CN113469576A

CN113469576A - High-load cell identification method and device, storage medium and electronic equipment

Info

Publication number: CN113469576A
Application number: CN202110842362.5A
Authority: CN
Inventors: 陈永红
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2021-07-26
Filing date: 2021-07-26
Publication date: 2021-10-01

Abstract

The present disclosure provides a high-load cell identification method, apparatus, storage medium, and electronic device; relates to the technical field of communication. The method comprises the following steps: acquiring original performance data of a target cell, wherein the original performance data comprises first performance data and original second performance data; fitting the first performance data and the original second performance data to obtain linear second performance data corresponding to the first performance data; when the load of the target cell is a full load critical point, determining the resource amount of the target cell when the target cell reaches a sensing inflection point according to the linear second performance data; and calculating a specific Reynolds demand index corresponding to the original second performance data of the target period according to the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, and identifying the network load of the target cell according to the specific Reynolds demand index. The method and the device can accurately quantize and identify the high-load cell, and the output result is more accurate.

Description

High-load cell identification method and device, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of communications technologies, and in particular, to a high-load cell identification method, a high-load cell identification apparatus, a computer-readable storage medium, and an electronic device.

Background

With the development of 4/5G networks and the increase of users, the demand for network optimization is higher and higher. For example, in the case of limited resources, the network capacity of a cell needs to be deeply and finely analyzed, and whether the cell meets the condition of capacity expansion and the amount of resources required for capacity expansion can be identified to optimize the network capacity of the cell.

In the related art, a cell to be expanded may be identified according to a specified expansion threshold. For example, if a certain cell is continuous for 4 weeks in a month, and the capacity expansion threshold is met during busy hours for 7 days or more in each week, the cell may be defined as a cell to be expanded. The capacity expansion threshold can be large flow, for example, the average utilization rate of downlink PRBs in a cell during busy hour is more than 50%, and the throughput of the cell during busy hour is more than 6 GB; it can also mean that multiple users are satisfied, for example, the average utilization rate of downlink PRBs in a cell is greater than 50% during busy hours, and the maximum average number of users for RRC connection establishment is greater than 200.

However, when the cell to be expanded is identified by using the expansion threshold, accurate expansion of the cell cannot be performed, which may cause a small amount of resources to be invested, and thus, the practical problem is not solved. And the resource utilization rate is low due to resource waste caused by excessive resource input.

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

The present disclosure provides a high-load cell identification method, a high-load cell identification apparatus, a computer-readable storage medium, and an electronic device, so as to overcome, at least to some extent, the problems that a cell to be expanded cannot be accurately identified and accurate expansion cannot be performed due to limitations of related technologies.

The present disclosure provides a high-load cell identification method, including:

acquiring original performance data of a target cell, wherein the original performance data comprises first performance data and original second performance data;

fitting the first performance data and the original second performance data to obtain linear second performance data corresponding to the first performance data;

when the load of the target cell is a full load critical point, determining the resource amount of the target cell when the target cell reaches a perception inflection point according to the linear second performance data;

and calculating a specific Reynolds demand index corresponding to the original second performance data of the target period according to the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, and identifying the network load of the target cell according to the specific Reynolds demand index.

In an exemplary embodiment of the present disclosure, the obtaining raw performance data of the target cell includes:

extracting the original performance data of the target cell from the performance data of the background network management;

and selecting the first performance data and the original second performance data from the original performance data as sample data to fit the sample data.

In an exemplary embodiment of the present disclosure, the fitting the first performance data and the original second performance data to obtain linear second performance data corresponding to the first performance data includes:

performing linear fitting on the first performance data and the original second performance data, and obtaining a corresponding fitting curve when the fitting error is minimum;

and calculating the first performance data and the fitting parameters in the fitting curve to obtain linear second performance data corresponding to the first performance data.

In an exemplary embodiment of the present disclosure, the determining, according to the linear second performance data, an amount of resources when the target cell reaches a sensing inflection point when the target cell load is a full load critical point includes:

fitting the first performance data and the original second performance in a target interval, and determining a perception inflection point of the target cell;

and when the load of the target cell is a full load critical point, calculating according to the linear second performance data and the original second performance data in the target interval to obtain the resource amount when the target cell reaches a sensing inflection point.

In an exemplary embodiment of the disclosure, the calculating, according to the linear second performance data and a resource amount when a target cell reaches a sensing inflection point, a reynolds demand index corresponding to original second performance data of a target period includes:

according to

T＝(R_P-R_f)/β_p

Obtaining the specific Reynolds demand index of the target cell;

wherein T is the specific Reynolds demand index, R, of the target cell_PFor linear second performance data, R_fOriginal second performance data, beta, for a target period of time_pAnd the resource quantity when the target cell reaches the sensing inflection point.

In an exemplary embodiment of the present disclosure, the method further comprises:

when the target cell is a high-load cell, determining the resource demand of the target cell according to the mapping relation between the specific Reynolds demand index and the resource demand, and accurately expanding the capacity of the target cell according to the resource demand.

In an exemplary embodiment of the present disclosure, the first performance data is an average number of users for RRC connection establishment, and the original second performance data is an average utilization rate of downlink PRBs and/or a downlink traffic of user plane PDCP.

when a cell to be evaluated meets a preset condition, selecting the cell to be evaluated as a target cell; the preset condition is that the average utilization rate of the downlink PRB is greater than a preset utilization rate threshold value.

The present disclosure provides a high-load cell identification apparatus, including:

the system comprises a performance data acquisition module, a performance data acquisition module and a performance data acquisition module, wherein the performance data acquisition module is used for acquiring original performance data of a target cell, and the original performance data comprises first performance data and original second performance data;

the first data determining module is used for fitting the first performance data and the original second performance data to obtain linear second performance data corresponding to the first performance data;

a second data determining module, configured to determine, according to the linear second performance data, a resource amount when the target cell reaches a sensing inflection point when the load of the target cell is a full load critical point;

and the network load identification module is used for calculating a specific Reynolds demand index corresponding to the original second performance data of the target time period according to the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, and identifying the network load of the target cell according to the specific Reynolds demand index.

The present disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any one of the above.

The present disclosure provides an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of any one of the above via execution of the executable instructions.

Exemplary embodiments of the present disclosure may have some or all of the following benefits:

in the high-load cell identification method provided by the exemplary embodiment of the present disclosure, first performance data and original second performance data of a target cell are obtained; fitting the first performance data and the original second performance data to obtain linear second performance data; when the load of the target cell is a full load critical point, determining the resource amount of the target cell when the target cell reaches a sensing inflection point according to the linear second performance data; and calculating a specific Reynolds demand index corresponding to the original second performance data of the target period according to the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, and identifying the network load of the target cell according to the specific Reynolds demand index. On one hand, the distribution relation of the first performance data and the original second performance data is obtained by fitting to carry out modeling, and the specific Reynolds demand index of the target cell is calculated according to the modeling result, parameters do not need to be artificially defined in the process, so that artificial interference can be avoided, and the calculation result is more objective; on the other hand, by calculating the special Reynolds demand index of the target cell, the resource demand of the target cell can be accurately quantized and subjected to accurate capacity expansion, and the resource utilization rate 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 is a schematic diagram illustrating an exemplary system architecture to which a high-load cell identification method and apparatus according to an embodiment of the present disclosure may be applied;

fig. 2 schematically shows a flow chart of a high load cell identification method according to one embodiment of the present disclosure;

FIG. 3 schematically shows a flow chart of calculating linear second performance data according to an embodiment of the present disclosure;

FIG. 4 schematically illustrates a schematic diagram of a resource demand variation curve according to one embodiment of the present disclosure;

figure 5 schematically shows a flow diagram of calculating an amount of resources when a cell reaches a perceptual knee in accordance with one embodiment of the present disclosure;

FIG. 6 schematically illustrates a schematic diagram of a suppression index curve according to one embodiment of the present disclosure;

fig. 7 schematically shows a block diagram of a high load cell identification apparatus according to one embodiment of the present disclosure;

FIG. 8 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an 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.

Fig. 1 is a schematic diagram illustrating a system architecture of an exemplary application environment to which a high-load cell identification method and apparatus according to an embodiment of the present disclosure may be applied.

As shown in fig. 1, the system architecture 100 may include one or more of

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few. The

terminal devices

101, 102, 103 may be various electronic devices including, but not limited to, desktop computers, portable computers, smart phones, tablet computers, and the like. It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation. For example, the server 105 may be a server cluster formed by a plurality of servers, and the server may obtain the original performance data of the target cell, and perform fitting on the first performance data and the original second performance data in the original performance data to obtain linear second performance data when resources are not under pressure. When the load of the target cell is the full load critical point, the amount of resources when the target cell reaches the sensing inflection point may be determined. When the server obtains the original second performance data of the target cell in the latest period, the special Reynolds demand index of the target cell in the period can be obtained by utilizing the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, and then the network load state of the target cell can be identified according to the special Reynolds demand index of the period. When the target cell is determined to be a high-load cell, the resource demand of the cell can be quantified, so that the wireless network of the cell can be accurately optimized.

The high-load cell identification method provided by the embodiment of the present disclosure is generally executed by the server 105, and accordingly, the high-load cell identification apparatus is generally disposed in the server 105, and after the execution of the server is completed, the server can send the identification result to the terminal device, and the terminal device displays the identification result to the user. However, it is easily understood by those skilled in the art that the high-load cell identification method provided in the embodiment of the present disclosure may also be executed by one or more of the

terminal devices

101, 102, and 103, and correspondingly, the high-load cell identification apparatus may also be disposed in the

terminal devices

101, 102, and 103, for example, after being executed by the terminal device, the identification result may be directly displayed on a display screen of the terminal device, which is not particularly limited in the exemplary embodiment.

The technical solution of the embodiment of the present disclosure is explained in detail below:

in the application process of an LTE (Long Term Evolution ) network, a cell to be expanded can be identified according to a specified expansion threshold, and the LTE network is a rough threshold control valve. For example, if a certain cell is continuous for 4 weeks in a month, and the capacity expansion threshold is met during busy hours for 7 days or more in each week, the cell may be defined as a cell to be expanded. The capacity expansion threshold can be large flow, for example, the average utilization rate of downlink PRBs in a cell during busy hour is more than 50%, and the throughput of the cell during busy hour is more than 6 GB; it can also refer to multiple users, such as the average utilization of downlink PRBs in a cell is greater than 50% during self-busy hours and the maximum number of active RRC links is greater than 200.

When the capacity expansion threshold is used for identifying the cell to be expanded, the longer the time for meeting the capacity expansion threshold is, the cell can only show that the resource is continuously required in the time period, and the degree of the requirement on the resource cannot be reflected. When the resources are actually put into use, the limited resources need to be put into the most demanding point positions, so that the resource utilization rate is improved. When excessive resources are input and secondary dismantling and application are carried out, the difficulty is high, the overall network layout and the networking structure are possibly influenced, resource waste is caused, and the resource utilization rate is reduced. In addition, the method cannot quantify the degree of suppression of the cell to be expanded. For example, the super-busy cell can be alleviated only by expanding specific carriers, or network depression can be alleviated by means of network equalization and the like, and hardware investment is not needed. Therefore, the quantization of the conventional scheme is not sufficiently embodied, and accurate capacity expansion cannot be performed on the cell.

Based on one or more of the above problems, the present exemplary embodiment provides a high-load cell identification method, which may be applied to the server 105, and may also be applied to one or more of the

terminal devices

101, 102, and 103, which is not particularly limited in the present exemplary embodiment. Referring to fig. 2, the high load cell identification method may include the following steps S210 to S240:

s210, acquiring original performance data of a target cell, wherein the original performance data comprises first performance data and original second performance data;

s220, fitting the first performance data and the original second performance data to obtain linear second performance data corresponding to the first performance data;

step S230, when the load of the target cell is a full load critical point, determining the resource amount of the target cell when the target cell reaches a perception inflection point according to the linear second performance data;

and S240, calculating a special Reynolds demand index corresponding to the original second performance data of the target time period according to the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, and identifying the network load of the target cell according to the special Reynolds demand index.

The above steps of the present exemplary embodiment will be described in more detail below.

In step S210, original performance data of the target cell is obtained, where the original performance data includes the first performance data and the original second performance data.

In this example embodiment, the raw Performance data refers to a Performance Indicator that can be used to measure network quality, such as a Key Performance Indicator (KPI). In KPI monitoring and optimization, several important indicators can be selected to reflect the overall performance of the network. For example, the first performance data and the original second performance data in the original performance data may be selected to analyze the network performance. The first performance data may be an average number of users in RRC (Radio Resource Control) connection establishment, and the average number of users in RRC connection establishment may count the average number of simultaneous RRC connections, and by presetting a measurement time interval for sampling, the number of simultaneous RRC connections in the target cell may be obtained and averaged. The original second performance data may be a load index, such as an average utilization rate of downlink PRBs (Physical Resource blocks), where the average utilization rate of the downlink PRBs is an average occupied downlink PRBs/cell PRB number × 100%, and may be used to reflect a use condition of the downlink Physical resources. The original second performance data may also be an index of the throughput in busy hours of the cell, and the throughput in busy hours of the cell may adopt the traffic of an air interface, and is divided into the number of bytes of uplink and downlink traffic, and the index reflects the uplink and downlink traffic of the air interface. For example, it can be user plane PDCP downlink traffic. The user plane PDCP downlink traffic may be used to reflect the traffic condition of the system PDCP layer. It should be noted that the original second performance data in this example refers to an actual downlink PRB average utilization rate or an actual user plane PDCP downlink traffic when the target cell is limited by resources.

When the cell to be evaluated meets the preset condition, the cell to be evaluated can be selected as a target cell. The preset condition may be that the average utilization of the downlink PRBs is greater than a preset utilization threshold. For example, when the average utilization rate of the downlink PRBs of a certain cell to be evaluated is greater than 70%, the cell may be selected as a target cell, so as to perform quantitative identification on the network load of the cell. In other examples, a cell with an average utilization rate of downlink PRBs greater than 50% may also be used as the target cell. It should be noted that the preset condition for selecting the target cell may be set according to actual requirements, which is not specifically limited in this disclosure.

The wireless communication network can make each base station actively report the performance monitoring data in the base station to the network manager. Therefore, when the original performance data of the target cell is obtained, the original performance data of the target cell can be extracted from the background network management performance data, and several important indexes are selected from the original performance data as sample data. For example, performance data such as network bandwidth resources, the average number of users for RRC connection establishment, the average utilization of downlink PRBs, downlink traffic of the user plane PDCP, and the average user rate of the target cell in the last 7 days may be extracted from the network manager. The KPIs of the latest 7 days of a plurality of target cells can also be extracted from the network manager at the same time so as to quantitatively identify the network load of the target cells. It will be appreciated that any number of sample data may be acquired, depending on implementation needs. Subsequently, statistics may be performed on the selected sample data, such as fitting and analyzing the sample data. Specifically, the network depression degree of the target cell and the corresponding reynolds demand index can be determined by fitting and calculating performance data such as the average number of users established by RRC connection, the average utilization rate of downlink PRBs, the downlink flow of the user plane PDCP, and the average user rate, and then the resource demand of the target cell can be accurately quantified based on the reynolds demand index of the target cell and the network bandwidth resource. In other examples, the raw performance data may also be obtained by performing a static test using a drive test tool, which is not specifically limited by this disclosure.

In step S220, the first performance data and the original second performance data are fitted to obtain linear second performance data corresponding to the first performance data.

In a preferred embodiment, the network depression degree can be obtained based on a fitting algorithm of the reynolds demand index and the original performance data, and further capacity expansion can be performed according to the depressed resource amount. The resource demand degree of the target cell can be reflected by the magnitude of the special Reynolds demand index. For example, the larger the reynolds demand index is, the higher the degree of network depression of the target cell is, and the larger the resource demand is. Conversely, the smaller the resource demand. And finally, quantifying the resource amount required to be input into the target cell according to the corresponding relation between the Terraynolds demand index and the resource demand.

In particular, it may be according to:

T＝(R_P-R_f)/β_p

and calculating the specific Reynolds demand index of the target cell. Wherein: t is the specific Reynolds demand index, R, of the target cell_PThe second linear performance data may represent a linear resource amount corresponding to a traffic edge of the target cell when the resource is not limited, such as an average utilization rate of a downlink PRB and a downlink traffic of a user plane PDCP when the resource is not limited. R_fThe original second performance data may represent the amount of resources when the target cell reaches the smooth maximum value when the resources are limited, such as the actual downlink PRB average utilization and the user plane PDCP downlink traffic of the target cell at a certain time period. Beta is a_pThe amount of resources when the target cell reaches the sensing inflection point.

For example, for a target cell in a certain time period, the actual downlink PRB average utilization rate of the target cell in the time period may be obtained from the network manager, such as the actual downlink PRB average utilization rate is 50% (i.e. R)_f). Under the condition of unlimited resources, the average utilization rate of the target cell at the downlink PRB at the moment can reach 65 percent (namely R)_P)。R_P-R_fMay represent the degree of network depression of the target cell. When R is_PGreater than R_fIn time, the larger the difference between the two is, the larger the network depression degree of the target cell can be shown. The smaller the difference, the smaller the degree of network depression of the target cell can be indicated. When R is_PLess than R_fIt can be shown that the network of the target cell is not depressed and is in an idle state.

In the process of calculating the specific Reynolds demand index of the target cell, the original second performance data R_fFor example, the average utilization rate of the downlink PRB and the downlink flow of the user plane PDCP can be directly obtained from the network management. And for linear second performance data R_PThe first performance data and the original second performance data need to be fitted to obtain linear second performance data R corresponding to the first performance data_P. In an exemplary manner, the first and second electrodes are,the method can be used for fitting the average user number established by the RRC connection and the average utilization rate of the downlink PRB, and can also be used for fitting the average user number established by the RRC connection and the downlink flow of the PDCP. Referring to fig. 3, linear second performance data R may be calculated according to steps S310 and S320_P。

And S310, performing linear fitting on the first performance data and the original second performance data, and obtaining a corresponding fitting curve when the fitting error is minimum.

Preferably, the linear second performance data R may be calculated by linear fitting the first performance data and the original second performance data and from the fitting result_P. For example, linear fitting may be performed on the average number of users established for RRC connection in a large amount of sample data and the average utilization rate of actual downlink PRB, so as to obtain a distribution relationship between the average number of users established for RRC connection and the average utilization rate of downlink PRB, for example, a one-dimensional linear relationship y ═ kx + b may be obtained.

Specifically, referring to fig. 4, fig. 4 shows a scatter diagram and a linear fitting curve for establishing the average user number and the average utilization rate of downlink PRBs by RRC connection generated by using a large amount of sample data. Each discrete point in the scatter diagram may represent a value of an actual downlink PRB average utilization corresponding to an average number of users for RRC connection establishment. By formulating a large number of discrete points in the scatter diagram, the analytic expression continuously approximates to the discrete points, and when the fitting error is minimum, partial discrete points with large deviation can be discarded, so that the final linear fitting curve is obtained as y being 3.6783x + 93.424. Wherein, the abscissa x is the average number of users for RRC connection establishment, the ordinate y is the average utilization rate of downlink PRB, and R²The fitting degree in the one-dimensional linear relation can be expressed, and R can be known by the linear fitting curve in FIG. 4²0.6049, it can be shown that 60.49% of the sample data satisfies the analytical expression y 3.6783x + 93.424. In addition, the fitting error can be sum of squares, the sum of squares of errors of corresponding points of the fitting data and the original data can be calculated, and the smaller the sum of squares is, the better the model selection and fitting effect is, and the more accurate the data prediction is. The fitting error may also be a mean square error, a root mean square, etc., which is not specifically limited by this disclosure.

And S320, calculating the first performance data and fitting parameters in a fitting curve to obtain linear second performance data corresponding to the first performance data.

For example, when the distribution relationship between the average number of users established by the RRC connection and the average utilization rate of the downlink PRB is a one-dimensional linear relationship y ═ kx + b, a large amount of sample data may be counted to obtain fitting parameters k and b. Different first performance data are input, and corresponding linear second performance data can be obtained through calculation. Further, taking an example that a linear fit curve y obtained by fitting in fig. 4 is 3.6783x +93.424, when x is the number of different average users established in RRC connection, a corresponding y value may be obtained by calculation according to the linear fit curve y being 3.6783x +93.424, that is, the average utilization rate of downlink PRBs when resources are not limited may be obtained.

In this example, a one-dimensional linear relationship graph of the average number of users established by RRC connection and the average utilization rate of downlink PRBs can be obtained through a large number of data scattering points and fitting, that is, a trend graph of resources and users can be obtained, and a resource demand change curve containing 90% of data volume can be obtained by discarding 10% of abrupt data.

In this example, a data model of resources and users can be established through the distribution relationship of the existing resources and users, so that the resource demand of the PRB under the condition of a large user amount, that is, the suppression index, can be predicted, and the resource demand of the high-load cell can be determined subsequently according to the severity of the suppression index. The process does not need to artificially define parameters, avoids artificial interference results, and has objective output results.

In step S230, when the load of the target cell is a full load critical point, determining the resource amount of the target cell when the target cell reaches a sensing inflection point according to the linear second performance data.

According to the method and the device, in the process of calculating the special Reynolds demand index of the target cell, not only is the relation between resources and services considered, but also the coefficient of a sensing inflection point is introduced, quantitative identification of the high-load cell is combined with user use, and the problem of real sensing of the user can be fully considered in resource capacity expansion.

In particular, according to T ═ (R)_P-R_f)/β_pWhen the reynolds demand index T of the target cell is calculated, the linear second performance data R may be calculated according to step S220_PThe original second performance data R can be directly extracted from the network management_f. Currently, there is also a need to determine the amount of resources β when the target cell reaches the sensing knee point_p. In one example, referring to fig. 5, the resource amount β when the target cell reaches the sensing inflection point may be determined according to steps S510 and S520_p。

Step S510, fitting first performance data and original second performance in a target interval, and determining a perception inflection point of the target cell;

when the average utilization rate of the downlink PRB of the target cell is between 70% and 90%, the network corresponding to the cell is at the critical point of a depression state, and therefore R can be selected correspondingly_fThe average utilization rate of the downlink PRB is 70-90%. The passing interval is 70 percent and 90 percent]The average utilization rate of the downlink PRB is fitted with the average number of users established by the corresponding RRC connection, and the sensing inflection point of the target cell can be obtained through fitting. In other examples, 70% to 90% of the user average rate corresponding to the average utilization rate of the downlink PRB may also be selected to perform fitting to determine the sensing inflection point of the target cell, and it should be noted that the user average rate may also be directly obtained from the network manager.

And S520, when the load of the target cell is a full load critical point, calculating according to the linear second performance data and the original second performance data in the target interval to obtain the resource amount when the target cell reaches a sensing inflection point.

When the load of the target cell is a critical point of full load, that is, when the device resource of the target cell is just running to full load, the reynolds demand index T corresponding to the target cell is 100%, which is equivalent to that the network of the target cell is just at the edge of depression under the existing network bandwidth resource. For further explanation of the corresponding relationship between the reynolds demand index and the resource, for example, when the calculated reynolds demand index of the target cell is 150%, it is indicated that the cell is currently in a suppressed state, 1.5 times of resources are actually needed to relieve network busy of the target cell, that is, 0.5 times of bandwidth resources need to be invested on the basis of the existing network bandwidth resources.

Specifically, when the resource amount of the sensing inflection point of the target cell is calculated, T is 100%, and R is_fLinear second performance data R for sensing average utilization of downlink PRB at inflection point_PThe average utilization rate of the downlink PRBs when the resource corresponding to the sensing inflection point is not limited is calculated according to step S220. When T, R_P、R_fWhen known, can be based on T ═ R (R)_P-R_f)/β_pCalculating to obtain the resource amount beta when the target cell reaches the sensing inflection point_p。

Illustratively, referring to fig. 6, fig. 6 shows a suppression index curve that may be used to represent the degree of network suppression of the target cell. In fig. 6, the abscissa indicates the average number of users for RRC connection establishment, the ordinate indicates the average utilization rate of downlink PRBs (also referred to as the downlink channel PRB resource utilization rate) and the downlink traffic of the user plane PDCP pdu (also referred to as the downlink data amount of the user plane PDCP sdu), and fig. 6 further includes a plurality of scattered points 601, where each scattered point indicates the average utilization rate of downlink PRBs corresponding to the average number of users for RRC connection establishment in a certain period (e.g., in one hour) of the target cell. The slope 602 is a resource demand change curve obtained by fitting the average number of users established by RRC connection and the average utilization of linear downlink PRBs, and corresponds to the linear fit curve in fig. 4. A vertical line 603 and a vertical line 606 are used to remove 10% of the abrupt data, the vertical line 604 is used to indicate the position where the network of the target cell starts to be in the depression state and also is the starting point for calculating the sensing inflection point, the vertical line 605 is used to indicate the resource demand line when the average number of users established by the RRC connection of the target cell is 49 people in the nearest time period (such as the nearest hour), and the intersection 607 between the resource demand line 605 and the resource demand variation curve 602 is the average utilization rate R of the downlink PRBs when the network is not depressed_PActual downlink PRB average utilization ratio R in the period_fCan be derived from the depression index curve, i.e. corresponding to the intersection point 608 in the graph, or can be extracted directly from the network management, R_PAnd R_fIndicates the target in the time periodThe degree of network depression of the cell. Similarly, the oblique line 609 is a resource demand change curve obtained by fitting the average number of users established by RRC connection and the downlink traffic of the user plane PDCP, and each of the plurality of scattered points 610 represents the downlink traffic of the user plane PDCP corresponding to the average number of users established by RRC connection in a certain time period in the target cell.

In step S240, a reynolds demand index corresponding to the original second performance data in the target period is calculated according to the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, and the network load of the target cell is identified according to the reynolds demand index.

The linear second performance data R of the target cell may be calculated according to step S220_P，R_PThe average utilization rate of the downlink PRB when the resource is not limited and the resource amount beta when the target cell reaches the sensing inflection point can be calculated and obtained according to the step S230_p. Then, the original second performance data R of the target period can be extracted from the network management_fAnd according to:

T＝(R_P-R_f)/β_p

and calculating to obtain the specific Reynolds demand index of the target cell in the target time period. For example, the actual downlink PRB average utilization of the target cell in the latest period may be extracted to calculate the reynolds demand index of the target cell in the latest period. For example, when the specific reynolds demand index of the target cell is greater than the specific reynolds demand index threshold, for example, the specific reynolds demand index first threshold may be set to 100%, and the specific reynolds demand index second threshold may be set to 150%, and when the specific reynolds demand index T of the target cell is less than 100%, it may be determined that the target cell is a cell with normal network load, that is, the network of the target cell is not under the existing network bandwidth resources. When the specific reynolds demand index of the target cell is more than or equal to 100% and less than 150%, it may be determined that the target cell is a high-load cell, that is, the network of the target cell is in a suppressed state under the existing network bandwidth resources, and the suppressed degree is low. When the reynolds demand index T of the target cell is greater than or equal to 150%, it may be determined that the target cell is an ultra-high load cell, that is, the network of the target cell has a higher degree of depression under the existing network bandwidth resources. For example, when the reynolds number T of the target cell is 110%, it corresponds to the current network capacity that needs to be expanded by 10%, and the priority for expansion is low for an ultra-high load cell, and network optimization may be performed by means of carrier balancing or the like. When the reynolds demand index T is more than 150%, the network depression cannot be relieved by an optimization means, and the problem can be solved only by a capacity expansion mode.

When a plurality of ultrahigh-load cells need to be subjected to capacity expansion, the calculated specific reynolds demand indexes of each cell can be sorted, for example, sorted in a descending order, and the higher the ranking, the greater the network suppression degree of the cell is, and the greater the corresponding resource demand is. Therefore, the priority of capacity expansion of each cell can be determined according to the sorted special Reynolds demand indexes, and the resource investment efficiency can be determined to the maximum extent according to the priority level and the demand degree under the condition of resource limitation.

When the target cell is accurately expanded, the resource demand of the target cell can be determined according to the mapping relation between the special Reynolds demand index and the resource demand, and the target cell is accurately expanded according to the resource demand. Illustratively, the specific reynolds demand index of the target cell is positively correlated with the resource demand. For example, the existing network bandwidth resource of the target cell is 5M bandwidth, and when the reynolds demand index is 150%, the corresponding resource demand is 7.5M bandwidth, that is, the user demand can be met only when the resource of 2.5M bandwidth needs to be put into the target cell, and the solution can also be achieved by optimization means such as carrier balancing. When the reynolds number is 250%, the corresponding resource demand amount may be 12.5M bandwidth, that is, the user demand can be satisfied when the resource of 7.5M bandwidth needs to be invested into the target cell.

It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these 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.

Further, in the present exemplary embodiment, a high-load cell identification apparatus is also provided. Referring to fig. 7, the high-load cell identifying apparatus 700 may include a performance data acquiring module 710, a first data determining module 720, a second data determining module 730, and a network load identifying module 740, wherein:

a performance data obtaining module 710, configured to obtain original performance data of a target cell, where the original performance data includes first performance data and original second performance data;

a first data determining module 720, configured to fit the first performance data and the original second performance data to obtain linear second performance data corresponding to the first performance data;

a second data determining module 730, configured to determine, according to the linear second performance data, a resource amount when the target cell reaches a sensing inflection point when the load of the target cell is a full load critical point;

and the network load identification module 740 is configured to calculate, according to the linear second performance data and the resource amount when the target cell reaches the sensing inflection point, a reynolds demand index corresponding to the original second performance data in the target time period, and identify the network load of the target cell according to the reynolds demand index.

In an alternative embodiment, the performance data acquisition module 710 includes:

a performance data obtaining unit, configured to extract original performance data of the target cell from background network management performance data;

and the performance data selecting unit is used for selecting the first performance data and the original second performance data from the original performance data as sample data so as to fit the sample data.

In an alternative embodiment, the first data determination module 720 includes:

the performance data fitting unit is used for performing linear fitting on the first performance data and the original second performance data and obtaining a corresponding fitting curve when a fitting error is minimum;

and the performance data determining unit is used for calculating the first performance data and the fitting parameters in the fitting curve to obtain linear second performance data corresponding to the first performance data.

In an alternative embodiment, the second data determining module 730 includes:

a sensing inflection point determining unit, configured to fit first performance data and original second performance in a target interval, and determine a sensing inflection point of the target cell;

and an inflection point resource amount determining unit, configured to calculate, when the target cell load is a full load critical point, a resource amount when the target cell reaches a sensing inflection point according to the linear second performance data and the original second performance data in the target interval.

In an alternative embodiment, the network load identification module 740 is configured to:

according to

T＝(R_P-R_f)/β_p

Obtaining the specific Reynolds demand index of the target cell;

In an optional embodiment, the high-load cell identification apparatus 700 further includes:

and the resource demand determining module is used for determining the resource demand of the target cell according to the mapping relation between the specific Reynolds demand index and the resource demand when the target cell is a high-load cell, and accurately expanding the capacity of the target cell according to the resource demand.

In an optional implementation manner, the first performance data is an average number of users for RRC connection establishment, and the original second performance data is an average utilization rate of a downlink PRB and/or a downlink traffic of a user plane PDCP.

the target cell selection module is used for selecting the cell to be evaluated as a target cell when the cell to be evaluated meets a preset condition; the preset condition is that the average utilization rate of the downlink PRB is greater than a preset utilization rate threshold value.

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

Each module in the above apparatus may be a general-purpose processor, including: a central processing unit, a network processor, etc.; but may also be a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The modules may also be implemented in software, firmware, etc. The processors in the above device may be independent processors or may be integrated together.

Exemplary embodiments of the present disclosure also provide 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 an electronic device to perform the steps according to various exemplary embodiments of the disclosure described in the above-mentioned "exemplary methods" section of this specification, when the program product is run on the electronic device. The program product may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on an electronic 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).

The exemplary embodiment of the present disclosure also provides an electronic device capable of implementing the above method. An electronic device 800 according to such an exemplary embodiment of the present disclosure is described below with reference to fig. 8. The electronic device 800 shown in fig. 8 is only an example and should not bring any limitations to the functionality and scope of use of the embodiments of the present disclosure.

As shown in fig. 8, electronic device 800 may take the form of a general purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one memory unit 820, a bus 830 connecting the various system components including the memory unit 820 and the processing unit 810, and a display unit 840.

The storage unit 820 stores program code that may be executed by the processing unit 810 to cause the processing unit 810 to perform steps according to various exemplary embodiments of the present disclosure described in the "exemplary methods" section above in this specification. For example, processing unit 810 may perform any one or more of the method steps of fig. 2-5.

The storage unit 820 may include readable media in the form of volatile storage units, such as a random access storage unit (RAM)821 and/or a cache storage unit 822, and may further include a read only storage unit (ROM) 823.

Storage unit 820 may also include a program/utility 824 having a set (at least one) of program modules 825, such program modules 825 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 830 may be any 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 800 may also communicate with one or more external devices 900 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 800, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 800 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 850. Also, the electronic device 800 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 860. As shown, the network adapter 860 communicates with the other modules of the electronic device 800 via the bus 830. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 800, 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 exemplary embodiments of the present disclosure.

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.

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.

It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A high-load cell identification method is characterized by comprising the following steps:

2. The method of claim 1, wherein the obtaining raw performance data of the target cell comprises:

3. The method according to claim 1, wherein the fitting the first performance data and the original second performance data to obtain linear second performance data corresponding to the first performance data comprises:

4. The method of claim 3, wherein the determining the amount of resources for the target cell to reach the sensing inflection point according to the linear second performance data when the target cell load is the full load critical point comprises:

5. The method for identifying the high-load cell according to claim 1, wherein the calculating a reynolds demand index corresponding to the original second performance data in the target period according to the linear second performance data and the resource amount of the target cell reaching the sensing inflection point includes:

according to

T＝(R_P-R_f)/β_p

Obtaining the specific Reynolds demand index of the target cell;

6. The method of claim 1, further comprising:

7. The method according to claim 1, wherein the first performance data is an average number of users for RRC connection setup, and the original second performance data is an average utilization of downlink PRBs and/or user plane PDCP downlink traffic.

8. The method of claim 7, further comprising:

9. A high-load cell identification apparatus, comprising:

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of 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 method of any of claims 1-8 via execution of the executable instructions.