CN113068213A

CN113068213A - Network capacity evaluation processing method and device, electronic equipment and storage medium

Info

Publication number: CN113068213A
Application number: CN202010002169.6A
Authority: CN
Inventors: 辛潮; 戴鹏程; 万仁辉; 黄启虎; 张旭阳; 王洁丽; 张惠; 柏杨
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Group Design Institute Co Ltd
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Group Design Institute Co Ltd
Priority date: 2020-01-02
Filing date: 2020-01-02
Publication date: 2021-07-02
Anticipated expiration: 2040-01-02
Also published as: CN113068213B

Abstract

The embodiment of the invention discloses an evaluation processing method, a device, electronic equipment and a storage medium of network capacity, wherein the method comprises the following steps: the method comprises the steps of obtaining Deep Packet Inspection (DPI) signaling data, and determining a user perception index and a capacity performance index of the DPI signaling data according to a preset period; calculating and determining a correlation coefficient between the user perception index and the capacity performance index; and evaluating the network capacity of each cell according to the user perception index or the correlation coefficient to determine the cell with limited capacity. According to the embodiment of the invention, the user perception index and the capacity performance index are determined, and the correlation coefficient of the user perception index and the capacity performance index is calculated, so that the calculation complexity can be reduced, the influence degree of the network load on the user perception is quantized, the capacity-limited cell is quickly and accurately determined, and the whole evaluation and problem discovery of the whole cell are facilitated.

Description

Network capacity evaluation processing method and device, electronic equipment and storage medium

Technical Field

The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for evaluating network capacity, an electronic device, and a storage medium.

Background

With the update of mobile communication technology and the popularization of intelligent terminals, users have higher and higher requirements on wireless network dependence and network quality, which also brings new challenges to network optimization. The intensive development of speed-increasing and cost-reducing work and the wide use of flow-free data services lead to the rapid rise of mobile network flow, show explosive growth with annual speed increase of more than 100%, operators must continuously expand to ensure the quality of the network and the experience of users, and how to accurately evaluate the influence degree of the capacity load problem on the user perception and how to accurately and effectively find and solve the high load problem in a complex network environment becomes the most important work in network optimization.

For the problem of cell capacity, the traditional method of operators is to analyze by using network management performance data and field test data. Network management performance data, a network management system is utilized to obtain performance data such as flow, user number, resource utilization rate and the like of a cell level, capacity early warning thresholds are respectively set, and when part of performance indexes reach the early warning thresholds, capacity limitation is judged. And (4) field test, namely performing fixed point test on the high-flow cell by using network test equipment to obtain network quality related indexes and evaluating the influence degree of the capacity problem on the use of the user network.

Currently, the definition of an LTE network capacity-limited cell by an operator is roughly: the 4G daily average flow is greater than 15G, the maximum activated user number is greater than 40, the maximum RRC connection number is greater than 200, and the peak value wireless resource utilization rate is greater than 50%, namely the cell with limited capacity. The capacity evaluation methods of operators and equipment manufacturers are basically consistent, and the set threshold values have a small difference. The flow, the number of users, the number of connections and the utilization rate of wireless resources are all important parameters for evaluating capacity, the parameters of the current network cell and the cell air interface rate are obtained, points are scattered in a coordinate system, function fitting is carried out, a fitting function polynomial between each parameter and the cell air interface rate can be obtained, derivation is carried out on the function to find an inflection point, or the lowest rate standard is set, and the corresponding utilization rate value is calculated through the function and is used as a capacity early warning threshold. Fig. 1 is a fitting function provided by the prior art, which is an average fitting result of a total number of cells and does not represent the actual situation of each cell. For example, when radio utilization is 50%, the different cell rates may be very different, as much as tens of times. This is because the cell rate (or other performance indicators) is not only related to the capacity, but also related to many factors such as the coverage, interference, bandwidth, antenna, etc. of the cell itself, and the function obtained by such a fitting algorithm can only represent the performance variation trend of a part of cells, and cannot be characterized for cells whose coordinate points are far from the function curve. That is, there is no fitting function that can represent all cells, and a uniform inflection point cannot be found, and it is impossible to accurately evaluate whether there is a capacity limitation problem in a cell and the influence degree of the capacity problem on the user perception of the cell by using the early warning threshold alone.

Therefore, the fixed-point test used in the prior art has high cost, limited test site and test time, can only be used for single-point verification, and cannot be used for overall evaluation and problem discovery of a whole number of cells.

Disclosure of Invention

Because the existing methods have the above problems, embodiments of the present invention provide a method and an apparatus for evaluating and processing network capacity, an electronic device, and a storage medium.

In a first aspect, an embodiment of the present invention provides a method for evaluating and processing network capacity, where the method includes:

the method comprises the steps of obtaining Deep Packet Inspection (DPI) signaling data, and determining a user perception index and a capacity performance index of the DPI signaling data according to a preset period;

calculating and determining a correlation coefficient between the user perception index and the capacity performance index;

and evaluating the network capacity of each cell according to the user perception index or the correlation coefficient to determine the cell with limited capacity.

Optionally, the user perception index includes a transmission control protocol TCP wireless delay or a hypertext transfer protocol HTTP downlink rate;

the capacity performance index comprises the number of effective Radio Resource Control (RRC) connections, the utilization rate of a physical uplink resource module (PRB), the utilization rate of a downlink PRB and the utilization rate of a Physical Downlink Control Channel (PDCCH) control channel unit (CCE).

Optionally, the capacity performance indicator is equal to the number of valid RRC connections × MAX (uplink PRB utilization, downlink PRB utilization, PDCCH CCE utilization).

Optionally, the correlation coefficient ρ_X,YCalculated according to the following formula:

wherein, X is the random vector of the user perception index, and Y is the random vector of the capacity performance index.

Optionally, the preset period is 24 hours;

x is TCP wireless time delay or HTTP download rate per hour within 24 hours;

y is the hourly capacity performance index over 24 hours.

Optionally, the evaluating the network capacity of each cell according to the user perception index or according to the correlation coefficient to determine the capacity-limited cell specifically includes:

if the TCP wireless delay is judged to be larger than a delay preset value, or the HTTP downloading rate is smaller than a rate preset value, or the first correlation coefficient is larger than a first preset value, or the second correlation coefficient is smaller than a second preset value, determining the current cell as a capacity-limited cell;

wherein the first correlation coefficient is a correlation coefficient between the TCP wireless delay and the capacity performance index;

the second correlation coefficient is a correlation coefficient between the HTTP downlink rate and the capacity performance indicator.

Optionally, the method for evaluating and processing network capacity further includes:

and preferentially expanding the capacity of the capacity-limited cell.

In a second aspect, an embodiment of the present invention further provides an apparatus for evaluating and processing network capacity, including:

the index determining module is used for acquiring Deep Packet Inspection (DPI) signaling data and determining a user perception index and a capacity performance index of the DPI signaling data according to a preset period;

a correlation coefficient calculation module for calculating and determining a correlation coefficient between the user perception index and the capacity performance index;

and the limited cell determining module is used for evaluating the network capacity of each cell according to the user perception index or the correlation coefficient and determining the capacity-limited cell.

Optionally, the preset period is 24 hours;

x is TCP wireless time delay or HTTP download rate per hour within 24 hours;

y is the hourly capacity performance index over 24 hours.

Optionally, the restricted cell determining module is specifically configured to:

Optionally, the evaluation processing device of the network capacity further includes:

and the cell capacity expansion module is used for preferentially expanding the capacity of the capacity-limited cell.

In a third aspect, an embodiment of the present invention further provides an electronic device, including:

at least one processor; and

at least one memory communicatively coupled to the processor, wherein:

the memory stores program instructions executable by the processor, which when called by the processor are capable of performing the above-described methods.

In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium storing a computer program, which causes the computer to execute the above method.

According to the technical scheme, the embodiment of the invention can reduce the calculation complexity by determining the user perception index and the capacity performance index and calculating the correlation coefficient of the user perception index and the capacity performance index, quantize the influence degree of the network load on the user perception, quickly and accurately determine the capacity-limited cell, and facilitate the overall evaluation and problem discovery of the total number of cells.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic diagram of a fitting function of a capacity pre-warning threshold of a full-scale cell provided in the prior art;

fig. 2 is a schematic flowchart of a method for evaluating network capacity according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a service flow and a statistical node according to an embodiment of the present invention;

FIG. 4 is a diagram illustrating mathematical meanings of correlation coefficients provided in accordance with an embodiment of the present invention;

fig. 5 is a flowchart illustrating a method for evaluating network capacity according to another embodiment of the present invention;

fig. 6 is a schematic diagram illustrating a 24-hour index variation trend of a cell according to an embodiment of the present invention;

fig. 7 is a schematic structural diagram of an evaluation processing apparatus for network capacity according to an embodiment of the present invention;

fig. 8 is a logic block diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.

Fig. 2 is a schematic flowchart illustrating a method for evaluating network capacity according to this embodiment, where the method includes:

s201, obtaining DPI (Deep Packet Inspection) signaling data, and determining a user perception index and a capacity performance index of the DPI signaling data according to a preset period.

Wherein, the DPI signaling data is signaling data representing the quality of the wireless network.

The user perception index comprises TCP (Transmission Control Protocol) wireless time delay or HTTP (Hypertext Transfer Protocol) downlink rate;

the capacity performance index includes an effective RRC (Radio Resource Control) connection number, an uplink PRB (Physical Resource Block) utilization rate, a Downlink PRB utilization rate, and a PDCCH (Physical Downlink Control Channel) CCE (Control Channel Elements) utilization rate.

Specifically, performance indexes such as network air interface rate are mainly suitable for performance analysis of a physical layer, are related to hardware configurations such as antennas, bandwidth and terminals, have large difference among different cells, and are not suitable for evaluating user service perception. The comparison and verification proves that the high-level protocol indexes such as TCP wireless time delay, HTTP downlink rate and the like are more suitable for the evaluation of user service perception.

The TCP wireless delay index is the delay between the second and third handshakes in the TCP link establishment process.

The HTTP downlink rate is a rate perceived by the user as HTTP service.

Fig. 3 shows a schematic diagram of a transmission flow of primary traffic and a calculation node of indexes, which can be counted through user plane S1-U port DPI signaling. The DPI signaling collection machine is arranged below the SGW equipment, and the downlink index statistical node is arranged below an S1-U interface, so that the DPI signaling collection machine is used for representing the quality of a wireless network. For example, a TCP wireless delay index statistical node is directly related to a wireless environment, when capacity, coverage and interference of a wireless side are deteriorated, TCP wireless delay is correspondingly increased, which causes increase of wireless side delay in a link establishment and data transmission process, decrease of a service rate, corresponding deterioration of user perception, a current network index value of TCP wireless delay is generally between 50 and 500ms, and fluctuation range is large.

The capacity performance index generally includes the number of users, the utilization rate of wireless resources, the number of connections, the uplink and downlink flow and the like. The comparison proves that: the user number (effective RRC connection number), the uplink PRB utilization rate, the downlink PRB utilization rate and the PDCCH CCE utilization rate index are combined with the user plane and control plane information, the assessment accuracy is higher, and the overall load condition of the cell can be represented better.

S202, calculating and determining a correlation coefficient between the user perception index and the capacity performance index.

Wherein the correlation coefficient is used for measuring the linear correlation degree between the two groups of data sets.

Specifically, for analyzing the relationship between the user perception index and the capacity performance index, a Pearson correlation coefficient is introduced for analysis, and the Pearson correlation coefficient is used for measuring the linear correlation degree between two groups of data sets.

S203, evaluating the network capacity of each cell according to the user perception index or the correlation coefficient, and determining the cell with limited capacity.

And the capacity limited cell needs to be a cell subjected to capacity expansion.

According to the embodiment, the user perception index and the capacity performance index are determined, and the correlation coefficient of the user perception index and the capacity performance index is calculated, so that the calculation complexity can be reduced, the influence degree of the network load on the user perception is quantized, the capacity-limited cell is rapidly and accurately determined, and the whole evaluation and problem discovery of the whole cell are facilitated.

Further, in addition to the above method embodiments, the capacity performance indicator is equal to the number of valid RRC connections × MAX (uplink PRB utilization rate, downlink PRB utilization rate, PDCCH CCE utilization rate).

Specifically, in order to facilitate calculation of the correlation coefficient, a capacity coefficient, that is, a capacity performance index may be defined, the capacity coefficient and the capacity performance index are unified, and a parameter of the capacity coefficient represents a current overall resource use condition of the cell, so that problem judgment is clearer.

The capacity performance index is determined through the capacity coefficient, so that the calculation of the correlation coefficient is facilitated, and the problem judgment is clearer.

Further, on the basis of the above method embodiment, the correlation coefficient ρ_X,YCalculated according to the following formula:

Specifically, the correlation coefficient is a dimensionless index in the range of-1.0 to 1.0, and represents a cosine value of an included angle between two n-dimensional random vectors, values of the cosine value are-1 to 1, and the closer to 1, the smaller the vector included angle is, the larger the positive correlation between the two vectors is, as shown in fig. 4. The following table 1 shows the correlation strength of the variables judged by the value range of the correlation coefficient:

TABLE 1 Pearson correlation coefficient value range and meanings

Value range	Means of
		0.8 to 1.0	Very strong correlation
0.6 to 0.8	Strong correlation
		0.4 to 0.6	Moderate degree of correlation
0.2 to 0.4	Weak correlation
		0.0 to 0.2	Very weak or no correlation
-0.0 to-0.2	Very weak or no correlation
		-0.2 to-0.4	Weak negative correlation
-0.4 to-0.6	Moderate degree of negative correlation
		-0.6 to-0.8	Strong negative correlation
-0.8 to-1.0	Extremely strong negative correlation

The correlation coefficient is conveniently calculated through the formula, and different correlation meanings are conveniently determined through different value ranges.

Further, on the basis of the above method embodiment, the preset period is 24 hours;

x is TCP wireless time delay or HTTP download rate per hour within 24 hours;

y is the hourly capacity performance index over 24 hours.

Specifically, the effective RRC connection number, that is, the current number of users in the cell, is multiplied by the maximum of the three utilization rates to obtain the capacity performance index, and the current network generally has the highest utilization rate of the downlink PRB. The radio resource usage change situation in one day of the cell can be known based on the capacity coefficient value of 24 hours.

The embodiment is used for solving the problem of how to accurately evaluate the capacity condition of the existing network by using a big data means. The capacity problem generally presents a time aggregation phenomenon, the perception index and the performance index of each cell in 24 hours all day are subjected to correlation calculation, the cell with the user perception index and the capacity performance index degraded at the same time is found, the capacity-limited cell is accurately positioned, and the influence degree of the capacity problem on user perception is evaluated through a correlation coefficient.

Further, on the basis of the above method embodiment, S103 specifically includes:

The preset time delay value is a preset threshold value used for judging TCP wireless time delay.

The preset rate value is a preset threshold value used for judging the HTTP downloading rate.

The first preset value is a preset threshold value used for judging the first correlation coefficient.

The second preset value is a preset threshold value used for judging the first correlation coefficient.

Specifically, wireless factors influencing user perception mainly include coverage, interference and capacity problems, the coverage and interference problems mainly are geographic aggregation phenomena and are not directly related to time, and only the capacity problems can obviously change in idle time and busy time of a day, so that cells with poor perception quality caused by capacity load factors can be accurately found through 24-hour correlation calculation.

Taking the TCP wireless time delay or HTTP download rate of 24 hours a day of a cell as an X array in a correlation coefficient formula, taking the capacity coefficient of 24 hours as a Y array, and taking N as 24, carrying out correlation calculation on the 24 arrays of numbers, and obtaining the correlation between perception and performance indexes. The TCP wireless time delay is positively correlated with the capacity coefficient, and the HTTP downloading rate is negatively correlated with the capacity coefficient. The overall calculation complexity is not high, and the rapid statistical evaluation of the whole number of cells can be realized through a program.

As can be seen from table 1, when the Pearson correlation coefficient is greater than 0.6, it represents that the two sets of data are strongly correlated, and it indicates that the perceptual index degradation is strongly correlated with the capacity problem in the capacity analysis. It is therefore considered that the capacity problem causes the index degradation when the TCP radio delay is greater than 0.6 or the HTTP download rate is less than-0.6. Through big data statistics, the TCP wireless time delay of a cell is more than 150ms, or the HTTP download rate is lower than 2Mbps, which obviously influences the service perception of a user. That is, when the TCP radio delay is greater than 150ms and the correlation coefficient is greater than 0.6, it can be determined that the cell is the capacity-limited cell.

Thus, the capacity-restricted cell is determined by the following two conditions:

condition 1: the TCP wireless time delay of the cell is more than 150ms (or the HTTP download rate is lower than 2 Mbps);

condition 2: the result of calculating the Pearson correlation coefficient by the TCP wireless delay index and the capacity coefficient index is more than 0.6 (or the result of calculating the Pearson correlation coefficient by the HTTP download rate index and the capacity coefficient index is less than-0.6) 24 hours a day.

As shown in fig. 5, when specifically determining a capacity-limited cell, the method specifically includes the following steps:

a1, collecting DPI signaling data, and performing service perception quality evaluation on a full cell based on indexes such as TCP wireless time delay of day granularity, HTTP downlink rate and the like;

and A2, judging the cell quality. And (4) the cells meeting the condition 1 have poor perception quality, and the next analysis is carried out. Judging the cell which does not meet the condition 1 as a cell with a non-capacity problem, and not processing the cell;

a3, extracting a complete signaling perception index and a complete capacity performance index of 24-hour granularity in one day for a perception quality difference cell, and performing Pearson correlation coefficient calculation;

a4, judging the relevance. The cell meeting the condition 2 is determined as a perception quality difference cell strongly related to the capacity problem, that is, a capacity limited cell, and needs to be preferentially expanded. And judging the cell which does not meet the condition 2 as a cell with no capacity problem and not processing the cell.

The embodiment is different from the traditional single mode of defining the early warning threshold, and based on the characteristics of each cell, the influence degree of the cell load on the user perception is quantified by using a scientific calculation method, so that the cell with limited capacity is accurately found.

Further, on the basis of the above method embodiment, the method for evaluating and processing network capacity further includes:

and preferentially expanding the capacity of the capacity-limited cell.

Specifically, the cell service perception condition can be judged through the TCP wireless time delay or the HTTP rate, the influence degree of wireless resource load on cell index degradation can be judged through the correlation coefficient, and the cell with severe and poor indexes and high correlation coefficient is selected for preferential expansion.

Taking a certain cell as an example, the correlation coefficient of the TCP wireless delay and the capacity is 0.75, and the index change trend in 24 hours is shown in fig. 6, it can be obviously found that the delay index value fluctuates with the capacity coefficient, the interval is 80 to 350ms, the delay exceeds 150ms in the busy hour at noon and in the busy hour at night, the user perception is obviously affected, and the cell belongs to a cell with limited capacity.

According to the embodiment, the user perception index and the capacity performance index are combined through calculation of the correlation coefficient, the influence degree of the network load on the user perception is quantized, the problem of limited capacity is accurately found, and the method and the device can be suitable for evaluation of various network element load conditions.

Fig. 7 is a schematic structural diagram illustrating an evaluation processing apparatus for network capacity according to this embodiment, where the apparatus includes: an index determining module 701, a correlation coefficient calculating module 702, and a restricted cell determining module 703, wherein:

the index determining module 701 is configured to acquire Deep Packet Inspection (DPI) signaling data, and determine a user perception index and a capacity performance index of the DPI signaling data according to a preset period;

the correlation coefficient calculation module 702 is configured to calculate and determine a correlation coefficient between the user perception index and the capacity performance index;

the restricted cell determining module 703 is configured to perform evaluation processing on the network capacity of each cell according to the user perception index or according to the correlation coefficient, and determine a capacity-restricted cell.

Specifically, the index determining module 701 obtains Deep Packet Inspection (DPI) signaling data, and determines a user perception index and a capacity performance index of the DPI signaling data according to a preset period; the correlation coefficient calculation module 702 calculates and determines a correlation coefficient between the user perception index and the capacity performance index; the restricted cell determining module 703 evaluates the network capacity of each cell according to the user perception index or according to the correlation coefficient, and determines a capacity-restricted cell.

Further, on the basis of the above device embodiment, the user perception indicator includes a transmission control protocol TCP wireless delay or a hypertext transfer protocol HTTP downlink rate;

Further, in addition to the above device embodiment, the capacity performance indicator is equal to the number of active RRC connections × MAX (uplink PRB utilization rate, downlink PRB utilization rate, PDCCH CCE utilization rate).

Further, on the basis of the above device embodiment, the correlation coefficient ρ_X,YCalculated according to the following formula:

Further, on the basis of the embodiment of the device, the preset period is 24 hours;

x is TCP wireless time delay or HTTP download rate per hour within 24 hours;

y is the hourly capacity performance index over 24 hours.

Further, on the basis of the above apparatus embodiment, the restricted cell determining module 703 is specifically configured to:

Further, on the basis of the above device embodiment, the device for evaluating and processing network capacity further includes:

The evaluation processing device for network capacity described in this embodiment may be used to execute the above method embodiments, and the principle and technical effect are similar, which are not described herein again.

Referring to fig. 8, the electronic device includes: a processor (processor)801, a memory (memory)802, and a bus 803;

wherein the content of the first and second substances,

the processor 801 and the memory 802 communicate with each other via the bus 803;

the processor 801 is configured to call program instructions in the memory 802 to perform the methods provided by the method embodiments described above.

The present embodiments disclose a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the methods provided by the above-described method embodiments.

The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the method embodiments described above.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

It should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An evaluation processing method for network capacity, comprising:

2. The method according to claim 1, wherein the user perception indicator includes TCP wireless latency or HTTP downlink rate;

3. The method according to claim 2, wherein the capacity performance indicator is an effective RRC connection number x MAX (uplink PRB utilization, downlink PRB utilization, PDCCH CCE utilization).

4. The method of claim 1, wherein the correlation coefficient p is an integer multiple of the correlation coefficient p_X,YCalculated according to the following formula:

5. The method according to claim 4, wherein the preset period is 24 hours;

x is TCP wireless time delay or HTTP download rate per hour within 24 hours;

y is the hourly capacity performance index over 24 hours.

6. The method for evaluating and processing network capacity according to any one of claims 2 to 5, wherein the determining a capacity-limited cell by evaluating and processing the network capacity of each cell according to the user perception index or according to the correlation coefficient specifically includes:

7. The method of claim 1, further comprising:

and preferentially expanding the capacity of the capacity-limited cell.

8. An evaluation processing apparatus for network capacity, comprising:

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for evaluating network capacity according to any one of claims 1 to 7 when executing the program.

10. A non-transitory computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the method for processing an assessment of network capacity according to any one of claims 1 to 7.