device, a medium and electronic equipment for selecting sites, which can solve the problem of address selection of the type on a large data volume, optimize candidate models, have low cost and can meet actual demands.
In a first aspect, an embodiment of the present application provides a method for selecting a site, where the method includes:
determining a primary point set of the net points by adopting a set coverage model according to the client distribution points in the arrangement area;
screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets;
adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining the optimal solution of the number of each candidate point; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the allocated client distribution points is shortest;
and determining a serial route from the candidate points to the client distribution points by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial route to obtain a final selection result of the candidate points.
In a second aspect, an embodiment of the present application provides an apparatus for selecting a site, where the apparatus includes:
the initial point set determining module is used for determining initial point sets of the net points by adopting a set coverage model according to the client distribution points in the arrangement area;
the candidate point set determining module is used for screening the initial point set according to a preset rule, and determining the screened initial point and the existing mesh point as the candidate point set;
the optimal solution determining module is used for restraining the number of candidate points of the candidate point set by adopting a P-median model and determining an optimal solution of the number of each candidate point; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the allocated client distribution points is shortest;
and the final selection result determining module is used for determining a serial route from the candidate point to the client distribution point of the vehicle by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial route to obtain a final selection result of the candidate points.
In a third aspect, embodiments of the present application provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor implements a method for site selection for a cable or drop as described in embodiments of the present application.
In a fourth aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and capable of being executed by the processor, where the processor executes the computer program to implement a method for selecting an advertisement website according to an embodiment of the present application.
According to the technical scheme provided by the embodiment of the application, a primary point set of the net points is determined by adopting a set coverage model according to the client distribution points in the arrangement area; screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets; adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining the optimal solution of the number of each candidate point; and determining the serial routes from the candidate points to the client distribution points by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial routes to obtain the final selection result of the candidate points. By executing the technical scheme, the method for solving the site selection problem on the basis of large data volume can be used for optimizing the candidate model, is low in cost and can meet actual requirements.
Drawings
FIG. 1 is a flow chart of a method for selecting a drop network point according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a first choice point model provided in accordance with an embodiment of the present application;
fig. 3 is a schematic structural diagram of an address selecting device for collecting a throwing site according to a second embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present application are shown in the drawings.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example 1
Fig. 1 is a flowchart of a method for selecting an address of a cable and drop network in accordance with an embodiment of the present application, where the embodiment is applicable to a situation where an address of a cable and drop network is selected, and the method may be performed by an address selecting device of the cable and drop network in accordance with an embodiment of the present application, where the device may be implemented by software and/or hardware, and may be integrated in an electronic device for selecting an address of a cable and drop network.
As shown in FIG. 1, the method for selecting the site of the cable-cast net comprises the following steps:
s110, determining a primary point set of the mesh points by adopting a set coverage model according to the client distribution points in the arrangement area.
In the present embodiment, the arrangement region may be a range of regions. For example, the area may be an area 2 kilometers from an existing dot radius, or an area 3 kilometers from an existing dot radius, etc.
In this embodiment, the client distribution point may be a location of the client on the map, or may be an address of the client. Preferably, the customer distribution point may be the customer's address.
In this embodiment, the set of preliminary points may be composed of new preliminary points composed of customer distribution points.
In this embodiment, the aggregate coverage model may be a model for selecting the initial selection point of the pair.
In this embodiment, a set coverage model is used to determine a set of initial points of the mesh points, first, all the client distribution points are used as candidate points, and then the set coverage model selects the least number of points from the candidate points as initial points, so that the set coverage model covers all the client distribution points within the average service radius range of the existing mesh points.
Fig. 2 is a schematic diagram of a primary point model provided in this embodiment, as shown in fig. 2, in which a small circle represents a customer distribution point, a center of a large circle represents a primary point, and a size of the large circle is determined based on an average service radius. The scheme aims at enabling the number of the primary selection points to be as small as possible on the basis that the large circle covers all the small circles.
Illustratively, the initial point set of the mesh points is determined by using the set coverage model, and the following specific steps can be adopted: let d be the average service radius of the existing network point, P be the customer distribution point and the key value pair formed by the customer distribution points within d, the key be the customer distribution point P, the value be the set s of all the customer distribution points with the distance less than or equal to d. Step 1: selecting the key value pair with the largest s number in P, and taking out the key value P1 into a primary selection point set; step 2: updating P, deleting P1, and deleting a client distribution point set corresponding to P1 in all s; step 3: repeating the step 1 and the step 2 until all the customer distribution points are covered; step 4: outputting the initial point set.
In this technical solution, optionally, before determining the initial set of points of the mesh point by adopting the set coverage model according to the client distribution points in the layout area, the method further includes:
acquiring customer distribution points in an arrangement area;
and preprocessing the client distribution point by adopting at least one longitude and latitude grabbing platform according to the longitude and latitude data of the client distribution point.
In this embodiment, the latitude and longitude data of the client distribution point may be the latitude and longitude data of the client address. The longitude and latitude are the combination of longitude and latitude to form a coordinate system, which is called a geographic coordinate system, and the coordinate system is a spherical coordinate system which uses a spherical surface of a three-dimensional space to define a space on the earth and can mark any position on the earth. The customer distribution point can be accurately determined by determining the longitude and latitude.
In this embodiment, the capturing platform may be a hundred-degree map capturing platform or a high-german map capturing platform.
In this embodiment, according to the longitude and latitude data of the client distribution point, at least one longitude and latitude capturing platform is adopted to preprocess the client distribution point, firstly, client address data is cleaned, including special characters, irrelevant information, building information and the like are removed, and the address after cleaning retains address information of an entity; and secondly, acquiring longitude and latitude data according to the address data. After longitude and latitude data are obtained, gridding the grabbed longitude and latitude data, and reserving the accuracy after decimal points. The preprocessing of the customer distribution points is completed. Preferably, 4 precision after the decimal point may be preserved. The platform needs to be replaced for grabbing under the condition that a plurality of addresses correspond to the same longitude and latitude.
By preprocessing the client distribution points, longitude and latitude data of the client distribution points with unified rules can be obtained, and data support can be provided for subsequent calculation.
And S120, screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets.
In this embodiment, the candidate point set may be a candidate set that is previously used as a screen point. It can be understood that the primary selection points are screened, and the screened primary selection points and the existing mesh points are determined to be a candidate point set, and the set can meet part of requirements of being taken as the throwing mesh points.
In this embodiment, the preset rule may be a rule having a certain requirement on the number of client distribution points included in the coverage area of the initial selection point, or may be a rule having a certain requirement on the radius of the initial selection point. It can be understood that the primary selection point set is screened by the preset rule, and the primary selection point meeting a certain condition can be used as a candidate point. For example, the number of included customer distribution points may be more than 5 primary points as candidate points; it is also possible to use as candidate points the primary selection points having a radius greater than 2 km.
In this technical scheme, optionally, screening the initial selection point set according to a preset rule includes:
and screening the primary selection point set according to the distance between the primary selection point and the existing network points and the number of the client distribution points in the preset service radius of each primary selection point.
The distance between the initial selection point and the existing mesh point can be a straight line distance.
The number of the customer distribution points in the preset service radius of each primary selection point can be determined when the primary selection point set is determined.
In this embodiment, the distance between the primary selected point and the existing mesh point is used to screen the primary selected point set, that is, the distance between the primary selected point and the existing mesh point is greater than the average service radius of the existing mesh point as a screening condition; or the distance between the initial selection point and the existing mesh point is equal to the average service radius of the existing mesh point, and the initial selection point set is screened by taking the average service radius as a screening condition.
In this embodiment, the number of customer distribution points in the preset service radius of each primary selection point is selected, and the primary selection point set is selected by taking the number of customer distribution points in the preset service radius of each primary selection point as a selection condition; or the number of the customer distribution points in the preset service radius of each initial selection point is smaller than a certain threshold value as a screening condition to screen the initial selection point set. The threshold value can be set according to site selection requirements.
And screening the primary selection point set according to the distance between the primary selection point and the existing network points and the number of the customer distribution points in the preset service radius of each primary selection point, fully considering the positions of the candidate points relative to the customers, screening out the primary selection points meeting the maximum capacity of the network points and the traffic of the customers, and improving the picking efficiency.
In this technical scheme, optionally, according to the distance between the primary selection point and the existing network point and the number of customer distribution points in the preset service radius of each primary selection point, the screening of the primary selection point set includes:
if the distance between the primary selected point and the existing network point is smaller than the preset service radius, deleting the primary selected point; the method comprises the steps of,
and if the number of the customer distribution points in the preset service radius of the initial selection point is smaller than a set threshold value, deleting the initial selection point.
In this embodiment, the preset service radius may be a radius set according to requirements when the mesh point is constructed. For example, the preset service radius may be 2 km or 3 km.
In this embodiment, the threshold may be set according to the site selection requirement. For example, the threshold may be 10 or 15. I.e. the number of customer distribution points within the pre-set service radius of the initial selection point is at least 10, or 15.
In this embodiment, the primary selected points whose distance from the primary selected point to the existing mesh point is smaller than the preset service radius and the primary selected points whose number of client distribution points within the preset service radius of the primary selected points is smaller than the set threshold are deleted, and the remaining primary selected points are used as candidate points to form a candidate point set together with the existing mesh point.
The method has the advantages that the distance between the primary selected point and the existing network point and the number of the customer distribution points in the preset service radius of each primary selected point are screened, primary selected points which do not meet the conditions are deleted, the positions of candidate points relative to customers are fully considered, primary selected points meeting the maximum capacity of the network point and the traffic of the customers can be screened, and the picking efficiency is improved.
S130, adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining an optimal solution of the number of each candidate point; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the assigned client distribution points is shortest.
In this embodiment, the P-median model may refer to finding the appropriate location for P facilities and assigning each demand point to a particular facility for a given number and location of demand sets and a set of candidate facility locations, respectively, to minimize transportation costs between the plant and the demand points. It can be appreciated that the constraint on the number of candidate points in the candidate point set by using the P-median model may be that the weighted distance between the candidate point and the assigned client distribution point is calculated, and the weighted distance is the shortest as the optimal solution of the candidate point data.
In this embodiment, the constraint on the number of candidate points of the candidate point set by using the P-median model may be that the number of candidate points of the candidate point set is deleted, or that the number of candidate points of the candidate point set is increased, and preferably, the number of candidate points of the candidate point set is deleted.
In this technical solution, optionally, a constraint interval for constraining the number of candidate points of the candidate point set is determined by using the following formula:
k∈[m×(1-30％)，m×(1+30％)]；
where k is the number of candidate points and m is the number of existing dots in the candidate point set.
In this embodiment, the number of candidate points may be determined according to the number of existing dots in the candidate point set. For example, when the number of existing dots in the candidate dot set is 10, the number of candidate dots is 7-13.
For example, let l be customer traffic, d be the distance from the primary point to the customer distribution point, m be the number of existing points in the area, J initialize to the primary point set, for interval [ m (1-30%), m (1+30%)]Each integer value k within. Wherein the area may be determined by an existing mesh point. The method comprises the following specific steps: step 1: calculating the distance from each customer distribution point to all points in J; step 2, setting a circulation variable p=the number of initial selection point sets, calculating the nearest site selection point of a client distribution point, taking the product of the distance and the traffic volume l as a weight p, and taking a target C as the sum of all p; step 3: taking any one of the addressing points J in the pass J, reassigning the client distribution point assigned to the J to other addressing points according to the nearest principle, calculating a new assigned target, and marking the new assigned target as C j The method comprises the steps of carrying out a first treatment on the surface of the Step 4, when J is taken through the set J, J0 is selected to enable C j0 ＝max{C j -a }; and delete j0 in the collection; and 5, stopping the algorithm when p=k, otherwise, returning to the step 2.
It can be understood that, if the initial selection point set includes 15 initial selection points and the existing mesh points are 10, the interval is 7-13, that is, the number of candidate points in the interval is 7-13, the target of the number of candidate points in the interval is calculated, and the optimal solution of the number of candidate points is determined. Firstly, calculating the distance from each customer distribution point to the distribution point, distributing each customer distribution point to a candidate point closest to the distribution point, and then selecting and taking one candidate point; wherein the target satisfies a minimum condition; and sequentially repeating until the candidate point data is 7, and ending the calculation. An optimal solution for each candidate point number may be determined.
The number of the candidate points can be determined according to the number of the existing dots in the candidate point set, and the client distribution points and the dot capacity can be fully considered by determining the number of the candidate points, so that the efficiency of the batch delivery can be improved.
In this technical solution, optionally, after constraining the number of candidate points of the candidate point set by using a P-median model and determining an optimal solution for each number of candidate points, the method further includes:
and adopting a balance algorithm to redistribute the service range by using each number of candidate points in the constraint range.
In this embodiment, the delivery range of the candidate points may be adjusted by using a balancing algorithm, so that the throughput of the mesh points satisfies the constraint condition, and the service range of each optimized number of candidate points is obtained. The constraint condition may be that the daily throughput of the assignment result is less than the minimum throughput, or that the dot throughput is less than 80% of the maximum dot capacity.
The balancing algorithm calculates the service range of each number of candidate points by the following specific steps: let d be the average service radius of the existing mesh point, step 1: assigning clients to the site selection points by taking the distance between the client distribution points and the candidate points as priority, and assigning the clients to next-nearest candidate points when the capacity of the candidate points reaches the maximum capacity; step 2: if the assignment result in the step 1 has candidate points with daily throughput smaller than the minimum throughput, limiting the maximum capacity of the candidate points to 80% of the original value, and repeating the step 1; step 3: step 2, when the candidate points with daily throughput smaller than the minimum daily throughput still exist in the assignment result after repeating the step 5, replacing the candidate points, and repeating the steps 1 and 2 until all the candidate points meet the constraint of the website throughput; step 4: and calculating the average service radius under the new assignment, deleting the candidate points of the number if the average service radius is larger than d, gradually increasing the number of the candidate points, and repeating the steps 1, 2 and 3.
By adopting a balance algorithm, the service range is redistributed by the candidate points of each quantity in the constraint range, so that the distribution of the client distribution points in each candidate point can be balanced, and the distribution and delivery efficiency is improved.
And S140, determining a serial route from the candidate points to the client distribution points by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial route to obtain a final selection result of the candidate points.
In this embodiment, the time window constraint may be that the sum of the vehicle travel time and the processing time after reaching the customer address is less than the single pass processing time period. The single-trip processing time period can be the time from the departure to the return of the vehicle. For example, when the vehicle starts 8 a.m., comes back 12 a.m., the single pass treatment is 4 hours long. It can be understood that, after the candidate point is determined, if the sum of the processing time after the vehicle starts to travel from the candidate point and reaches the customer address is smaller than the single-pass processing time, it is indicated that the processing time after the vehicle travels and reaches the customer address is shorter at this time, and the route is better.
In the present embodiment, the vehicle capacity constraint condition may be that the load capacity of the vehicle is less than or equal to the vehicle maximum capacity. For example, if the maximum vehicle capacity is 10L, the vehicle load may be 5L or 8L. An optimal vehicle configuration may be determined based on the maximum capacity of the vehicle and the load of the vehicle.
In this embodiment, the preset algorithm may be a path planning algorithm. The method is used for planning the running path of the vehicle so that the running path from the candidate point to the client distribution point of the vehicle is the shortest.
In this embodiment, a serial route from the candidate point to the customer distribution point is determined by a preset algorithm, and after the serial route is determined, the optimal number of candidate points may be determined according to the serial route. For example, when the number of candidate points is 8, the serial route is more excellent than the serial route when the number of candidate points is 9, and the optimal number of candidate points is 8. Wherein the optimal number of candidate points can be determined according to the shortest sum of the serial routes. After determining the optimal number of candidate points, the optimal number of candidate points satisfying the number can be selected from the candidate point set as the final selection result of the candidate points.
In this technical scheme, optionally, according to the time window constraint condition and the vehicle capacity constraint condition, a serial route from the candidate point to the client distribution point of the vehicle is determined by adopting a preset algorithm, and the optimal number of candidate points is determined by using the serial route, so as to obtain a final selection result of the candidate points, including:
determining a serial route from a candidate point to a customer distribution point by using an ortools solver according to the time window constraint condition and the vehicle capacity constraint condition;
taking the shortest serial route as an optimizing condition, and determining the shortest serial route as the optimal candidate point number by using the candidate points of each number;
and taking the optimal candidate point number and an optimal solution of the optimal candidate point number as a final selection result of the candidate points.
In this embodiment, for different numbers of obtained location points and service ranges, according to time window constraint and vehicle capacity constraint conditions, an ortools solver is utilized to solve optimal vehicle configuration and serial driving routes, and according to the shortest driving route, the number of optimal candidate points is determined, the service range, resource configuration and vehicle path planning corresponding to the optimal location points are obtained, and the final selection result of the candidate points is determined. The ortools algorithm is an open source software suite for optimization, and is used for solving the problems of vehicle path, flow, integer and linear programming, constraint programming and the like.
It can be appreciated that according to the time window constraint condition and the vehicle capacity constraint condition, then the serial route from the candidate point to the client distribution point of the vehicle can be determined by utilizing the ortools algorithm, and then the shortest serial route is selected as the optimizing condition, so that the optimal candidate point number can be determined. After determining the optimal number of candidate points, an optimal solution of the optimal number of candidate points can be predetermined as a final selection result according to the optimal number of candidate points. For example, the candidate point set includes 3 candidate points, the candidate points are A, B and C, the number of determined candidate points is 2, serial routes between the client distribution points and the candidate points are calculated respectively, when the serial routes between the client distribution points and the candidate points a and B are 5 km, the serial routes between the client distribution points and the candidate points a and C are 10 km, and the serial routes between the client distribution points and the candidate points B and C are 15 km, the candidate points a and B can be used as final selection results of the candidate points.
And determining the optimal candidate point number according to the shortest serial route serving as the optimizing condition, and determining an optimal solution of the optimal candidate point number according to the optimal candidate point number and the preset optimal solution of the optimal candidate point number as a final selection result of the candidate points. The method fully considers the distribution points, the net point quantity and the serial routes of the clients, solves the problem of address selection of the type on a large data quantity, optimizes the candidate model, has low cost and can meet the actual requirements.
According to the technical scheme provided by the embodiment of the application, a primary point set of the net points is determined by adopting a set coverage model according to the client distribution points in the arrangement area; screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets; adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining the optimal solution of the number of each candidate point; and determining the serial routes from the candidate points to the client distribution points by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial routes to obtain the final selection result of the candidate points. By executing the technical scheme, the method for solving the site selection problem on the basis of large data volume can be used for optimizing the candidate model, is low in cost and can meet actual requirements.
Example two
Fig. 3 is a schematic diagram of an address selecting device for collecting a throwing site according to a second embodiment of the present application. As shown in fig. 3, the site selection device for the cable-drop network comprises:
a preliminary choice point set determining module 310, configured to determine a preliminary choice point set of the mesh points by adopting a set coverage model according to the client distribution points in the layout area;
the candidate point set determining module 320 is configured to screen the initial point set according to a preset rule, and determine the screened initial point and the existing mesh point as a candidate point set;
the optimal solution determining module 330 is configured to constrain the number of candidate points of the candidate point set by using a P-median model, and determine an optimal solution for the number of candidate points; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the allocated client distribution points is shortest;
and the final selection result determining module 340 is configured to determine a serial route from the candidate point to the client distribution point by using a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determine the optimal number of candidate points according to the serial route, so as to obtain a final selection result of the candidate points.
In this technical solution, optionally, the apparatus further includes:
and the service range reassignment module is used for adopting a balance algorithm to reassign the service range by using each number of candidate points in the constraint range.
In this technical solution, optionally, the apparatus further includes:
the client distribution point acquisition module is used for acquiring client distribution points in the arrangement area;
and the data preprocessing module is used for preprocessing the client distribution points by adopting at least one longitude and latitude grabbing platform according to the longitude and latitude data of the client distribution points.
In this technical solution, optionally, a constraint interval for constraining the number of candidate points of the candidate point set is determined by using the following formula:
k∈[m×(1-30％)，m×(1+30％)]；
where k is the number of candidate points and m is the number of existing dots in the candidate point set.
In this technical solution, optionally, the candidate point set determining module 320 includes:
and the primary selection point set screening unit is used for screening the primary selection point set according to the distance between the primary selection point and the existing network points and the number of client distribution points in the preset service radius of each primary selection point.
In this technical scheme, optionally, the primary selection point collection screening unit is specifically used for:
if the distance between the primary selected point and the existing network point is smaller than the preset service radius, deleting the primary selected point; the method comprises the steps of,
and if the number of the customer distribution points in the preset service radius of the initial selection point is smaller than a set threshold value, deleting the initial selection point.
In this embodiment, optionally, the final selection result determining module 340 includes:
the serial route determining unit is used for determining a serial route from a candidate point to a customer distribution point of the vehicle by using an ortools solver according to the time window constraint condition and the vehicle capacity constraint condition;
a candidate point number determining unit configured to determine, as an optimal candidate point number, a shortest one of the serial routes determined by the candidate points of each number, with the shortest serial route as a optimizing condition;
and the final selection result determining unit is used for determining the optimal solution of the optimal candidate point number in advance as the final selection result of the candidate points according to the optimal candidate point number.
The product can execute the method provided by the embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
Example III
A third embodiment of the present application also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method of site selection for a drop, the method comprising:
determining a primary point set of the net points by adopting a set coverage model according to the client distribution points in the arrangement area;
screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets;
adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining the optimal solution of the number of each candidate point; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the allocated client distribution points is shortest;
and determining a serial route from the candidate points to the client distribution points by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial route to obtain a final selection result of the candidate points.
Storage media refers to any of various types of memory electronic devices or storage electronic devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk or tape devices; computer system memory or random access memory such as DRAM, DDR RAM, SRAM, EDO RAM, lanbas (Rambus) RAM, etc.; nonvolatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a computer system in which the program is executed, or may be located in a different second computer system connected to the computer system through a network (such as the internet). The second computer system may provide program instructions to the computer for execution. The term "storage medium" may include two or more storage media that may reside in different unknowns (e.g., in different computer systems connected by a network). The storage medium may store program instructions (e.g., embodied as a computer program) executable by one or more processors.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present application is not limited to the above-mentioned site selection operation of the cable-drop site, and may also perform the related operations in the site selection method of the cable-drop site provided in any embodiment of the present application.
Example IV
The fourth embodiment of the present application provides an electronic device, in which the location device of the pick-and-place point provided in the embodiments of the present application may be integrated, where the electronic device may be configured in a system, or may be a device that performs some or all of the functions in the system. Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present application. As shown in fig. 4, the present embodiment provides an electronic device 400, which includes: one or more processors 420; the storage device 410 is configured to store one or more programs, where the one or more programs are executed by the one or more processors 420, so that the one or more processors 420 implement the method for locating a cable drop provided in the embodiments of the present application, and the method includes:
determining a primary point set of the net points by adopting a set coverage model according to the client distribution points in the arrangement area;
screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets;
adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining the optimal solution of the number of each candidate point; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the allocated client distribution points is shortest;
and determining a serial route from the candidate points to the client distribution points by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial route to obtain a final selection result of the candidate points.
Of course, those skilled in the art will appreciate that the processor 420 also implements the solution of the method for locating a cable/socket provided in any embodiment of the present application.
The electronic device 400 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 4, the electronic device 400 includes a processor 420, a storage device 410, an input device 430, and an output device 440; the number of processors 420 in the electronic device may be one or more, one processor 420 being taken as an example in fig. 4; the processor 420, the storage device 410, the input device 430, and the output device 440 in the electronic device may be connected by a bus or other means, as exemplified by connection via a bus 450 in fig. 4.
The storage device 410 is used as a computer readable storage medium for storing a software program, a computer executable program, and a module unit, such as program instructions corresponding to the method for selecting an advertisement screen in the embodiment of the present application.
The storage device 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, the storage 410 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, storage device 410 may further include memory located remotely from processor 420, which may be connected via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may be used to receive input numeric, character information, or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 440 may include an electronic device such as a display screen, a speaker, etc.
The electronic equipment provided by the embodiment of the application can solve the problem of solving the site selection problem on a large data volume, optimize the candidate model, has low cost and can meet the actual demand.
The device, the medium and the electronic equipment for selecting the site of the cable and the drop provided in the embodiment can execute the method for selecting the site of the cable and the drop provided in any embodiment of the application, and have the corresponding functional modules and beneficial effects of executing the method. Technical details not described in detail in the above embodiments can be referred to the method for locating the cable-drop network points provided in any embodiment of the present application.
Note that the above is only a preferred embodiment of the present application and the technical principle applied. Those skilled in the art will appreciate that the present application is not limited to the particular embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the present application. Therefore, while the present application has been described in connection with the above embodiments, the present application is not limited to the above embodiments, but may include many other equivalent embodiments without departing from the spirit of the present application, the scope of which is defined by the scope of the appended claims.
Claims (7)
1. The site selection method for the cable-throw net points is characterized by comprising the following steps:
determining a primary point set of the net points by adopting a set coverage model according to the client distribution points in the arrangement area;
screening the initial selection point set according to a preset rule, and determining the screened initial selection point and the existing mesh point as candidate point sets;
adopting a P-median model to restrict the number of candidate points of the candidate point set, and determining the optimal solution of the number of each candidate point; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the allocated client distribution points is shortest;
according to the time window constraint condition and the vehicle capacity constraint condition, determining a serial route from a candidate point to a client distribution point by adopting a preset algorithm, and determining the optimal number of candidate points by using the serial route to obtain a final selection result of the candidate points;
the constraint interval for constraining the number of candidate points of the candidate point set is determined by adopting the following formula:
k∈[m×(1-30％)，m×(1+30％)]；
wherein k is the number of candidate points, and m is the number of existing dots in the candidate point set;
the screening the initial selection point set according to a preset rule comprises the following steps:
screening the primary selection point set according to the distance between the primary selection point and the existing network points and the number of client distribution points in the preset service radius of each primary selection point;
determining a serial route from a candidate point to a client distribution point by adopting a preset algorithm according to a time window constraint condition and a vehicle capacity constraint condition, determining the optimal number of candidate points by using the serial route, and obtaining a final selection result of the candidate points, wherein the method comprises the following steps of:
determining a serial route from a candidate point to a customer distribution point by using an ortools solver according to the time window constraint condition and the vehicle capacity constraint condition;
taking the shortest serial route as an optimizing condition, and determining the shortest serial route as the optimal candidate point number by using the candidate points of each number;
and taking the optimal candidate point number and an optimal solution of the optimal candidate point number as a final selection result of the candidate points.
2. The method of claim 1, wherein after constraining the number of candidate points for the set of candidate points using a P-median model to determine an optimal solution for each candidate point number, the method further comprises:
and adopting a balance algorithm to redistribute the service range by using each number of candidate points in the constraint range.
3. The method of claim 1, wherein prior to determining the initial set of points for the mesh point using the set overlay model based on the customer distribution points in the placement area, the method further comprises:
acquiring customer distribution points in an arrangement area;
and preprocessing the client distribution point by adopting at least one longitude and latitude grabbing platform according to the longitude and latitude data of the client distribution point.
4. The method of claim 1, wherein screening the set of primary points based on a distance between the primary points and existing points and a number of customer distribution points within a predetermined service radius for each primary point comprises:
if the distance between the primary selected point and the existing network point is smaller than the preset service radius, deleting the primary selected point; the method comprises the steps of,
and if the number of the customer distribution points in the preset service radius of the initial selection point is smaller than a set threshold value, deleting the initial selection point.
5. The site selection device for the cable-throw net points is characterized by comprising the following steps:
the initial point set determining module is used for determining initial point sets of the net points by adopting a set coverage model according to the client distribution points in the arrangement area;
the candidate point set determining module is used for screening the initial point set according to a preset rule, and determining the screened initial point and the existing mesh point as the candidate point set;
the optimal solution determining module is used for restraining the number of candidate points of the candidate point set by adopting a P-median model and determining an optimal solution of the number of each candidate point; the P-median model aims at that the weighted distance from the candidate points of the current candidate point number to the allocated client distribution points is shortest;
the final selection result determining module is used for determining a serial route from the candidate point to the client distribution point of the vehicle by adopting a preset algorithm according to the time window constraint condition and the vehicle capacity constraint condition, and determining the optimal number of the candidate points by using the serial route to obtain a final selection result of the candidate points;
the constraint interval for constraining the number of candidate points of the candidate point set is determined by adopting the following formula:
k∈[m×(1-30％)，m×(1+30％)]；
wherein k is the number of candidate points, and m is the number of existing dots in the candidate point set;
wherein the candidate point set determining module includes:
the primary selection point set screening unit is used for screening the primary selection point set according to the distance between the primary selection point and the existing network points and the number of client distribution points in the preset service radius of each primary selection point;
wherein, the final selection result determining module includes:
the serial route determining unit is used for determining a serial route from a candidate point to a customer distribution point of the vehicle by using an ortools solver according to the time window constraint condition and the vehicle capacity constraint condition;
a candidate point number determining unit configured to determine, as an optimal candidate point number, a shortest one of the serial routes determined by the candidate points of each number, with the shortest serial route as a optimizing condition;
and the final selection result determining unit is used for determining the optimal solution of the optimal candidate point number in advance as the final selection result of the candidate points according to the optimal candidate point number.
6. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of site selection for a cable or drop as claimed in any one of claims 1 to 4.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of addressing a cable drop as claimed in any one of claims 1-4 when executing the computer program.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107967590A (en) * | 2017-12-13 | 2018-04-27 | 浙江理工大学 | A kind of optimization constructing method of Urban Underground Logistics System |
| CN109583622A (en) * | 2018-10-15 | 2019-04-05 | 顺丰科技有限公司 | A kind of Distribution Center choosing method, device, server and storage medium |
| CN111383051A (en) * | 2020-03-02 | 2020-07-07 | 杭州比智科技有限公司 | Method and device for selecting address of entity object, computing equipment and computer storage medium |
| CN111461470A (en) * | 2019-01-21 | 2020-07-28 | 顺丰科技有限公司 | Method and device for selecting addresses in transit field and computer readable storage medium |
-
2020
- 2020-12-03 CN CN202011415088.5A patent/CN112541624B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107967590A (en) * | 2017-12-13 | 2018-04-27 | 浙江理工大学 | A kind of optimization constructing method of Urban Underground Logistics System |
| CN109583622A (en) * | 2018-10-15 | 2019-04-05 | 顺丰科技有限公司 | A kind of Distribution Center choosing method, device, server and storage medium |
| CN111461470A (en) * | 2019-01-21 | 2020-07-28 | 顺丰科技有限公司 | Method and device for selecting addresses in transit field and computer readable storage medium |
| CN111383051A (en) * | 2020-03-02 | 2020-07-07 | 杭州比智科技有限公司 | Method and device for selecting address of entity object, computing equipment and computer storage medium |
Non-Patent Citations (4)
| Title |
|---|
| 快递包装回收网络中回收点选址研究;周晓晔;《包装工程》;第第41 卷卷(第第11 期期);全文 * |
| 杜志平.《物流系统工程》.中国财富出版社,2014,第130页. * |
| 终端物流车辆综合调度管理研究;朱秀峰;;中国商贸(第12期);第130页 * |
| 集合覆盖模型在京东快递配送中心选址问题中的研究;赖垠淳;《现代物流》;全文 * |
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