CN113095943A - Position determining method, position determining device, electronic equipment and readable storage medium - Google Patents
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Abstract
The embodiment of the disclosure provides a position determining method, which is applied to the field of finance or the technical field of computers. The method comprises the following steps: obtaining a position determination request for determining the position of a self-service teller machine network point; responding to a position determination request, and acquiring a first group, wherein the first group comprises M individuals, each individual is used for representing a candidate position of a self-service teller machine website, and M is more than or equal to 3; processing the first population by using a differential evolution algorithm to obtain a target individual, wherein the target individual is obtained under the condition that an iteration termination condition of the differential evolution algorithm is met; and determining the candidate position characterized by the target individual as the target position of the self-service teller machine network. The disclosed embodiments also provide a position determination device, an electronic device, a computer-readable storage medium and a computer program product.
Description
Technical Field
The present disclosure relates to the field of finance or computer technology, and more particularly, to a position determination method, a position determination apparatus, an electronic device, a computer-readable storage medium, and a computer program product.
Background
The position determination problem of the self-service teller machine network point can be realized in a manual mode.
In the course of implementing the disclosed concept, the inventors found that there is at least a technical problem in the related art that needs to be time-consuming and costly.
Disclosure of Invention
In view of the above, the disclosed embodiments provide a position determining method, a position determining apparatus, an electronic device, a computer-readable storage medium, and a computer program product.
One aspect of the embodiments of the present disclosure provides a position determination method, including: obtaining a position determination request for determining the position of a self-service teller machine network point; responding to the position determination request, and acquiring a first population, wherein the first population comprises M individuals, each individual is used for representing a candidate position of a self-service teller machine network point, and M is more than or equal to 3; processing the first population by using a differential evolution algorithm to obtain a target individual, wherein the target individual is obtained under the condition that an iteration termination condition of the differential evolution algorithm is met; and determining the candidate position characterized by the target individual as the target position of the self-service teller machine website.
According to an embodiment of the present disclosure, the processing the first population by using a differential evolution algorithm to obtain a target individual includes: under the condition that the iteration termination condition of the differential evolution algorithm is determined not to be met, processing part or all individuals in the first population by using a mutation operator to obtain a variant population, wherein the iteration termination condition comprises that the iteration number reaches the maximum iteration number or the coverage distance of the individuals meets a preset condition, the coverage distance is determined according to candidate positions and N positions of the residents represented by the individuals, and N is more than or equal to 1; processing the first population and the variant population by using a crossover operator to obtain a crossover population; processing the first population and the cross population by using a selection operator to obtain a second population; under the condition that the iteration termination condition of the differential evolution algorithm is not met, continuously executing the operation of determining the next generation population until the iteration termination condition is met; and determining the individuals in the population meeting the iteration termination condition of the differential evolution algorithm as the target individuals.
According to an embodiment of the present disclosure, the processing the first population and the cross population by using a selection operator to obtain a second population includes: determining a coverage distance of said individuals in said first population for the same individual in said first population and said crossover population; determining a coverage distance of said individuals in said cross population; determining said individuals in said first population as individuals in said second population if it is determined that the coverage distance of said individuals in said first population is less than or equal to the coverage distance of said individuals in said crossover population; and determining the individual in the cross population as an individual in the second population when it is determined that the coverage distance of the individual in the first population is greater than the coverage distance of the individual in the cross population.
According to an embodiment of the present disclosure, the determining the coverage distance according to the candidate position characterized by the individual and the N positions of the residential sites includes: the coverage distance is the maximum distance of N distances determined according to the candidate position represented by the individual and the positions of the N residential points; or, the coverage distance is an average distance of N distances determined according to the candidate position characterized by the individual and the N positions of the residential points.
According to an embodiment of the present disclosure, further comprising: acquiring a residential point network topological graph, wherein the residential point network topological graph comprises T residential point positions; and determining the N residential point positions from the T residential point positions.
According to an embodiment of the present disclosure, further comprising: acquiring target map information, wherein the target map information comprises an area position corresponding to each residential area in a plurality of residential areas; determining the position of a residential point corresponding to the residential area according to the area position; and constructing the residential point network topological graph according to the residential point positions.
According to an embodiment of the present disclosure, the obtaining of the target map information includes: and acquiring the target map information from the map application program by using a crawler tool.
According to an embodiment of the present disclosure, further comprising: acquiring initial map information; and performing data cleaning on the initial map information to obtain the target map information.
Another aspect of the present disclosure provides a position determination apparatus including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a position determination request for determining the position of a self-service teller machine network; a second obtaining module, configured to respond to the location determination request, and obtain a first population, where the first population includes M individuals, each of the M individuals is used to represent a candidate location of a self-service teller machine website, and M is greater than or equal to 3; an algorithm processing module, configured to process the first population by using a differential evolution algorithm to obtain a target individual, where the target individual is an individual obtained under a condition that an iteration termination condition of the differential evolution algorithm is satisfied; and the first determination module is used for determining the candidate position represented by the target individual as the target position of the self-service teller machine website.
Another aspect of an embodiment of the present disclosure provides an electronic device including: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
Another aspect of the embodiments of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of an embodiment of the present disclosure provides a computer program product comprising computer executable instructions for implementing the method as described above when executed.
According to the embodiment of the disclosure, as the technical means that the target position of the self-service teller machine network point can be obtained by processing the first population by using the differential evolution algorithm is adopted, the technical problems of long time consumption and high cost in manual determination of the position of the self-service teller machine network point in the related technology are at least partially overcome, and the technical effects of saving time and reducing cost are further achieved.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an exemplary system architecture to which the location determination methods and apparatus may be applied, according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a position determination method according to an embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart for processing a first population using a differential evolution algorithm to obtain a target individual according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow chart for processing a first population and a cross population with a selection operator to obtain a second population according to an embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart for determining N residential site locations in accordance with an embodiment of the disclosure;
FIG. 6 schematically shows a flow chart for obtaining a residential neighborhood network topology according to an embodiment of the present disclosure;
FIG. 7 schematically illustrates a flow chart for obtaining target map information, according to an embodiment of the disclosure;
FIG. 8 schematically illustrates a block diagram of a position determining apparatus according to an embodiment of the present disclosure; and
fig. 9 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
In this context, it is to be understood that the terminology referred to may be technical means or other conclusive technical terminology for implementing a part of the disclosure. For example, the term may include.
And (3) a differential evolution algorithm: the differential evolution algorithm is a heuristic search algorithm based on population. The basic idea is as follows: starting from a randomly generated initial population, new individuals are generated by summing the vector difference of any two individuals in the population with the third individual, then the new individuals are compared with the corresponding individuals in the current generation population, if the fitness of the new individuals is better than that of the current individuals, the new individuals are used for replacing the old individuals in the next generation, otherwise, the old individuals are still stored. Through continuous evolution, excellent individuals are reserved, inferior individuals are eliminated, and search is guided to approach to the optimal solution.
Individual: a vector representing a solution space in the differential evolution algorithm, each individual representing a solution of the solution space in the differential evolution algorithm.
Population: in the term of the differential evolution algorithm, a plurality of individuals form a population.
Network topology diagram: the connection expression mode of things is composed of nodes and edges, and the nodes are connected by the edges.
Where a convention analogous to "A, B, at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides a position determining method, which is applied to the financial field, the computer technical field or other fields. The method comprises the steps of obtaining a position determination request for determining the position of the self-service teller machine network point; responding to a position determination request, and acquiring a first group, wherein the first group comprises M individuals, each individual is used for representing a candidate position of a self-service teller machine website, and M is more than or equal to 3; processing the first population by using a differential evolution algorithm to obtain a target individual, wherein the target individual is obtained under the condition that an iteration termination condition of the differential evolution algorithm is met; and determining the candidate position characterized by the target individual as the target position of the self-service teller machine network. The present disclosure also provides a position determination apparatus, an electronic device, a computer-readable storage medium, and a computer program product.
Fig. 1 schematically illustrates an exemplary system architecture 100 to which the location determination methods and apparatus may be applied, according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired and/or wireless communication links, and so forth.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various messenger client applications such as, for example only, a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, and/or social platform software.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103.
For example, the background management server may respond to the acquired position determination request, acquire a first population, where the first population includes M individuals, each individual is used to represent a candidate position of the self-service teller machine website, M is greater than or equal to 3, process the first population by using a differential evolution algorithm, obtain a target individual, where the target individual is an individual obtained under the condition that an iteration termination condition of the differential evolution algorithm is satisfied, and determine a candidate position represented by the target individual as a target position of the self-service teller machine website.
It should be noted that the position determining method provided by the embodiment of the present disclosure may be generally executed by the terminal device 101, 102, or 103, or may also be executed by another terminal device different from the terminal device 101, 102, or 103. Accordingly, the position determining apparatus provided in the embodiments of the present disclosure may also be disposed in the terminal device 101, 102, or 103, or disposed in another terminal device different from the terminal device 101, 102, or 103.
The location determination method provided by the embodiments of the present disclosure may also be performed by the server 105. Accordingly, the location determination device provided by the embodiments of the present disclosure may be generally disposed in the server 105. The location determination method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the location determination apparatus provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. .
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Fig. 2 schematically shows a flow chart of a position determination method according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S201 to S204.
In operation S201, a location determination request for determining a location of a self-service teller machine site is acquired.
According to the embodiment of the disclosure, the method provided by the embodiment of the disclosure can be reasonably expanded to other fields besides the financial field by a person skilled in the art. For example, in addition to an automated teller machine (atm) site, the methods provided by embodiments of the present disclosure may also be applicable to location determination for other types of service sites, such as retail stores, restaurants, or supermarkets.
In operation S202, in response to the location determination request, a first population is obtained, wherein the first population includes M individuals, each individual is used for representing a candidate location of the self-service teller machine website, and M is larger than or equal to 3.
In operation S203, the first population is processed by using a differential evolution algorithm to obtain target individuals, where the target individuals are individuals obtained under the condition that an iteration termination condition of the differential evolution algorithm is satisfied.
In operation S204, the candidate location characterized by the target individual is determined as a target location of the self-service teller machine website.
According to the embodiment of the disclosure, a mathematical model for the position determination problem of the self-service teller machine network point is established, and the mathematical model is solved by using a differential evolution algorithm, so that a target position suitable for setting the self-service teller machine network point is obtained. Because the differential evolution algorithm has stronger searching capability and robustness, a target position suitable for setting the self-service teller machine network point can be selected from a plurality of candidate positions.
According to the embodiment of the disclosure, as the technical means that the target position of the self-service teller machine network point can be obtained by processing the first population by using the differential evolution algorithm is adopted, the technical problems of long time consumption and high cost in manual determination of the position of the self-service teller machine network point in the related technology are at least partially overcome, and the technical effects of saving time and reducing cost are further achieved.
The method shown in fig. 2 is further described with reference to fig. 3-7 in conjunction with specific embodiments.
Fig. 3 schematically shows a flowchart for processing a first population by using a differential evolution algorithm to obtain a target individual according to an embodiment of the present disclosure.
As shown in fig. 3, the method includes operations S301 to S303.
In operation S301, an iteration termination condition of the differential evolution algorithm is not satisfied? (ii) a If yes, perform operation S302; if not, operation S303 is performed.
In operation S302, some or all of the individuals in the first population are processed by using a mutation operator to obtain a mutation population, and operation S304 is performed.
According to the embodiment of the disclosure, the iteration termination condition includes that the iteration number reaches the maximum iteration number or the coverage distance of the individual meets a preset condition, the coverage distance is determined according to the candidate position represented by the individual and the positions of N residential points, and N is larger than or equal to 1.
In operation S303, an individual in the population that satisfies an iteration termination condition of the differential evolution algorithm is determined as a target individual.
In operation S304, the first population and the variant population are processed by using a crossover operator to obtain a crossover population.
In operation S305, the first population and the cross population are processed by using a selection operator to obtain a second population, and operation S301 is performed.
According to an embodiment of the present disclosure, the g-th population may be represented as x (g). The ith individual in the g population may be represented as Xi(g)＝(xi，1(g)，xi，2(g)，...，xi，j(g)，...，xi，n-1(g)，xi，n(g) ). Wherein i 1, 2.. said., M-1, M denotes the number of individuals comprised by the first population. g represents the number of generations of the population. j 1, 2, n, n represents the dimension of an individual.
According to an embodiment of the present disclosure, a mutation population may be obtained by processing some or all of the individuals selected from the first population X (g ═ 1) with a mutation operator. An individual X in the first population X (g ═ 1) can be treated in the manner provided by equation (1) belowi(g ═ 1) to obtain individual H in the variant population H (g ═ 1)i(g＝1)。
Hi(g＝1)＝Xr1(g＝1)+λ(Xr2(g＝1)-Xr3(g＝1)) (1)
Wherein r1 ≠ r2 ≠ r3 ≠ i, Xr1(g＝1)、Xr2(g ═ 1) and Xr3(g ═ 1) denotes in each case a different individual X of the first group X (g ═ 1)i(g-1). λ represents a scaling factor. H (g) represents the g-th variant population.
According to an embodiment of the present disclosure, processing X in the first population X (g ═ 1) may be processed in a manner provided by the following equation (2)i，j(g ═ 1) and H in the variant population H (g ═ 1)i，j(g ═ 1), and V in the crossover population V (g ═ 1) was obtainedi，j(g＝1)。
Wherein V (g) represents the g-th crossover population. v. ofi，j(g) Represents the j-th dimension vector of the i-th individual in the g-th cross population. cr denotes the cross probability, cr ∈ [0, 1 ]]. rand (0, 1) is represented by [0, 1 ]]Obeying uniformly distributed random numbers.
According to the embodiment of the disclosure, the first population and the variant population are processed by using the crossover operator to obtain the crossover population, so that the diversity of the population can be increased, and the accuracy of determining the positions of the self-service teller machine network points can be improved.
Fig. 4 schematically shows a flow chart for processing a first population and a cross population with a selection operator to obtain a second population according to an embodiment of the disclosure.
As shown in fig. 4, the method includes operations S401 to S404.
In operation S401, for the same individual in the first population and the cross population, the coverage distance of the individual in the first population is determined.
In operation S402, coverage distances of individuals in the cross population are determined.
In operation S403, are the coverage distances of the individuals in the first population less than or equal to the coverage distances of the individuals in the cross population? (ii) a If yes, perform operation S404; if not, operation S405 is performed.
In operation S404, individuals in the first population are determined as individuals in the second population.
In operation S405, individuals in the cross population are determined as individuals in the second population.
According to an embodiment of the present disclosure, an individual X in the first population X (g ═ 1) may be processed in the manner of the following equation (3)i(g ═ 1) and individual V in crossover population V (g ═ 1)i(g ═ 1) to give individual X in the second population X (g ═ 2)i(g＝2)。
Where f () represents the coverage distance.
According to an embodiment of the present disclosure, the coverage distance of an individual in the second population is less than or equal to the coverage distance of the same individual in the first population.
According to an embodiment of the present disclosure, the coverage distance may be a maximum distance of N distances determined from the candidate position characterized by the individual and the N positions of the residential points.
According to an embodiment of the disclosure, a distance between a candidate position characterized by an individual and each of N location of population points may be determined, resulting in N distances. The maximum distance is determined as the coverage distance from the N distances.
For example, a spherical area covering all the residential points can be made by taking the candidate position characterized by the individual as the center of sphere, and the radius of the spherical area can be the coverage distance. The radius of the spherical area is the maximum distance among the distances from the candidate position to the respective residential points.
According to an embodiment of the present disclosure, the coverage distance may be an average distance of N distances determined from the candidate position characterized by the individual and N populated positions.
According to an embodiment of the disclosure, a distance between a candidate position characterized by an individual and each of N location of population points may be determined, resulting in N distances. And determining the average distance of the N distances, and taking the average distance as the coverage distance, wherein the average distance can represent the average condition of the candidate position from each resident position.
According to the embodiment of the disclosure, the average distance can represent the average condition of the candidate position from each residential point position, so that the distance from the selected candidate position to each residential point position is appropriate by taking the average distance as the coverage distance, and the service quality of the self-service teller machine website can be improved to the maximum extent.
FIG. 5 schematically shows a flow chart for determining N residential site locations according to an embodiment of the disclosure.
As shown in fig. 5, the method includes operations S401 to S404 and operations S501 to S502. Operations S401 to S404 are the same as or similar to the method described in fig. 4, and are not described again here.
In operation S501, a residential point network topology is acquired, wherein the residential point network topology includes T residential point locations.
In operation S502, N residential point locations are determined from the T residential point locations.
According to an embodiment of the present disclosure, T and N are both positive integers. The size relationship between T and N is not specifically limited in the embodiments of the present disclosure, for example, N may be equal to T, but is not limited thereto, and N may also be smaller than T.
Fig. 6 schematically shows a flow chart for obtaining a residential network topology according to an embodiment of the present disclosure.
As shown in fig. 6, the method includes operations S501 to S502 and operations S601 to S603. Operations S501 to S502 are the same as or similar to the method described in fig. 5, and are not described herein again.
In operation S601, target map information including an area position corresponding to each of a plurality of residential areas is acquired.
According to the embodiment of the present disclosure, the residential area may be, for example, a district-level administrative unit, such as a district B in city a, but is not limited thereto, and the residential area may also be, for example, a preset range in a map, where the preset range may include a coverage distance range of 50 meters, 200 meters, or 500 meters, and may also be a block with an area range of about 5 kilometers, about 10 kilometers, and the like.
In operation S602, a location of a residential point corresponding to a residential area is determined according to an area location.
According to the embodiment of the present disclosure, the residential site may be a residential area, but is not limited thereto, and may also be a place with dense people flow, such as a mall.
In operation S603, a residential point network topology is constructed from the residential point locations.
According to the embodiment of the present disclosure, when the residential site is a residential cell, for example, the position of the geometric center of each residential cell on the map may be taken as the position of the residential site. After the positions of the residential points are determined, all the residential points are connected with each other by lines, and the connecting line between the two residential points is used as an edge. Then, the linear distance of every two residential points in all the residential points is determined, and the linear distance of the two residential points is marked on the edge connecting the two residential points. Therefore, a residential point network topological graph can be obtained, each point in the residential point network topological graph represents one residential point, and the straight line distance of two residential points is marked on the connecting line of any two residential points.
According to the embodiments of the present disclosure, the straight-line distance between two residential points may be obtained by means of actual measurement or may be acquired from the target map information.
According to an embodiment of the present disclosure, operation S601 may include the following operations.
And acquiring from the map application program by utilizing a crawler tool.
According to the embodiment of the disclosure, the crawler tool can be used for acquiring the longitude and latitude information of the residential points from the map application program, and then the target map information is determined according to the longitude and latitude information of the residential points.
For example, the longitude and latitude information of the residential point A and the residential point B is crawled by a crawler tool, and the coordinate is (X)1，Y1) The coordinates of the residential point B are (X)2，Y2) Wherein X is1And X2Denotes longitude, Y1And Y2Indicating the latitude. The distance between the residential point a and the residential point B is then calculated using the following equation (4).
d＝R*arcos[cos(Y1)*cos(Y2)*cos(X1-X2)+sin(Y1)*sin(Y2)] (4)
Wherein, X1Longitude, Y, representing the residential Point A1Indicating the latitude of the residential point a. X2Longitude, Y, representing the residential point B2Indicating the latitude of the residential point B. d represents the distance between the residential point a and the residential point B. R represents the radius of the earth, and R is 6371.0 km.
Fig. 7 schematically shows a flowchart of obtaining target map information according to an embodiment of the present disclosure.
As shown in fig. 7, the method includes operations S501 to S502, operations S601 to S603, or operation S6011, and operations S701 to S702. Operations S501 to S502 are the same as or similar to the method described in fig. 5, and operations S601 to S603 are the same as or similar to the method described in fig. 6, and are not repeated here.
In operation S701, initial map information is acquired.
In operation S702, data cleaning is performed on the initial map information to obtain target map information.
Data cleansing, according to embodiments of the present disclosure, may refer to a process of re-examining and verifying data with the purpose of deleting duplicate data, deleting more data, deleting or correcting incomplete data and erroneous data, resulting in more consistent data.
According to the embodiment of the disclosure, the initial residential points in the initial map information can be subjected to data processing, and the target map information can be obtained.
According to an embodiment of the present disclosure, there may be some initial population points with some repeatability among the plurality of initial population points. For example, there may be some number of initial dwellings that are relatively close, i.e., only one dwellings need to be determined from the relatively close initial dwellings. Therefore, the initial residents in the several positions which are relatively close to each other can be combined into one resident, but the present invention is not limited to this, and one initial resident can be selected from the initial residents in the several positions which are relatively close to each other as the resident.
According to the embodiment of the disclosure, whether several initial residential points with relatively close positions can be combined into one residential point or not can be judged according to the position information of the initial residential points. For example, whether the distance between at least two initial residential points meets the preset merging condition or not can be judged according to the position information of each initial residential point, if so, several initial residential points with relatively close positions can be merged into one residential point, and if not, several initial residential points with relatively close positions cannot be merged into one residential point.
For example, the preset merging condition may be set to a distance of less than 20 meters, that is, two by two, among several initial residents located relatively close to each other, it is determined whether the distance between the initial residents is less than 20 meters. If there are two initial dwellings that are less than 20 meters apart, then it is determined that the two initial dwellings can be merged. Without being limited thereto, it may also be possible that the distances between three or more initial population points meet a preset merging condition, i.e., the distances between two initial population points of the three or more initial population points are each less than 20 meters, and it is determined that the set of three or more initial population points may be merged.
FIG. 8 schematically shows a block diagram of a position determining apparatus according to an embodiment of the present disclosure.
As shown in fig. 8, the position determining apparatus 800 may include a first obtaining module 810, a second obtaining module 820, an algorithm processing module 830, and a first determining module 840.
A first obtaining module 810 is configured to obtain a location determination request for determining a location of a self-service teller machine website.
And a second obtaining module 820, configured to obtain a first population in response to the location determination request, where the first population includes M individuals, each individual is used to characterize a candidate location of the self-service teller machine website, and M is greater than or equal to 3.
The algorithm processing module 830 is configured to process the first population by using a differential evolution algorithm to obtain a target individual, where the target individual is an individual obtained under the condition that an iteration termination condition of the differential evolution algorithm is satisfied.
A first determining module 840, configured to determine the candidate location characterized by the target individual as a target location of the self-service teller machine website.
According to the embodiment of the disclosure, as the technical means that the target position of the self-service teller machine network point can be obtained by processing the first population by using the differential evolution algorithm is adopted, the technical problems of long time consumption and high cost in manual determination of the position of the self-service teller machine network point in the related technology are at least partially overcome, and the technical effects of saving time and reducing cost are further achieved.
According to an embodiment of the present disclosure, the algorithm processing module 830 may include a first algorithm processing unit, a second algorithm processing unit, a third algorithm processing unit, an iteration unit, and a first determination unit.
The first algorithm processing unit is used for processing part or all of the individuals in the first population by using a mutation operator under the condition that the iteration termination condition of the differential evolution algorithm is determined not to be met, so as to obtain a variant population, wherein the iteration termination condition comprises that the iteration frequency reaches the maximum iteration frequency or the coverage distance of the individual meets a preset condition, the coverage distance is determined according to the candidate position represented by the individual and the positions of N residential points, and N is more than or equal to 1.
And the second algorithm processing unit is used for processing the first population and the variation population by using a crossover operator to obtain a crossover population.
And the third algorithm processing unit is used for processing the first population and the cross population by using the selection operator to obtain a second population.
And the iteration unit is used for continuously operating the first algorithm processing unit under the condition that the iteration termination condition of the differential evolution algorithm is not met until the iteration termination condition is met.
The first determining unit is used for determining the individuals in the population meeting the iteration termination condition of the differential evolution algorithm as target individuals.
According to an embodiment of the present disclosure, the third algorithm processing unit may include a first determination subunit, a second determination subunit, a third determination subunit, and a fourth determination subunit.
And the first determining subunit is used for determining the coverage distance of the individuals in the first population aiming at the same individual in the first population and the cross population.
And the second determining subunit is used for determining the coverage distance of the individuals in the cross population.
And a third determining subunit, configured to determine the individual in the first population as an individual in the second population if it is determined that the coverage distance of the individual in the first population is less than or equal to the coverage distance of the individual in the cross population.
And the fourth determining subunit is used for determining the individuals in the cross population as the individuals in the second population under the condition that the coverage distance of the individuals in the first population is determined to be larger than the coverage distance of the individuals in the cross population.
According to an embodiment of the present disclosure, determining the coverage distance according to the candidate position characterized by the individual and the N-number of residential location may include the following operations.
The coverage distance is the largest distance of the N distances determined from the candidate position characterized by the individual and the N positions of the populated points. Or
The coverage distance is an average distance of N distances determined from the candidate position characterized by the individual and the N positions of the populated points.
According to an embodiment of the present disclosure, the position determining apparatus 800 may further include a third obtaining module and a second determining module.
And the third acquisition module is used for acquiring a residential point network topological graph, wherein the residential point network topological graph comprises T residential point positions.
And the second determination module is used for determining N residential point positions from the T residential point positions.
According to an embodiment of the present disclosure, the position determining apparatus 800 may further include a fourth obtaining module, a third determining module, and a constructing module.
The fourth acquisition module is used for acquiring target map information, wherein the target map information comprises area positions corresponding to each residential area in a plurality of residential areas.
And the third determining module is used for determining the position of the residential point corresponding to the residential area according to the area position.
And the building module is used for building a residential point network topological graph according to the position of the residential point.
According to an embodiment of the present disclosure, the fourth obtaining module may include a obtaining unit.
And the acquisition unit is used for acquiring the target map information from the map application program by utilizing the crawler tool.
According to an embodiment of the present disclosure, the position determining apparatus 800 may further include a fourth obtaining module and a data washing module.
And the fourth acquisition module is used for acquiring the initial map information.
And the data cleaning module is used for carrying out data cleaning on the initial map information to obtain target map information.
Any of the modules, units, sub-units, or at least part of the functionality of any of them according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, units and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, units, and sub-units according to the embodiments of the present disclosure may be implemented at least partially as a hardware Circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a Circuit, or implemented by any one of or a suitable combination of software, hardware, and firmware. Alternatively, one or more of the modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as computer program modules, which, when executed, may perform the corresponding functions.
For example, any plurality of the first obtaining module 810, the second obtaining module 820, the algorithm processing module 830 and the first determining module 840 may be combined into one module/unit/sub-unit to be implemented, or any one of the modules/units/sub-units may be split into a plurality of modules/units/sub-units. Alternatively, at least part of the functionality of one or more of these modules/units/sub-units may be combined with at least part of the functionality of other modules/units/sub-units and implemented in one module/unit/sub-unit. According to an embodiment of the present disclosure, at least one of the first obtaining module 810, the second obtaining module 820, the algorithm processing module 830 and the first determining module 840 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented in any one of three implementations of software, hardware and firmware, or in a suitable combination of any of them. Alternatively, at least one of the first obtaining module 810, the second obtaining module 820, the algorithm processing module 830 and the first determining module 840 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.
It should be noted that the position determining device portion in the embodiment of the present disclosure corresponds to the position determining method portion in the embodiment of the present disclosure, and the description of the position determining device portion specifically refers to the position determining method portion, and is not repeated herein.
Fig. 9 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure. The electronic device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 9, an electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 902 or a program loaded from a storage portion 908 into a Random Access Memory (RAM) 903. Processor 901 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 901 may also include on-board memory for caching purposes. The processor 901 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM903, various programs and data necessary for the operation of the electronic apparatus 900 are stored. The processor 901, the ROM902, and the RAM903 are connected to each other through a bus 904. The processor 901 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM902 and/or the RAM 903. Note that the programs may also be stored in one or more memories other than the ROM902 and the RAM 903. The processor 901 may also perform various operations of the method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 909, and/or installed from the removable medium 911. The computer program, when executed by the processor 901, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples may include, but are not limited to: a portable Computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM) or flash Memory), a portable compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the preceding. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM902 and/or the RAM903 described above and/or one or more memories other than the ROM902 and the RAM 903.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method provided by the embodiments of the present disclosure, when the computer program product is run on an electronic device, the program code being adapted to cause the electronic device to carry out the position determination method provided by the embodiments of the present disclosure.
The computer program, when executed by the processor 901, performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal on a network medium, and downloaded and installed through the communication section 909 and/or installed from the removable medium 911. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device through any kind of Network, including a Local Area Network (LAN) or Wide Area Network (WAN), or may be connected to external computing devices (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (12)
1. A method of position determination, comprising:
obtaining a position determination request for determining the position of a self-service teller machine network point;
responding to the position determination request, and acquiring a first population, wherein the first population comprises M individuals, each individual is used for representing a candidate position of a self-service teller machine website, and M is more than or equal to 3;
processing the first population by using a differential evolution algorithm to obtain a target individual, wherein the target individual is obtained under the condition that an iteration termination condition of the differential evolution algorithm is met; and
determining the candidate position characterized by the target individual as the target position of the self-service teller machine network.
2. The method of claim 1, wherein the processing the first population using a differential evolution algorithm to obtain target individuals comprises:
under the condition that the iteration termination condition of the differential evolution algorithm is determined not to be met, processing part or all individuals in the first population by using a mutation operator to obtain a variant population, wherein the iteration termination condition comprises that the iteration number reaches the maximum iteration number or the coverage distance of the individuals meets a preset condition, the coverage distance is determined according to candidate positions and N positions of the residents represented by the individuals, and N is more than or equal to 1;
processing the first population and the variation population by using a crossover operator to obtain a crossover population;
processing the first population and the cross population by using a selection operator to obtain a second population;
under the condition that the iteration termination condition of the differential evolution algorithm is determined not to be met, continuing to execute the operation of determining the next generation population until the iteration termination condition is met; and
and determining the individuals in the population meeting the iteration termination condition of the differential evolution algorithm as the target individuals.
3. The method of claim 2, wherein said processing said first population and said cross population with a selection operator to obtain a second population comprises:
determining, for the same individual in the first population and the crossover population, a coverage distance of the individual in the first population;
determining a coverage distance of the individuals in the crossover population;
determining the individuals in the first population as individuals in the second population if it is determined that the coverage distance of the individuals in the first population is less than or equal to the coverage distance of the individuals in the crossover population; and
determining the individuals in the cross population as individuals in the second population if it is determined that the coverage distance of the individuals in the first population is greater than the coverage distance of the individuals in the cross population.
4. A method according to claim 2 or 3, wherein the coverage distance is determined from the candidate location characterized by the individual and N populated locations, comprising:
the coverage distance is the maximum distance in N distances determined according to the candidate position characterized by the individual and the N positions of the residential points; or
The coverage distance is an average distance of N distances determined according to the candidate position characterized by the individual and the N positions of the residential points.
5. The method of claim 3, further comprising:
acquiring a residential point network topological graph, wherein the residential point network topological graph comprises T residential point positions; and
determining the N number of residential location positions from the T number of residential location positions.
6. The method of claim 5, further comprising:
acquiring target map information, wherein the target map information includes an area position corresponding to each of a plurality of residential areas;
determining the position of a residential point corresponding to the residential area according to the area position; and
and constructing the residential point network topological graph according to the residential point position.
7. The method of claim 6, wherein the obtaining target map information comprises:
and acquiring the target map information from a map application program by using a crawler tool.
8. The method of any of claims 5-7, further comprising:
acquiring initial map information; and
and carrying out data cleaning on the initial map information to obtain the target map information.
9. A position determining apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a position determination request for determining the position of a self-service teller machine network;
the second obtaining module is used for responding to the position determining request and obtaining a first population, wherein the first population comprises M individuals, each individual is used for representing a candidate position of a self-service teller machine website, and M is more than or equal to 3;
the algorithm processing module is used for processing the first population by using a differential evolution algorithm to obtain a target individual, wherein the target individual is obtained under the condition that an iteration termination condition of the differential evolution algorithm is met; and
a first determining module, configured to determine a candidate location characterized by the target individual as a target location of the self-service teller machine website.
10. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-8.
11. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 8.
12. A computer program product comprising a computer program which, when executed by a processor, is adapted to carry out the method of any one of claims 1 to 8.
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