CN111489194A - Map information processing method and device, readable storage medium and electronic equipment - Google Patents
Map information processing method and device, readable storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses a map information processing method, a map information processing device, a readable storage medium and electronic equipment. According to the embodiment of the invention, map information is obtained, a plurality of target areas and an initial number corresponding to each target area are determined according to the map information, and the initial numbers of at least one target area form an initial number vector; determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs; and re-dividing the at least one target area into at least one new delivery range according to the update code. By the method, the distribution range is automatically divided, and waste of human resources is reduced.
Description
Technical Field
The invention relates to the field of data processing, in particular to a map information processing method, a map information processing device, a readable storage medium and electronic equipment.
Background
With the development of science and technology and the progress of society, industries such as express delivery and take-out bring more and more convenience to daily life of people, in the distribution process, the distribution range needs to be divided firstly, then distribution resources are configured according to the distribution range, and in order to improve the efficiency of the distribution resources, the distribution range needs to be divided reasonably.
In the prior art, distribution ranges are divided offline by workers, each distribution range can be composed of several distribution grids, and a platform configures a plurality of capacity teams for the distribution range to be responsible for orders of merchants in the distribution range; the distribution range is divided according to the offline distribution of workers, so that manpower resources are wasted, and the problem of unreasonable division also exists.
In summary, how to divide the distribution range is a problem to be solved at present.
Disclosure of Invention
In view of this, embodiments of the present invention provide a map information processing method, an apparatus, a readable storage medium, and an electronic device, which can automatically divide a distribution range, and reduce waste of human resources.
In a first aspect, an embodiment of the present invention provides a map information processing method, where the method includes: obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas; determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are the geographical areas defined in advance in the map information, and the initial numbers are used for identifying the initial distribution range to which each target area belongs; composing the initial number of the at least one target region into an initial number vector; determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs; in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration; and re-dividing the at least one target area into at least one new delivery range according to the update code.
With reference to the first aspect, in a first implementation manner of the first aspect, the embodiment of the present invention specifically includes, for each iteration of the genetic algorithm: performing mutation operation on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector; determining at least one partitioning scheme of the candidate delivery ranges according to the at least one variation vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one variation vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, an embodiment of the present invention further includes: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one variant child vector as a new first vector, and carrying out variant operation and/or crossover operation.
With reference to the first implementation manner of the first aspect, in a third implementation manner of the first aspect, each iteration of the genetic algorithm further includes determining an update number corresponding to the target region according to the candidate partition scheme.
With reference to the first implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the embodiment of the present invention further includes, for each iteration of the genetic algorithm:
and screening the at least one variation vector according to a set rule to determine at least one screened variation vector.
With reference to the first aspect, in a fifth implementation manner of the first aspect, the embodiment of the present invention specifically includes, for each iteration of the genetic algorithm: determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors; determining at least one partitioning scheme of candidate delivery ranges according to the at least one cross vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one cross vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
With reference to the fifth implementation manner of the first aspect, in a sixth implementation manner of the first aspect, an embodiment of the present invention further includes, for each iteration of the genetic algorithm: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
With reference to the fifth implementation manner of the first aspect, in a seventh implementation manner of the first aspect, an embodiment of the present invention further includes, for each iteration of the genetic algorithm: and determining an updating number corresponding to the target area according to the candidate partition scheme.
With reference to the fifth implementation manner of the first aspect, in an eighth implementation manner of the first aspect, each iteration of the genetic algorithm further includes: and screening the at least one cross vector according to a set rule to determine at least one screened variation vector.
With reference to the first aspect, in a ninth implementation manner of the first aspect, the embodiment of the present invention further includes, for each iteration of the genetic algorithm: responding to the number of the crossing operation and/or the mutation operation being equal to a second set threshold; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
With reference to any one of the second implementation manner of the first aspect to the ninth implementation manner of the first aspect, in a tenth implementation manner of the first aspect, the score of the partitioning scheme is determined according to a circulation feature, a time interval feature, and a co-delivery feature, where the circulation feature is used to characterize whether to acquire a task and deliver the task in the same delivery range, and the co-delivery feature is used to characterize whether to acquire the task in different target areas and then deliver the task to the same target area.
In a second aspect, an embodiment of the present invention provides a map information processing apparatus, including: the system comprises an acquisition unit, a distribution unit and a distribution unit, wherein the acquisition unit is used for acquiring map information which comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas; the determining unit is used for determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are geographical areas defined in advance in the map information, and the initial numbers are used for identifying initial distribution ranges to which the target areas belong; a processing unit, configured to compose the initial number of the at least one target region into an initial number vector; the processing unit is further configured to determine an update number corresponding to the target region according to a genetic algorithm by using the initial number vector as a parent vector of a first iteration, where the update number is used to identify a new distribution range to which the target region belongs; in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration; and the dividing unit is used for dividing the at least one target area into at least one new distribution range again according to the updating code.
In a third aspect, the present invention provides a computer-readable storage medium on which computer program instructions are stored, the computer program instructions, when executed by a processor, implementing the method according to any one of the first aspect or any one of the first aspect implementation manners.
In a fourth aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory is used to store one or more computer program instructions, where the one or more computer program instructions are executed by the processor to implement the following steps: obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas; determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are the geographical areas defined in advance in the map information, and the initial numbers are used for identifying the initial distribution range to which each target area belongs; composing the initial number of the at least one target region into an initial number vector; determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs; in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration; and re-dividing the at least one target area into at least one new delivery range according to the update code.
With reference to the fourth aspect, in a first implementation manner of the fourth aspect, in the embodiment of the present invention, the processor specifically executes the following steps: performing mutation operation on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector; determining at least one partitioning scheme of the candidate delivery ranges according to the at least one variation vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one variation vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
With reference to the first implementation manner of the fourth aspect, in a second implementation manner of the fourth aspect, the processor further performs the following steps: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one variant child vector as a new first vector, and carrying out a variant operation.
With reference to the first implementation manner of the fourth aspect, in a third implementation manner of the fourth aspect, the processor further performs the following steps: and determining an updating number corresponding to the target area according to the candidate partition scheme.
With reference to the first implementation manner of the fourth aspect, in a fourth implementation manner of the fourth aspect, the processor further performs the following steps: and screening the at least one variation vector according to a set rule to determine at least one screened variation vector.
With reference to the fourth aspect, in a fifth implementation manner of the first aspect, in the embodiment of the present invention, the processor specifically executes the following steps: determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors; determining at least one partitioning scheme of candidate delivery ranges according to the at least one cross vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one cross vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
With reference to the fifth implementation manner of the fourth aspect, in a sixth implementation manner of the fourth aspect, the processor further performs the following steps: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
With reference to the fifth implementation manner of the fourth aspect, in a seventh implementation manner of the fourth aspect, an embodiment of the present invention further includes, for each iteration of the genetic algorithm: and determining an updating number corresponding to the target area according to the candidate partition scheme.
With reference to the fifth implementation manner of the fourth aspect, in an eighth implementation manner of the fourth aspect, the processor further performs the following steps: and screening the at least one cross vector according to a set rule to determine at least one screened variation vector.
With reference to the fourth aspect, in a ninth implementation manner of the fourth aspect, the processor further performs the following steps: responding to the number of the crossing operation and/or the mutation operation being equal to a second set threshold; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
With reference to any one of the second implementation manner of the fourth aspect to the ninth implementation manner of the fourth aspect, in a tenth implementation manner of the fourth aspect, the score of the division scheme is determined according to a circulation feature, a time interval feature, and a delivery feature, where the circulation feature is used to characterize whether to acquire tasks and deliver tasks in the same delivery range, and the delivery feature is used to characterize whether to acquire tasks in different target areas and deliver the tasks to the same target area.
According to the embodiment of the invention, map information is obtained, a plurality of target areas and an initial number corresponding to each target area are determined according to the map information, and the initial numbers of at least one target area form an initial number vector; determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs; and re-dividing the at least one target area into at least one new delivery range according to the update code. By the method, the distribution range is automatically divided, and waste of human resources is reduced.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:
FIG. 1 is a schematic view of a delivery range in the prior art;
fig. 2 is a flowchart of a map information processing method of the first embodiment of the present invention;
FIG. 3 is a schematic view of the distribution range of the first embodiment of the present invention;
fig. 4 is a flowchart of a map information processing method of the first embodiment of the present invention;
fig. 5 is a flowchart of a map information processing method of the first embodiment of the present invention;
FIG. 6 is a diagram of an application scenario of the second embodiment of the present invention;
fig. 7 is a schematic diagram of a map information processing apparatus of a third embodiment of the present invention;
fig. 8 is a schematic view of an electronic apparatus according to a fourth embodiment of the present invention.
Detailed Description
The present disclosure is described below based on examples, but the present disclosure is not limited to only these examples. In the following detailed description of the present disclosure, certain specific details are set forth. It will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. Well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present disclosure.
Further, those of ordinary skill in the art will appreciate that the drawings provided herein are for illustrative purposes and are not necessarily drawn to scale.
Unless the context clearly requires otherwise, throughout this specification, the words "comprise", "comprising", and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, what is meant is "including, but not limited to".
In the description of the present disclosure, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, in the description of the present disclosure, "a plurality" means two or more unless otherwise specified.
In the prior art, the distribution range is divided offline by workers, each distribution range can be composed of several distribution grids, as shown in fig. 1, the workers divide the distribution grids according to geographic shapes, order density, natural barriers and the like, and the distribution grids comprise a distribution grid 1, a distribution grid 2, a distribution grid 3, a distribution grid 4, a distribution grid 5, a distribution grid 6 and a distribution grid 7; assuming that a worker divides the seven distribution grids into two distribution ranges, specifically, the distribution grid 1, the distribution grid 2, the distribution grid 3 and the distribution grid 4 are one distribution range, the distribution grid 5, the distribution grid 6 and the distribution grid 7 are another distribution range, and the platform allocates transportation resources for the distribution ranges to take charge of orders of merchants in the distribution ranges, since the distribution grid 1, the distribution grid 2, the distribution grid 3 and the distribution grid 4 are office areas, and the distribution grid 5, the distribution grid 6 and the distribution grid 7 are residential areas, the number of orders in different distribution ranges in different time periods is unbalanced, for example, the number of orders in the office areas in the working days is large, and the number of orders in the residential areas is small; the order number of residential areas is more and the order number of office areas is less during non-working days; because the distribution resources are configured according to the distribution range, the distribution resources may be idle in a manner of dividing the distribution range; after the distribution resources receive the orders in the distribution range, the distribution resources need to distribute the orders for the users outside the distribution range, the more the orders outside the distribution range are, the greater the distance the distribution resources need to run is, and after the distribution is finished, the merchants in the distribution range in charge of the distribution resources need to get the orders, and the round trip distance is increased, so how to optimize the division of the distribution range to reduce the problem of unbalanced time period distribution of different business circles, and increase the orders of the distribution resources circulating in the business circles is a problem to be solved at present.
In the embodiment of the present invention, the delivery range may also be referred to as a business area, and the delivery grid may also be referred to as a geographic area, a target area, and a grid.
Fig. 2 is a flowchart of a map information processing method of the first embodiment of the present invention. As shown in fig. 2, the method specifically includes the following steps:
step S200, obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas.
Step S201, determining a plurality of target areas and an initial number corresponding to each target area according to the map information, where the target areas are the predefined geographical areas in the map information, and the initial numbers are used to identify an initial distribution range to which each target area belongs.
For example, as shown in fig. 3, it is assumed that one area includes 9 target areas, each target area corresponds to an initial number, and it is assumed that the 9 target areas are divided into 3 distribution ranges, which are a, b, and c, respectively, for example, the initial numbers of the target areas 1, 2, 3, 4, 5, 6, 7, 8, and 9 are a, the initial numbers of the target areas 1, 2, and 3 are b, the initial numbers of the target areas 4, 5, and 6 are b, and the initial numbers of the target areas 7, 8, and 9 are c.
Step S202, the initial numbers of the at least one target area form an initial number vector.
For example, the initial numbers of the target regions shown in fig. 3 are combined into a vector { a, a, a, b, b, b, c, c, c }, in this embodiment of the present invention, the vector formed by the initial numbers is specifically determined according to actual situations, and this embodiment of the present invention does not limit this, and the above is merely an exemplary illustration.
Step S203, determining an update number corresponding to the target area according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target area belongs; in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; alternatively, at least one cross vector is determined by a crossing operation of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors for the current iteration.
Each iteration of the genetic algorithm described in step S203 is described in detail below by two specific examples, which are as follows:
as shown in fig. 4, the first embodiment includes the following steps:
in step S400, a mutation operation is performed on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector.
For example, randomly mutating the { a, a, a, b, b, b, c, c, c } into three initial numbers, wherein the { a, a, a, b, b, b, c, c, c } is a first vector, and mutating three progeny mutation vectors, namely { a, a, b, b, b, c, a, c, c }, { a, a, a, a, b, b, c, c }, { b, b, a, a, b, b, c, c }; and screening the three variant vectors according to a set rule to determine at least one screened descendant vector, and screening { a, a, b, b, c, a, c, c } and { b, b, a, b, b, b, c, c, c } in the three variant vectors according to the set rule to determine one variant vector { a, a, a, a, b, b, b, c, c } assuming that target areas with the same number are adjacent.
Step S401, determining at least one partitioning scheme of the candidate distribution range according to the at least one variation vector.
Specifically, { a, a, a, a, b, b, c, c } is determined as the partition scheme of the candidate delivery range.
Step S402, responding to the candidate partitioning scheme with the partitioning scheme score larger than or equal to a first set threshold value in the at least one partitioning scheme.
Specifically, the task scheduling method is determined according to a circulation feature, a time interval feature and a simultaneous delivery feature, wherein the circulation feature is used for representing whether to acquire the tasks and deliver the tasks in the same delivery range, and the simultaneous delivery feature is used for representing whether to acquire the tasks in different target areas and then deliver the tasks to the same target area. The score of the division scheme is determined according to the circulation characteristic, the time interval characteristic and the simultaneous transmission characteristic weight calculation.
The higher the score of the division scheme is, the more reasonable the division of the distribution range is proved, the more the circulation in the distribution range is, the more the tasks acquired in different target areas are distributed to the same target area, and the better the balance of the tasks in different time periods is.
Optionally, the update number corresponding to the target area is determined according to the candidate partition scheme.
Step S403, determining at least one variant vector corresponding to the candidate partition scheme as a candidate child vector.
And S404, exiting iteration, and determining the current candidate child vector as a final candidate child vector.
Alternatively, another possibility in step S402 is: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a set threshold.
Step S405, determining the at least one variant child vector as a new first vector, and performing a variant operation and/or a crossover operation.
A second embodiment, as shown in fig. 5, includes the following steps:
step S500, determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors.
For example, randomly mutating the { a, a, a, b, b, b, c, c, c } into three initial numbers, wherein the { a, a, a, b, b, b, c, c, c } is a first vector, and mutating three progeny mutation vectors, namely { a, a, b, b, b, c, a, c, c }, { a, a, a, a, b, b, c, c }, { b, b, a, a, b, b, c, c }; and screening the three variation vectors according to a set rule, determining at least one screened child vector, and if the set rule is that target areas with the same number are adjacent, determining { a, a, b, b, b, c, a, c, c } in the three variation vectors according to the set rule, and performing cross operation on { b, b, a, a, b, b, c, c }, { a, a, a, b, b, b, c, c } one variation vector in the three variation vectors, and determining { b, b, a, a, b, b, b, c, c }, { a, a, a, b, b, c, c }, to determine { a, a, a, b, b, c, c }, { b, a, b, b, c, c }.
Step S501, determining at least one partitioning scheme of the candidate distribution range according to the at least one cross vector.
Step S502, responding to the candidate partition scheme with the partition scheme score larger than or equal to the first set threshold value in the at least one partition scheme.
Step S503, determining at least one cross vector corresponding to the candidate partition scheme as a candidate child vector.
And step S504, exiting iteration, and determining the current candidate child vector as a final candidate child vector.
Optionally, another possibility in step S502 is: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold.
And step S505, determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
A third specific embodiment, as shown in fig. 4 or 5, includes the following steps:
and step S406 or step S506, responding to the number of the crossing operation and/or the mutation operation being equal to the second set threshold, and performing step S404 or step S504 in response.
Optionally, if the number of the crossover operations and/or mutation operations is smaller than a second set threshold, returning to step S400 or step S500.
The embodiment of the invention determines the update code of the target area through the two specific embodiments.
Step S204, the at least one target area is divided into at least one new distribution range again according to the update codes.
In the embodiment of the invention, the target distribution area is divided into 3 new distribution ranges again according to { a, a, a, a, b, b, c, c }.
For example, { a, a, a, a, b, b, c, c } is determined as the update number corresponding to the target area, specifically, the update number of the target area 1 is a, the update number of the target area 2 is a, the update number of the target area 3 is a, the update number of the target area 4 is a, the update number of the target area 5 is b, the update number of the target area 6 is b, the update number of the target area 7 is c, the update number of the target area 8 is c, and the update number of the target area 9 is c.
In the embodiment of the invention, as the dividing scheme of { a, a, a, a, b, b, b, c, c } meets the set conditions, the more reasonable the distribution range is divided according to the scheme, the more the circulation in the distribution range is, the more the tasks acquired in different target areas are distributed to the same target area, and the better the balance of the tasks in different time periods is.
Fig. 6 is an application scenario diagram of a second embodiment of the present invention, including a server, a target resource distribution terminal, a merchant terminal, and the like, where the server may also be referred to as a platform, a system, and the like, the target resource distribution terminal and the merchant terminal may be a mobile phone, a tablet, and the like, and may be capable of positioning and acquiring a start point or an end point of a task, the number of the target resource distribution terminal and the number of the merchant terminals is at least one, and the server may further acquire map information from a third service platform. Obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas; determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are the geographical areas defined in advance in the map information, and the initial numbers are used for identifying the initial distribution range to which each target area belongs; composing the initial number of the at least one target region into an initial number vector; determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs; in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration; and re-dividing the at least one target area into at least one new delivery range according to the update code. By the method, the distribution range is automatically divided, and waste of human resources is reduced. And the distribution range is optimized, the distribution range is more reasonably divided, and the efficiency of target distribution resources is improved.
Fig. 7 is a schematic diagram of a map information processing apparatus of a third embodiment of the present invention. As shown in fig. 7, the apparatus of the present embodiment includes an acquisition unit 71, a determination unit 72, a processing unit 73, and a dividing unit 74.
The acquiring unit 71 is configured to acquire map information, where the map information includes a plurality of predefined geographic areas and distribution ranges corresponding to the geographic areas; a determining unit 72, configured to determine, according to the map information, a plurality of target areas and an initial number corresponding to each target area, where the target areas are the geographical areas predefined in the map information, and the initial numbers are used to identify an initial distribution range to which each target area belongs; a processing unit 73, configured to compose the initial number of the at least one target area into an initial number vector; the processing unit 73 is further configured to determine, by using the initial number vector as a parent vector of a first iteration, an update number corresponding to the target region according to a genetic algorithm, where the update number is used to identify a new distribution range to which the target region belongs; in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration; a dividing unit 74, configured to re-divide the at least one target area into at least one new delivery range according to the update code.
Further, the processing unit is specifically configured to: performing mutation operation on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector; determining at least one partitioning scheme of the candidate delivery ranges according to the at least one variation vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one variation vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
Further, the processing unit is specifically configured to: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one variant child vector as a new first vector, and performing variant operation and/or cross operation.
Further, the processing unit is further configured to: and determining an updating number corresponding to the target area according to the candidate partition scheme.
Further, the device also comprises a screening unit: and the method is used for screening the at least one variation vector according to a set rule and determining the screened at least one variation vector.
Further, the processing unit is specifically configured to: determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors; determining at least one partitioning scheme of candidate delivery ranges according to the at least one cross vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one cross vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
Further, the processing unit is further configured to: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
Further, the processing unit is further configured to: each iteration of the genetic algorithm further comprises: and determining an updating number corresponding to the target area according to the candidate partition scheme.
Further, the device also comprises a screening unit: and the system is used for screening the at least one cross vector according to a set rule and determining at least one screened variation vector.
Further, the processing unit is further configured to: responding to the number of the crossing operation and/or the mutation operation being equal to a second set threshold; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
Further, the score of the division scheme is determined according to a circulation feature, a time interval feature and a simultaneous delivery feature, wherein the circulation feature is used for representing whether to acquire the tasks and deliver the tasks in the same delivery range, and the simultaneous delivery feature is used for representing whether to acquire the tasks in different target areas and then deliver the tasks to the same target area.
Fig. 8 is a schematic view of an electronic apparatus according to a fourth embodiment of the present invention. In this embodiment, the electronic device is a server. It should be understood that other electronic devices, such as raspberry pies, are also possible. As shown in fig. 8, the electronic device: includes at least one processor 801; and a memory 802 communicatively coupled to the at least one processor 801; and a communication component 803 communicatively coupled to the scanning device, the communication component 803 receiving and transmitting data under control of the processor 801; wherein the memory 802 stores instructions executable by the at least one processor 801 to implement: obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas; determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are the geographical areas defined in advance in the map information, and the initial numbers are used for identifying the initial distribution range to which each target area belongs; composing the initial number of the at least one target region into an initial number vector; determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs; in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration; and re-dividing the at least one target area into at least one new delivery range according to the update code. Further, the processor specifically executes the following steps: performing mutation operation on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector; determining at least one partitioning scheme of the candidate delivery ranges according to the at least one variation vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one variation vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
Further, the processor performs the steps of: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one variant child vector as a new first vector, and carrying out variant operation and/or crossover operation.
Further, the processor performs the steps of: and determining an updating number corresponding to the target area according to the candidate partition scheme.
Further, the processor performs the steps of: and screening the at least one variation vector according to a set rule to determine at least one screened variation vector.
Further, the processor specifically executes the following steps: determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors; determining at least one partitioning scheme of candidate delivery ranges according to the at least one cross vector; in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; determining at least one cross vector corresponding to the candidate partitioning scheme as a candidate child vector; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
Further, the processor performs the steps of: in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold; and determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
Further, each iteration of the genetic algorithm further comprises: and determining an updating number corresponding to the target area according to the candidate partition scheme.
Further, the processor performs the steps of: and screening the at least one cross vector according to a set rule to determine at least one screened variation vector.
Further, the processor performs the steps of: responding to the number of the crossing operation and/or the mutation operation being equal to a second set threshold; and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
Further, the score of the division scheme is determined according to a circulation feature, a time interval feature and a simultaneous delivery feature, wherein the circulation feature is used for representing whether to acquire the tasks and deliver the tasks in the same delivery range, and the simultaneous delivery feature is used for representing whether to acquire the tasks in different target areas and then deliver the tasks to the same target area.
Specifically, the electronic device includes: one or more processors 801 and a memory 802, one processor 801 being illustrated in fig. 8. The processor 801 and the memory 802 may be connected by a bus or other means, and fig. 8 illustrates an example of a connection by a bus. Memory 802, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The processor 801 executes various functional applications of the device and data processing by running nonvolatile software programs, instructions, and modules stored in the memory 802, that is, implements the map information processing method described above.
The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store a list of options, etc. Further, the memory 802 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 embodiments, the memory 802 may optionally include memory located remotely from the processor 801, which may be connected to an external device 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.
One or more modules are stored in the memory 802, and when executed by the one or more processors 801, perform the map information processing method in any of the method embodiments described above.
The product can execute the method provided by the embodiment of the application, has corresponding functional modules and beneficial effects of the execution method, and can refer to the method provided by the embodiment of the application without detailed technical details in the embodiment.
A fifth embodiment of the invention is directed to a non-volatile storage medium storing a computer-readable program for causing a computer to perform some or all of the above-described method embodiments.
That is, as can be understood by those skilled in the art, all or part of the steps in the method for implementing the embodiments described above may be implemented by a program instructing related hardware, where the program is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be understood by those of ordinary skill in the art that the foregoing embodiments are specific embodiments for practicing the invention, and that various changes in form and details may be made therein without departing from the spirit and scope of the invention in practice.
The embodiment of the application discloses A1 and a map information processing method, which comprises the following steps:
obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas;
determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are the geographical areas defined in advance in the map information, and the initial numbers are used for identifying the initial distribution range to which each target area belongs;
composing the initial number of the at least one target region into an initial number vector;
determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs;
in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration;
and re-dividing the at least one target area into at least one new delivery range according to the update code.
A2, the method according to a1, wherein each iteration of the genetic algorithm specifically comprises:
performing mutation operation on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector;
determining at least one partitioning scheme of the candidate delivery ranges according to the at least one variation vector;
in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
determining at least one variation vector corresponding to the candidate partitioning scheme as a candidate child vector;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
A3, the method of A2, each iteration of the genetic algorithm further comprising:
in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
and determining the at least one variant child vector as a new first vector, and performing variant operation and/or cross operation.
A4, the method of A2, each iteration of the genetic algorithm further comprising:
and determining an updating number corresponding to the target area according to the candidate partition scheme.
A5, the method of A2, each iteration of the genetic algorithm further comprising:
and screening the at least one variation vector according to a set rule to determine at least one screened variation vector.
A6, the method according to a1, wherein each iteration of the genetic algorithm specifically comprises:
determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors;
determining at least one partitioning scheme of candidate delivery ranges according to the at least one cross vector;
in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
determining at least one cross vector corresponding to the candidate partitioning scheme as a candidate child vector;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
A7, the method of A6, each iteration of the genetic algorithm further comprising:
in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
and determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
A8, the method of A6, each iteration of the genetic algorithm further comprising:
and determining an updating number corresponding to the target area according to the candidate partition scheme.
A9, the method of A6, each iteration of the genetic algorithm further comprising:
and screening the at least one cross vector according to a set rule to determine at least one screened variation vector.
A10, the method of A1, each iteration of the genetic algorithm further comprising:
responding to the number of the crossing operation and/or the mutation operation being equal to a second set threshold;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
A11, the method according to any one of A2-A10, wherein the score of the dividing scheme is determined according to circulation characteristics, time interval characteristics and co-delivery characteristics, wherein the circulation characteristics are used for characterizing whether tasks are acquired and delivered in the same delivery range, and the co-delivery characteristics are used for characterizing whether tasks are acquired in different target areas and then delivered to the same target area.
The embodiment of the application discloses B1, a map information processing device, this device includes:
the system comprises an acquisition unit, a distribution unit and a distribution unit, wherein the acquisition unit is used for acquiring map information which comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas;
the determining unit is used for determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are geographical areas defined in advance in the map information, and the initial numbers are used for identifying initial distribution ranges to which the target areas belong;
a processing unit, configured to compose the initial number of the at least one target region into an initial number vector;
the processing unit is further configured to determine an update number corresponding to the target region according to a genetic algorithm by using the initial number vector as a parent vector of a first iteration, where the update number is used to identify a new distribution range to which the target region belongs;
in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration;
and the dividing unit is used for dividing the at least one target area into at least one new distribution range again according to the updating code.
The embodiment of the application discloses C1, a computer readable storage medium, on which computer program instructions are stored, which when executed by a processor implement the method according to any one of A1-A11.
The embodiment of the application discloses a D1 electronic device, comprising a memory and a processor, wherein the memory is used for storing one or more computer program instructions, and the one or more computer program instructions are executed by the processor to realize the following steps:
obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas;
determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are the geographical areas defined in advance in the map information, and the initial numbers are used for identifying the initial distribution range to which each target area belongs;
composing the initial number of the at least one target region into an initial number vector;
determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs;
in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration;
and re-dividing the at least one target area into at least one new delivery range according to the update code.
D2, the electronic device as recited in D1, the processor specifically performs the following steps:
performing mutation operation on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector;
determining at least one partitioning scheme of the candidate delivery ranges according to the at least one variation vector;
in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
determining at least one variation vector corresponding to the candidate partitioning scheme as a candidate child vector;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
D3, the electronic device as recited in D2, the processor further performing the steps of:
in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
and determining the at least one variant child vector as a new first vector, and carrying out variant operation and/or crossover operation.
D4, the electronic device as recited in D2, the processor further performing the steps of:
and determining an updating number corresponding to the target area according to the candidate partition scheme.
D5, the electronic device as D2 and the processor thereof further execute the following steps:
and screening the at least one variation vector according to a set rule to determine at least one screened variation vector.
D6, the electronic device as recited in D1, the processor specifically performs the following steps:
determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors;
determining at least one partitioning scheme of candidate delivery ranges according to the at least one cross vector;
in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
determining at least one cross vector corresponding to the candidate partitioning scheme as a candidate child vector;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
D7, the electronic device as recited in D6, the processor further performing the steps of:
in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
and determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
D8, the electronic device of D6, each iteration of the genetic algorithm further comprising:
and determining an updating number corresponding to the target area according to the candidate partition scheme.
D9, the electronic device as recited in D6, the processor further performing the steps of:
and screening the at least one cross vector according to a set rule to determine at least one screened variation vector.
D10, the electronic device as recited in D1, the processor further performing the steps of:
responding to the number of the crossing operation and/or the mutation operation being equal to a second set threshold;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
D11, the electronic device according to any one of D2-D10, the score of the division scheme is determined according to a circulation characteristic, a time interval characteristic and a simultaneous transmission characteristic, wherein the circulation characteristic is used for representing whether tasks are acquired and distributed in the same distribution range, and the simultaneous transmission characteristic is used for representing whether the tasks are acquired in different target areas and then distributed to the same target area.
Claims (10)
1. A map information processing method, characterized by comprising:
obtaining map information, wherein the map information comprises a plurality of pre-defined geographic areas and distribution ranges corresponding to the geographic areas;
determining a plurality of target areas and an initial number corresponding to each target area according to the map information, wherein the target areas are the geographical areas defined in advance in the map information, and the initial numbers are used for identifying the initial distribution range to which each target area belongs;
composing the initial number of the at least one target region into an initial number vector;
determining an update number corresponding to the target region according to a genetic algorithm by taking the initial number vector as a parent vector of the initial iteration, wherein the update number is used for identifying a new distribution range to which the target region belongs;
in each iteration of the genetic algorithm, performing mutation operation on the first vector by randomly changing a preset number of numbers in the parent vector to determine at least one mutation vector so as to determine candidate child vectors of the current iteration; or determining at least one cross vector by crossing operations of numbers in different parent vectors and/or different variant vectors to determine candidate child vectors of the current iteration;
and re-dividing the at least one target area into at least one new delivery range according to the update code.
2. The method according to claim 1, wherein each iteration of the genetic algorithm comprises in particular:
performing mutation operation on the first vector by randomly changing a predetermined number of numbers in the parent vector to determine at least one mutation vector;
determining at least one partitioning scheme of the candidate delivery ranges according to the at least one variation vector;
in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
determining at least one variation vector corresponding to the candidate partitioning scheme as a candidate child vector;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
3. The method of claim 2, wherein each iteration of the genetic algorithm further comprises:
in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
and determining the at least one variant child vector as a new first vector, and performing variant operation and/or cross operation.
4. The method of claim 2, wherein each iteration of the genetic algorithm further comprises:
and determining an updating number corresponding to the target area according to the candidate partition scheme.
5. The method of claim 2, wherein each iteration of the genetic algorithm further comprises:
and screening the at least one variation vector according to a set rule to determine at least one screened variation vector.
6. The method according to claim 1, wherein each iteration of the genetic algorithm comprises in particular:
determining at least one crossing vector by crossing operations of numbers in different parent vectors and/or different variant vectors;
determining at least one partitioning scheme of candidate delivery ranges according to the at least one cross vector;
in response to the presence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
determining at least one cross vector corresponding to the candidate partitioning scheme as a candidate child vector;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
7. The method of claim 6, wherein each iteration of the genetic algorithm further comprises:
in response to an absence of a candidate partitioning scheme of the at least one partitioning scheme having a partitioning scheme score greater than or equal to a first set threshold;
and determining the at least one cross vector as a new first vector, and performing mutation operation and/or cross operation.
8. The method of claim 6, wherein each iteration of the genetic algorithm further comprises:
and determining an updating number corresponding to the target area according to the candidate partition scheme.
9. The method of claim 6, wherein each iteration of the genetic algorithm further comprises:
and screening the at least one cross vector according to a set rule to determine at least one screened variation vector.
10. The method of claim 1, wherein each iteration of the genetic algorithm further comprises:
responding to the number of the crossing operation and/or the mutation operation being equal to a second set threshold;
and exiting the iteration, and determining the current candidate child vector as a final candidate child vector.
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