CN116828530A - Capacity expansion scheme determining method and device - Google Patents
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Abstract
The application discloses a capacity expansion scheme determining method and device, which are used for predicting a traffic suppression point of a cell in advance and determining a capacity expansion scheme according to the traffic suppression point, so that capacity expansion is performed on the traffic suppression point of the cell in advance, the traffic suppression of the cell is avoided, and the situation that the cell is jammed is avoided. The method comprises the following steps: acquiring first flow information of at least one first cell, wherein the first flow information comprises the change conditions of the number of users in the first cell and the use flow; fitting a first association relation between the number of users and the used flow according to the first flow information, wherein the first cell is a cell in which a flow suppression point appears; obtaining the similarity between each first cell and each second cell; determining a flow suppression point of each second cell according to the first association relation and the similarity, wherein the flow suppression point comprises a point in the second cell, wherein the point is increased in flow speed and reduced along with the increase of the number of users; and determining the capacity expansion scheme of at least one second cell according to the traffic suppression point.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a method and apparatus for determining a capacity expansion scheme.
Background
In a wireless network cell, with the increase of the number of network users and the increase of the user traffic DOU, the traffic carried by the wireless cell is gradually increased, and the cell network equipment may be difficult to meet the increasing user traffic demand, so that the cell needs to be expanded in time to carry more network cell users and network traffic, thereby avoiding network congestion, user experience reduction and user loss caused by being unable to carry more network cell users and network traffic.
Some common wireless network capacity expansion schemes mainly rely on real-time monitoring of wireless network equipment, and when detecting that a network cell index exceeds a preset threshold value, an operator judges that a wireless network cell is congested, and further starts capacity expansion. Therefore, how to expand the capacity of the cell in time becomes a problem to be solved.
Disclosure of Invention
The application provides a capacity expansion scheme determining method and device, which are used for predicting a flow suppression point of a cell in advance so as to predict and obtain the time length of the cell reaching the flow suppression point, and determining the capacity expansion scheme according to the time length, so that capacity expansion is performed based on the flow suppression point of the cell in advance, the situation that the cell is blocked is avoided, the situation that the cell is jammed is avoided, and the user experience is improved.
In a first aspect, the present application provides a method for determining a capacity expansion scheme, including: firstly, acquiring first flow information of at least one first cell, wherein the first flow information comprises information of the number of users in the at least one first cell and the change condition of the use flow; then fitting a first association relation between the number of users and the used flow according to the first flow information, wherein the first cell is a cell in which a flow suppression point appears; subsequently obtaining a similarity between each of the at least one first cell and each of the at least one second cell; then, according to the first association relation and the similarity, determining the flow suppression point of each second cell, wherein the flow suppression point comprises a point in the second cell, wherein the point is increased in flow speed and reduced along with the increase of the number of users; and determining a capacity expansion scheme of at least one second cell according to the traffic suppression point, wherein the capacity expansion scheme comprises a scheme for expanding the bandwidth of the second cell.
Therefore, in the embodiment of the application, the association relationship between the number of users of the first cell generating the traffic suppression point and the generated traffic and the similarity between cells can be utilized to fit the association relationship between the number of users of the second cell generating no traffic suppression point and the traffic, the traffic suppression point of the second cell is predicted based on the association relationship, and the expansion scheme is determined based on the traffic suppression point, so that the traffic suppression point of the second cell can be predicted more accurately in advance, the capacity expansion is performed before the network traffic suppression occurs, the expansion period is covered, and the loss caused by the traffic suppression is avoided.
In a possible implementation manner, the foregoing capacity expansion scheme for determining at least one second cell according to the traffic suppression point may include: acquiring the time length of each second cell from the current reaching of the traffic suppression point; and determining the capacity expansion scheme of at least one second cell according to the duration.
Therefore, in the embodiment of the application, after the duration that each second cell reaches the flow suppression point from the current time is predicted, the capacity expansion scheme of each second cell can be determined according to the duration, so that capacity expansion is performed on the flow suppression point possibly generated by each second cell in advance, and the situation that each second cell is congested is avoided.
In one possible implementation manner, the number of the second cells may be plural, and the foregoing capacity expansion scheme for determining at least one second cell according to the duration may include: scoring the plurality of second cells according to the time length to obtain a value score of each second cell, wherein the value score is used for representing the priority of capacity expansion of each second cell; and obtaining a capacity expansion scheme according to the value score of each second cell.
In the embodiment of the application, when a plurality of second cells exist, the capacity expansion priorities of the second cells can be ordered according to the time length from each second cell to the predicted flow rate depression point, and the capacity expansion priority of each second cell is represented by the value score, so that the capacity expansion of the cell which is more required to be expanded can be performed in time, and congestion is avoided.
In a possible implementation manner, the foregoing obtaining a duration of each second cell from the current reaching of the traffic suppression point may include: and acquiring the duration of each second cell from the current reaching of the traffic suppression point through a preset time sequence prediction model.
Therefore, in the embodiment of the application, the time sequence prediction model can be used for accurately predicting the time length of each second cell reaching the flow suppression point from the current state, so that each cell can be expanded in time later, and the congestion of the cell is avoided.
In a possible implementation manner, the acquiring the first traffic information of the at least one first cell may include: acquiring initial flow information of at least one first cell; and clustering the initial flow information to obtain first flow information.
Therefore, in the embodiment of the application, the obtained initial traffic information of each first cell can be clustered, and the screening of the abnormal values is realized in a clustering mode, so that the obtained first traffic information can reflect the relation between the users of each first cell and the use traffic.
In one possible embodiment, the acquiring the similarity between each of the at least one first cell and each of the at least one second cell includes: acquiring first cell information of each first cell, wherein the first cell information comprises at least one of first cell characteristic information, first switching data, first cell RRC average user quantity or cell flow; acquiring second cell information of each second cell, wherein the second cell information comprises at least one of second cell characteristic information, second switching data, second cell RRC average user quantity or cell flow; and calculating the similarity according to the first cell information of each first cell and the second cell information of each second cell.
Therefore, in the embodiment of the application, the similarity between each first cell and each second cell can be calculated according to the characteristics of multiple dimensions of each first cell and each second cell, so that the accurate similarity is obtained.
In a second aspect, the present application provides a capacity expansion scheme determining apparatus, including:
the system comprises an acquisition module, a first traffic information acquisition module and a second traffic acquisition module, wherein the acquisition module is used for acquiring first traffic information of at least one first cell, and the first traffic information comprises information of the number of users in the at least one first cell and the change condition of the use traffic;
the fitting module is used for fitting a first association relation between the number of users and the use flow according to the first flow information;
a similarity calculation module, configured to obtain a similarity between each first cell in the at least one first cell and each second cell in the at least one second cell;
the prediction module is used for determining a flow suppression point of each second cell according to the first association relation and the similarity, wherein the flow suppression point comprises a point in the second cell, wherein the point is increased in flow speed and reduced along with the increase of the number of users;
and the capacity expansion module is used for determining the capacity expansion scheme of at least one second cell according to the flow rate depression point, wherein the capacity expansion scheme comprises a scheme for expanding the bandwidth of the second cell.
In one possible implementation, the capacity expansion module is specifically configured to: acquiring the time length of each second cell from the current reaching of the traffic suppression point; and determining the capacity expansion scheme of at least one second cell according to the duration.
In one possible implementation manner, the number of the second cells is a plurality of, and the capacity expansion module is specifically configured to: scoring the plurality of second cells according to the time length to obtain a value score of each second cell, wherein the value score is used for representing the priority of capacity expansion of each second cell; and obtaining a capacity expansion scheme according to the value score of each second cell.
In one possible implementation manner, the capacity expansion module is specifically configured to obtain, through a preset time sequence prediction model, a duration of time from when each second cell reaches the traffic suppression point.
In one possible implementation manner, the acquiring module is specifically configured to: acquiring initial flow information of at least one first cell; and clustering the initial flow information to obtain first flow information.
In one possible implementation manner, the similarity calculation module is specifically configured to: acquiring first cell information of each first cell, wherein the first cell information comprises at least one of first cell characteristic information, first switching data, first cell RRC average user quantity or cell flow; acquiring second cell information of each second cell, wherein the second cell information comprises at least one of second cell characteristic information, second switching data, second cell RRC average user quantity or cell flow; and calculating the similarity according to the first cell information of each first cell and the second cell information of each second cell.
In a third aspect, an embodiment of the present application provides a capacity expansion scheme determining apparatus, including: the processor and the memory are interconnected through a line, and the processor invokes the program code in the memory to perform the processing-related function in the capacity expansion scheme determining method according to any one of the first aspect. Alternatively, the expansion scheme determining means may be a chip.
In a fourth aspect, an embodiment of the present application provides a capacity expansion scheme determining device, which may also be referred to as a digital processing chip or a chip, where the chip includes a processing unit and a communication interface, where the processing unit obtains program instructions through the communication interface, where the program instructions are executed by the processing unit, where the processing unit is configured to perform a function related to processing as in the first aspect or any optional implementation manner of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect or any of the alternative embodiments of the first aspect.
In a sixth aspect, embodiments of the present application provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect or any of the alternative embodiments of the first aspect.
Drawings
FIG. 1 is a schematic diagram of a network framework to which the present application is applied;
fig. 2 is a schematic flow chart of a method for determining a capacity expansion scheme according to the present application;
fig. 3 is a schematic flow chart of another capacity expansion scheme determining method provided by the application;
FIG. 4 is a schematic diagram of a flow variation trend provided by the present application;
FIG. 5 is a schematic view of another flow variation trend provided by the present application;
fig. 6 is a schematic diagram of an association relationship between the number of users and the traffic provided by the present application;
fig. 7 is a schematic diagram of another association relationship between the number of users and the traffic provided by the present application;
FIG. 8 is a schematic diagram of another association relationship between the number of users and the traffic provided by the present application;
fig. 9 is a schematic structural diagram of a capacity expansion scheme determining device provided by the application;
fig. 10 is a schematic structural diagram of another capacity expansion scheme determining device provided by the application.
Detailed Description
The following description of the technical solutions according to the embodiments of the present application will be given with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The capacity expansion scheme determination method provided by the application can be applied to various communication systems, such as a wireless network, a wired network or a combination of the wireless network and the wired network. The wireless network includes, but is not limited to: a fifth Generation mobile communication technology (5 th-Generation, 5G) system, a Long term evolution (Long term evolution, LTE) system, a global system for mobile communication (global system for mobile communication, GSM) or code division multiple access (code division multiple access, CDMA) network, a wideband code division multiple access (wideband code division multiple access, WCDMA) network, the internet of things, wireless fidelity (wireless fidelity, wiFi), bluetooth (blue), zigbee (Zigbee), radio frequency identification technology (radio frequency identification, RFID), long Range (Lora) wireless communication, near field wireless communication (near field communication, NFC). The wired network may include a network of fiber optic communications or coaxial cables, etc.
The present application is exemplified by a wireless network. In general, in a wireless network architecture, access devices may be deployed in multiple areas, a user may access a wireless network by connecting the access devices, and the range covered by each access device may be referred to as a cell.
The access device may include a base station (eNodeB, eNB) in long term evolution (long term evolution, LTE), a base station (gndeb, gNB) in New Radio, NR), and so on. For example, from a product aspect, the base station is a device with a central control function, such as a macro base station, a micro base station, a hot spot (pico), a home base station (femeta), a Transmission Point (TP), a Relay (Relay), an Access Point (AP), and the like.
The user referred to by the present application may be a terminal, or may be a carrier device of a user account, etc. For example, the user may include an account logged in to the device, or may refer to a device that carries the account, etc. For example, the user equipment may be a terminal device, such as a mobile phone, a computer, a bracelet, a smart watch, a data card, a sensor, a Station (STA), etc.
When there are multiple access devices, one of the access devices may be used as a management node, or a separate management node may be provided, where the management node is used to manage the multiple access devices, such as allocating a frequency band, controlling on or off, and so on. It will be appreciated that the method provided by the application may be performed by the management node to enable management of the cells covered by the respective access devices.
The network architecture to which the present application is applied may be illustrated in fig. 1, where a plurality of access devices are included, and a coverage area of one or more access devices may be regarded as a cell, or one or more access devices may be set in a cell. In a wireless network cell, as the number of network users increases and the user traffic (dataflow of usage, DOU) increases, the traffic carried by the wireless cell increases, and it may be difficult for a cell network device to meet the increasing user traffic demand, so that the cell needs to be expanded in time to carry more network cell users and network traffic, thereby avoiding network congestion, user experience reduction and user loss caused by being unable to carry more network cell users and network traffic.
In some common capacity expansion modes, when a cell is busy, when the carrier busy rate is greater than a threshold (such as 60%), the throughput of the cell is greater than the threshold (such as 3GB or 0.7GB upstream), and the 'RRC average connection number' is greater than the threshold (such as 50), the capacity expansion is performed by increasing the carrier frequency; when the cell is busy, the carrier frequency is increased when the 'RRC maximum connection number' is larger than the threshold (such as 200). According to the capacity expansion method of the capacity expansion threshold, the capacity expansion of the network equipment can be conveniently carried out, but due to the fact that the actual network condition is complex, the single threshold judgment method cannot take the difference of the carrying capacity of the network equipment caused by factors such as cell users, service differences and the like into consideration; in addition, the capacity expansion method judged by the threshold value performs early warning when the corresponding network index of the cell exceeds the threshold value, the capacity expansion period for carrying out capacity expansion is longer at the moment, cell congestion can occur in the capacity expansion period, and the user experience is reduced and the clients are lost.
In other capacity expansion modes, a machine learning model for judging the state of the network equipment can be trained by acquiring network performance data of the network equipment, and the state of the network equipment is determined by the judging model and the network equipment performance data acquired in real time, so that whether capacity expansion processing is performed on the target network equipment is determined. However, the method for judging the state of the network equipment based on the machine learning model can judge and analyze the state of the network equipment in real time, and has real-time performance, but the method cannot discover whether the network equipment needs to be expanded in advance or not, and cannot know the time required for reaching the state of the network equipment to be expanded.
Therefore, the application provides a capacity expansion scheme determining method, which predicts the flow suppression points of cells through the flow change condition of the cells with flow suppression and the similarity among the cells, so as to determine the capacity expansion scheme according to the predicted suppression points, thereby expanding the capacity before the network flow suppression occurs, covering the capacity expansion period and avoiding the loss caused by the flow suppression.
Referring to fig. 2, a flow chart of a capacity expansion scheme determining manner provided by the present application is as follows.
201. First traffic information of at least one first cell is acquired.
In the present application, the cells may be divided into a plurality of types, and in order to facilitate the distinction between a first cell, i.e., a cell acquired to generate traffic with a depression, and a second cell, i.e., a cell not generating traffic depression. Traffic throttling, i.e. the increase in the number of users in a cell, is reduced, and it can be understood that as the number of users increases, the increase in the number of users is reduced, i.e. the network bandwidth may not meet the traffic demand of the users, and congestion may be caused.
The number of the first cells may be one or more, and the number of the second cells may be one or more.
The first traffic information includes information of the number of users in at least one first cell and the change condition of the usage traffic in a period of time, for example, the number of users in a plurality of first cells and the change condition of the usage traffic in one month can be collected in an hour unit.
In one possible implementation, step 201 may specifically include: and acquiring initial flow information of one or more first cells, namely, the value of the number of users in a period of time and the value of the corresponding generated flow, screening the acquired initial flow information, and deleting abnormal values in the initial flow information, such as a value far away from a curve.
Optionally, clustering the initial flow information in a clustering mode, such as an unsupervised clustering method of DBSCAN, hierarchical clustering and the like, and removing clustered outliers; and the data is thinned, so that the influence of uneven data distribution on the fitting result is reduced.
202. And fitting a first association relation between the number of users and the used flow according to the first flow information.
Wherein, after obtaining the first traffic information of at least one first cell, the association relationship between the number of users and the usage traffic is fitted based on the first traffic information, which is called a first association relationship for convenience of distinction.
It can be understood that the information on the number of users and the usage traffic in the cell where the traffic suppression has occurred is obtained to fit the correlation between the number of users and the usage traffic.
203. And obtaining the similarity between at least one first cell and at least one second cell.
Wherein the degree of similarity between each first cell and each second cell, i.e. the degree of similarity between each cell that has generated traffic suppression and each cell that has not generated traffic suppression, can be calculated.
In particular, the similarity may be calculated including, but not limited to, at least one dimension of cell characteristics, handover data, cell RRC average number of users, or cell traffic. The cell characteristics may include information such as the number of devices in the cell, the number of single boards of the devices, the system of the devices, the bandwidth supported by the devices, and the like, and the handover data may include data generated by the user switching the cell, such as switching time, switched cell information, and the like, where the handover data may be used to characterize the degree of association between the cells, and the average number of users in the cell RRC, that is, the number of users in the cell that establish RRC connection, and the cell traffic, that is, the traffic generated by the devices in the cell, and the like.
If the first cell information of each first cell can be acquired, the first cell information includes at least one of first cell feature information, first handover data, a first cell RRC average user number or cell traffic, and second cell information of a second cell is acquired, the second cell information includes at least one of second cell feature information, second handover data, a second cell RRC average user number or cell traffic; the similarity between each second cell and the at least one first cell is then calculated based on the first cell information of each first cell and the second cell information of each second cell. Therefore, in the embodiment of the application, the similarity between the cells can be measured from each dimension, so that more accurate similarity is obtained.
Alternatively, in calculating the similarity, the similarity may be calculated by an algorithm such as cosine similarity, minkowski distance, or the like. As can be expressed as:
similarity＝cal_sim(S.P,S.H,S.U,S.Q)
s is a cell set, wherein the cell set comprises a first cell and a second cell, P is cell characteristic information, H is handover data, U is the average number of users in a cell RRC, and Q is cell flow.
It should be noted that, the execution sequence of the step 201 and the step 203 is not limited in the present application, the step 201 may be executed first, the step 203 may be executed first, the step 201 and the step 203 may be executed simultaneously, and the present application is not limited in this respect, specifically, the present application may be adjusted according to the actual application scenario.
204. And determining the traffic suppression point of each second cell according to the first association relation and the similarity.
The change condition of the number of users and the usage flow rate of the second cells in the past period of time can be collected, so as to be convenient for distinguishing second flow rate information, and then the second association relation of each second cell, namely the relation between the number of users and the usage flow rate in the second cells, is fitted based on the second flow rate information, the first association relation and the similarity.
Specifically, it may be understood that, based on the similarity between the first cell and the second cell, the first association relationship is adjusted, and the second association relationship is obtained by combining the generated number of users and the usage flow rate of the second cell, so that the second association relationship is matched with the actual number of users and the usage flow rate of the second cell, and the relationship between the number of users and the usage flow rate in the second cell can be reflected.
After the second association relationship is obtained, the traffic suppression point of the second cell, that is, the point of the second association relationship where the traffic speed increases and decreases with the increase of the number of users, can be predicted based on the second association relationship.
It will be appreciated that as the number of users increases, the flow rate increases, which would otherwise be the case, due to the bandwidth limitations of the device leading to a decrease in flow rate increase, there may be situations where the user's flow demand cannot be met.
As can be expressed as: calculating the traffic suppression point of the traffic non-suppressed cell according to the known traffic suppression point of the traffic suppressed cell:
A new ＝f(similarity,A)
i.e. the similarity and A are used as the input of the association relation algorithm, and the flow suppression point is output.
Therefore, the traffic suppression point of the second cell can be identified based on the similarity between cells and the trend of the change of the cell in which the traffic suppression point has been generated, so that the traffic suppression point to be generated by the cell can be predicted,
205. and determining the capacity expansion scheme of at least one second cell according to the traffic suppression point.
After the traffic suppression point is determined, a capacity expansion scheme of the second cell, that is, a scheme for expanding the bandwidth of the second cell, can be determined based on the information of the traffic suppression point.
The specific capacity expansion scheme may include information such as a time point of expansion of the second cell, expansion priority, an amount of expanded frequency band, and an increased number of access devices. For example, when a second cell exists today, the latest capacity expansion time point, capacity expansion frequency band, or increased access device of the second cell can be determined. When a plurality of second cells exist, the priorities of the plurality of second cells can be ordered, so that the capacity expansion schemes of the plurality of second cells, such as the latest capacity expansion time point, the capacity expansion priority, the capacity expansion mode and the like of each cell, are determined.
Therefore, in the embodiment of the application, the association relationship between the number of users of the first cell generating the traffic suppression point and the generated traffic and the similarity between cells can be utilized to fit the association relationship between the number of users of the second cell not generating the traffic suppression point and the traffic, the traffic suppression point of the second cell is predicted based on the association relationship, and the expansion scheme is determined based on the traffic suppression point, so that the traffic suppression point of the second cell can be predicted more accurately in advance, expansion is performed before network traffic suppression occurs, the expansion period is covered, and loss caused by traffic suppression is avoided.
In one possible implementation manner, a duration of time from the current reaching of the traffic suppression point by the second cell may be determined, and then a capacity expansion scheme of the second cell, such as information of a latest capacity expansion time, a size of a capacity expansion band, an increased number of devices, a capacity expansion priority, and the like, is determined according to the duration of time. Therefore, in the embodiment of the application, the capacity expansion scheme can be determined according to the time length of the second cell reaching the traffic suppression point, and the traffic suppression point of the second cell can be dealt with in advance.
In particular, when there are a plurality of second cells, a value score for each second cell may be determined according to a length of time for which each second cell reaches a traffic suppression point, and the value score may be used to represent a capacity expansion priority of each second cell. If the number of users is generally larger, the duration is shorter, or the number of users is increased, the value score of the cells is generally higher, i.e. the priority can be set to be higher, i.e. the capacity of the second cells can be expanded according to the value score. Therefore, in the embodiment of the application, when a plurality of second cells exist, the plurality of cells can be evaluated according to the time period that the plurality of second cells reach the traffic suppression point respectively, and the value score of each second cell can be obtained and used for representing the capacity expansion priority, so that the capacity expansion scheme of the plurality of second cells can be determined according to the value score of each second cell, and the capacity expansion can be respectively carried out.
Optionally, when determining the duration of time for each second cell to reach the traffic suppression point, the duration of time for each second cell to reach the strength suppression point may be calculated according to a preset time sequence prediction model, so that the duration of time for each second cell to reach the traffic suppression point from the current time may be predicted very accurately.
The foregoing describes the flow of the method provided by the present application, and for convenience of understanding, the flow of the method provided by the present application is further described below in conjunction with a more specific application scenario.
Referring to fig. 3, a flow chart of another capacity expansion scheme determining method provided by the application is shown.
First, the first cell is a cell in which a traffic suppression point has been generated, the second cell is a cell in which a traffic suppression point has not been generated, the number of the first cells may be one or more, and the number of the second cells may be one or more.
The first traffic information and the first cell information may be collected from the related data of the first cell and the second cell information may be collected from the related data of the second cell.
First flow information: the number of users and traffic usage in the first cell, which may include a history period, may be collected in units of a certain unit time, such as the number of users and the resulting traffic per hour in 30 days of the history.
First cell information: the information related to the first cell, such as the number of devices in the first cell, the type of the device board, the network system corresponding to the devices, the geographical location of the cell, and the like, may be included.
The second cell information is similar to the first cell, and may include information such as the number of devices in the second cell, the type of the device board, the network system corresponding to the devices, and the geographic location of the cell.
After the data is acquired, the following steps can be performed based on the acquired data:
301. a relationship between the number of users in the first cell and the traffic is fitted.
After the first traffic information is obtained, a relationship between the number of users in the first cell and the generated traffic, i.e. a first association relationship, may be fitted based on the first traffic information.
For example, the number of users in the first cell and the traffic data may be collected in units of hours, and then a relationship between the number of users and the traffic at the level of hours may be fitted, so as to determine a traffic suppression point of the first cell. In general, the trend of no traffic suppression point can be as shown in fig. 4, that is, the number of users and the traffic are in a linear relationship. And when there is a traffic depression point, i.e. the traffic acceleration decreases as the number of users increases, as shown in figure 5,
For example, cell C can be acquired, and RRC average user number U of cell C t Cell downlink flow Q t . In order to reduce the influence of abnormal data on the result, the data is firstly filtered to remove abnormal values. Using U with outliers removed t Q and t and performing curve fitting to obtain a flow suppression point A of the suppression cell. For example, an unsupervised clustering method such as Density-based clustering method (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) with noise, hierarchical clustering and the like can be adopted, and outliers after clustering are removed; and the data is thinned, so that the influence of uneven data distribution on the fitting result is reduced.
The relationship between the number of users and the traffic can then be fitted. If a piecewise fit can be performed, this is expressed as:
f 1 (x)＝k 1 *x+y 0 -k 1 *x 0
where x represents the number of users, f represents the flow generated, and f is a coefficient, typically a preset value, or can be adjusted according to an actual algorithm.
After the relationship between the number of users and the traffic in the first cells is obtained by fitting, the traffic suppression point of each first cell can be identified according to the relationship, for example, several association relationships may be shown in fig. 6, fig. 7 and fig. 8, where a point where the traffic increases and begins to decrease as the number of users increases is taken as the traffic suppression point, which indicates that subsequent network resources from the traffic suppression point may not meet the traffic demand of the users, resulting in network congestion, a part of users may not access the network or use the network resources, and so on.
302. And calculating the similarity between the first cell and the second cell.
One or more first cells and one or more second cells may be considered as a set of cells, with similarities between cells within the set of cells.
Specifically, for the cell set S, the cell similarity is calculated by acquiring data including cell characteristic information P, handover data H, average number of users U of the cell RRC, and cell traffic Q.
For example, cosine similarity may be used to calculate the similarity, expressed as:
c 1 ,c 2 ,c 3 ,……,c n calculating the inter-cell similarity W, W of each cell in the cell set S as the feature vector formed by the feature information of n cells in the cell set S ij Is the similarity of cells i, j.
For another example, the minkowski distance may also be used to calculate the similarity, as expressed as:
wherein D is ij For the minkowski distance of cell i, j, D will be ij Normalizing and further obtaining the similarity of the cells i and j:
303. and predicting a flow depression point.
After the first association relationship and the similarity between the cells are obtained, the traffic suppression point of the second cell can be predicted based on the first association relationship.
For example, by inter-cell similarity W, and the resulting traffic suppression point (U A ,Q A ) Obtaining the traffic suppression point of the current non-suppressed cellSimilarity-based computation methods such as collaborative filtering may be employed, as represented by:
wherein the method comprises the steps ofThe number of the traffic suppression point users for m current non-suppressed cells is +.>The number of the traffic suppression point users for the ith current non-suppressed cell is +.>Traffic suppression point traffic values for m current non-suppressed cells, +.>And the traffic suppression point traffic value of the i-th current non-suppressed cell.
Also, for example, the inter-cell similarity W, and the cell traffic suppression point (U A ,Q A ) Obtaining the traffic suppression point of the current non-suppressed cellIncluding but not limited to similarity-based computation methods such as tag propagation, as expressed by:
wherein the method comprises the steps ofThe output result of predicting the number of users of the cell aggregate flow suppression point is that mu is the absorptivity and 0<μ<And 1, n is the total cell number of the cell set.
Wherein the method comprises the steps ofMu is absorptivity and 0 for output cell aggregate traffic suppression point traffic prediction result<μ<And 1, n is the total cell number of the cell set.
304. And calculating the time length for reaching the flow depression point.
Wherein, a traffic suppression point A can be predicted, wherein A is RRC average user number-downlink traffic binary group (U A ,Q A ). Then, the average user number of the cell RRC and the downlink flow can be predicted by utilizing time sequence prediction to obtain the predicted value U of the average user number of the cell RRC and the downlink flow in future time t ,Q t 。
Predicting the result U of predicting the average number of RRC users and the downlink flow of a future time cell according to the time sequence t ,Q t Calculating cell arrival traffic pressureSuppression pointThe time required is:
wherein the method comprises the steps ofThe average number of RRC users (i.e., the number of users who have established RRC connections) and downlink traffic corresponding to the traffic suppression point of a single cell.
305. And determining a capacity expansion scheme of the second cell.
When a plurality of second cells exist, the capacity expansion values of the plurality of second cells can be scored, and the ranking is carried out according to the scoring result, which is equivalent to marking the capacity expansion priority of each cell through the value scoring, so that a final capacity expansion scheme is obtained.
According to the time T of the cell reaching the suppression state A and the information of the number U of the cell, the flow Q, other environmental characteristics K and the like, sequencing the capacity expansion priority of the cell, and giving out final cell capacity expansion recommendation:
wherein: t (T) now Omega as the current time 1 、ω 2 、ω 3 、ω 4 The weight coefficients corresponding to the time dimension, the user dimension, the flow dimension and the cell environment characteristic respectively can be preset values or can be values adjusted according to actual application scenes.
In general, the shorter the time that the cell reaches the traffic suppression point, the higher the value score, and the longer the time that the cell reaches the traffic suppression point, the lower the value score; the more users in a cell, the higher the value score, the fewer the users in a cell, the lower the value score, the more traffic is used in a cell, the higher the value score, the less traffic is used in a cell, the lower the value score, and so on.
For example, the calculated cell scores may be as shown in table 1:
TABLE 1
In general, the higher the score of a cell, the higher the capacity expansion priority for the cell. Therefore, when a plurality of cells exist, the cells with higher capacity expansion requirements can be expanded preferentially according to the value scores of the cells, so that the available traffic of the cells can meet the user requirements, traffic suppression points are avoided, and the user experience is improved.
The embodiment of the application provides a network agility capacity expansion method based on the suppression point prediction, which can be used for not only the traffic suppression point prediction at the cell level but also the agility capacity expansion at the site level, the cell cluster level, the regional level, the city level and the like, and implements the network agility capacity expansion at different levels. And the cell capacity expansion recommended time table is given by predicting the occurrence time of the traffic suppression point, so that capacity expansion is carried out before network traffic suppression occurs, the capacity expansion period is covered, and the loss caused by traffic suppression is avoided. The method is beneficial to operators to grasp the capacity expansion rhythm, smooth investment and avoid risks and losses caused by peak investment.
The foregoing describes a method flow provided by the present application, and the following describes an apparatus for performing the method flow. Referring to fig. 9, a schematic structural diagram of a capacity expansion scheme determining device provided by the present application may include:
An obtaining module 901, configured to obtain first traffic information of at least one first cell, where the first traffic information includes information of a number of users in the at least one first cell and a change situation of a usage traffic;
a fitting module 902, configured to fit a first association relationship between the number of users and the usage traffic according to the first traffic information;
a similarity calculation module 903, configured to obtain a similarity between each of the at least one first cell and each of the at least one second cell;
the prediction module 904 is configured to determine, according to the first association relationship and the similarity, a traffic suppression point of each second cell, where the traffic suppression point includes a point in the second cell where the traffic speed increases and decreases with the increase of the number of users;
the capacity expansion module 905 is configured to determine a capacity expansion scheme of at least one second cell according to the traffic suppression point, where the capacity expansion scheme includes a scheme for expanding a bandwidth of the second cell.
In one possible implementation, the capacity expansion module 905 is specifically configured to: acquiring the time length of each second cell from the current reaching of the traffic suppression point; and determining the capacity expansion scheme of at least one second cell according to the duration.
In one possible implementation, the number of the second cells is a plurality, and the capacity expansion module 905 is specifically configured to: scoring the plurality of second cells according to the time length to obtain a value score of each second cell, wherein the value score is used for representing the priority of capacity expansion of each second cell; and obtaining a capacity expansion scheme according to the value score of each second cell.
In a possible implementation manner, the capacity expansion module 905 is specifically configured to obtain, through a preset time sequence prediction model, a duration of time for each second cell to reach the traffic suppression point from the current time.
In one possible implementation manner, the obtaining module 901 is specifically configured to: acquiring initial flow information of at least one first cell; and clustering the initial flow information to obtain first flow information.
In one possible implementation, the similarity calculation module 903 is specifically configured to: acquiring first cell information of each first cell, wherein the first cell information comprises at least one of first cell characteristic information, first switching data, first cell RRC average user quantity or cell flow; acquiring second cell information of each second cell, wherein the second cell information comprises at least one of second cell characteristic information, second switching data, second cell RRC average user quantity or cell flow; and calculating the similarity according to the first cell information of each first cell and the second cell information of each second cell.
Referring to fig. 10, a schematic structure diagram of another capacity expansion scheme determining device provided by the present application is as follows.
The expansion scheme determining device may comprise a processor 1001, a memory 1002 and a transceiver 1003. The processor 1001 and the memory 1002 are interconnected by a line. Wherein program instructions and data are stored in memory 1002.
The memory 1002 stores program instructions and data corresponding to the steps in fig. 2 to 8.
The processor 1001 is configured to perform the method steps performed by the capacity expansion scheme determining device according to any of the embodiments shown in fig. 2 to 8.
A transceiver 1003 for performing the steps of receiving or transmitting data performed by the capacity expansion scheme determining device according to any of the embodiments shown in fig. 2 to 8. Wherein the transceiver 1003 is an optional module.
There is also provided in an embodiment of the present application a computer-readable storage medium having stored therein a program for generating a vehicle running speed, which when run on a computer causes the computer to perform the steps of the method described in the embodiments shown in the foregoing fig. 2-8.
Alternatively, the capacity expansion scheme determining device shown in fig. 10 described above is a chip.
The embodiment of the application also provides a capacity expansion scheme determining device, which can also be called as a digital processing chip or a chip, wherein the chip comprises a processing unit and a communication interface, the processing unit obtains program instructions through the communication interface, the program instructions are executed by the processing unit, and the processing unit is used for executing the method steps executed by the capacity expansion scheme determining device shown in any one of the embodiments of the foregoing fig. 2-8.
The embodiment of the application also provides a digital processing chip. The digital processing chip has integrated therein circuitry and one or more interfaces for implementing the above-described processor 1001, or the functions of the processor 1001. When the memory is integrated into the digital processing chip, the digital processing chip may perform the method steps of any one or more of the preceding embodiments. When the digital processing chip is not integrated with the memory, the digital processing chip can be connected with the external memory through the communication interface. The digital processing chip realizes the actions executed by the capacity expansion scheme determining device in the embodiment according to the program codes stored in the external memory.
Embodiments of the present application also provide a computer program product which, when run on a computer, causes the computer to perform the steps performed by the expansion scheme determining device in the method described in the embodiments of fig. 2-8 described above.
The capacity expansion scheme determining device provided by the embodiment of the application can be a chip, and the chip comprises: a processing unit, which may be, for example, a processor, and a communication unit, which may be, for example, an input/output interface, pins or circuitry, etc. The processing unit may execute the computer-executable instructions stored in the storage unit, so that the chip in the server performs the device search method described in the embodiment shown in fig. 2 to 8. Optionally, the storage unit is a storage unit in the chip, such as a register, a cache, etc., and the storage unit may also be a storage unit in the wireless access device side located outside the chip, such as a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a random access memory (random access memory, RAM), etc.
In particular, the aforementioned processing unit or processor may be a central processing unit (central processing unit, CPU), a Network Processor (NPU), a graphics processor (graphics processing unit, GPU), a digital signal processor (digital signal processor, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC) or field programmable gate array (field programmable gate array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The general purpose processor may be a microprocessor or may be any conventional processor or the like.
The processor referred to in any of the foregoing may be a general purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the programs of the methods of fig. 2-8 described above.
It should be further noted that the above-described apparatus embodiments are merely illustrative, and that the units described as separate units may or may not be physically separate, and that units shown as units may or may not be physical units, may be located in one place, or may be distributed over 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 this embodiment. In addition, in the drawings of the embodiment of the device provided by the application, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines.
From the above description of the embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general purpose hardware, or of course by means of special purpose hardware including application specific integrated circuits, special purpose CPUs, special purpose memories, special purpose components, etc. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions can be varied, such as analog circuits, digital circuits, or dedicated circuits. However, a software program implementation is a preferred embodiment for many more of the cases of the present application. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a readable storage medium, such as a floppy disk, a U-disk, a removable hard disk, a Read Only Memory (ROM), a random access memory (random access memory, RAM), a magnetic disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method according to the embodiments of the present application.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing is merely illustrative embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present application, and the application should be covered. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (16)
1. A method for determining a capacity expansion scheme, comprising:
acquiring first flow information of at least one first cell, wherein the first flow information comprises information of the number of users in the at least one first cell and the change condition of the use flow;
fitting a first association relation between the number of users and the use flow according to the first flow information;
acquiring the similarity between each first cell in the at least one first cell and each second cell in the at least one second cell;
determining a traffic suppression point of each second cell according to the first association relation and the similarity, wherein the traffic suppression point comprises a point of increasing and decreasing traffic along with the increase of the number of users in the second cell;
and determining a capacity expansion scheme of the at least one second cell according to the traffic suppression point, wherein the capacity expansion scheme comprises a scheme for expanding the bandwidth of the second cell.
2. The method of claim 1, wherein the determining the capacity expansion scheme of the at least one second cell from the traffic suppression point comprises:
acquiring the time length of each second cell from the current reaching of the traffic suppression point;
And determining the capacity expansion scheme of the at least one second cell according to the duration.
3. The method of claim 2, wherein the number of second cells is a plurality, and the determining the capacity expansion scheme of the at least one second cell according to the duration comprises:
scoring the plurality of second cells according to the duration to obtain a value score of each second cell, wherein the value score is used for representing the priority of capacity expansion of each second cell;
and obtaining the capacity expansion scheme according to the value score of each second cell.
4. A method according to claim 2 or 3, wherein said obtaining a time period from when said each second cell has reached said traffic suppression point comprises:
and acquiring the time length of each second cell from the current reaching of the flow suppression point through a preset time sequence prediction model.
5. The method according to any of claims 1-4, wherein the obtaining first traffic information of at least one first cell comprises:
acquiring initial flow information of the at least one first cell;
and clustering the initial flow information to obtain the first flow information.
6. The method according to any of claims 1-5, wherein the obtaining a similarity between each of the at least one first cell and each of the at least one second cell comprises:
acquiring first cell information of each first cell, wherein the first cell information comprises at least one of first cell characteristic information, first switching data, first cell RRC average user quantity or cell traffic;
acquiring second cell information of each second cell, wherein the second cell information comprises at least one of second cell characteristic information, second switching data, second cell RRC average user quantity or cell flow;
and calculating the similarity according to the first cell information of each first cell and the second cell information of each second cell.
7. A capacity expansion scheme determining apparatus, comprising:
the system comprises an acquisition module, a first traffic information acquisition module and a second traffic acquisition module, wherein the acquisition module is used for acquiring first traffic information of at least one first cell, and the first traffic information comprises information of the number of users in the at least one first cell and the change condition of the use traffic;
the fitting module is used for fitting a first association relation between the number of users and the use flow according to the first flow information;
A similarity calculation module, configured to obtain a similarity between each first cell of the at least one first cell and each second cell of the at least one second cell;
the prediction module is used for determining a flow suppression point of each second cell according to the first association relation and the similarity, wherein the flow suppression point comprises a point in the second cell, wherein the point is increased along with the increase of the number of users, and the flow speed is increased and reduced;
and the capacity expansion module is used for determining a capacity expansion scheme of the at least one second cell according to the traffic suppression point, wherein the capacity expansion scheme comprises a scheme for expanding the bandwidth of the second cell.
8. The apparatus of claim 7, wherein the capacity expansion module is specifically configured to:
acquiring the time length of each second cell from the current reaching of the traffic suppression point;
and determining the capacity expansion scheme of the at least one second cell according to the duration.
9. The apparatus of claim 8, wherein the number of second cells is plural, and the capacity expansion module is specifically configured to:
scoring the plurality of second cells according to the duration to obtain a value score of each second cell, wherein the value score is used for representing the priority of capacity expansion of each second cell;
And obtaining the capacity expansion scheme according to the value score of each second cell.
10. The device according to claim 8 or 9, wherein,
the capacity expansion module is specifically configured to obtain, through a preset time sequence prediction model, a duration of time from when each second cell reaches the traffic suppression point.
11. The apparatus according to any one of claims 7-10, wherein the acquisition module is specifically configured to:
acquiring initial flow information of the at least one first cell;
and clustering the initial flow information to obtain the first flow information.
12. The apparatus according to any one of claims 7-11, wherein the similarity calculation module is specifically configured to:
acquiring first cell information of each first cell, wherein the first cell information comprises at least one of first cell characteristic information, first switching data, first cell RRC average user quantity or cell traffic;
acquiring second cell information of each second cell, wherein the second cell information comprises at least one of second cell characteristic information, second switching data, second cell RRC average user quantity or cell flow;
And calculating the similarity according to the first cell information of each first cell and the second cell information of each second cell.
13. A capacity expansion scheme determining device, comprising a processor coupled to a memory, the memory storing a program, the memory storing program instructions that when executed by the processor implement the method of any of claims 1 to 6.
14. A computer readable storage medium comprising a program which, when executed by a processing unit, performs the method of any of claims 1 to 6.
15. A capacity expansion scheme determining device, characterized by comprising a processing unit and a communication interface, the processing unit obtaining program instructions via the communication interface, the program instructions, when executed by the processing unit, implementing the method of any of claims 1 to 6.
16. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.
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