CN113891369B - Network optimization method, device and storage medium - Google Patents
Network optimization method, device and storage medium Download PDFInfo
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- CN113891369B CN113891369B CN202111276628.0A CN202111276628A CN113891369B CN 113891369 B CN113891369 B CN 113891369B CN 202111276628 A CN202111276628 A CN 202111276628A CN 113891369 B CN113891369 B CN 113891369B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
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- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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 realize 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 includes 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 a plurality of 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 a 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 coverage of a network is directly related to customer experience of an operator, and how to optimize the overall coverage quality of the network by adjusting a base station is one of important problems of the operator.
The conventional single base station adjustment is easy to consider only the signal problem in the service range of the base station, so that the defect that a new problem appears when a problem is solved after adjustment is caused, and the effective coordination among a plurality of base stations is not facilitated.
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 DCN service flow cannot be realized in the prior art.
In order to achieve the above purpose, the present application adopts the following technical scheme:
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 includes 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 a plurality of 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 a 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 the plurality of base stations one by one according to the position information;
according to the connection information, connecting adjacent base stations in the plurality of base stations, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are accessed by the access terminals in sequence; the edge distance is the number of access terminals which are sequentially accessed by adjacent base stations;
constructing a topological structure diagram of a plurality of base stations, and determining the topological structure diagram 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 center nodes meeting preset conditions from a plurality of nodes; a central node corresponds to a category;
determining categories of nodes except for the plurality of center 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;
the class of each node is determined as the class of the base station corresponding to each node.
Optionally, determining the category of the other nodes in the plurality of nodes except the plurality of center nodes based on the breadth-first search algorithm until the category of each node in the plurality of nodes is determined includes:
performing target operation on a plurality of nodes based on a breadth-first search algorithm to obtain categories of other nodes; the target operation is as follows:
acquiring the category of the adjacent node of the m-th layer of the first center node; m is an integer greater than 0; the first central node is any central node in a plurality of central nodes; the adjacent node of the m-th 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 m layer is not marked with the category, determining the category of the first center node as the category of the adjacent node of the m layer;
when the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the m-n-th layer adjacent node of the second central node, determining the category of the second central node as the category of the adjacent node of the m-th layer; n is an integer less than m and greater than 1; the second central node is any central node except the first central node among the plurality of central nodes;
when the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the (m+n) -th layer adjacent node of the second central node, determining the category of the first central node as the category of the adjacent node of the m-th layer;
determining a first total distance and a second total distance when the m-th layer neighboring node is marked as the category of the second center node and the m-th layer neighboring node is the m-th layer neighboring node of the second center node; 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 m-th 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 m-th layer;
The first total distance is the sum of distances between adjacent nodes of the m-th layer and each node in the first node set; the first node set is a node set under the category of the first center node; the second total distance is the sum of distances between the adjacent node of the m-th layer and each node in the second node set; the second set of nodes is a set of nodes under the category of the second central node.
In a second aspect, there is provided a network optimization apparatus, comprising: an acquisition unit and a processing unit;
the acquisition unit is used for acquiring the position information and the connection information of each base station in the plurality of base stations in the area to be optimized; the connection information includes access information of an access terminal under each base station;
the processing unit is used for determining the topological relation of the plurality of base stations according to the position information and the connection information;
the processing unit is also used for clustering the plurality of base stations according to the topological relation 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 the plurality of base stations one by one according to the position information;
according to the connection information, connecting adjacent base stations in the plurality of base stations, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are accessed by the access terminals in sequence; the edge distance is the number of access terminals which are sequentially accessed by adjacent base stations;
Constructing a topological structure diagram of a plurality of base stations, and determining the topological structure diagram as the topological relation of the plurality of base stations.
Optionally, the processing unit is specifically configured to:
selecting a plurality of center nodes meeting preset conditions from a plurality of nodes; a central node corresponds to a category;
determining categories of nodes except for the plurality of center 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;
the class of each node is determined as the class of the base station corresponding to each node.
Optionally, the processing unit is specifically configured to:
performing target operation on a plurality of nodes based on a breadth-first search algorithm to obtain categories of other nodes; the target operation is as follows:
acquiring the category of the adjacent node of the m-th layer of the first center node; m is an integer greater than 0; the first central node is any central node in a plurality of central nodes; the adjacent node of the m-th 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 m layer is not marked with the category, determining the category of the first center node as the category of the adjacent node of the m layer;
When the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the m-n-th layer adjacent node of the second central node, determining the category of the second central node as the category of the adjacent node of the m-th layer; n is an integer less than m and greater than 1; the second central node is any central node except the first central node among the plurality of central nodes;
when the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the (m+n) -th layer adjacent node of the second central node, determining the category of the first central node as the category of the adjacent node of the m-th layer;
determining a first total distance and a second total distance when the m-th layer neighboring node is marked as the category of the second center node and the m-th layer neighboring node is the m-th layer neighboring node of the second center node; 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 m-th 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 m-th layer;
the first total distance is the sum of distances between adjacent nodes of the m-th layer and each node in the first node set; the first node set is a node set under the category of the first center node; the second total distance is the sum of distances between the adjacent node of the m-th layer and each node in the second node set; the second set of nodes is a set of nodes under the category of the second central node.
In a third aspect, a network optimization apparatus is provided that 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 according to the first aspect.
The network optimization device may be a network device or may be a part of a device in the network device, for example, a chip system in the network device. The system on a chip is configured to support the network device to implement the functions involved in the first aspect and any one of its possible implementations, e.g. to receive, determine, and offload data and/or information involved in the network optimization method described above. The chip system includes a chip, and may also include other discrete devices or circuit structures.
In a fourth aspect, there is provided a computer readable storage medium comprising computer executable instructions which, when run 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 network optimization method according to the first aspect and any one of its possible designs.
It should be noted that, the above-mentioned computer instructions may be stored in whole or in part 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.
The description of the second, third, fourth and fifth aspects of the present invention may refer to the detailed description of the first aspect; the advantages of the second aspect, the third aspect, the fourth aspect and the fifth aspect may be referred to as analysis of the advantages of the first aspect, and will not be described here.
In this application, the names of the above-mentioned network optimization apparatuses do not constitute limitations on the devices or function modules themselves, and in actual implementations, these devices or function modules may appear under other names. Insofar as the function of each device or function module is similar to that of the present invention, it falls within the scope of the claims of the present invention and the equivalents thereof.
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 one of the above aspects, in the present application, after the location information and the connection information of each of the plurality of base stations in the area to be optimized are obtained, the topology relationship of the plurality of base stations may be determined according to the location information and the connection information, and the plurality of base stations may be clustered according to the topology relationship, so as to obtain a plurality of categories. Subsequently, the network in the area to be optimized can be optimized according to a 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 plurality of base stations according to the position information and the connection information, and can accurately cluster the plurality of base stations in the area to be optimized according to the service capability and the geographic position, thereby realizing more reasonable network optimization.
Drawings
Fig. 1 is a schematic hardware structure of a network optimization device according to an embodiment of the present application;
fig. 2 is a schematic hardware structure of another network optimization device according to an embodiment of the present application;
fig. 3 is a schematic flow chart 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 following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, the terms "first", "second", and the like are used to distinguish the same item or similar items having substantially the same function and effect, and those skilled in the art will understand that the terms "first", "second", and the like are not limited in number and execution order.
As described in the background art, conventional single base station adjustment is easy to consider only signal problems within the service range of the base station, which causes the disadvantage that a new problem occurs when a problem is solved after adjustment, and is not beneficial to forming effective coordination among a plurality of base stations.
In view of the above problems, the present application proposes a network optimization method, including: after the network optimizing device obtains the position information and the connection information of each base station in the area to be optimized, the network optimizing device can determine the topological relation of the base stations according to the position information and the connection information, and cluster the base stations according to the topological relation to obtain a plurality of categories. Subsequently, the network optimization device may optimize the network in the area to be optimized according to a 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 plurality of base stations according to the position information and the connection information, and can accurately cluster the plurality of base stations in the area to be optimized according to the service capability and the geographic position, thereby realizing more reasonable network optimization.
The network optimizing device may be a device for optimizing a network, a chip in the device, or a system on a chip in the device.
Alternatively, the network optimization device may implement the functions to be implemented by the network optimization device through a Virtual Machine (VM) deployed on a physical machine.
Fig. 1 shows a schematic hardware structure of a network optimization device according to an embodiment of the present application. As shown in fig. 1, the network optimization device comprises 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 one processor or a collective name of a plurality of processing elements. For example, the processor 11 may be a general-purpose central processing unit (central processing unit, CPU), or may be another general-purpose processor. Wherein the general purpose processor may be a microprocessor or any conventional processor or the like.
As an example, processor 11 may include one or more CPUs, such as CPU 0 and CPU 1 shown in fig. 1.
In a possible implementation, the memory 12 may exist separately from the processor 11, and the memory 12 may be connected to the processor 11 through the bus 14 for storing instructions or program code. The processor 11, when calling and executing instructions or program code stored in the memory 12, is capable of implementing the network optimization method provided by the embodiment of the invention.
In another possible implementation, the memory 12 may also be integrated with the processor 11.
A communication interface 13 for connecting with other devices via a communication network. The communication network may be an ethernet, a radio access network, a wireless local area network (wireless local area networks, WLAN), etc. The communication interface 13 may include a receiving unit for receiving data, and a transmitting unit for transmitting data.
It should be noted that the structure shown in fig. 1 does not constitute a limitation of the network optimization device. The network optimization device may include more or less components than those shown in fig. 1, or may combine certain components, or may be 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 be as described above with reference to the processor 11. 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 an external interface (corresponding to the communication interface 13) of the network optimization device.
It should be noted that the structure shown in fig. 1 (or fig. 2) does not constitute a limitation of the network optimization device, and the network optimization device may include more or less components than those shown in fig. 1 (or fig. 2), or may combine some components, or may be arranged in different components.
The network optimization method provided by the embodiment of the application is described in detail below with reference to the accompanying drawings.
As shown in fig. 3, the network optimization method provided in the application embodiment includes: S301-S304.
S301, the network optimization device acquires position information and connection information of each base station in the plurality of base stations in the area to be optimized.
The connection information includes access information of access terminals under each base station.
Specifically, the network optimization device may acquire, 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 a latitude and longitude of each base station.
The connection information may be data of a terminal connected to each base station and connection start and disconnection times in the first period.
S302, the network optimization device determines the topological relation of the plurality of base stations according to the position information and the connection information.
Specifically, after the location information and the connection information of each of the plurality of base stations in the area to be optimized are obtained, the network optimization device may determine the topology relationship of the plurality of base stations according to the location information and the connection information.
Optionally, the topology relationship of the plurality of base stations may be an undirected weighted graph, a topology structure chart, or other structure charts or structure tables for representing the topology relationship, which is not limited in the embodiment of the present application.
In one implementation manner, the method for determining the topological relation of the plurality of base stations by the network optimization device according to the position 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 according to the position information.
Specifically, when the network optimization device determines the topological relation of the plurality of base stations according to the position information and the connection information, a plurality of nodes corresponding to the plurality of base stations one by one can be determined according to the position information.
For example, the network optimization device may define an area to be optimized (such as a ground city), and each base station is a node of the undirected weighted graph.
S3022, the network optimization device connects adjacent base stations in the plurality of base stations according to the connection information, and determines the edge distance of the adjacent base stations.
Specifically, when the network optimization device determines the topological relation of the plurality of base stations according to the position information and the connection information, the network optimization device can connect adjacent base stations in the plurality of base stations according to the connection information and determine the edge distance of the adjacent base stations.
The adjacent base stations are base stations which are sequentially accessed by the access terminal; the edge distance is the number of access terminals that the neighboring base station accesses in sequence.
It should be appreciated that for any two base stations of the plurality of base stations, the number N of access terminals with which an adjacent connection is established during the second time period is taken as the edge distance between the two base stations.
Note that, the adjacent connection means that the access terminal is connected to the a, the next time is connected to the B, and after a period of time, the access terminal is connected to the base station C, and A, B is an adjacent connection, B, C is an adjacent connection, and A, C is not.
The first time period and the second time period are artificially defined time periods, the two time periods have no association relationship, but the duration of the second time period is smaller than that of the first time period.
S3023, the network optimization device constructs a topology structure diagram of the plurality of base stations and determines the topology structure diagram as a topology relation of the plurality of base stations.
Specifically, after determining a plurality of nodes corresponding to the plurality of base stations one by one according to the location information, and connecting adjacent base stations in the plurality of base stations according to the connection information and determining the edge distances 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.
For example, there are four base stations a, B, C, D in a certain area. In the second 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.
And S303, the network optimization device clusters the plurality of base stations according to the topological relation to obtain a plurality of categories.
Specifically, after determining the topological relation of the plurality of base stations according to the location information and the connection information, the network optimization device may cluster the plurality of 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 network optimization scene of the base stations is oriented, and the following requirements are met in consideration of the optimization cost:
1. the categories are 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 extremely unbalanced phenomenon is not suitable to occur. Adjacent base stations are clustered together as much as possible to form a regular range.
2. The initial clusters may typically be specified according to a partial principle, such as administrative districts.
3. In each cluster in the graph, the edge distance of the edges within each category is as large as possible, and the edge distance of the edges between categories should be as small as possible (i.e., the smaller the association between different categories).
4. The base stations cannot be 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 a topological relation to obtain a plurality of categories, the isolated points in the graph are removed first).
In this case, the network optimization device clusters the plurality of base stations according to the topological relation, so as to obtain a plurality of categories, and the method specifically comprises the following steps:
s3031, the network optimization device selects a plurality of center nodes meeting preset conditions from a plurality of nodes.
Wherein a central node corresponds to a category.
Specifically, when the network optimization device clusters the plurality of base stations according to the topological relation to obtain a plurality of categories, a plurality of center nodes meeting preset conditions can be selected from the plurality of nodes.
Alternatively, the preset condition may be set manually. Such as street, administrative district, etc. Random generation can be adopted when no condition exists.
S3032, the network optimization device determines the categories of other nodes except the plurality of central nodes in the plurality of nodes based on the breadth-first search algorithm until the category of each node in the plurality of nodes is determined.
Specifically, after selecting a plurality of central nodes satisfying a preset condition from the plurality of nodes, the network optimization device may determine, based on the breadth-first search algorithm, a category of other nodes than the plurality of central nodes among the plurality of nodes until a category of each node among the plurality of nodes is determined.
Optionally, the network optimization device determines the categories of the nodes except for the plurality of central nodes in the plurality of nodes based on the breadth-first search algorithm, until the method for 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 breadth-first search algorithm so as to obtain the category of other nodes.
The target operation specifically comprises S1-S5:
s1, acquiring the category of the adjacent node of the m-th layer of the first center node.
Wherein m is an integer greater than 0; the first central node is any central node in a plurality of central nodes; the adjacent node of the m-th 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.
Specifically, breadth-first search algorithms are one of the simplest graph search algorithms, which are prototypes of many important graph algorithms. Breadth-first search algorithms belong to a blind search method, with the aim of systematically developing and checking all nodes in the graph for results. In other words, it does not take into account the possible locations of the results, searching through the entire graph until the results are found.
By way of example, knowing the graph g= (V, E) and a source vertex s, breadth-first search algorithm looks up the edges of G in a systematic way to "find" all vertices that s can reach and calculate the distance s to all these vertices (the minimum number of edges), which algorithm can simultaneously generate a breadth-first tree with a root of 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.
S2, when the adjacent node of the m layer is not marked with the category, determining the category of the first center node as the category of the adjacent node of the m layer.
Specifically, when the adjacent node of the m layer is not marked with the category, it is indicated that the adjacent node of the m layer is not clustered yet. In this case, the category of the first center node may be determined as the category of the neighbor node of the m-th layer.
S3, when the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the m-n-th layer adjacent node of the second central node, determining the category of the second central node as the category of the adjacent node of the m-th layer.
Wherein n is an integer less than m and greater than 1; the second center node is any center node except the first center node among the plurality of center nodes.
Specifically, when the adjacent node of the m-th layer is marked as the category of the second center node and the adjacent node of the m-th layer is the m-n-th layer adjacent node of the second center node, the adjacent node of the m-th layer is indicated to be clustered by the category corresponding to the second center node, and the distance between the adjacent node of the m-th layer and the second center node is smaller than the distance between the adjacent node of the m-th layer and the first center node. In this case, the category of the second center node may be determined as the category of the neighbor node of the m-th layer.
Illustratively, when m=2 (representing a neighboring node traversing the second layer), for the center node a, the neighboring node C is the second layer neighboring node of the center node a; but now the neighboring node C has been marked as being of the class of the central node B and is the first layer node of the central node B. Because of 1<2, the class of the neighboring node C remains as the original class, i.e., the class of the center node B.
S4, determining the category of the first center node as the category of the adjacent node of the m-th layer when the adjacent node of the m-th layer is marked as the category of the second center node and the adjacent node of the m-th layer is the (m+n) -th layer adjacent node of the second center node.
Specifically, when the adjacent node of the m-th layer is marked as the category of the second center node and the adjacent node of the m-th layer is the (m+n) -th layer adjacent node of the second center node, the adjacent node of the m-th layer is clustered by the category corresponding to the second center node, and the distance between the adjacent node of the m-th layer and the second center node is larger than the distance between the adjacent node of the m-th layer and 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 m-th layer.
Illustratively, when m=2 (representing a neighboring node traversing the second layer), for the center node a, the neighboring node C is the second layer neighboring node of the center node a; but now the neighboring node C has been marked as being of the class of the central node B and is the third level node of the central node B. Because of 2<3, the class of the neighboring node C is updated to the class of the center node a.
S5, when the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the m-th layer adjacent node 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 m-th 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 m-th layer.
Wherein the first total distance is the sum of distances between adjacent nodes of the mth layer and each node in the first set of nodes; the first node set is a node set under the category of the first center node; the second total distance is the sum of distances between the adjacent node of the m-th layer and each node in the second node set; the second set of nodes is a set of nodes under the category of the second central node.
Specifically, when the adjacent node of the m-th layer is marked as the category of the second center node and the adjacent node of the m-th layer is the m-th layer adjacent node of the second center node, the adjacent node of the m-th layer is indicated to be clustered by the category corresponding to the second center node, and the distance between the adjacent node of the m-th layer and the second center node is equal to the distance between the adjacent node of the m-th layer and the first center 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 m-th 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 m-th layer.
S3033, the network optimization device determines the class of each node as the class of the base station corresponding to each node.
Specifically, in the breadth-first search algorithm, the categories of the nodes other than the plurality of center nodes are determined until the category of each node in the plurality of nodes is determined, and then the category of each node is determined as the category of the base station corresponding to each node.
And S304, the network optimizing device optimizes the network in the area to be optimized according to a 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 type, for example, all base stations in the first type, the coverage area is defined, and when network optimization (such as capacity expansion and antenna angle adjustment) is performed, the coverage area is optimized as a whole, so that the aims of no coverage dead angle, maximum combined coverage area, optimal coverage quality and the like in the area are realized.
The application provides a network optimization method, which comprises the following steps: after the network optimizing device obtains the position information and the connection information of each base station in the area to be optimized, the network optimizing device can determine the topological relation of the base stations according to the position information and the connection information, and cluster the base stations according to the topological relation to obtain a plurality of categories. Subsequently, the network optimization device may optimize the network in the area to be optimized according to a 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 plurality of base stations according to the position information and the connection information, and can accurately cluster the plurality of base stations in the area to be optimized according to the service capability and the geographic position, thereby realizing more reasonable network optimization.
The foregoing description of the solution provided in the embodiments of the present application has been mainly presented in terms of a method. To achieve the above functions, it includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven 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.
The embodiment of the application may divide the functional modules of the network optimization device according to the above 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 modules may be implemented in hardware or in software functional modules. Optionally, the division of the modules in the embodiments of the present application is schematic, which is merely a logic function division, and other division manners may be actually implemented.
Fig. 5 is a schematic structural diagram of a network optimization device 400 according to an embodiment of the present application. Such as for performing the network optimization method shown in fig. 3. The network optimization apparatus 400 includes: comprising 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 of a plurality of base stations in an area to be optimized; the connection information includes access information of an access terminal under each base station;
a processing unit 402, configured to determine a topological relation of the plurality of base stations according to the location information and the connection information;
the processing unit 402 is further configured to cluster the plurality of base stations according to the topological relation to obtain a plurality of categories;
the processing unit 402 is further configured to optimize the network in the area to be optimized according to a plurality of categories.
Optionally, the processing unit 402 is specifically configured to:
determining a plurality of nodes corresponding to the plurality of base stations one by one according to the position information;
according to the connection information, connecting adjacent base stations in the plurality of base stations, and determining the edge distance of the adjacent base stations; the adjacent base stations are base stations which are accessed by the access terminals in sequence; the edge distance is the number of access terminals which are sequentially accessed by adjacent base stations;
Constructing a topological structure diagram of a plurality of base stations, and determining the topological structure diagram as the topological relation of the plurality of base stations.
Optionally, the processing unit 402 is specifically configured to:
selecting a plurality of center nodes meeting preset conditions from a plurality of nodes; a central node corresponds to a category;
determining categories of nodes except for the plurality of center 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;
the class of each node is determined as the class of the base station corresponding to each node.
Optionally, the processing unit 402 is specifically configured to:
performing target operation on a plurality of nodes based on a breadth-first search algorithm to obtain categories of other nodes; the target operation is as follows:
acquiring the category of the adjacent node of the m-th layer of the first center node; m is an integer greater than 0; the first central node is any central node in a plurality of central nodes; the adjacent node of the m-th 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 m layer is not marked with the category, determining the category of the first center node as the category of the adjacent node of the m layer;
When the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the m-n-th layer adjacent node of the second central node, determining the category of the second central node as the category of the adjacent node of the m-th layer; n is an integer less than m and greater than 1; the second central node is any central node except the first central node among the plurality of central nodes;
when the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the (m+n) -th layer adjacent node of the second central node, determining the category of the first central node as the category of the adjacent node of the m-th layer;
determining a first total distance and a second total distance when the m-th layer neighboring node is marked as the category of the second center node and the m-th layer neighboring node is the m-th layer neighboring node of the second center node; 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 m-th 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 m-th layer;
the first total distance is the sum of distances between adjacent nodes of the m-th layer and each node in the first node set; the first node set is a node set under the category of the first center node; the second total distance is the sum of distances between the adjacent node of the m-th layer and each node in the second node set; the second set of nodes is a set of nodes under the category of the second central node.
Embodiments of the present application also provide a computer-readable storage medium including computer-executable instructions. When the computer executes the instructions on the computer, the computer is caused to perform the steps performed by the network optimization device in the network optimization method provided in the above embodiment.
The embodiment of the present application further provides a computer program product, which can be directly loaded into a memory and contains software codes, and the computer program product can implement each step executed by the network optimization device in the network optimization method provided in the above embodiment after being loaded and executed by a computer.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using a software program, it 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. When the computer-executable instructions are loaded and executed on a computer, the processes or functions in accordance with embodiments of the present application are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, a website, computer, server, or data center via a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices including one or more servers, data centers, etc. that can be integrated with the media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to implement all or part of the functions described above.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and the division of modules or units, for example, is merely a logical function division, and other manners of division are possible when actually implemented. For example, multiple units or components may be combined or may be integrated into another device, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions for causing a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the method described in the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (6)
1. A method of 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 access terminals under each base station;
determining the topological relation of the plurality of base stations according to the position information and the connection information, wherein the method comprises the following steps: determining a plurality of nodes corresponding to the plurality of base stations one by one according to the position information; according to the connection information, connecting adjacent base stations in the plurality of base stations, 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 which are sequentially accessed by the adjacent base stations; constructing a topological structure diagram of the plurality of base stations, and determining the topological structure diagram as a topological relation of the plurality of base stations;
clustering the plurality of base stations according to the topological relation to obtain a plurality of categories, including: selecting a plurality of center nodes meeting preset conditions from the plurality of nodes; a central node corresponds to a category; determining categories of nodes of the plurality of nodes except the plurality of center nodes based on a breadth-first search algorithm until the category of each node of the plurality of nodes is determined; determining the category of each node as the category of the base station corresponding to each node;
And optimizing the network in the area to be optimized according to the categories.
2. The network optimization method of claim 1, wherein determining the categories of nodes of the plurality of nodes other than the plurality of center nodes based on the breadth-first search algorithm until the category of each of the plurality of nodes is determined comprises:
performing target operations on the plurality of nodes based on the breadth-first search algorithm to obtain categories of the other nodes; the target operation is as follows:
acquiring the category of the adjacent node of the m-th layer of the first center node; m is an integer greater than 0; the first central node is any central node in the plurality of central nodes; the adjacent node of the m-th 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 m layer is not marked with the category, determining the category of the first center node as the category of the adjacent node of the m layer;
when the adjacent node of the m-th layer is marked as the category of a second central node and the adjacent node of the m-th layer is the m-n-th layer adjacent node of the second central node, determining the category of the second central node as the category of the adjacent node of the m-th layer; the n is an integer less than the m and greater than 1; the second central node is any central node except the first central node in the plurality of central nodes;
When the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the (m+n) -th layer adjacent node of the second central node, determining the category of the first central node as the category of the adjacent node of the m-th layer;
determining a first total distance and a second total distance when the m-th layer neighboring node has been marked as a category of the second center node and the m-th layer neighboring node is an m-th layer neighboring node of the second center node; determining a category of the first center node as a category of the m-th layer neighboring node when the first total distance is smaller than the second total distance; determining a category of the second center node as a category of the m-th layer neighboring node when the first total distance is greater than the second total distance;
the first total distance is the sum of distances between adjacent nodes of the mth layer and each node in a 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 between adjacent nodes of the mth layer and each node in a second set of nodes; the second node set is a node set under the category of the second central node.
3. A network optimization device, comprising: an acquisition unit and a processing unit;
the acquisition unit is used for acquiring the position information and the connection information of each base station in the plurality of base stations in the area to be optimized; the connection information comprises access information of access terminals under each base station;
the processing unit is used for determining the topological relation of the plurality of base stations according to the position information and the connection information; the processing unit is specifically configured to: determining a plurality of nodes corresponding to the plurality of base stations one by one according to the position information; according to the connection information, connecting adjacent base stations in the plurality of base stations, 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 which are sequentially accessed by the adjacent base stations; constructing a topological structure diagram of the plurality of base stations, and determining the topological structure diagram as a topological relation of the plurality of base stations;
the processing unit is further configured to cluster the plurality of base stations according to the topological relation to obtain a plurality of categories; the processing unit is specifically configured to: selecting a plurality of center nodes meeting preset conditions from the plurality of nodes; a central node corresponds to a category; determining categories of nodes of the plurality of nodes except the plurality of center nodes based on a breadth-first search algorithm until the category of each node of the plurality of nodes is determined; determining the category of each node as the category of the base station corresponding to each node;
The processing unit is further configured to optimize the network in the area to be optimized according to the multiple categories.
4. The network optimization device of claim 3, wherein the processing unit is specifically configured to:
performing target operations on the plurality of nodes based on the breadth-first search algorithm to obtain categories of the other nodes; the target operation is as follows:
acquiring the category of the adjacent node of the m-th layer of the first center node; m is an integer greater than 0; the first central node is any central node in the plurality of central nodes; the adjacent node of the m-th 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 m layer is not marked with the category, determining the category of the first center node as the category of the adjacent node of the m layer;
when the adjacent node of the m-th layer is marked as the category of a second central node and the adjacent node of the m-th layer is the m-n-th layer adjacent node of the second central node, determining the category of the second central node as the category of the adjacent node of the m-th layer; the n is an integer less than the m and greater than 1; the second central node is any central node except the first central node in the plurality of central nodes;
When the adjacent node of the m-th layer is marked as the category of the second central node and the adjacent node of the m-th layer is the (m+n) -th layer adjacent node of the second central node, determining the category of the first central node as the category of the adjacent node of the m-th layer;
determining a first total distance and a second total distance when the m-th layer neighboring node has been marked as a category of the second center node and the m-th layer neighboring node is an m-th layer neighboring node of the second center node; determining a category of the first center node as a category of the m-th layer neighboring node when the first total distance is smaller than the second total distance; determining a category of the second center node as a category of the m-th layer neighboring node when the first total distance is greater than the second total distance;
the first total distance is the sum of distances between adjacent nodes of the mth layer and each node in a 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 between adjacent nodes of the mth layer and each node in a second set of nodes; the second node set is a node set under the category of the second central node.
5. 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;
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 any one of claims 1-2.
6. A computer readable storage medium comprising computer executable instructions which, when run on a computer, cause the computer to perform the network optimization method of any one of claims 1-2.
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