CN113891369A - Network optimization method, device and storage medium - Google Patents
Network optimization method, device and storage medium Download PDFInfo
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- CN113891369A CN113891369A CN202111276628.0A CN202111276628A CN113891369A CN 113891369 A CN113891369 A CN 113891369A CN 202111276628 A CN202111276628 A CN 202111276628A CN 113891369 A CN113891369 A CN 113891369A
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Abstract
The application provides a network optimization method, a network optimization device and a storage medium, relates to the technical field of communication, and can achieve more reasonable network optimization. The network optimization method comprises the following steps: acquiring position information and connection information of each base station in a plurality of base stations in an area to be optimized; the connection information comprises access information of an access terminal under each base station; determining the topological relation of a plurality of base stations according to the position information and the connection information; clustering the base stations according to the topological relation to obtain a plurality of categories; and optimizing the network in the area to be optimized according to the plurality of categories.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a network optimization method, apparatus, and storage medium.
Background
The quality of the coverage quality of the network directly relates to the customer experience of the operator, and how to optimize the overall coverage quality of the network by adjusting the base station is one of the important problems of the operator.
The traditional single base station adjustment easily only considers the signal problem in the service range of the base station, thus causing the defect that a new problem appears when one problem is solved after the adjustment, and being not beneficial to forming effective coordination among a plurality of base stations.
Disclosure of Invention
The application provides a network optimization method, a network optimization device and a storage medium, which solve the technical problem that the prior art cannot realize a DCN service flow.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, a network optimization method is provided, including:
acquiring position information and connection information of each base station in a plurality of base stations in an area to be optimized; the connection information comprises access information of an access terminal under each base station;
determining the topological relation of a plurality of base stations according to the position information and the connection information;
clustering the base stations according to the topological relation to obtain a plurality of categories;
and optimizing the network in the area to be optimized according to the plurality of categories.
Optionally, determining the topological relation of the plurality of base stations according to the location information and the connection information includes:
determining a plurality of nodes corresponding to a plurality of base stations one by one according to the position information;
connecting adjacent base stations in the plurality of base stations according to the connection information, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are sequentially accessed by the access terminal; the side distance is the number of access terminals accessed by adjacent base stations in sequence;
and constructing a topological structure chart of the plurality of base stations, and determining the topological structure chart as the topological relation of the plurality of base stations.
Optionally, clustering the plurality of base stations according to the topological relation to obtain a plurality of categories, including:
selecting a plurality of central nodes meeting preset conditions from the plurality of nodes; one central node corresponds to one category;
determining the categories of other nodes except the plurality of central nodes in the plurality of nodes based on a breadth-first search algorithm until the category of each node in the plurality of nodes is determined;
and determining the category of each node as the category of the base station corresponding to each node.
Optionally, determining the category of the other nodes except the central nodes in the plurality of nodes based on the breadth-first search algorithm until determining the category of each node in the plurality of nodes includes:
based on the breadth-first search algorithm, target operation is executed on the multiple nodes to obtain the categories of other nodes; the target operation is:
acquiring the category of the adjacent node of the mth layer of the first central node; m is an integer greater than 0; the first central node is any one of a plurality of central nodes; the adjacent node of the mth layer is an adjacent node connected with the adjacent node of the (m-1) th layer; when m is 1, the adjacent node of the 1 st layer is an adjacent node connected with the first central node;
when the adjacent node of the mth layer is not marked with the category, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the m-nth layer of the second central node, determining the category of the second central node as the category of the adjacent node of the mth layer; n is an integer less than m and greater than 1; the second central node is any one of the plurality of central nodes except the first central node;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the m + nth layer of the second central node, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, determining a first total distance and a second total distance; when the first total distance is smaller than the second total distance, determining the category of the first center node as the category of the adjacent node of the mth layer; when the first total distance is greater than the second total distance, determining the category of the second center node as the category of the adjacent node of the mth layer;
the first total distance is the sum of the distances from the adjacent nodes of the mth layer to each node in the first node set; the first node set is a node set under the category of the first central node; the second total distance is the sum of the distances from the adjacent nodes of the mth layer to each node in the second node set; the second node set is a set of nodes under the category of the second center node.
In a second aspect, a network optimization apparatus is provided, including: an acquisition unit and a processing unit;
an obtaining unit, configured to obtain location information and connection information of each base station in a plurality of base stations in an area to be optimized; the connection information comprises access information of an access terminal under each base station;
the processing unit is used for determining the topological relation of the base stations according to the position information and the connection information;
the processing unit is also used for clustering the base stations according to the topological relation so as to obtain a plurality of categories;
and the processing unit is also used for optimizing the network in the area to be optimized according to the plurality of categories.
Optionally, the processing unit is specifically configured to:
determining a plurality of nodes corresponding to a plurality of base stations one by one according to the position information;
connecting adjacent base stations in the plurality of base stations according to the connection information, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are sequentially accessed by the access terminal; the side distance is the number of access terminals accessed by adjacent base stations in sequence;
and constructing a topological structure chart of the plurality of base stations, and determining the topological structure chart as the topological relation of the plurality of base stations.
Optionally, the processing unit is specifically configured to:
selecting a plurality of central nodes meeting preset conditions from the plurality of nodes; one central node corresponds to one category;
determining the categories of other nodes except the plurality of central nodes in the plurality of nodes based on a breadth-first search algorithm until the category of each node in the plurality of nodes is determined;
and determining the category of each node as the category of the base station corresponding to each node.
Optionally, the processing unit is specifically configured to:
based on the breadth-first search algorithm, target operation is executed on the multiple nodes to obtain the categories of other nodes; the target operation is:
acquiring the category of the adjacent node of the mth layer of the first central node; m is an integer greater than 0; the first central node is any one of a plurality of central nodes; the adjacent node of the mth layer is an adjacent node connected with the adjacent node of the (m-1) th layer; when m is 1, the adjacent node of the 1 st layer is an adjacent node connected with the first central node;
when the adjacent node of the mth layer is not marked with the category, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the m-nth layer of the second central node, determining the category of the second central node as the category of the adjacent node of the mth layer; n is an integer less than m and greater than 1; the second central node is any one of the plurality of central nodes except the first central node;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the m + nth layer of the second central node, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, determining a first total distance and a second total distance; when the first total distance is smaller than the second total distance, determining the category of the first center node as the category of the adjacent node of the mth layer; when the first total distance is greater than the second total distance, determining the category of the second center node as the category of the adjacent node of the mth layer;
the first total distance is the sum of the distances from the adjacent nodes of the mth layer to each node in the first node set; the first node set is a node set under the category of the first central node; the second total distance is the sum of the distances from the adjacent nodes of the mth layer to each node in the second node set; the second node set is a set of nodes under the category of the second center node.
In a third aspect, a network optimization device is provided and includes a memory and a processor. The memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus. When the network optimization device is running, the processor executes the computer executable instructions stored in the memory to cause the network optimization device to perform the network optimization method of the first aspect.
The network optimization device may be a network device, or may be a part of a device in the network device, such as a system on chip in the network device. The system on chip is configured to support the network device to implement the functions involved in the first aspect and any one of the possible implementations thereof, for example, to receive, determine, and offload data and/or information involved in the network optimization method. The chip system includes a chip and may also include other discrete devices or circuit structures.
In a fourth aspect, a computer-readable storage medium is provided, which comprises computer-executable instructions, which, when executed on a computer, cause the computer to perform the network optimization method of the first aspect.
In a fifth aspect, there is provided a computer program product, which, when run on a computer, causes the computer to perform the method of network optimization according to the first aspect and any of its possible designs.
It should be noted that all or part of the computer instructions may be stored on the first computer storage medium. The first computer storage medium may be packaged together with the processor of the network optimization device, or may be packaged separately from the processor of the network optimization device, which is not limited in this application.
For the description of the second, third, fourth and fifth aspects of the present invention, reference may be made to the detailed description of the first aspect; in addition, for the beneficial effects of the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to the beneficial effect analysis of the first aspect, and details are not repeated here.
In the present application, the names of the network optimization devices mentioned above do not limit the devices or functional modules themselves, and in actual implementation, the devices or functional modules may appear by other names. Insofar as the functions of the respective devices or functional blocks are similar to those of the present invention, they are within the scope of the claims of the present invention and their equivalents.
These and other aspects of the invention will be more readily apparent from the following description.
The technical scheme provided by the application at least brings the following beneficial effects:
based on any aspect, in the present application, after the position information and the connection information of each of the plurality of base stations in the area to be optimized are obtained, the topological relations of the plurality of base stations may be determined according to the position information and the connection information, and the plurality of base stations may be clustered according to the topological relations to obtain a plurality of categories. Subsequently, the network in the area to be optimized can be optimized according to the plurality of categories. Because the connection information comprises the access information of the access terminal under each base station, the method and the device cluster the base stations according to the position information and the connection information, and can accurately cluster the base stations in the area to be optimized according to the service capacity and the geographic position, thereby realizing more reasonable network optimization.
Drawings
Fig. 1 is a schematic hardware structure diagram of a network optimization device according to an embodiment of the present disclosure;
fig. 2 is a schematic hardware structure diagram of another network optimization device according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a network optimization method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a network optimization method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a network optimization device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
For the convenience of clearly describing the technical solutions of the embodiments of the present application, in the embodiments of the present application, the terms "first" and "second" are used to distinguish the same items or similar items with basically the same functions and actions, and those skilled in the art can understand that the terms "first" and "second" are not used to limit the quantity and execution order.
As described in the background, conventional single base station adjustment easily considers only the signal problem within the service range of the base station, which causes the disadvantage that a new problem appears when one problem is solved after adjustment, and is not favorable for forming effective cooperation among a plurality of base stations.
In order to solve the above problem, the present application provides a network optimization method, including: after the network optimization device acquires the position information and the connection information of each base station in the plurality of base stations in the area to be optimized, the network optimization device can determine the topological relation of the plurality of base stations according to the position information and the connection information, and cluster the plurality of base stations according to the topological relation to obtain a plurality of categories. Subsequently, the network optimization device can optimize the network in the area to be optimized according to the plurality of categories. Because the connection information comprises the access information of the access terminal under each base station, the method and the device cluster the base stations according to the position information and the connection information, and can accurately cluster the base stations in the area to be optimized according to the service capacity and the geographic position, thereby realizing more reasonable network optimization.
The network optimization device may be a device for optimizing a network, a chip in the device, or a system on chip in the device.
Optionally, the network optimization device may also implement a function to be implemented by the network optimization device through a Virtual Machine (VM) deployed on a physical machine.
Fig. 1 shows a hardware structure diagram of a network optimization device provided in an embodiment of the present application. As shown in fig. 1, the network optimization device includes a processor 11, a memory 12, a communication interface 13, and a bus 14. The processor 11, the memory 12 and the communication interface 13 may be connected by a bus 14.
The processor 11 is a control center of the network optimization device, and may be a single processor or a collective term for a plurality of processing elements. For example, the processor 11 may be a general-purpose Central Processing Unit (CPU), or may be another general-purpose processor. Wherein a general purpose processor may be a microprocessor or any conventional processor or the like.
For one embodiment, processor 11 may include one or more CPUs, such as CPU 0 and CPU 1 shown in FIG. 1.
The memory 12 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
In a possible implementation, the memory 12 may be present separately from the processor 11, and the memory 12 may be connected to the processor 11 via a bus 14 for storing instructions or program code. The processor 11, when calling and executing the instructions or program codes stored in the memory 12, can implement the network optimization method provided by the embodiment of the present invention.
In another possible implementation, the memory 12 may also be integrated with the processor 11.
And a communication interface 13 for connecting with other devices through a communication network. The communication network may be an ethernet network, a radio access network, a Wireless Local Area Network (WLAN), or the like. The communication interface 13 may comprise a receiving unit for receiving data and a transmitting unit for transmitting data.
The bus 14 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 1, but it is not intended that there be only one bus or one type of bus.
It is to be noted that the structure shown in fig. 1 does not constitute a limitation of the network optimization device. In addition to the components shown in fig. 1, the network optimization device can include more or fewer components than shown, or some components can be combined, or a different arrangement of components.
Fig. 2 shows another hardware structure of the network optimization device in the embodiment of the present application. As shown in fig. 2, the communication device may include a processor 21 and a communication interface 22. The processor 21 is coupled to a communication interface 22.
The function of the processor 21 may refer to the description of the processor 11 above. The processor 21 also has a memory function, and the function of the memory 12 can be referred to.
The communication interface 22 is used to provide data to the processor 21. The communication interface 22 may be an internal interface of the communication device, or may be an external interface (corresponding to the communication interface 13) of the network optimization device.
It is noted that the configuration shown in fig. 1 (or fig. 2) does not constitute a limitation of the network optimization device, which may include more or less components than those shown in fig. 1 (or fig. 2), or combine some components, or a different arrangement of components, in addition to those shown in fig. 1 (or fig. 2).
The network optimization method provided by the embodiment of the present application is described in detail below with reference to the accompanying drawings.
As shown in fig. 3, the network optimization method provided in the embodiment of the present application includes: S301-S304.
S301, the network optimization device acquires the position information and the connection information of each base station in a plurality of base stations in the area to be optimized.
The connection information includes access information for access terminals under each base station.
Specifically, the network optimization device may obtain, from the network management server, location information and connection information of each of the plurality of base stations in the area to be optimized.
The location information may be longitude and latitude of each base station.
The connection information may be data of a terminal that has connected to the base station and connection start and disconnection times of each base station in the first time period.
S302, the network optimization device determines the topological relation of the base stations according to the position information and the connection information.
Specifically, after obtaining the location information and the connection information of each of the plurality of base stations in the area to be optimized, the network optimization device may determine the topological relation of the plurality of base stations according to the location information and the connection information.
Optionally, the topological relation of the multiple base stations may be an undirected weighted graph, or a topological structure graph, or may be another structure graph or structure table for representing the topological relation, which is not limited in this embodiment of the present application.
In an implementation manner, the method for determining the topological relations of the plurality of base stations by the network optimization device according to the location information and the connection information specifically includes:
s3021, the network optimization device determines a plurality of nodes corresponding to the plurality of base stations one by one based on the location information.
Specifically, when determining the topological relation of the plurality of base stations according to the location information and the connection information, the network optimization device may determine a plurality of nodes corresponding to the plurality of base stations one to one according to the location information.
Illustratively, the network optimization device may define an area to be optimized (e.g., a city), and each base station is a node of the undirected weighted graph.
S3022, the network optimization apparatus connects the neighboring base stations among the plurality of base stations according to the connection information, and determines the edge distances of the neighboring base stations.
Specifically, when determining the topological relation of the plurality of base stations according to the location information and the connection information, the network optimization device may connect the adjacent base stations of the plurality of base stations according to the connection information, and determine the edge distance of the adjacent base stations.
Wherein, the adjacent base stations are base stations which are sequentially accessed by the access terminal; the side distance is the number of access terminals sequentially accessed by the adjacent base stations.
It should be understood that, in the second time period, for any two base stations in the plurality of base stations, the number N of access terminals with which adjacent connections are established is taken as the edge distance between the two base stations.
If the access terminal is connected to a, B at the next time, and connected to base station C after a while, the access terminal is connected to a through a neighbor connection A, B, B, C and A, C.
The first time period and the second time period are artificially defined time periods, the two time periods have no correlation relation, but the duration of the second time period is less than that of the first time period.
S3023, the network optimization apparatus constructs a topology structure diagram of the plurality of base stations, and determines the topology structure diagram as a topological relationship among the plurality of base stations.
Specifically, after determining a plurality of nodes corresponding to the plurality of base stations one to one according to the location information, and connecting adjacent base stations among the plurality of base stations according to the connection information and determining the edge distance of the adjacent base stations, the network optimization device may construct a topology structure diagram of the plurality of base stations and determine the topology structure diagram as a topology relationship of the plurality of base stations.
Illustratively, there are four base stations a, B, C, D in a certain area. In the second time period, 10 terminals are connected with the base station A, B in sequence, 20 terminals are connected with the base station A, C in sequence, and 3 terminals are connected with the result base station B, D in sequence, so that the constructed topology structure diagram is shown in fig. 4.
S303, clustering the base stations by the network optimization device according to the topological relation to obtain a plurality of categories.
Specifically, after determining the topological relation of the base stations according to the location information and the connection information, the network optimization device may cluster the base stations according to the topological relation to obtain a plurality of categories.
When the network optimization device clusters a plurality of base stations according to the topological relation to obtain a plurality of categories, the clustering has the following requirements in consideration of the optimization cost due to the fact that the network optimization scene of the base stations is oriented:
1. each category is as uniform as possible. Considering that the base station optimization requires the cooperation of a plurality of base stations in an area, the phenomenon of extreme imbalance is not suitable to occur. The adjacent base stations are clustered together as much as possible to form a regular range.
2. The initial clustering may be generally specified according to a partial rule, such as administrative districts.
3. In each cluster in the graph, the edge distance of the edge in each class is as large as possible, and the edge distance of the edge between classes should be as small as possible (i.e., the connection between different classes is smaller).
4. The base stations can not be mutually matched and optimized among the isolated points, so that the isolated points are not considered in the clustering (namely, when a plurality of base stations are clustered according to the topological relation to obtain a plurality of categories, the isolated points in the graph are removed firstly).
In this case, the method for clustering the plurality of base stations by the network optimization device according to the topological relation to obtain the plurality of categories specifically includes:
s3031, the network optimization apparatus selects a plurality of central nodes satisfying a preset condition from the plurality of nodes.
Wherein one central node corresponds to one category.
Specifically, the network optimization device clusters the base stations according to the topological relation to obtain a plurality of categories, and may select a plurality of central nodes satisfying a preset condition from the plurality of nodes.
Alternatively, the preset condition may be set manually. Such as street, administrative area, etc. In the absence of a condition, a random generation mode can be adopted.
S3032, the network optimization apparatus determines the category of the other nodes except the plurality of central nodes in the plurality of nodes based on the breadth first search algorithm until determining the category of each node in the plurality of nodes.
Specifically, after selecting a plurality of central nodes satisfying a preset condition from the plurality of nodes, the network optimization device may determine the category of other nodes than the plurality of central nodes in the plurality of nodes based on a breadth-first search algorithm until determining the category of each node in the plurality of nodes.
Optionally, the method for determining, by the network optimization device, the category of the other nodes except the plurality of central nodes in the plurality of nodes based on the breadth-first search algorithm until determining the category of each node in the plurality of nodes specifically includes:
the network optimization device executes target operation on a plurality of nodes based on a breadth-first search algorithm so as to obtain the categories of other nodes.
The target operation specifically comprises S1-S5:
and S1, acquiring the category of the adjacent node of the mth layer of the first central node.
Wherein m is an integer greater than 0; the first central node is any one of a plurality of central nodes; the adjacent node of the mth layer is an adjacent node connected with the adjacent node of the (m-1) th layer; when m is 1, the adjacent node of the 1 st level is an adjacent node connected to the first central node.
Specifically, the breadth-first search algorithm is one of the simplest graph search algorithms, and is a prototype of many important graph algorithms. The breadth-first search algorithm is a blind search method, which aims to systematically expand and check all nodes in a graph to find a result. In other words, it does not take into account the possible locations of the results and searches through the entire graph until a result is found.
Illustratively, given a graph G ═ V, E and a source vertex s, the breadth-first search algorithm looks for the G edges in a systematic manner, to "find" all vertices that s can reach, and computes the distance s to all those vertices (the minimum number of edges), which simultaneously generates a breadth-first tree rooted at s and including all reachable vertices. For any vertex v reachable from s, the path from s to v in the breadth-first tree corresponds to the shortest path from s to v in graph G, i.e., the path containing the smallest number of edges. The algorithm is equally applicable to directed graphs and undirected graphs.
And S2, when the adjacent node of the mth layer does not mark the category, determining the category of the first central node as the category of the adjacent node of the mth layer.
Specifically, when the adjacent node of the mth layer does not mark a category, it indicates that the adjacent node of the mth layer has not been clustered. In this case, the category of the first center node may be determined as the category of the neighbor node of the mth layer.
And S3, when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, determining the category of the second central node as the category of the adjacent node of the mth layer.
Wherein n is an integer less than m and greater than 1; the second central node is any one of the plurality of central nodes except the first central node.
Specifically, when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the m-nth layer adjacent node of the second central node, it is indicated that the adjacent node of the mth layer has been clustered by the category corresponding to the second central node, and the distance from the adjacent node of the mth layer to the second central node is smaller than the distance from the adjacent node of the mth layer to the first central node. In this case, the category of the second center node may be determined as the category of the neighbor node of the mth layer.
For example, when m is 2 (representing the neighboring nodes traversing the second level), for the central node a, the neighboring node C is the second-level neighboring node of the central node a; but this time the neighboring node C has been marked as a category of the central node B and is the first level node of the central node B. Since 1<2, the class of the neighboring node C is maintained as the original class, i.e., the class of the center node B.
And S4, when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the m + n th layer of the second central node, determining the category of the first central node as the category of the adjacent node of the mth layer.
Specifically, when the adjacent node of the mth layer is marked as the category of the second center node and the adjacent node of the mth layer is the m + nth layer adjacent node of the second center node, it is indicated that the adjacent node of the mth layer has been clustered by the category corresponding to the second center node, and the distance from the adjacent node of the mth layer to the second center node is greater than the distance from the adjacent node of the mth layer to the first center node. In this case, the category of the first center node may be determined as the category of the neighbor node of the mth layer.
For example, when m is 2 (representing the neighboring nodes traversing the second level), for the central node a, the neighboring node C is the second-level neighboring node of the central node a; but this time the neighboring node C has been marked as a category of the central node B and is a third level node of the central node B. Since 2<3, the category of the neighboring node C is updated to the category of the central node a.
S5, when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, determining a first total distance and a second total distance; when the first total distance is smaller than the second total distance, determining the category of the first center node as the category of the adjacent node of the mth layer; and when the first total distance is greater than the second total distance, determining the category of the second center node as the category of the adjacent node of the mth layer.
The first total distance is the sum of distances from adjacent nodes of the mth layer to each node in the first node set; the first node set is a node set under the category of the first central node; the second total distance is the sum of the distances from the adjacent nodes of the mth layer to each node in the second node set; the second node set is a set of nodes under the category of the second center node.
Specifically, when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, it is indicated that the adjacent node of the mth layer has been clustered by the category corresponding to the second central node, and the distance from the adjacent node of the mth layer to the second central node is equal to the distance from the adjacent node of the mth layer to the first central node. In this case, the first total distance and the second total distance may be determined.
When the first total distance is smaller than the second total distance, determining the category of the first center node as the category of the adjacent node of the mth layer; and when the first total distance is greater than the second total distance, determining the category of the second center node as the category of the adjacent node of the mth layer.
S3033, the network optimization device determines the category of each node as the category of the base station corresponding to each node.
Specifically, the category of the other nodes except the plurality of central nodes in the plurality of nodes is determined based on the breadth-first search algorithm, and after the category of each node in the plurality of nodes is determined, the category of each node is determined as the category of the base station corresponding to each node.
S304, the network optimization device optimizes the network in the area to be optimized according to the plurality of categories.
Specifically, after determining the category of each node as the category of the base station corresponding to each node, the network optimization device optimizes the network in the area to be optimized according to the plurality of categories.
For each class, such as all base stations in the first class, a coverage range is defined, and when network optimization (such as capacity expansion and antenna angle adjustment) is performed, the coverage range is optimized as a whole, so that the aims of no coverage dead angle, maximum combined coverage range, optimal coverage quality and the like in an area are fulfilled.
The application provides a network optimization method, which comprises the following steps: after the network optimization device acquires the position information and the connection information of each base station in the plurality of base stations in the area to be optimized, the network optimization device can determine the topological relation of the plurality of base stations according to the position information and the connection information, and cluster the plurality of base stations according to the topological relation to obtain a plurality of categories. Subsequently, the network optimization device can optimize the network in the area to be optimized according to the plurality of categories. Because the connection information comprises the access information of the access terminal under each base station, the method and the device cluster the base stations according to the position information and the connection information, and can accurately cluster the base stations in the area to be optimized according to the service capacity and the geographic position, thereby realizing more reasonable network optimization.
The scheme provided by the embodiment of the application is mainly introduced from the perspective of a method. To implement the above functions, it includes hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiment of the present application, the network optimization device may be divided into the functional modules according to the method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. Optionally, the division of the modules in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 5 is a schematic structural diagram of a network optimization apparatus 400 according to an embodiment of the present disclosure. Such as a method for performing the network optimization shown in fig. 3. The network optimization apparatus 400 includes: the method comprises the following steps: an acquisition unit 401 and a processing unit 402;
an obtaining unit 401, configured to obtain location information and connection information of each base station in a to-be-optimized area; the connection information comprises access information of an access terminal under each base station;
a processing unit 402, configured to determine a topological relation between the plurality of base stations according to the location information and the connection information;
the processing unit 402 is further configured to cluster the base stations according to the topological relation to obtain multiple categories;
the processing unit 402 is further configured to optimize the network in the area to be optimized according to the multiple categories.
Optionally, the processing unit 402 is specifically configured to:
determining a plurality of nodes corresponding to a plurality of base stations one by one according to the position information;
connecting adjacent base stations in the plurality of base stations according to the connection information, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are sequentially accessed by the access terminal; the side distance is the number of access terminals accessed by adjacent base stations in sequence;
and constructing a topological structure chart of the plurality of base stations, and determining the topological structure chart as the topological relation of the plurality of base stations.
Optionally, the processing unit 402 is specifically configured to:
selecting a plurality of central nodes meeting preset conditions from the plurality of nodes; one central node corresponds to one category;
determining the categories of other nodes except the plurality of central nodes in the plurality of nodes based on a breadth-first search algorithm until the category of each node in the plurality of nodes is determined;
and determining the category of each node as the category of the base station corresponding to each node.
Optionally, the processing unit 402 is specifically configured to:
based on the breadth-first search algorithm, target operation is executed on the multiple nodes to obtain the categories of other nodes; the target operation is:
acquiring the category of the adjacent node of the mth layer of the first central node; m is an integer greater than 0; the first central node is any one of a plurality of central nodes; the adjacent node of the mth layer is an adjacent node connected with the adjacent node of the (m-1) th layer; when m is 1, the adjacent node of the 1 st layer is an adjacent node connected with the first central node;
when the adjacent node of the mth layer is not marked with the category, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the m-nth layer of the second central node, determining the category of the second central node as the category of the adjacent node of the mth layer; n is an integer less than m and greater than 1; the second central node is any one of the plurality of central nodes except the first central node;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the m + nth layer of the second central node, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, determining a first total distance and a second total distance; when the first total distance is smaller than the second total distance, determining the category of the first center node as the category of the adjacent node of the mth layer; when the first total distance is greater than the second total distance, determining the category of the second center node as the category of the adjacent node of the mth layer;
the first total distance is the sum of the distances from the adjacent nodes of the mth layer to each node in the first node set; the first node set is a node set under the category of the first central node; the second total distance is the sum of the distances from the adjacent nodes of the mth layer to each node in the second node set; the second node set is a set of nodes under the category of the second center node.
Embodiments of the present application also provide a computer-readable storage medium, which includes computer-executable instructions. When the computer executes the instructions to run on the computer, the computer is enabled to execute the steps executed by the network optimization device in the network optimization method provided by the embodiment.
The embodiments of the present application further provide a computer program product, where the computer program product may be directly loaded into the memory and contains a software code, and after the computer program product is loaded and executed by a computer, the computer program product can implement each step executed by the network optimization device in the network optimization method provided in the foregoing embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented using a software program, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The processes or functions according to the embodiments of the present application are generated in whole or in part when the computer-executable instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that can be accessed by a computer or can comprise one or more data storage devices, such as servers, data centers, and the like, that can be integrated with the media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and there may be other division ways in actual implementation. For example, various elements or components may be combined or may be integrated into another device, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product 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 according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for network optimization, comprising:
acquiring position information and connection information of each base station in a plurality of base stations in an area to be optimized; the connection information comprises access information of an access terminal under each base station;
determining the topological relation of the base stations according to the position information and the connection information;
clustering the base stations according to the topological relation to obtain a plurality of categories;
and optimizing the network in the area to be optimized according to the plurality of categories.
2. The method of claim 1, wherein the determining the topological relationship of the plurality of base stations according to the location information and the connection information comprises:
determining a plurality of nodes which are in one-to-one correspondence with the plurality of base stations according to the position information;
connecting adjacent base stations in the plurality of base stations according to the connection information, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are sequentially accessed by the access terminal; the side distance is the number of access terminals sequentially accessed by the adjacent base stations;
and constructing a topological structure chart of the plurality of base stations, and determining the topological structure chart as the topological relation of the plurality of base stations.
3. The method of claim 2, wherein the clustering the plurality of base stations according to the topological relation to obtain a plurality of categories comprises:
selecting a plurality of central nodes meeting preset conditions from the plurality of nodes; one central node corresponds to one category;
determining the category of other nodes in the plurality of nodes except the plurality of central nodes based on a breadth-first search algorithm until the category of each node in the plurality of nodes is determined;
and determining the category of each node as the category of the base station corresponding to each node.
4. The method according to claim 3, wherein the determining the category of the nodes other than the central nodes in the plurality of nodes based on the breadth-first search algorithm until the determining the category of each node in the plurality of nodes comprises:
based on the breadth-first search algorithm, executing target operation on the plurality of nodes to obtain the categories of the other nodes; the target operation is:
acquiring the category of the adjacent node of the mth layer of the first central node; m is an integer greater than 0; the first central node is any one of the plurality of central nodes; the adjacent node of the mth layer is an adjacent node connected with the adjacent node of the (m-1) th layer; when m is 1, the adjacent node of the 1 st layer is the adjacent node connected with the first central node;
when the adjacent node of the mth layer is not marked with the category, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of a second central node and the adjacent node of the mth layer is the adjacent node of the mth-nth layer of the second central node, determining the category of the second central node as the category of the adjacent node of the mth layer; n is an integer less than m and greater than 1; the second central node is any one of the plurality of central nodes except the first central node;
when the adjacent node of the mth layer is marked as the class of the second central node and the adjacent node of the mth layer is the adjacent node of the m + nth layer of the second central node, determining the class of the first central node as the class of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, determining a first total distance and a second total distance; determining the category of the first center node as the category of the adjacent node of the mth layer when the first total distance is smaller than the second total distance; when the first total distance is greater than the second total distance, determining the category of the second center node as the category of the adjacent node of the mth layer;
the first total distance is the sum of distances from adjacent nodes of the mth layer to each node in the first node set; the first node set is a node set under the category of the first central node; the second total distance is the sum of distances from the adjacent node of the mth layer to each node in the second node set; the second node set is a set of nodes under the category of the second central node.
5. A network optimization apparatus, comprising: an acquisition unit and a processing unit;
the acquiring unit is used for acquiring the position information and the connection information of each base station in a plurality of base stations in the area to be optimized; the connection information comprises access information of an access terminal under each base station;
the processing unit is configured to determine a topological relation between the plurality of base stations according to the location information and the connection information;
the processing unit is further configured to cluster the base stations according to the topological relation to obtain multiple categories;
the processing unit is further configured to optimize the network in the area to be optimized according to the multiple categories.
6. The network optimization device according to claim 5, wherein the processing unit is specifically configured to:
determining a plurality of nodes which are in one-to-one correspondence with the plurality of base stations according to the position information;
connecting adjacent base stations in the plurality of base stations according to the connection information, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are sequentially accessed by the access terminal; the side distance is the number of access terminals sequentially accessed by the adjacent base stations;
and constructing a topological structure chart of the plurality of base stations, and determining the topological structure chart as the topological relation of the plurality of base stations.
7. The network optimization device according to claim 6, wherein the processing unit is specifically configured to:
selecting a plurality of central nodes meeting preset conditions from the plurality of nodes; one central node corresponds to one category;
determining the category of other nodes in the plurality of nodes except the plurality of central nodes based on a breadth-first search algorithm until the category of each node in the plurality of nodes is determined;
and determining the category of each node as the category of the base station corresponding to each node.
8. The network optimization device according to claim 7, wherein the processing unit is specifically configured to:
based on the breadth-first search algorithm, executing target operation on the plurality of nodes to obtain the categories of the other nodes; the target operation is:
acquiring the category of the adjacent node of the mth layer of the first central node; m is an integer greater than 0; the first central node is any one of the plurality of central nodes; the adjacent node of the mth layer is an adjacent node connected with the adjacent node of the (m-1) th layer; when m is 1, the adjacent node of the 1 st layer is the adjacent node connected with the first central node;
when the adjacent node of the mth layer is not marked with the category, determining the category of the first central node as the category of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of a second central node and the adjacent node of the mth layer is the adjacent node of the mth-nth layer of the second central node, determining the category of the second central node as the category of the adjacent node of the mth layer; n is an integer less than m and greater than 1; the second central node is any one of the plurality of central nodes except the first central node;
when the adjacent node of the mth layer is marked as the class of the second central node and the adjacent node of the mth layer is the adjacent node of the m + nth layer of the second central node, determining the class of the first central node as the class of the adjacent node of the mth layer;
when the adjacent node of the mth layer is marked as the category of the second central node and the adjacent node of the mth layer is the adjacent node of the mth layer of the second central node, determining a first total distance and a second total distance; determining the category of the first center node as the category of the adjacent node of the mth layer when the first total distance is smaller than the second total distance; when the first total distance is greater than the second total distance, determining the category of the second center node as the category of the adjacent node of the mth layer;
the first total distance is the sum of distances from adjacent nodes of the mth layer to each node in the first node set; the first node set is a node set under the category of the first central node; the second total distance is the sum of distances from the adjacent node of the mth layer to each node in the second node set; the second node set is a set of nodes under the category of the second central node.
9. A network optimization device comprising a memory and a processor; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus;
the processor executes the computer-executable instructions stored by the memory when the network optimization device is running to cause the network optimization device to perform the network optimization method of any of claims 1-4.
10. A computer-readable storage medium, comprising computer-executable instructions that, when executed on a computer, cause the computer to perform the network optimization method of any one of claims 1-4.
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