CN114245392A - 5G network optimization method and system - Google Patents
5G network optimization method and system Download PDFInfo
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- CN114245392A CN114245392A CN202111564131.9A CN202111564131A CN114245392A CN 114245392 A CN114245392 A CN 114245392A CN 202111564131 A CN202111564131 A CN 202111564131A CN 114245392 A CN114245392 A CN 114245392A
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H04W24/02—Arrangements for optimising operational condition
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- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/06—Optimizing the usage of the radio link, e.g. header compression, information sizing, discarding information
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- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/56—Allocation or scheduling criteria for wireless resources based on priority criteria
- H04W72/566—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient
- H04W72/569—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient of the traffic information
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Abstract
The invention provides a 5G network optimization method and system, and relates to the technical field of network optimization. The method comprises the following steps: and arranging in a map model according to the target 5G network planning scheme and the actual positions of all target terminals, and performing simulation operation in the map model. And if the analog simulation data of the target terminal meets the preset conditions, acquiring the service type of the target terminal. And matching in a preset service library to obtain a router channel, all service influence factors and a preset proportion corresponding to the service type. And inputting the actual service demand data and the service types of the target terminals meeting the preset conditions into the trained AI model in sequence according to the scheduling priority so as to obtain the latest specific gravity value. And adjusting the router channel corresponding to the target terminal to obtain an optimal channel. The purpose of optimizing the 5G network is achieved, and the problem that when the network is not smooth and fluent is solved, the installed 5G station is detached and adjusted.
Description
Technical Field
The invention relates to the technical field of network optimization, in particular to a 5G network optimization method and system.
Background
With the development and progress of social informatization, people have stronger demand for high-speed mobile communication technology, and mobile communication gradually develops from traditional 2G, 3G and 4G to 5G. Due to the introduction of a plurality of advanced technologies such as an SON technology, a D2D technology, a heterogeneous ultra-dense deployment analysis technology, an SDN technology and the like, the 5G can realize network bandwidth of several giga, so as to provide high-quality network services such as high-definition videos, large online tours, intelligent tourism and the like for people.
Compared with 2G, 3G and 4G communication technologies, 5G communication technologies have faster and more stable network speed, and operators are also expanding 5G trial points. And when the operator performs 5G pilot amplification, the operator generally needs to plan the 5G site. After an operator installs a 5G site according to a planning scheme, if the problems of unsmooth and unsmooth network and the like occur, the set 5G site cannot be dismantled and adjusted, and the installed 5G network cannot be reasonably optimized in the prior art, so that not only is the user experience poor, but also the popularization of 5G is affected.
Disclosure of Invention
The invention aims to provide a 5G network optimization method and a system, which are used for solving the problems that in the prior art, after an operator installs a 5G station according to a planning scheme, if the network is not smooth, not smooth and the like, the set 5G station cannot be dismantled and adjusted, and the installed 5G network cannot be reasonably optimized.
The embodiment of the invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides a 5G network optimization method, which includes the following steps: and acquiring the actual positions and actual service demand data of all target terminals. And acquiring a target 5G network planning scheme, arranging in a map model according to the target 5G network planning scheme and the actual positions of all target terminals, and performing simulation operation in the map model. And in the simulation operation process, acquiring the simulation data of all the target terminals. And comparing the simulation data with the actual service demand data, and if the simulation data of the target terminal meets the preset conditions, acquiring the service type of the target terminal. And matching in a preset service library according to the service types to obtain router channels corresponding to the service types, all service influence factors and preset proportion of each service influence factor, and determining the scheduling priority of the target terminal. And inputting the actual service demand data and the service types of the target terminals meeting the preset conditions into the trained AI model in sequence according to the scheduling priority so as to obtain the latest specific gravity value. And adjusting the router channel corresponding to the target terminal according to all the service influence factors of the target terminal and the latest specific weight value of each service influence factor to obtain an optimal channel.
In some embodiments of the present invention, the actual traffic demand data includes actual traffic and actual network rate, and the simulation data includes simulation traffic and simulation network rate. The step of comparing the simulation data with the actual service requirement data, and if the simulation data of the target terminal meets the preset condition, acquiring the service type of the target terminal includes: and comparing the simulated traffic of the target terminal with the actual traffic of the target terminal, and comparing the simulated network rate of the target terminal with the actual network rate of the target terminal. And if the simulated service volume of the target terminal is lower than the actual service volume of the target terminal or the simulated network rate of the target terminal is lower than the actual network rate of the target terminal, acquiring the service types of all the target terminals.
In some embodiments of the present invention, before the step of performing matching in a preset service library according to the service category to obtain a router channel corresponding to the service category, all service impact factors, and a preset proportion of each service impact factor, and determining the scheduling priority of the target terminal, the method further includes: and acquiring historical service data of all service types, wherein any historical service data comprises a router channel, service influence factors and the actual proportion of each service influence factor. According to all historical service data of the same service type, counting the router channel with the highest use frequency and the preset number of service influence factors with the highest occurrence frequency, and determining the preset proportion of each service influence factor. And binding the router channel of the same service type, the service influence factor and the preset proportion of each service influence factor, and storing the bound router channel, the service influence factor and the preset proportion of each service influence factor to a preset service library.
In some embodiments of the present invention, the step of binding the router channel, the service impact factor, and the preset specific gravity of each service impact factor of the same service type and storing the bound router channel, the service impact factor, and the preset specific gravity of each service impact factor to a preset service library includes: and configuring the scheduling priority of each service influence factor from high to low according to the same router channel and the occurrence frequency of each service influence factor. And storing the router channels, the service influence factors and the preset proportion of each service influence factor of the same service type into a preset service library according to the scheduling priority.
In some embodiments of the present invention, the step of obtaining the target 5G network planning scheme, arranging in the map model according to the target 5G network planning scheme and the actual positions of all the target terminals, and performing simulation operation in the map model includes: and detecting the simulation operation process in real time, and optimizing in a ring splitting mode when the 5G flow of any target terminal exceeds the 5G capacity threshold.
In some embodiments of the present invention, before the step of sequentially inputting the actual service requirement data and the service category of the target terminal meeting the preset condition into the trained AI model according to the scheduling priority to obtain the latest specific gravity value, the method further includes: and establishing an AI model. A plurality of samples are obtained, the samples including historical traffic data and corresponding traffic categories. And training the AI model by using a plurality of samples to obtain the trained AI model.
In some embodiments of the invention, the step of establishing the AI model includes: and constructing an AI model by a random forest algorithm and a convolutional neural network algorithm.
In a second aspect, an embodiment of the present application provides a 5G network optimization system, which includes: and the target terminal parameter acquisition module is used for acquiring the actual positions and the actual service demand data of all the target terminals. And the network planning scheme acquisition module is used for acquiring a target 5G network planning scheme, arranging the target 5G network planning scheme and the actual positions of all the target terminals in the map model according to the target 5G network planning scheme and carrying out simulation operation in the map model. And the analog simulation data acquisition module is used for acquiring analog simulation data of all target terminals in the process of analog operation. And the service type acquisition module is used for comparing the analog simulation data with the actual service demand data, and acquiring the service type of the target terminal if the analog simulation data of the target terminal meets the preset condition. And the scheduling priority determining module is used for matching in a preset service library according to the service types to obtain the router channels corresponding to the service types, all the service influence factors and the preset proportion of each service influence factor, and determining the scheduling priority of the target terminal. And the proportion adjusting module is used for sequentially inputting the actual service demand data and the service types of the target terminals meeting the preset conditions into the trained AI model according to the scheduling priority so as to obtain the latest proportion value. And the optimal channel obtaining module is used for adjusting the router channel corresponding to the target terminal according to all the service influence factors of the target terminal and the latest specific weight value of each service influence factor to obtain the optimal channel.
In some embodiments of the present invention, the actual traffic demand data includes actual traffic and actual network rate, and the simulation data includes simulation traffic and simulation network rate. The service type acquiring module includes: and the comparison unit is used for comparing the simulated service volume of the target terminal with the actual service volume of the target terminal and comparing the simulated network rate of the target terminal with the actual network rate of the target terminal. And the judging unit is used for acquiring the service types of all the target terminals if the simulated service volume of the target terminal is lower than the actual service volume of the target terminal or the simulated network rate of the target terminal is lower than the actual network rate of the target terminal.
In some embodiments of the present invention, the 5G network optimization system further includes: and the historical service data acquisition module is used for acquiring historical service data of all service types, and any historical service data comprises a router channel, service influence factors and the actual proportion of each service influence factor. And the preset proportion determining module is used for counting the router channel with the highest use frequency and the preset number of service influence factors with the highest occurrence frequency according to all historical service data of the same service type, and determining the preset proportion of each service influence factor. And the binding module is used for binding the router channel of the same service type, the service influence factor and the preset proportion of each service influence factor and then storing the bound router channel, the service influence factor and the preset proportion of each service influence factor into a preset service library.
In some embodiments of the present invention, the binding module includes: and the scheduling priority configuration unit is used for configuring the scheduling priority of each service influence factor from high to low according to the same router channel and the occurrence frequency of each service influence factor. And the storage unit is used for storing the router channels, the service influence factors and the preset proportion of each service influence factor of the same service type into a preset service library according to the scheduling priority.
In some embodiments of the present invention, the network planning scheme obtaining module includes: and the ring-splitting optimization unit is used for detecting the simulation operation process in real time, and optimizing in a ring-splitting mode when the 5G flow of any target terminal exceeds the 5G capacity threshold.
In some embodiments of the present invention, the 5G network optimization system further includes: and the AI model establishing module is used for establishing an AI model. The system comprises a sample acquisition module, a data processing module and a data processing module, wherein the sample acquisition module is used for acquiring a plurality of samples, and the samples comprise historical business data and corresponding business types. And the AI model training module is used for training the AI model by utilizing a plurality of samples so as to obtain the trained AI model.
In some embodiments of the invention, the AI model building module includes: and the AI model building unit is used for building an AI model through a random forest algorithm and a convolutional neural network algorithm.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; a processor. The program or programs, when executed by a processor, implement the method of any of the first aspects as described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method according to any one of the first aspect described above.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
the invention provides a 5G network optimization method and a system, which comprises the following steps: and acquiring the actual positions and actual service demand data of all target terminals. And acquiring a target 5G network planning scheme, arranging in a map model according to the target 5G network planning scheme and the actual positions of all target terminals, and performing simulation operation in the map model. And in the simulation operation process, acquiring the simulation data of all the target terminals. And comparing the simulation data with the actual service demand data, and if the simulation data of the target terminal meets the preset conditions, acquiring the service type of the target terminal. And matching in a preset service library according to the service types to obtain router channels corresponding to the service types, all service influence factors and preset proportion of each service influence factor, and determining the scheduling priority of the target terminal. And inputting the actual service demand data and the service types of the target terminals meeting the preset conditions into the trained AI model in sequence according to the scheduling priority so as to obtain the latest specific gravity value. And adjusting the router channel corresponding to the target terminal according to all the service influence factors of the target terminal and the latest specific weight value of each service influence factor to obtain an optimal channel. When the 5G site is installed according to the target 5G network planning scheme and the network state is found to be poor in the using process, the 5G site and all the target terminals are arranged on the map model according to the actual positions of all the target terminals and the target 5G network planning scheme, network simulation is carried out on the map model, the position relation between all the 5G sites and the target terminals can be visually seen through the map model, and the simulation service flow of each 5G site and the simulation data of each target terminal in the simulation process can be reflected. And then comparing the analog simulation data of each target terminal with the actual service requirement data to screen out the target terminals meeting the preset conditions. And the service type of the target terminal is matched and compared with a preset service library, so that the router channel corresponding to the service type, all service influence factors and the preset proportion of each service influence factor can be quickly found out. And then sequentially inputting the target terminals meeting the preset conditions to the trained AI model for analysis according to the scheduling priority so as to find the latest weight ratio which is most matched with the actual service demand data and the service types. Therefore, the router channel corresponding to the target terminal, namely the optimal channel, can be found according to all the service influence factors of the target terminal and the latest specific gravity value of each service influence factor, the purpose of selecting the optimal channel for the target terminal is achieved, the purpose of reasonably optimizing the 5G network is also achieved, and the problem that when the network is not smooth and fluent is caused, the installed 5G stations are detached and adjusted is solved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a 5G network optimization method according to an embodiment of the present invention;
fig. 2 is a block diagram of a 5G network optimization system according to an embodiment of the present invention;
fig. 3 is a schematic structural block diagram of an electronic device according to an embodiment of the present invention.
Icon: a 100-5G network optimization system; 110-target terminal parameter obtaining module; 120-a network planning scheme acquisition module; 130-analog simulation data acquisition module; 140-service class acquisition module; 150-a scheduling priority determination module; 160-specific gravity adjusting module; 170-optimal channel obtaining module; 101-a memory; 102-a processor; 103-communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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 application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not construed as indicating or implying relative importance.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of an element identified by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the description of the present application, it should be noted that if the terms "upper", "lower", "inner", "outer", etc. are used to indicate an orientation or positional relationship based on that shown in the drawings or that the application product is usually placed in use, the description is merely for convenience and simplicity, and it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore should not be construed as limiting the present application.
In the description of the present application, it should also be noted that, unless otherwise explicitly stated or limited, the terms "disposed" and "connected" should be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present application can be understood in a specific case by those of ordinary skill in the art.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Examples
Referring to fig. 1, fig. 1 is a flowchart illustrating a 5G network optimization method according to an embodiment of the present disclosure. A5G network optimization method comprises the following steps:
s110: acquiring actual positions and actual service demand data of all target terminals;
s120: acquiring a target 5G network planning scheme, arranging in a map model according to the target 5G network planning scheme and the actual positions of all target terminals, and performing simulation operation in the map model;
wherein the target 5G network planning scheme comprises the number of 5G sites and the location of each 5G site. The 5G sites and all target terminals can be arranged on the map model according to the location of each site and the actual locations of all target terminals.
Specifically, a network simulation technique may be used to perform network simulation on the map model to obtain all simulation data during the simulation operation. The network simulation is carried out through the map model, so that the position relation between all the 5G sites and the target terminal can be visually seen, and the simulation service flow of each 5G site and the simulation data of each target terminal in the simulation process can be reflected.
It should be noted that the network simulation technology is a high and new technology that simulates network behavior by using mathematical modeling and statistical analysis methods, and simulates transmission of network traffic by establishing statistical models of network devices and network links, thereby obtaining network performance data required for network design and optimization.
S130: acquiring analog simulation data of all target terminals in the process of analog operation;
s140: comparing the simulation data with the actual service demand data, and if the simulation data of the target terminal meets the preset conditions, acquiring the service type of the target terminal;
the preset condition may be that the simulated traffic of the target terminal is lower than the actual traffic of the target terminal, or that the simulated network rate of the target terminal is lower than the actual network rate of the target terminal. And finding out the target terminals meeting the preset conditions so as to screen out the target terminals needing to be processed.
S150: matching in a preset service library according to the service types to obtain router channels corresponding to the service types, all service influence factors and preset proportion of each service influence factor, and determining the scheduling priority of the target terminal;
the preset service library comprises router channels corresponding to various service types, all service influence factors and preset proportion of the service influence factors. And matching and comparing the service type of the target terminal with a preset service library, so that the router channel, all service influence factors and the preset proportion of each service influence factor corresponding to the service type can be quickly found out.
S160: according to the scheduling priority, inputting actual service demand data and service types of the target terminal meeting preset conditions into a trained AI model in sequence to obtain a latest specific gravity value;
specifically, the target terminals meeting the preset conditions are sequentially processed according to the scheduling priority, and the trained AI model can analyze the actual service demand data and the service type of each target terminal meeting the preset conditions to find the latest weight ratio which is most matched with the actual service demand data and the service type.
In the implementation process, the target terminals meeting the preset conditions can be sequentially processed according to the sequence of the dispatching priorities from high to low, so that the purpose of preferentially meeting the service types with high priorities is achieved.
S170: and adjusting the router channel corresponding to the target terminal according to all the service influence factors of the target terminal and the latest specific weight value of each service influence factor to obtain an optimal channel.
Specifically, because the router channels of the same service type, the service impact factors and the preset specific gravity of each service impact factor are bound in the preset service library, after the preset weight of any service impact factor is updated to the latest specific gravity value, the router channel corresponding to the target terminal at the moment can be found through the preset service library, and the router channel is the optimal channel.
In the implementation process, when the 5G site is installed according to the target 5G network planning scheme and the network state is found to be poor in the using process, the 5G site and all the target terminals are arranged on the map model according to the actual positions of all the target terminals and the target 5G network planning scheme, network simulation is carried out on the map model, the position relation between all the 5G sites and the target terminals can be visually seen through the map model, and the simulation service flow of each 5G site and the simulation data of each target terminal in the simulation process can be reflected. And then comparing the analog simulation data of each target terminal with the actual service requirement data to screen out the target terminals meeting the preset conditions. And the service type of the target terminal is matched and compared with a preset service library, so that the router channel corresponding to the service type, all service influence factors and the preset proportion of each service influence factor can be quickly found out. And then sequentially inputting the target terminals meeting the preset conditions to the trained AI model for analysis according to the scheduling priority so as to find the latest weight ratio which is most matched with the actual service demand data and the service types. Therefore, the router channel corresponding to the target terminal, namely the optimal channel, can be found according to all the service influence factors of the target terminal and the latest specific gravity value of each service influence factor, the purpose of selecting the optimal channel for the target terminal is achieved, the purpose of reasonably optimizing the 5G network is also achieved, and the problem that when the network is not smooth and fluent is caused, the installed 5G stations are detached and adjusted is solved.
In some embodiments of this embodiment, the actual traffic demand data includes actual traffic volume and actual network rate, and the simulation data includes simulation traffic volume and simulation network rate. The step of comparing the simulation data with the actual service requirement data, and if the simulation data of the target terminal meets the preset condition, acquiring the service type of the target terminal includes: and comparing the simulated traffic of the target terminal with the actual traffic of the target terminal, and comparing the simulated network rate of the target terminal with the actual network rate of the target terminal. And if the simulated service volume of the target terminal is lower than the actual service volume of the target terminal or the simulated network rate of the target terminal is lower than the actual network rate of the target terminal, acquiring the service types of all the target terminals. Specifically, target terminals with simulated traffic volume lower than the actual traffic volume or simulated network rate lower than the actual network rate are all regarded as terminals that need network processing, and target terminals that need network optimization are further comprehensively collected.
In some embodiments of this embodiment, before the step of performing matching in a preset service library according to the service category to obtain a router channel, all service impact factors, and a preset specific gravity of each service impact factor corresponding to the service category, and determining the scheduling priority of the target terminal, the method further includes: and acquiring historical service data of all service types, wherein any historical service data comprises a router channel, service influence factors and the actual proportion of each service influence factor. According to all historical service data of the same service type, counting the router channel with the highest use frequency and the preset number of service influence factors with the highest occurrence frequency, and determining the preset proportion of each service influence factor. And binding the router channel of the same service type, the service influence factor and the preset proportion of each service influence factor, and storing the bound router channel, the service influence factor and the preset proportion of each service influence factor to a preset service library. Therefore, the preset service library comprises the router channels corresponding to the current various service types, all the service influence factors and the preset proportion of the various service influence factors, and the router channels, the service influence factors and the preset proportion of the various service influence factors of the same service type in the preset service library are bound, so that the accuracy of data in the preset service library is ensured.
In some embodiments of this embodiment, the step of storing the router channels, the service impact factors, and the preset specific gravity of each service impact factor of the same service category to a preset service library after binding the router channels, the service impact factors, and the preset specific gravity of each service impact factor of the same service category includes: and configuring the scheduling priority of each service influence factor from high to low according to the same router channel and the occurrence frequency of each service influence factor. And storing the router channels, the service influence factors and the preset proportion of each service influence factor of the same service type into a preset service library according to the scheduling priority. Specifically, for the same router channel, the more frequently occurring traffic impact factors are, the higher the scheduling priority is. The router channels, the service impact factors and the preset proportion of each service impact factor of the same service type can be stored in a preset service library according to the sequence from high to low scheduling priority. The ordering of the data in the preset service library is ensured, and the data in the preset service library is also convenient to call.
In some embodiments of this embodiment, the step of obtaining the target 5G network planning scheme, arranging in a map model according to the target 5G network planning scheme and actual positions of all target terminals, and performing simulation operation in the map model includes: and detecting the simulation operation process in real time, and optimizing in a ring splitting mode when the 5G flow of any target terminal exceeds the 5G capacity threshold. Thereby meeting the requirement of 5G transmission bandwidth.
In some embodiments of this embodiment, before the step of sequentially inputting the actual service demand data and the service category of the target terminal meeting the preset condition into the trained AI model according to the scheduling priority to obtain the latest specific gravity value, the method further includes: and establishing an AI model. A plurality of samples are obtained, the samples including historical traffic data and corresponding traffic categories. And training the AI model by using a plurality of samples to obtain the trained AI model.
In some embodiments of this embodiment, the step of establishing the AI model includes: and constructing an AI model by a random forest algorithm and a convolutional neural network algorithm. Therefore, the AI model can analyze complex service requirement data and service types.
The random forest algorithm is a classifier comprising a plurality of decision trees, and the output class of the random forest algorithm is determined by the mode of the class output by an individual tree. The convolutional neural network algorithm is a feedforward neural network which comprises convolutional calculation and has a deep structure, and is one of the representative algorithms of deep learning. The convolutional neural network has the characteristic learning ability and can carry out translation invariant classification on input information according to the hierarchical structure of the convolutional neural network.
Referring to fig. 2, fig. 2 is a block diagram illustrating a 5G network optimization system 100 according to an embodiment of the present invention. The embodiment of the present application provides a 5G network optimization system 100, which includes: and a target terminal parameter obtaining module 110, configured to obtain actual positions and actual service requirement data of all target terminals. And the network planning scheme obtaining module 120 is configured to obtain a target 5G network planning scheme, arrange the target 5G network planning scheme and actual positions of all target terminals in a map model according to the target 5G network planning scheme, and perform simulation operation in the map model. The analog simulation data obtaining module 130 is configured to obtain analog simulation data of all target terminals during a simulation operation. And the service type obtaining module 140 is configured to compare the simulation data with the actual service requirement data, and if the simulation data of the target terminal meets a preset condition, obtain the service type of the target terminal. And a scheduling priority determining module 150, configured to perform matching in a preset service library according to the service type, to obtain a router channel, all service impact factors, and a preset proportion of each service impact factor corresponding to the service type, and determine a scheduling priority of the target terminal. And the specific gravity adjusting module 160 is configured to sequentially input actual service demand data and service types of the target terminal meeting the preset condition into the trained AI model according to the scheduling priority, so as to obtain a latest specific gravity value. The optimal channel obtaining module 170 is configured to adjust a router channel corresponding to the target terminal according to all service impact factors of the target terminal and the latest specific gravity of each service impact factor, so as to obtain an optimal channel.
Specifically, when the 5G site is installed according to the target 5G network planning scheme and the network state is found to be poor in the using process, the 5G site and all the target terminals are arranged on the map model according to the actual positions of all the target terminals and the target 5G network planning scheme, network simulation is performed on the map model, the position relation between all the 5G sites and the target terminals can be visually seen through the map model, and the simulation service flow of each 5G site and the simulation data of each target terminal in the simulation process can be reflected. And then comparing the analog simulation data of each target terminal with the actual service requirement data to screen out the target terminals meeting the preset conditions. And the service type of the target terminal is matched and compared with a preset service library, so that the router channel corresponding to the service type, all service influence factors and the preset proportion of each service influence factor can be quickly found out. And then sequentially inputting the target terminals meeting the preset conditions to the trained AI model for analysis according to the scheduling priority so as to find the latest weight ratio which is most matched with the actual service demand data and the service types. Therefore, the router channel corresponding to the target terminal, namely the optimal channel, can be found according to all the service influence factors of the target terminal and the latest specific gravity value of each service influence factor, the purpose of selecting the optimal channel for the target terminal is achieved, the purpose of reasonably optimizing the 5G network is also achieved, and the problem that when the network is not smooth and fluent is caused, the installed 5G stations are detached and adjusted is solved.
In some embodiments of this embodiment, the actual traffic demand data includes actual traffic volume and actual network rate, and the simulation data includes simulation traffic volume and simulation network rate. The service category acquiring module 140 includes: and the comparison unit is used for comparing the simulated service volume of the target terminal with the actual service volume of the target terminal and comparing the simulated network rate of the target terminal with the actual network rate of the target terminal. And the judging unit is used for acquiring the service types of all the target terminals if the simulated service volume of the target terminal is lower than the actual service volume of the target terminal or the simulated network rate of the target terminal is lower than the actual network rate of the target terminal. Specifically, target terminals with simulated traffic volume lower than the actual traffic volume or simulated network rate lower than the actual network rate are all regarded as terminals that need network processing, and target terminals that need network optimization are further comprehensively collected.
In some embodiments of this embodiment, the 5G network optimization system 100 further includes: and the historical service data acquisition module is used for acquiring historical service data of all service types, and any historical service data comprises a router channel, service influence factors and the actual proportion of each service influence factor. And the preset proportion determining module is used for counting the router channel with the highest use frequency and the preset number of service influence factors with the highest occurrence frequency according to all historical service data of the same service type, and determining the preset proportion of each service influence factor. And the binding module is used for binding the router channel of the same service type, the service influence factor and the preset proportion of each service influence factor and then storing the bound router channel, the service influence factor and the preset proportion of each service influence factor into a preset service library. Therefore, the preset service library comprises the router channels corresponding to the current various service types, all the service influence factors and the preset proportion of the various service influence factors, and the router channels, the service influence factors and the preset proportion of the various service influence factors of the same service type in the preset service library are bound, so that the accuracy of data in the preset service library is ensured.
In some embodiments of this embodiment, the binding module includes: and the scheduling priority configuration unit is used for configuring the scheduling priority of each service influence factor from high to low according to the same router channel and the occurrence frequency of each service influence factor. And the storage unit is used for storing the router channels, the service influence factors and the preset proportion of each service influence factor of the same service type into a preset service library according to the scheduling priority. The ordering of the data in the preset service library is ensured, and the data in the preset service library is also convenient to call.
In some embodiments of this embodiment, the network planning scheme obtaining module 120 includes: and the ring-splitting optimization unit is used for detecting the simulation operation process in real time, and optimizing in a ring-splitting mode when the 5G flow of any target terminal exceeds the 5G capacity threshold. Thereby meeting the requirement of 5G transmission bandwidth.
In some embodiments of this embodiment, the 5G network optimization system 100 further includes: and the AI model establishing module is used for establishing an AI model. The system comprises a sample acquisition module, a data processing module and a data processing module, wherein the sample acquisition module is used for acquiring a plurality of samples, and the samples comprise historical business data and corresponding business types. And the AI model training module is used for training the AI model by utilizing a plurality of samples so as to obtain the trained AI model.
In some embodiments of this embodiment, the AI model building module includes: and the AI model building unit is used for building an AI model through a random forest algorithm and a convolutional neural network algorithm. Therefore, the AI model can analyze complex service requirement data and service types.
Referring to fig. 3, fig. 3 is a schematic structural block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device comprises a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, such as program instructions/modules corresponding to the 5G network optimization system 100 provided in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 101, so as to execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 3 or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A5G network optimization method is characterized by comprising the following steps:
acquiring actual positions and actual service demand data of all target terminals;
acquiring a target 5G network planning scheme, arranging in a map model according to the target 5G network planning scheme and the actual positions of all target terminals, and performing simulation operation in the map model;
in the simulation operation process, acquiring simulation data of all the target terminals;
comparing the analog simulation data with the actual service demand data, and if the analog simulation data of the target terminal meets a preset condition, acquiring the service type of the target terminal;
matching in a preset service library according to the service type to obtain a router channel corresponding to the service type, all service influence factors and preset proportion of each service influence factor, and determining the scheduling priority of the target terminal;
inputting actual service demand data and service types of the target terminal meeting preset conditions into a trained AI model in sequence according to the scheduling priority so as to obtain a latest specific gravity value;
and adjusting the router channel corresponding to the target terminal according to all the service influence factors of the target terminal and the latest specific weight value of each service influence factor to obtain an optimal channel.
2. The 5G network optimization method of claim 1, wherein the actual traffic demand data comprises actual traffic volume and actual network rate, and the simulation data comprises simulation traffic volume and simulation network rate;
comparing the simulation data with the actual service demand data, and if the simulation data of the target terminal meets a preset condition, acquiring the service type of the target terminal, wherein the step comprises the following steps:
comparing the simulated traffic of the target terminal with the actual traffic of the target terminal, and comparing the simulated network rate of the target terminal with the actual network rate of the target terminal;
and if the simulated service volume of the target terminal is lower than the actual service volume of the target terminal or the simulated network rate of the target terminal is lower than the actual network rate of the target terminal, acquiring the service types of all the target terminals.
3. The 5G network optimization method according to claim 1, wherein, before the step of matching in a preset service library according to the service type to obtain a router channel, all service impact factors and a preset proportion of each service impact factor corresponding to the service type, and determining the scheduling priority of the target terminal, the method further comprises:
acquiring historical service data of all service types, wherein any historical service data comprises a router channel, service influence factors and actual proportions of the service influence factors;
according to all historical service data of the same service type, counting a router channel with the highest use frequency and a preset number of service influence factors with the highest occurrence frequency, and determining the preset proportion of each service influence factor;
and binding the router channel of the same service type, the service influence factor and the preset proportion of each service influence factor, and storing the bound router channel, the service influence factor and the preset proportion of each service influence factor to a preset service library.
4. The 5G network optimization method according to claim 3, wherein the step of storing the router channels, the service impact factors and the preset specific gravity of each service impact factor of the same service type to a preset service library after binding the router channels, the service impact factors and the preset specific gravity of each service impact factor comprises:
according to the same router channel, configuring the scheduling priority of each service influence factor from high to low according to the occurrence frequency of each service influence factor;
and storing the router channels, the service influence factors and the preset proportion of each service influence factor of the same service type into a preset service library according to the scheduling priority.
5. The 5G network optimization method according to claim 1, wherein the step of obtaining the target 5G network planning scheme, arranging in a map model according to the target 5G network planning scheme and the actual positions of all target terminals, and performing simulation operation in the map model comprises:
and detecting a simulation operation process in real time, and optimizing in a ring-splitting mode when the 5G flow of any one target terminal exceeds a 5G capacity threshold.
6. The 5G network optimization method according to claim 1, wherein before the step of sequentially inputting the actual service requirement data and the service type of the target terminal satisfying the preset condition into the trained AI model according to the scheduling priority to obtain the latest specific gravity value, the method further comprises:
establishing an AI model;
obtaining a plurality of samples, wherein the samples comprise historical service data and corresponding service types;
and training the AI model by using the plurality of samples to obtain the trained AI model.
7. The 5G network optimization method according to claim 6, wherein the step of establishing an AI model comprises:
and constructing an AI model by a random forest algorithm and a convolutional neural network algorithm.
8. A 5G network optimization system, comprising:
the target terminal parameter acquisition module is used for acquiring the actual positions and actual service demand data of all target terminals;
the network planning scheme acquisition module is used for acquiring a target 5G network planning scheme, arranging the target 5G network planning scheme and the actual positions of all target terminals in a map model according to the target 5G network planning scheme and carrying out simulation operation on the map model;
the analog simulation data acquisition module is used for acquiring analog simulation data of all the target terminals in the process of analog operation;
the service type acquisition module is used for comparing the simulation data with the actual service demand data, and acquiring the service type of the target terminal if the simulation data of the target terminal meets a preset condition;
a scheduling priority determining module, configured to perform matching in a preset service library according to the service category to obtain a router channel, all service impact factors, and a preset proportion of each service impact factor corresponding to the service category, and determine a scheduling priority of the target terminal;
the proportion adjusting module is used for sequentially inputting the actual service demand data and the service types of the target terminal meeting the preset conditions into the trained AI model according to the scheduling priority so as to obtain the latest proportion value;
and the optimal channel obtaining module is used for adjusting the router channel corresponding to the target terminal according to all the service influence factors of the target terminal and the latest specific weight value of each service influence factor to obtain the optimal channel.
9. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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