CN114189475A

CN114189475A - Network load optimization method and device, computer storage medium and electronic equipment

Info

Publication number: CN114189475A
Application number: CN202111537857.3A
Authority: CN
Inventors: 周笑笑; 谢卓罡; 卢哲钊; 姚莉; 贝旭峰
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-12-15
Filing date: 2021-12-15
Publication date: 2022-03-15
Anticipated expiration: 2041-12-15
Also published as: CN114189475B

Abstract

The disclosure relates to a network load optimization method and device, a computer storage medium and electronic equipment, and relates to the technical field of big data processing, wherein the method comprises the following steps: acquiring high-load monitoring points in a target range, and acquiring a high-load monitoring model through first network key data of the high-load monitoring points; verifying the high-load monitoring model through second network key data to obtain target monitoring points in the high-load monitoring points, and obtaining the target monitoring model through the network key data of the target monitoring points; acquiring a neighboring cell monitoring point of a target monitoring point, and acquiring a neighboring cell monitoring model through third network key data of the neighboring cell monitoring point; obtaining a first predicted value and a second predicted value through a target monitoring model and a neighboring cell monitoring model, and obtaining a load balancing suggestion list of high-load monitoring points according to the first predicted value and the second predicted value; and optimizing the network load of the high-load monitoring point according to the load balancing suggestion list. The present disclosure improves the efficiency of network load optimization.

Description

Network load optimization method and device, computer storage medium and electronic equipment

Technical Field

The embodiment of the disclosure relates to the technical field of big data processing, in particular to a network load optimization method and device, a computer-readable storage medium and an electronic device.

Background

In existing networks, especially in larger cities, the use of networks has a significant tidal effect and periodicity effect, and the network load varies with time during the day.

In the existing wireless optimization system, monitoring of network key data and adjustment of network parameters are relatively separated, when a network has a load imbalance, a network optimization engineer usually obtains the network key data from a network key data monitoring system to perform manual analysis, and then manually adjusts staticized parameters in a network manager, which has a large time delay, and the adjustment of the static parameters of different monitoring points has poor portability, consumes a large amount of resources, and results in low network optimization efficiency.

Therefore, it is desirable to provide a new network load optimization method.

It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Disclosure of Invention

An object of the present disclosure is to provide a network load optimization method, a network load optimization apparatus, a computer-readable storage medium, and an electronic device, thereby overcoming, at least to some extent, the problem of low network optimization efficiency due to the limitations and disadvantages of the related art.

According to an aspect of the present disclosure, there is provided a network load optimization method, including:

acquiring high-load monitoring points in a target range and first network key data of the high-load monitoring points, and acquiring a high-load monitoring model through the first network key data;

verifying the high-load monitoring model through second network key data to obtain target monitoring points in the high-load monitoring points, and obtaining a target monitoring model through the network key data of the target monitoring points;

acquiring a neighboring cell monitoring point of the target monitoring point and third network key data of the neighboring cell monitoring point, and acquiring a neighboring cell monitoring model through the third network key data;

obtaining a first predicted value of the high-load monitoring point and a second predicted value of the neighboring monitoring point of the high-load monitoring point through the target monitoring model and the neighboring monitoring model, and obtaining a load balancing suggestion list of the high-load monitoring point according to the first predicted value and the second predicted value;

and optimizing the network load of the high-load monitoring point according to the load balancing suggestion list.

In an exemplary embodiment of the present disclosure, acquiring a high load monitoring point within a target range and first network key data of the high load monitoring point includes:

acquiring monitoring points in a target range and network key data of the monitoring points;

filtering the network key data of the monitoring point according to a first missing threshold value to obtain filtered network key data;

processing the filtering network key data through an interpolation method to obtain target network key data in the target range;

and distinguishing the monitoring points according to a preset high-load monitoring point judgment condition to obtain high-load monitoring points in the target range and first network key data corresponding to the high-load monitoring points.

In an exemplary embodiment of the present disclosure, distinguishing the monitoring points according to a preset high-load monitoring point judgment condition includes:

when the frequency band of the monitoring point is 800M and the bandwidth is 5M, acquiring the utilization rate of a physical resource block, the total flow, the number of users and the maximum wireless resource control connection number of the monitoring point in a self busy hour;

in a preset time period, when the self-busy hour occurs at any monitoring point, the utilization rate of a physical resource block is not less than a first preset utilization rate, and the frequency that the total flow is not less than the first preset flow is greater than a preset frequency; or

When the utilization rate of the physical resource blocks in the self busy hour is not less than a first preset utilization rate, the number of the used users is not less than a first preset number of the used users, and the number of times that the maximum wireless resource control connection number is not less than the first preset number of the used users is greater than a preset number of times, determining the monitoring point as a high-load monitoring point;

and the self-busy hour is the hour with the maximum total flow of the packet data convergence protocol layer in 24 hours.

when the frequency band of the monitoring point is 1.8G/2.1G, and the bandwidth is 20M, acquiring the utilization rate of a physical resource block, the total flow, the number of users and the maximum wireless resource control connection number of the monitoring point during self-busy hour;

in a preset time period, when the self-busy hour occurs at any monitoring point, the utilization rate of a physical resource block is not less than a second preset utilization rate, and the frequency that the total flow is not less than the second preset flow is greater than the preset frequency; or

When the utilization rate of the physical resource blocks in the self busy hour is not less than a second preset utilization rate, the number of the used users is not less than a second preset number of the used users, and the number of times that the maximum wireless resource control connection number is not less than the second preset number of the used users is greater than a preset number of times, determining the monitoring point as a high-load monitoring point;

In an exemplary embodiment of the present disclosure, the network load optimization method further includes:

acquiring the utilization rate of a downlink physical resource block, the maximum wireless resource control connection number and the total flow of a packet data convergence protocol layer, which are included in the first network key data;

training the preset neural network model by utilizing the downlink physical resource utilization rate of the high-load monitoring points in the target range to obtain a resource utilization rate prediction model;

training the preset neural network model by using the maximum wireless resource control connection number of the high-load monitoring points in the target range to obtain a resource control connection number prediction model;

and training the preset neural network model by using the total flow of the packet data convergence protocol layer of the high-load monitoring points in the target range to obtain a flow prediction model.

In an exemplary embodiment of the present disclosure, verifying the high load monitoring model through second network key data to obtain a target monitoring point of the high load monitoring points includes:

acquiring second network key data of the high-load monitoring point within a second preset time period, and inputting the second network key data into the high-load monitoring model to obtain a high-load prediction result;

respectively inputting the utilization rate of the downlink physical resource block, the maximum wireless resource control connection number and the total flow of the packet data convergence protocol layer of the high-load monitoring point into a resource utilization rate prediction model, a resource control connection number prediction model and a flow prediction model to obtain a resource block utilization rate prediction value, a connection number prediction value and a flow prediction value;

verifying the high load prediction result, the resource block utilization rate prediction value, the connection number prediction value and the flow prediction value to obtain the prediction accuracy;

and determining any monitoring point in the high-load monitoring points as a target monitoring point according to the accuracy of the high-load occurrence time point in the high-load prediction result of any monitoring point, the prediction accuracy and recall rate of the high-load occurrence time point, the occurrence frequency of the high-load occurrence time point and the prediction accuracy.

In an exemplary embodiment of the present disclosure, acquiring a neighboring monitoring point of the target monitoring point includes:

acquiring the coverage range of the target monitoring point and the coverage range of the monitoring points included in the target range;

determining a polygon covered by the target monitoring point according to the coverage range of the target monitoring point and determining a polygon covered by the monitoring point according to the coverage range of the detection point included in the target range;

when the fact that the covering polygon of the target monitoring point is overlapped with the covering polygon of the monitoring point is determined, the monitoring point is determined to be a neighbor monitoring point of the target monitoring point, and a neighbor table is generated according to the neighbor monitoring point;

and acquiring the neighboring cell monitoring points with the number of times of reaching high load in a third preset time period, which is greater than the third preset number of times, in the neighboring cell table, and deleting the neighboring cell monitoring points from the neighboring cell table.

In an exemplary embodiment of the present disclosure, obtaining a load balancing suggestion list of the high load monitoring point according to the first predicted value and the second predicted value includes:

acquiring a high load occurrence time point included in the first predicted value and a predicted value of a neighboring cell monitoring point included in the neighboring cell table included in the second predicted value, and deleting the neighboring cell monitoring point in the neighboring cell table according to the high load occurrence time point and the predicted value of the neighboring cell monitoring point to obtain a neighboring cell monitoring point of a target monitoring point;

sequencing the monitoring points of the adjacent cell according to the frequency band and the priority;

and acquiring the sorted neighbor monitoring points, and taking the sorted neighbor monitoring points as a load balancing suggestion list of the high-load monitoring points.

According to an aspect of the present disclosure, there is provided a network load optimization apparatus, including:

the high-load monitoring model training module is used for acquiring high-load monitoring points in a target range and first network key data of the high-load monitoring points, and training a preset neural network model through the first network key data to obtain a high-load monitoring model;

the target monitoring model training module is used for verifying the high-load monitoring model through second network key data to obtain target monitoring points in the high-load monitoring points, and training the preset neural network model through the network key data of the target monitoring points to obtain a target monitoring model;

the neighbor cell monitoring model training module is used for acquiring a neighbor cell monitoring point of the target monitoring point and third network key data of the neighbor cell monitoring point, and training the preset neural network model through the third network key data to obtain a neighbor cell monitoring model;

and the suggestion list generation module is used for obtaining a first predicted value of the high-load monitoring point and a second predicted value of the neighboring monitoring point of the high-load monitoring point through the target monitoring model and the neighboring monitoring model, and obtaining a load balancing suggestion list of the high-load monitoring point according to the first predicted value and the second predicted value.

And the network load optimization module is used for optimizing the network load of the high-load monitoring point according to the load balancing suggestion list.

According to an aspect of the present disclosure, there is provided a readable storage medium, on which a computer program is stored, which when executed by a processor, implements the network load optimization method according to any of the above exemplary embodiments.

According to an aspect of the present disclosure, there is provided an electronic device including:

a processor; and

a memory for storing executable instructions of the processor;

wherein the processor is configured to perform the network load optimization method of any of the above exemplary embodiments via execution of the executable instructions.

On one hand, after the network key data of the monitoring point is obtained, a target monitoring model and a neighbor monitoring model are obtained through the network key data, a first predicted value of a high-load monitoring point and a second predicted value of a neighbor monitoring point of the high-load monitoring point are obtained through the target monitoring model and the neighbor monitoring model, a load balancing suggestion list of the high-load monitoring point is obtained according to the first predicted value and the second predicted value, and the network load of the high-load monitoring point is optimized according to the load balancing suggestion list, so that the problem that in the prior art, when the network load is unbalanced, a network optimization engineer needs to manually analyze the network key data firstly, and then manually adjust parameters in a network manager, and the network optimization efficiency is low is solved; on the other hand, after the high-load monitoring model is obtained, after the high-load monitoring model is verified through second network key data, a target monitoring point included in the high-load monitoring point is obtained, then the preset neural network model is trained through the network key data of the target monitoring point, the target monitoring model is obtained, the network key data of the high-load monitoring point is predicted through the target monitoring model, and the prediction accuracy is improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.

Fig. 1 schematically illustrates a flow chart of a network load optimization method according to an example embodiment of the present disclosure.

Fig. 2 schematically illustrates a block diagram of a network load optimization system according to an example embodiment of the present disclosure.

Fig. 3 schematically shows a flowchart of a method for acquiring high-load monitoring points and first network key data of the high-load monitoring points within a target range according to an example embodiment of the present disclosure.

Fig. 4 schematically shows a flowchart of a method for distinguishing monitoring points according to a preset high-load monitoring point judgment condition according to an exemplary embodiment of the present disclosure.

Fig. 5 schematically shows a flowchart of a method for distinguishing monitoring points according to a preset high-load monitoring point judgment condition according to an exemplary embodiment of the present disclosure.

Fig. 6 schematically illustrates a flow chart of a network load optimization method according to an example embodiment of the present disclosure.

Fig. 7 schematically shows a flowchart of a method for verifying a high load monitoring model by using second network key data to obtain a target monitoring point in high load monitoring points according to an exemplary embodiment of the present disclosure.

Fig. 8 schematically shows a flowchart of a method for acquiring a neighbor monitoring point of a target monitoring point according to an exemplary embodiment of the present disclosure.

Fig. 9 schematically shows a flowchart of a method for obtaining a recommendation of a load balancing list of high-load monitoring points according to a first predicted value and a second predicted value according to an example embodiment of the present disclosure.

Fig. 10 schematically illustrates a flow chart of a method of network load optimization according to an example embodiment of the present disclosure.

Fig. 11 schematically illustrates an apparatus block diagram for network load optimization according to an example embodiment of the present disclosure.

Fig. 12 schematically illustrates an electronic device for implementing the above-described network load optimization method according to an example embodiment of the present disclosure.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

In the existing network, especially a large city, the tidal effect and the periodic effect (such as weekend effect) are often obvious, for example, during working hours on a working day, users are mostly concentrated in office areas, while during working hours, users are mostly concentrated in residences, and during weekends or holidays, network loads in some markets, scenic spots, stations and other places are also obviously changed.

In the existing wireless optimization system, monitoring of network key data and adjustment of network parameters are relatively separated, network optimization personnel often manually acquire and analyze network monitoring data from a monitoring system of the network key data, and then manually acquire and adjust staticized parameters in a network management system, which often has a large time delay. In addition, one of the most common means for optimizing the high-load cell in the related art is to divide a large scene, configure reselection and switching parameters without difference, and a manufacturer has a characteristic function of load balancing but can only apply to the same station and the same manufacturer. The above schemes cannot match the service change of tidal effect, and when the number of high-load cells is obviously increased, manual adjustment cannot be completed in time, and the range of manual adjustment is extremely limited.

Based on one or more of the above problems, the present exemplary embodiment first provides a network load optimization method, which may be executed in a server, a server cluster, a cloud server, or the like; of course, those skilled in the art may also operate the method of the present invention on other platforms as needed, and this is not particularly limited in this exemplary embodiment. Referring to fig. 1, the network load optimization method may include the following steps:

s110, acquiring high-load monitoring points in a target range and first network key data of the high-load monitoring points, and acquiring a high-load monitoring model through the first network key data;

s120, verifying the high-load monitoring model through second network key data to obtain target monitoring points in the high-load monitoring points, and obtaining a target monitoring model through the network key data of the target monitoring points;

s130, acquiring a neighbor cell monitoring point of the target monitoring point and third network key data of the neighbor cell monitoring point, and obtaining a neighbor cell monitoring model through the third network key data;

s140, obtaining a first predicted value of the high-load monitoring point and a second predicted value of a neighboring monitoring point of the high-load monitoring point through the target monitoring model and the neighboring monitoring model, and obtaining a load balancing suggestion list of the high-load monitoring point according to the first predicted value and the second predicted value;

and S150, optimizing the network load of the high-load monitoring point according to the load balancing suggestion list.

According to the network load optimization method, on one hand, after the network key data of the monitoring points are obtained, a target monitoring model and a neighbor monitoring model are obtained through the network key data, a first predicted value of the high-load monitoring point and a second predicted value of the neighbor monitoring point of the high-load monitoring point are obtained through the target monitoring model and the neighbor monitoring model, a load balancing suggestion list of the high-load monitoring point is obtained according to the first predicted value and the second predicted value, and the network load of the high-load monitoring point is optimized according to the load balancing suggestion list, so that the problem that in the prior art, when the network load is unbalanced, a network optimization engineer needs to manually analyze the network key data firstly, and then manually adjust parameters in a network manager, and the network optimization efficiency is low is solved; on the other hand, after the high-load monitoring model is obtained, after the high-load monitoring model is verified through second network key data, a target monitoring point included in the high-load monitoring point is obtained, then the preset neural network model is trained through the network key data of the target monitoring point, the target monitoring model is obtained, the network key data of the high-load monitoring point is predicted through the target monitoring model, and the prediction accuracy is improved.

Hereinafter, each step involved in the network load optimization method of the exemplary embodiment of the present disclosure is explained and explained in detail.

First, an application scenario and an object of the exemplary embodiment of the present disclosure are explained and explained. Specifically, the exemplary embodiment of the present disclosure may be applied to network load optimization, and mainly researches how to improve the efficiency of network load optimization.

In the method, on the basis of received network key data of monitoring points in a target range, firstly, preprocessing the acquired network key data, eliminating the network key data with the deletion ratio larger than a preset deletion ratio threshold value, distinguishing the monitoring points, and acquiring high-load monitoring points included in the monitoring points and the network key data of the high-load monitoring points; then, training a preset neural network model through the network key data of the high-load monitoring points to obtain a high-load monitoring model, and verifying the high-load monitoring model through the network key data of another time period of the high-load monitoring points to obtain target monitoring points included in the high-load monitoring points; secondly, training a preset neural network model through network key data of a target monitoring point to obtain the target monitoring model, meanwhile, obtaining a neighboring monitoring point of the target monitoring point and network key data of the neighboring monitoring point, and training the preset neural network model through the network key data of the neighboring monitoring point to obtain a neighboring monitoring model; and finally, obtaining a first predicted value and a second predicted value of the high-load monitoring point and the adjacent monitoring point of the high-load monitoring point through the target monitoring model and the adjacent monitoring model, obtaining a load balancing suggestion list of the high-load monitoring point through the first predicted value and the second predicted value, and carrying out load balancing on the high-load monitoring point according to the load balancing suggestion list, so that the efficiency of network load optimization of the high-load monitoring point is improved.

Next, a network load optimization system related to the exemplary embodiment of the present disclosure is explained and explained. Referring to fig. 2, the network load optimization system may include a monitoring point classification module 210, a network key data preprocessing module 220, a model training module 230, and a load balancing module 240. The monitoring point classifying module 210 is configured to obtain a frequency band, a bandwidth, a PRB (Physical Resource Block) utilization rate, a maximum RRC (Radio Resource Control) connection number, a PDCP (Packet Data Convergence Protocol) layer total flow, a total flow, and a number of users of the monitoring point, and classify the monitoring point according to the obtained Data to obtain a high-load monitoring point; the network key data preprocessing module 220 is in network connection with the monitoring point classification module 210, and is used for acquiring network key data of monitoring points, filtering the network key data with the deletion ratio higher than a first deletion threshold value in the acquired network key data, filtering the network key data input into the model through a second deletion threshold value during prediction, and filling the rest network key data through an interpolation method; the model training module 230 is in network connection with the network key data preprocessing module 220 and is used for acquiring network key data of high-load monitoring points, training a preset neural network model through the network key data of the high-load monitoring points to obtain a high-load monitoring model, verifying the high-load monitoring model to obtain target monitoring points included in the high-load monitoring points, acquiring network key data of the target monitoring points, training the preset neural network model through the network key data of the target monitoring points to obtain the target monitoring model, acquiring neighbor monitoring points of the target monitoring points, and training the preset neural network model through the network key data of the neighbor monitoring points to obtain a neighbor monitoring model; and the load balancing module 240 is in network connection with the model training module 230 and is configured to acquire network key data of the high-load monitoring point and network key data of the neighboring monitoring point of the high-load monitoring point, input the network key data of the high-load monitoring point and the network key data of the neighboring monitoring point into the target monitoring model and the neighboring monitoring model respectively to acquire a first predicted value and a second predicted value, acquire a load balancing suggestion list of the high-load monitoring point according to the first predicted value and the second predicted value, and optimize the network load of the high-load monitoring point according to the load balancing suggestion list.

Hereinafter, steps S110 to S150 will be explained and explained in detail with reference to fig. 2.

In step S110, a high load monitoring point within a target range and first network key data of the high load monitoring point are obtained, and a high load monitoring model is obtained through the first network key data.

The target range may be any city, any community, or any public building, and is not specifically limited in this example embodiment. All monitoring points in the target range can be obtained, and the monitoring points in the target range are judged according to the preset high-load monitoring point judgment condition to obtain the high-load monitoring points in the target range. The first network key data may be network key data in self-busy hours within a preset time, for example, the first network key data may be network key data in self-busy hours for 60 days; the network key data of the monitoring point comprises: the utilization rate of downlink PRB, the maximum RRC connection number and the total flow of the PDCP layer. The method comprises the steps of training a preset neural network model through first network key data to obtain a high-load monitoring model, wherein the preset neural network model is an amazon probabilistic neural network DeepaR model, neurons of the preset neural network model adopt Memory cycle neural network model types, namely LSTM (Long Short-Term Memory) or GRU (Gate Current Unit), and the reasoning process is divided into two stages, namely a training process and a prediction process.

In this exemplary embodiment, referring to fig. 3, acquiring the high load monitoring point in the target range and the first network key data of the high load monitoring point may include steps S310 to S340:

s310, acquiring monitoring points in a target range and network key data of the monitoring points;

s320, filtering the network key data of the monitoring point according to a first missing threshold to obtain filtered network key data;

s330, processing the filtering network key data through an interpolation method to obtain target network key data in the target range;

and S340, distinguishing the monitoring points according to a preset high-load monitoring point judgment condition to obtain high-load monitoring points in the target range and first network key data corresponding to the high-load monitoring points.

Hereinafter, steps S310 to S340 will be explained and explained. Specifically, monitoring points in a target range and network key data of the monitoring points are obtained firstly, instability is brought to model prediction due to the fact that the situation that the deletion proportion is high often occurs in the network data, but the model can handle the situation that the data is really not high, so that the obtained network key data of the monitoring points can be filtered through a first deletion threshold value during model training, and the filtered network key data are obtained; the first absence threshold may be 0.3 or 0.4, and is not specifically limited in this exemplary embodiment; the filtering network key data can be filled through an interpolation method to obtain target network key data, the interpolation method is to estimate the approximate value of the function at the missing point by using the value of a known time point through a one-dimensional function, a fitting curve is required to pass through the known data point as far as possible, and the fitting variance is minimum, wherein the piecewise interpolation method takes the periodicity of a time sequence into consideration, different functions are fitted to different parts of the sequence, and the curves between the functions are smoothly butted, so that the accuracy of local fitting is improved; and finally, distinguishing the monitoring points according to a preset high-load monitoring point judgment condition to obtain the high-load monitoring points included in the monitoring points and first network key data corresponding to the high-load monitoring points.

Besides filtering the network key data of the monitoring points in the training stage, the network key data of the monitoring points can be filtered through the second missing threshold value in the prediction stage, and the prediction accuracy is improved.

Further, in this exemplary embodiment, referring to fig. 4, distinguishing the monitoring points according to a preset high-load monitoring point judgment condition may include steps S410 to S430:

s410, when the frequency band of the monitoring point is 800M and the bandwidth is 5M, acquiring the utilization rate of a physical resource block, the total flow, the number of users and the maximum wireless resource control connection number of the monitoring point in a self-busy hour;

step S420, in a preset time period, when the self-busy state occurs at any monitoring point, the utilization rate of a physical resource block is not less than a first preset utilization rate, and the frequency that the total flow is not less than the first preset flow is greater than a preset frequency; or

Step S430, when the utilization rate of the physical resource blocks in self busy hour is not less than a first preset utilization rate, the number of the users is not less than a first preset number of the users, and the number of times that the maximum wireless resource control connection number is not less than the first preset number of the connections is greater than a preset number of times, determining the monitoring point as a high-load monitoring point;

Hereinafter, steps S410 to S430 will be explained and explained. Specifically, at first judge the frequency channel and the bandwidth of monitoring point, when the frequency channel of monitoring point is 800M bandwidth and is 5M, can acquire monitoring point's PRB utilization ratio in busy hour, monitoring point total flow, monitoring point number of users, the biggest RRC connection number of monitoring point, and the judgement to the monitoring point can include, condition one: the PRB utilization rate of the monitoring points in the self busy hour is more than or equal to a first preset utilization rate, and the total flow of the monitoring points is more than or equal to a first preset flow; and a second condition: the PRB utilization rate of the monitoring points in the self-busy hour is more than or equal to a first preset utilization rate, the number of the using users of the monitoring points is more than or equal to a first preset number, and the maximum RRC connection number is more than or equal to the first preset connection number; when the frequency of meeting the condition one in the preset time of the monitoring point is greater than the preset frequency, or the frequency of meeting the condition two in the preset time is greater than the preset frequency, the monitoring point can be determined as a high-load monitoring point. Wherein, the self-busy time is the hour when the total flow of the PDCP layer is maximum in 24 hours. The first preset usage rate may be 50% or 55%, which is not specifically limited in this example embodiment, and the first preset flow rate may be 1.5GB or 2GB, which is not specifically limited in this example embodiment; when the scene of the monitoring point is a cell, the first preset number of uses may be the number of users using a network in the cell, and the numerical value of the first preset number of uses may be 75 or 80, which is not specifically limited in this example embodiment; the first preset number of connections may be 50 or 55, which is not specifically limited in this exemplary embodiment. The number of times that the condition I or the condition II is met within the preset time can be the preset number of times, the data of the monitoring points within the target range in the whole day of 30 days can be obtained, and when the self-busy time of any one of the monitoring points within 7 continuous days in the day of 30 days meets the condition I or meets the condition II, the monitoring point can be determined as the high-load monitoring point.

In addition, when the frequency band of the monitoring point is 1.8G/2.1G, and the bandwidth is 20M, referring to fig. 5, distinguishing the monitoring point according to the preset high-load monitoring point judgment condition may include steps S510 to S530:

step S510, when the frequency band of the monitoring point is 1.8G/2.1G and the bandwidth is 20M, acquiring the utilization rate of a physical resource block, the total flow, the number of users and the maximum wireless resource control connection number of the monitoring point in a self-busy hour;

step S520, in a preset time period, when the self-busy time of any monitoring point occurs, the utilization rate of a physical resource block is not less than a second preset utilization rate, and the frequency that the total flow is not less than the second preset flow is greater than the preset frequency; or

Step S530, when the utilization rate of the physical resource blocks in self busy hour is not less than a second preset utilization rate, the number of the used users is not less than a second preset number of used users, and the number of times that the maximum wireless resource control connection number is not less than the second preset number of connected users is greater than a preset number of times, determining the monitoring point as a high-load monitoring point;

Hereinafter, steps S510 to S530 will be explained and explained. Specifically, when the frequency band of the monitoring point is 20M for 1.8G/2.1G M bandwidth, the monitoring point can be obtained from the busy time PRB utilization ratio, the total flow of the monitoring point, the number of users of the monitoring point, and the maximum RRC connection number of the monitoring point, and the judgment on the monitoring point can include the condition one: the PRB utilization rate of the monitoring points in the self busy hour is more than or equal to a second preset utilization rate, and the total flow of the monitoring points is more than or equal to a second preset flow; and a second condition: the PRB utilization rate of the monitoring points in the self-busy hour is more than or equal to a second preset utilization rate, the number of the using users of the monitoring points is more than or equal to a second preset number of the using users, and the maximum RRC connection number is more than or equal to the second preset number of the connecting users; when the frequency of meeting the condition one in the preset time of the monitoring point is greater than the preset frequency, or the frequency of meeting the condition two in the preset time is greater than the preset frequency, the monitoring point can be determined as a high-load monitoring point. Wherein, the self-busy time is the hour when the total flow of the PDCP layer is maximum in 24 hours. The second preset usage rate may be 50% or 55%, which is not specifically limited in this example, and the second preset flow rate may be 6GB or 6.5GB, which is not specifically limited in this example; when the scene of the monitoring point is a cell, the first preset number of uses may be the number of users using a network in the cell, and the numerical value of the first preset number of uses may be 300 or 320, which is not specifically limited in this example embodiment; the first preset number of connections may be 200 or 250, which is not specifically limited in this exemplary embodiment. The number of times that the condition I or the condition II is met within the preset time can be the preset number of times, the data of the monitoring points within the target range in the whole day of 30 days can be obtained, and when the self-busy time of any one of the monitoring points within 7 continuous days in the day of 30 days meets the condition I or meets the condition II, the monitoring point can be determined as the high-load monitoring point.

After the high-load monitoring points are obtained, network key data of the high-load monitoring points can be obtained, the preset neural network model is trained through the obtained network key data of the high-load monitoring points, and the high-load monitoring model is obtained, wherein the preset neural network model can be a probability neural network DeepAR model of Amazon. In the related art, ARIMA (automated Integrated Moving Average model), Holt-Winters (Holt-wentt) method, exponential smooth Moving Average line, LSTM (Long Short-Term Memory network) neural network, and fbprophet (time series prediction) algorithm have the following limitations: (1) the method is limited and can predict a single variable, and cannot be suitable for predicting various network key data of a large number of monitoring points; (2) the requirement on the regularity of the time sequence is high, various assumed conditions need to be met, but network parameters of different monitoring points are complex and the conditions are variable; (3) the model in the related art generally cannot predict a long time window (for example, 24 hours a day), and the prediction error is accumulated continuously along with the extension of time; however, the deep ar model can solve the above-mentioned defects, and the deep ar model can learn the potential probability distribution parameters of the time series by memorizing the neural network structure, and sample the prediction result from the probability distribution model in the output stage, thereby improving the stability of the prediction result in a long time window and reducing the deviation of the prediction value.

In this example, the high-load monitoring points and the network key data of the high-load monitoring points are obtained by filtering and filling the network key data of the monitoring points and judging the monitoring points, so that the precision of model training is improved.

In step S120, the high-load monitoring model is verified through second network key data to obtain a target monitoring point among the high-load monitoring points, and a target monitoring model is obtained through the network key data of the target monitoring point.

The second network key data may be network key data of a high-load monitoring point in a self-busy time of 60 days, or may also be network key data of a high-load monitoring point in a self-busy time of 65 days, and in this exemplary embodiment, the second network key data is not specifically limited. And training a preset neural network model through the network key data of the target monitoring points to obtain a target monitoring model, wherein the preset neural network model is a DeepAR model.

In this example embodiment, a plurality of data included in the network key data may also be obtained, and the preset neural network model is trained through the plurality of data to obtain a corresponding prediction model, as shown in fig. 6, the network load optimization method may further include steps S610 to S640:

step S610, acquiring the utilization rate of a downlink physical resource block, the maximum wireless resource control connection number and the total flow of a packet data convergence protocol layer, which are included in the first network key data;

s620, training the preset neural network model by using the downlink physical resource utilization rate of the high-load monitoring points in the target range to obtain a resource utilization rate prediction model;

s630, training the preset neural network model by using the maximum wireless resource control connection number of the high-load monitoring points in the target range to obtain a resource control connection number prediction model;

and step 640, training the preset neural network model by using the total flow of the packet data convergence protocol layer of the high-load monitoring points in the target range to obtain a flow prediction model.

Hereinafter, steps S610 to S640 will be explained and explained. Specifically, the network key data includes: the method comprises the steps that the utilization rate of a downlink PRB, the maximum RRC connection number and the total flow of a PDCP layer can be obtained, the utilization rate of the downlink PRB in first network key data of a high-load monitoring point can be obtained, a preset neural network model is trained through the utilization rate of the downlink PRB, and a resource utilization rate prediction model is obtained; the maximum RRC connection number in the first network key data can be obtained, and a preset neural network model is trained through the maximum RRC connection number to obtain a resource control connection number prediction model; the total flow of the PDCP layer in the first network key data can be obtained, and the preset neural network model is trained through the total flow of the PDCP layer to obtain a flow prediction model. The deep AR model can also be used for simultaneously modeling and predicting a large quantity of network parameter time sequences with similar characteristics, so that the prediction efficiency of network key data is improved.

After the prediction models corresponding to various data included in the network key data are obtained, the target monitoring points included in the high-load monitoring points can be obtained through the various prediction models and the high-load monitoring model. Referring to fig. 7, verifying the high load monitoring model through the second network key data to obtain a target monitoring point in the high load monitoring points may include steps S710 to S740:

step S710, obtaining second network key data of the high-load monitoring point in a second preset time period, and inputting the second network key data into the high-load monitoring model to obtain a high-load prediction result;

s720, respectively inputting the utilization rate of a downlink physical resource block, the maximum wireless resource control connection number and the total flow of a packet data convergence protocol layer of the high-load monitoring point into a resource utilization rate prediction model, a resource control connection number prediction model and a flow prediction model to obtain a resource block utilization rate prediction value, a connection number prediction value and a flow prediction value;

step 730, verifying the high-load prediction result, the resource block utilization rate prediction value, the connection number prediction value and the flow prediction value to obtain the prediction accuracy;

and S740, determining any monitoring point in the high-load monitoring points as a target monitoring point according to the accuracy of the high-load occurrence time point in the high-load prediction result of any monitoring point, the prediction accuracy and recall rate of the high-load occurrence time point, the occurrence frequency of the high-load occurrence time point and the prediction accuracy.

Hereinafter, steps S710 to S740 will be explained and explained. Specifically, first, second network key data of the high-load monitoring point may be obtained, where the second network key data may be network key data of the high-load monitoring point in a self-busy hour for 10 days, or may be network key data of the high-load monitoring point for 15 days, which is not specifically limited in this example embodiment; after second network data of the high-load monitoring point within a second preset time period are obtained, the second network data can be input into the high-load monitoring model to obtain a high-load prediction result, wherein the high-load prediction result is a high-load occurrence time point of the predicted monitoring point; after the prediction result of the high-load monitoring point is obtained, the utilization rate of a downlink PRB, the maximum RRC connection number and the total flow of a PDCP layer, which are included in the network key data, can be respectively input into corresponding prediction models to respectively obtain a resource block utilization rate predicted value, a connection number predicted value and a flow predicted value; then, verifying the obtained resource block utilization rate predicted value, the connection number predicted value and the flow predicted value to obtain the accuracy of the predicted values; finally, determining the accuracy of the high load occurrence time point, the prediction accuracy and the recall rate of the high load occurrence time point, and the occurrence frequency and the prediction accuracy of the high load occurrence time point in the high load prediction result of any monitoring point; and obtaining target monitoring points included in the high-load monitoring points according to the accuracy of the high-load occurrence time points of the high-load monitoring points, the prediction accuracy and recall rate of the high-load occurrence time points, and the occurrence frequency and prediction accuracy of the high-load occurrence time points.

In this example embodiment, after the target monitoring points included in the high-load monitoring points are obtained, network key data within a preset time period of the target monitoring points may be obtained, and the preset neural network model is trained through the network key data to obtain the target monitoring model. Wherein, the preset neural network model is a DeepAR model.

In step S130, a neighboring cell monitoring point of the target monitoring point and third network key data of the neighboring cell monitoring point are obtained, and a neighboring cell monitoring model is obtained through the third network key data.

The monitoring point of the adjacent cell of the target monitoring point can be obtained by covering the polygon, and the grid of the monitoring point of the adjacent cell is overlapped with that of the target monitoring point; and training the preset neural network model through the third network key data to obtain a neighbor cell monitoring model, wherein the preset neural network model is a DeepAR model.

In this exemplary embodiment, referring to fig. 8, acquiring the neighboring monitoring point of the target monitoring point may include steps S810 to S840:

step S810, acquiring the coverage range of the target monitoring point and the coverage range of the monitoring points included in the target range;

s820, determining a polygon covered by the target monitoring point according to the coverage range of the target monitoring point and determining a polygon covered by the monitoring point according to the coverage range of the detection points included in the target range;

s830, when the fact that the covering polygon of the target monitoring point is overlapped with the covering polygon of the monitoring point is determined, the monitoring point is determined to be a neighbor monitoring point of the target monitoring point, and a neighbor table is generated according to the neighbor monitoring point;

step 840, obtaining the neighboring cell monitoring point with the number of times of reaching high load in the third preset time period included in the neighboring cell table larger than the third preset number of times, and deleting the neighboring cell monitoring point from the neighboring cell table.

Hereinafter, steps S810 to S840 will be explained and explained. Specifically, firstly, the coverage of the target monitoring point and the coverage of the monitoring points included in the target range can be obtained; then, covering the target monitoring points and the monitoring points included in the target range except the target monitoring points by using a covering polygon, wherein the covering polygon may be a grid of 20m × 20m or a grid of 30m × 30m, and the covering polygon is not specifically limited in this exemplary embodiment; after the target monitoring point and the monitoring points in the target range are covered, when grid overlapping exists between the covering polygon of any monitoring point included in the target range and the covering polygon of the target monitoring point, determining any monitoring point included in the target range as a neighbor monitoring point of the target monitoring point, and generating a neighbor table of the target monitoring point, wherein the neighbor table includes all neighbor monitoring points of the target monitoring point, which have grid overlapping. And after the neighbor cell table of the target monitoring point is obtained, the neighbor cell monitoring point with the number of times of reaching high load in a third preset time period in the neighbor cell table larger than the third preset number of times is obtained, and the monitoring point is deleted from the neighbor cell table. The third preset time period may be 7 days in the past, the third preset number of times may be 4 times, and the third preset time period and the third preset number of times are not specifically limited in this exemplary embodiment.

In this example embodiment, after the neighboring cell monitoring point of the target monitoring point is obtained, third network key data of the neighboring cell monitoring point may be obtained, and the preset neural network model is trained through the third network key data to obtain the neighboring cell monitoring model.

In step S140, a first predicted value of the high-load monitoring point and a second predicted value of the neighboring monitoring point of the high-load monitoring point are obtained through the target monitoring model and the neighboring monitoring model, and a load balancing suggestion list of the high-load monitoring point is obtained according to the first predicted value and the second predicted value.

Before the first predicted value and the second predicted value are obtained through the target monitoring model and the neighbor cell monitoring model, the network key data input into the target monitoring model and the network key data input into the neighbor cell monitoring model can be filtered through the second missing threshold value.

In this exemplary embodiment, referring to fig. 9, obtaining a load balancing suggestion list of the high load monitoring point according to the first predicted value and the second predicted value may include steps S910 to S930:

step S910, obtaining a high load occurrence time point included in the first predicted value and a predicted value of a neighboring cell monitoring point included in the neighboring cell table included in the second predicted value, and deleting the neighboring cell monitoring point in the neighboring cell table according to the high load occurrence time point and the predicted value of the neighboring cell monitoring point to obtain a neighboring cell monitoring point of a target monitoring point;

s920, sequencing the monitoring points of the adjacent cell according to the frequency band and the priority;

and S930, obtaining the sorted neighbor monitoring points, and taking the sorted neighbor monitoring points as a load balancing suggestion list of the high-load monitoring points.

Hereinafter, steps S910 to S930 will be explained and explained. Specifically, first, a high load occurrence time point of a high load monitoring point included in the first predicted value and a high load occurrence time point of a neighboring cell monitoring point included in the second predicted value are obtained, and the neighboring cell monitoring point included in the neighboring cell table is deleted according to the high load occurrence time point in the first predicted value and the high load occurrence time point in the second predicted value, specifically: and determining the adjusting time interval of the high-load monitoring point according to the high-load occurrence time point of the high-load monitoring point, and deleting the neighboring monitoring point from the neighboring table when the high-load occurrence time point of the neighboring monitoring point is within the preset time period of the adjusting time interval. The preset time period of the adjustment time interval may be 2 hours before and after the adjustment time interval, or 1 hour before and after the adjustment time interval, which is not specifically limited in this example embodiment. After the neighboring monitoring points of the target monitoring point are obtained, sorting can be performed according to the frequency bands and the priorities of the neighboring monitoring points, and specifically, when the frequency band of the neighboring monitoring point is higher, the ranking of the neighboring monitoring point is closer to the front; when the frequency bands of the adjacent monitoring points are the same, the adjacent monitoring points can be sorted according to the priority, and the ranking of the adjacent monitoring points with high priority (1.8G/2.1G) is positioned before the adjacent monitoring points with low priority (800M). When the adjacent monitoring points are in the same frequency band and the same priority level, the overlapping area ratio of the adjacent monitoring points to the target monitoring point can be obtained, and the adjacent monitoring points are sorted in a reverse order according to the overlapping area ratio to obtain the sorted adjacent monitoring points; after the sorted neighbor monitoring points are obtained, neighbor monitoring points positioned at the top N in the sorting can be obtained, and the top N neighbor monitoring points are used as a load balancing suggestion list of high-load monitoring points. Wherein N is a positive integer.

In the embodiment, on one hand, by predicting the monitoring points of the adjacent cells and screening the monitoring points of the adjacent cells, the monitoring points of the adjacent cells which can generate high load in the future are effectively filtered, and the invalidation or negative adjustment of the monitoring points with high load is avoided; on the other hand, corresponding load balancing suggestion lists are generated for different high-load monitoring points, so that the network quality is ensured while the load balancing efficiency of the high-load monitoring points is improved; on the other hand, comprehensive optional neighbor cell monitoring points are provided, so that network optimization personnel can select the monitoring points according to the actual production environment, and the actual effectiveness of load balancing is ensured.

Further, the load balancing suggestion list comprises: monitoring point information of the high-load monitoring points, adjustment time intervals of the high-load monitoring points, N adjacent monitoring points before sequencing, high-load occurrence time points of the adjacent monitoring points, basic information of the adjacent monitoring points, priorities of the adjacent monitoring points, load balancing time periods of the adjacent monitoring points and coverage relations between the high-load monitoring points and the adjacent monitoring points.

In step S150, the network load of the high-load monitoring point is optimized according to the load balancing suggestion list.

In this example embodiment, after the load balancing suggestion list of the high-load monitoring point is obtained, the network load of the high-load monitoring point may be optimized according to the load balancing suggestion list.

The network load optimization method provided by the disclosed example embodiment has at least the following advantages: on one hand, before the model training, the data input to the model are screened and filled, so that the complexity of the data and the training quantity of the model are reduced, and the training precision of the model is improved; on the other hand, different monitoring models are obtained by training the DeepAR model, potential probability distribution functions of the time sequence can be learned through the memory neural network structure, and prediction results are sampled from the probability distribution model in the output stage, so that the stability of the prediction results in a long-time window is improved, and the deviation of predicted values is reduced; by modeling and predicting a large batch of network key data time sequences with similar characteristics at the same time, the general rule of the overall data can be learned, and the adjustment can be performed according to the network key data of different monitoring points, so that the expenditure of training time and resources is reduced; on the other hand, when the load balancing suggestion list is determined, neighbor monitoring points with high load in the future can be filtered, and the invalidation or negative adjustment of the high-load monitoring points is avoided; corresponding load balancing suggestion lists are generated for different high-load monitoring points, so that the network quality is ensured while the load balancing efficiency of the high-load monitoring points is improved; and a comprehensive optional neighbor monitoring point is provided, so that network optimization personnel can select the monitoring point according to the actual production environment, and the actual effectiveness of load balancing is ensured.

Hereinafter, the network load optimization method according to the exemplary embodiment of the present disclosure is further explained and explained with reference to fig. 10. Fig. 10 is a logic diagram of an implementation of a network load optimization method, where the network load optimization method may include:

step S1010, inputting network key data of the high-load monitoring points into a high-load monitoring model as input data, and verifying the high-load monitoring model to obtain target monitoring points;

s1020, inputting the network key data of the target monitoring point into a preset neural network model to obtain a target monitoring model, wherein the network key data serve as input data;

step S1030, inputting the network key data of the high-load monitoring points into a target monitoring model by taking the network key data of the high-load monitoring points as input data to obtain a predicted value of the high-load monitoring points;

s1040, acquiring a neighbor cell monitoring point of the high-load monitoring point, inputting network key data of the neighbor cell monitoring point into a neighbor cell monitoring model to obtain a predicted value of the neighbor cell monitoring point;

s1050, obtaining a load balancing list of the high-load monitoring points according to the predicted values of the high-load monitoring points and the predicted values of the neighboring monitoring points;

and S1060, carrying out load balance adjustment on the high-load monitoring points according to the load balance list.

An exemplary embodiment of the present disclosure further provides a network load optimization apparatus, which is shown in fig. 11 and may include: a high load monitoring model training module 1110, a target monitoring model training module 1120, a neighborhood monitoring model training module 1130, a suggestion list generating module 1140, and a network load optimizing module 1150. Wherein:

the high-load monitoring model training module is used for acquiring high-load monitoring points in a target range and first network key data of the high-load monitoring points, and acquiring a high-load monitoring model through the first network key data;

the target monitoring model training module is used for verifying the high-load monitoring model through second network key data to obtain target monitoring points in the high-load monitoring points, and obtaining a target monitoring model through the network key data of the target monitoring points;

the neighbor cell monitoring model training module is used for acquiring a neighbor cell monitoring point of the target monitoring point and third network key data of the neighbor cell monitoring point, and obtaining a neighbor cell monitoring model through the third network key data;

The specific details of each module in the network load optimization apparatus have been described in detail in the corresponding network load optimization method, and therefore are not described herein again.

It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.

Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.

In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

An electronic device 1200 according to this embodiment of the disclosure is described below with reference to fig. 12. The electronic device 1200 shown in fig. 12 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 12, the electronic device 1200 is embodied in the form of a general purpose computing device. The components of the electronic device 1200 may include, but are not limited to: the at least one processing unit 1210, the at least one memory unit 1220, a bus 1230 connecting various system components (including the memory unit 1220 and the processing unit 1210), and a display unit 1240.

Wherein the storage unit stores program code that is executable by the processing unit 1210 to cause the processing unit 1210 to perform steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary methods" of this specification. For example, the processing unit 1210 may perform step S110 as shown in fig. 1: acquiring high-load monitoring points in a target range and first network key data of the high-load monitoring points, and training a preset neural network model through the first network key data to obtain a high-load monitoring model; s120: verifying the high-load monitoring model through second network key data to obtain target monitoring points in the high-load monitoring points, and training the preset neural network model through the network key data of the target monitoring points to obtain a target monitoring model; s130: acquiring a neighboring cell monitoring point of the target monitoring point and third network key data of the neighboring cell monitoring point, and training the preset neural network model through the third network key data to obtain a neighboring cell monitoring model; s140: obtaining a first predicted value of the high-load monitoring point and a second predicted value of the neighboring monitoring point of the high-load monitoring point through the target monitoring model and the neighboring monitoring model, and obtaining a load balancing suggestion list of the high-load monitoring point according to the first predicted value and the second predicted value; s150: and optimizing the network load of the high-load monitoring point according to the load balancing suggestion list.

The storage unit 1220 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)12201 and/or a cache memory unit 12202, and may further include a read only memory unit (ROM) 12203.

Storage unit 1220 may also include a program/utility 12204 having a set (at least one) of program modules 12205, such program modules 12205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

Bus 1230 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

The electronic device 1200 may also communicate with one or more external devices 1300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1200 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 1250. Also, the electronic device 1200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 1260. As shown, the network adapter 1260 communicates with the other modules of the electronic device 1200 via the bus 1230. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.

According to the program product for implementing the above method of the embodiments of the present disclosure, it may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).

Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

Claims

1. A method for optimizing network load, comprising:

2. The method according to claim 1, wherein obtaining high-load monitoring points within a target range and first network key data of the high-load monitoring points comprises:

3. The network load optimization method according to claim 2, wherein distinguishing the monitoring points according to a preset high-load monitoring point judgment condition comprises:

4. The network load optimization method according to claim 2, wherein distinguishing the monitoring points according to a preset high-load monitoring point judgment condition comprises:

5. The network load optimization method of claim 1, further comprising:

6. The method of claim 5, wherein verifying the high load monitoring model through second network key data to obtain a target monitoring point of the high load monitoring points comprises:

7. The method of claim 1, wherein obtaining the neighbor monitoring point of the target monitoring point comprises:

8. The method according to claim 7, wherein obtaining a load balancing suggestion list of the high load monitoring points according to the first predicted value and the second predicted value comprises:

9. A network load optimization device, comprising:

the suggestion list generation module is used for obtaining a first predicted value of the high-load monitoring point and a second predicted value of the neighboring monitoring point of the high-load monitoring point through the target monitoring model and the neighboring monitoring model, and obtaining a load balancing suggestion list of the high-load monitoring point according to the first predicted value and the second predicted value;

10. A readable storage medium on which a computer program is stored, which computer program, when being executed by a processor, is adapted to carry out the network load optimization method according to any one of claims 1 to 8.

11. An electronic device, comprising:

a processor; and

a memory for storing executable instructions of the processor; wherein the processor is configured to perform the network load optimization method of any of claims 1-8 via execution of the executable instructions.