CN108810915B - WiFi hotspot flow control method and device and computer readable storage medium - Google Patents
WiFi hotspot flow control method and device and computer readable storage medium Download PDFInfo
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- CN108810915B CN108810915B CN201810534377.3A CN201810534377A CN108810915B CN 108810915 B CN108810915 B CN 108810915B CN 201810534377 A CN201810534377 A CN 201810534377A CN 108810915 B CN108810915 B CN 108810915B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
- H04W16/225—Traffic simulation tools or models for indoor or short range network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
- H04W16/20—Network planning tools for indoor coverage or short range network deployment
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/10—Flow control between communication endpoints
Abstract
The invention discloses a WiFi hotspot flow control method, a WiFi hotspot flow control device and a computer readable storage medium, wherein the method comprises the steps of obtaining flow use information of a target WiFi hotspot, establishing a power law distribution model according to the flow use information, and verifying the power law distribution model by adopting a preset information verification criterion; when the verification is passed, the WiFi hotspots are predicted by adopting a power law distribution model, the WiFi hotspots are controlled and managed according to the prediction result, and under the application scene of the dynamic change of the existing WiFi network, the network cannot be effectively planned and dynamically adjusted, so that the problem of poor user experience is caused.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a WiFi hotspot flow control method, apparatus, and computer-readable storage medium.
Background
With the popularization of mobile terminals, more and more places are equipped with WIreless-Fidelity (WiFi) devices, and the mobile terminals can access the WiFi devices for network access through legal passwords provided by the places, so that great convenience is provided for users to access the network. With the development of digital services, for WiFi devices provided in public environments, the number of people users connect the WiFi devices and the WiFi traffic used by the users are increasing explosively, and the contradiction that the WiFi bearer connection number and the WiFi bearer traffic are unbalanced in time and space is increasingly prominent. For example, an excessive number of connections to WiFi devices may result in unbalanced loading of the WiFi devices, poor network signaling, and unstable network usage.
Disclosure of Invention
The technical problem to be solved by the present invention is to provide a WiFi hotspot flow control method, a terminal and a computer-readable storage medium, aiming at the technical problem that in the application scenario of the dynamic change of the existing WiFi network, the network cannot be effectively planned and dynamically adjusted, resulting in poor user experience.
In order to solve the technical problem, the invention provides a WiFi hotspot flow control method, which comprises the following steps:
acquiring traffic use information of a target WiFi hotspot;
establishing a power law distribution model according to the traffic use information;
verifying the power law distribution model by adopting a preset information verification criterion;
and when the verification is passed, predicting the WiFi hotspot by adopting a power law distribution model, and controlling and managing the WiFi hotspot according to a prediction result.
Optionally, the traffic usage information includes at least one of a connection number of connecting the WiFi hotspot and a traffic usage value of the WiFi hotspot, a connection number of the WiFi hotspot and a traffic usage duration of the WiFi hotspot, a traffic usage duration of the WiFi hotspot and a traffic usage value of the WiFi hotspot.
Optionally, the establishing of the power law distribution model according to the traffic usage information includes at least one of the following models:
the method comprises the steps that the connection number of a WiFi hotspot is used as an independent variable of a power law distribution mathematical model, and a flow use value of the WiFi hotspot is used as a dependent variable to obtain a first power law distribution model;
using the flow use duration of the WiFi hotspot as an independent variable of the power law distribution mathematical model, and using the connection number of the WiFi hotspot as a dependent variable to obtain a second power law distribution model;
the flow use duration of the WiFi hotspot is used as an independent variable of the power law distribution mathematical model, and the flow use value of the WiFi hotspot is used as a dependent variable to obtain a third power law distribution model;
and taking the flow use value of the WiFi hotspot as an independent variable of the power law distribution mathematical model, and taking the connection number of the WiFi hotspot as a dependent variable to obtain a fourth power law distribution model.
Optionally, the preset information verification criterion includes an AIC information criterion; when the power law distribution model comprises a model, verifying the power law distribution model by adopting a preset information verification criterion comprises the following steps:
taking the power law distribution model as a likelihood function of the AIC information criterion, and taking the number of parameters of the power law distribution model as the number of parameters of the AIC information criterion to obtain an AIC value; and comparing the AIC value with a preset threshold value, and taking the power law distribution model as a target power law distribution model when the AIC value is smaller than the preset threshold value.
Optionally, the preset information verification criterion includes an AIC information criterion; when the power law distribution model comprises at least two models, the verification of the power law distribution model by adopting a preset information verification criterion comprises the following steps:
taking the distribution of at least two power law distribution models as a likelihood function of an AIC information criterion, taking the quantity distribution of parameters of the at least two power law distribution models as the quantity of the parameters of the AIC information criterion, and obtaining at least two AIC values;
comparing the at least two AIC values with a preset threshold value, and when the at least two AIC values are smaller than the preset threshold value, the verification result is passed;
selecting a power law distribution model with the minimum AIC value from at least two power law distribution models as a target power law distribution model;
or the like, or, alternatively,
and randomly selecting one power law distribution model from at least two power law distribution models as a target power law distribution model.
Optionally, when the target power-law distribution model is the first power-law distribution model or the third power-law distribution model, the prediction result is a predicted traffic usage value; when the target power law distribution model is a second power law distribution model or a fourth power law distribution model, the prediction result is a prediction connection number;
the control management of the WiFi hotspot according to the prediction result comprises the following steps:
and performing control management on at least one of the connection number and the traffic usage value of the WiFi hotspot.
Optionally, the controlling and managing the connection number of the WiFi hotspot includes:
when the number of the current terminals connected with the WiFi hotspot is larger than a first threshold value, acquiring the terminal priority of each terminal connected with the WiFi hotspot, and sequentially disconnecting the connected terminals from low to high according to the terminal priority until the number of the terminal connections is smaller than or equal to a second threshold value;
and/or the first and/or second light sources,
and controlling the number of terminal connections subsequently accessed to the WiFi hotspot to be less than or equal to a third threshold value according to the maximum bearing connection number of the WiFi hotspot and the terminal connection number currently connected with the WiFi hotspot.
Optionally, the controlling and managing the traffic usage value of the WiFi hotspot includes:
when the current flow use value of the terminal connected with the WiFi hotspot is larger than a fourth threshold value, acquiring the terminal priority of each terminal connected with the WiFi hotspot, and sequentially disconnecting the connected terminals from low to high according to the terminal priority until the flow use value of the terminal is smaller than or equal to a fifth threshold value;
and/or the first and/or second light sources,
and controlling the flow use value of the terminal subsequently accessed to the WiFi hotspot to be less than or equal to a sixth threshold value according to the maximum load flow value of the WiFi hotspot and the current flow use value of the terminal connected with the WiFi hotspot.
Furthermore, the invention also provides a device, which comprises a processor, a memory and a communication bus;
the communication bus is used for realizing connection communication between the processor and the memory;
the processor is configured to execute one or more programs stored in the memory to implement the steps of the WiFi hotspot flow control method as described above.
Further, the present invention also provides a computer readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps of the WiFi hotspot traffic control method as described above.
Advantageous effects
The invention provides a WiFi hotspot flow control method, a WiFi hotspot flow control device and a computer readable storage medium, aiming at the problem that the network cannot be effectively planned and dynamically adjusted in the application scene of the existing WiFi network dynamic change, so that the user experience is poor, a power law distribution model is established according to the flow use information by acquiring the flow use information of a target WiFi hotspot, the power law distribution model is verified by adopting a preset information verification criterion, when the verification is passed, the WiFi hotspot is predicted by adopting the power law distribution model, and the control management is carried out on the WiFi hotspot according to the prediction result. The method analyzes the flow use information of the WiFi hotspot, establishes the power law distribution model, verifies the established power law distribution model by adopting a preset information verification criterion, ensures the accuracy and the validity of the power law distribution model, predicts the WiFi hotspot by adopting the power law distribution model after the power law distribution model passes the verification, can know the use condition of the WiFi hotspot in advance according to a preset result, further makes a corresponding control management strategy, realizes the management control of the WiFi hotspot to the maximum extent, and effectively realizes the network planning and the dynamic adjustment according to the management control of the WiFi hotspot.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a basic flowchart of a WiFi hotspot traffic control method provided in a first embodiment of the present invention;
FIG. 2 is a basic flowchart of a method for verifying a power law distribution model by using an AIC information criterion according to a first embodiment of the present invention;
fig. 3 is a detailed flowchart of a WiFi hotspot flow control method provided by a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus according to a third embodiment of the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
The terminal may be implemented in various forms. For example, the terminal described in the present invention may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palmtop computer, a Personal Digital Assistant (PDA), a Portable Media Player (PMP), a navigation device, a wearable device, a smart band, a pedometer, and the like, and a fixed terminal such as a Digital TV, a desktop computer, and the like. It can be understood that the terminal described in the present invention includes a WiFi module, WiFi belongs to short distance wireless transmission technology, the terminal can help the user to send and receive e-mail, browse web page and access streaming media, etc. through the WiFi module, it provides wireless broadband internet access for the user.
First embodiment
In order to solve the problem of poor user experience caused by the fact that a network cannot be effectively planned and dynamically adjusted in the existing application scenario of dynamic change of a WiFi network, the present embodiment provides a WiFi hotspot traffic control method, as shown in fig. 1, fig. 1 is a basic flow chart of the WiFi hotspot traffic control method provided by the present embodiment, and the WiFi hotspot traffic control method includes:
s101: and acquiring the traffic use information of the target WiFi hotspot.
In this embodiment, the obtaining of the traffic usage information of the target WiFi hotspot may be acquiring data of traffic used by the user after connecting to the target WiFi hotspot by using a wireless detection technology, where the traffic usage information may be historical traffic usage information or current traffic usage information, and the traffic usage information includes at least two of a connection number of connecting to the WiFi hotspot, a traffic usage value of the WiFi hotspot, and a traffic usage duration of the WiFi hotspot. It is understood that the traffic usage information may be at least one of a number of connections to connect the WiFi hotspot and a traffic usage value of the WiFi hotspot, a number of connections to the WiFi hotspot and a traffic usage duration of the WiFi hotspot, a traffic usage duration of the WiFi hotspot and a traffic usage value of the WiFi hotspot. Of course, in some embodiments, the traffic usage information may also include three information, i.e., the number of connections to connect the WiFi hotspot, the traffic usage value of the WiFi hotspot, and the traffic usage duration of the WiFi hotspot. Certainly, the number of connections to the WiFi hotspot is specifically how many terminals are connected to the WiFi hotspot; the flow use value of the WiFi hotspot is specifically how many WiFi flow values are used by the terminal after the WiFi hotspot is connected; the traffic use duration of the WiFi hotspot specifically is how long the terminal has been using the WiFi hotspot.
S102: and establishing a power law distribution model according to the traffic use information.
In this embodiment, the data model of the power law distribution model is y ═ cx ^ -r, where x and y are positive random variables (x is an independent variable and y is a dependent variable), and c and r are constants greater than zero. The established power law distribution models are different according to different acquired traffic use information, and mainly comprise at least one of a first power law distribution model, a second power law distribution model, a third power law distribution model and a fourth power law distribution model. When the traffic usage information includes the number of connections to the WiFi hotspot and the traffic usage value of the WiFi hotspot, establishing a power law distribution model of the WiFi hotspot includes: the method comprises the steps that the connection number of a WiFi hotspot is used as an independent variable x of a power law distribution mathematical model, the traffic use value of the WiFi hotspot is used as a dependent variable y and is substituted into y ═ cx ^ -r to obtain the values of c1 and r1, and then the first power law distribution data model is obtained as y1 ═ c1x ^ -r 1; in some embodiments, the traffic usage value of the WiFi hotspot may be used as an independent variable x of the power-law distribution mathematical model, the number of connections connecting the WiFi hotspot is used as a dependent variable y, and the y ═ cx ^ -r is substituted into the dependent variable y to obtain values of c4 and r4, and then the fourth power-law distribution data model is obtained as y4 ═ c4x ^ -r 4. Similarly, when the traffic use information comprises the number of connections connecting the WiFi hotspot and the traffic use duration of the WiFi hotspot, taking the traffic use duration of the WiFi hotspot as a dependent variable x of the power law distribution mathematical model, taking the number of connections connecting the WiFi hotspot as an independent variable y, substituting the y into cx ^ -r to obtain values of c2 and r2, and further obtaining the second power law distribution data model as y2 into c2x ^ -r 2; when the flow use information comprises a flow use value of a WiFi hotspot and a flow use duration of the WiFi hotspot, the flow use duration of the WiFi hotspot is used as an independent variable x of the power law distribution mathematical model, the flow use value of the WiFi hotspot is used as a dependent variable y, and the y is substituted into the cx ^ -r to obtain values of c3 and r3, and further the third power law distribution model is obtained as y3 ═ c3 x-r 3.
In some embodiments, when the traffic usage information includes the number of connections to the WiFi hotspot, the traffic usage value of the WiFi hotspot, and the traffic usage duration of the WiFi hotspot, the number of connections to the WiFi hotspot and the traffic usage duration of the WiFi hotspot may be used as dependent variables, and the traffic usage value of the WiFi hotspot may be used as an independent variable; for example, a weight a is set for the number x1 of connections with the WiFi hotspot, a weight b is set for the traffic duration x2 of the WiFi hotspot, x1 a + x2 b is used as x of the power law distribution mathematical model, the traffic usage value of the WiFi hotspot is used as y, and is substituted into y ═ cx ^ -r to obtain c5 and r5, and the fifth power law distribution data model is obtained as y5 ═ c5(x1 a + x2 b) ^ -r 5. In other embodiments, when the traffic usage information includes the number of connections to the WiFi hotspot, the traffic usage value of the WiFi hotspot, and the traffic usage duration of the WiFi hotspot, the traffic usage value of the WiFi hotspot and the traffic usage duration of the WiFi hotspot may also be used as dependent variables, and the number of connections to the WiFi hotspot is used as an independent variable, and the specific establishment of the power law distribution model is consistent as in the above-mentioned method, which is not described herein any more.
S103: and verifying the power law distribution model by adopting a preset information verification criterion.
In the present embodiment, the preset information criterion may be an Akaike Information Criterion (AIC) where the AIC information criterion is denoted as AIC ═ 2k1-2ln (L1), k1 is the number of parameters, and L1 is a likelihood function. When the power-law distribution model includes only one model and the preset information criterion includes the AIC information criterion, the verification of the power-law distribution model using the preset information verification criterion is specifically shown in fig. 2, where fig. 2 is a verification method of the AIC information criterion provided by this embodiment for the power-law distribution model:
s201: taking the power law distribution model as a likelihood function of an AIC information criterion; and taking the number of the parameters of the power law distribution model as the number of the parameters of the AIC information criterion to obtain the AIC value.
S202: and comparing the AIC value with a preset threshold value, wherein when the AIC value is smaller than the preset threshold value, the verification result is passed, and the power law distribution model is used as a target power law distribution model.
S203: and if the AIC value is larger than the preset threshold value, continuously acquiring the traffic use information of the WiFi hotspot.
The size of the preset threshold value can be flexibly adjusted according to actual requirements. For ease of understanding, verification of the power law distribution model for the AIC information criterion is illustrated herein. When the power law distribution model is a third power law distribution model, and the third power law distribution model comprises y3 ═ c3x ^ -r3, the number of the parameters of the third power law distribution model is 2 (namely x and y), and when y3 ═ c3x ^ -r3 is taken as the likelihood function L, AIC3 is 2 ^ -2ln (c3x ^ -r 3); and comparing the AIC3 with a preset threshold, and taking the third power-law distribution model as a target power-law distribution model if the AIC3 is smaller than the threshold.
It should be noted that, when the power law distribution includes at least two models and the preset information verification criterion includes the AIC information criterion, verifying the power law distribution model includes: taking the distribution of at least two power law distribution models as a likelihood function of an AIC information criterion, taking the quantity distribution of parameters of the at least two power law distribution models as the quantity of the parameters of the AIC information criterion, and obtaining at least two AIC values; comparing the at least two AIC values with a preset threshold value, and when the at least two AIC values are smaller than the preset threshold value, the verification result is passed; selecting a power law distribution model with the minimum AIC value from at least two power law distribution models as a target power law distribution model; or randomly selecting one power law distribution model from at least two power law distribution models as a target power law distribution model. Assuming that when the power-law distribution model includes a first power-law distribution model and a second power-law distribution model, and the first power-law distribution model includes y1 ═ c1x ^ -r1, the number of parameters of the first power-law distribution model is 2 (i.e., x and y), and y1 ^ c1x ^ -r1 is taken as a likelihood function, AIC1 is 2 ^ -2ln (c1x ^ -r 1); when the second power-law distribution model comprises y2 ═ c2x ^ -r2, the number of parameters of the second power-law distribution model is 2 (namely x and y), and y2 ^ c2x ^ -r2 is taken as a likelihood function, AIC2 ^ 2 ^ -2ln (c2x ^ -r 2); comparing the AIC1 and the AIC2 with a preset threshold value, and determining whether the AIC1 and the AIC2 are smaller than the preset threshold value, for example, when both the AIC1 and the AIC2 are smaller than the preset threshold value, selecting one power law distribution model from a first power law distribution model and a second power law distribution model as a target power law distribution model, and assuming that an AIC1 value is smaller than a value of ACI2, selecting the first power law distribution model corresponding to the ACI1 as the target power law distribution model, that is, selecting the power law distribution model with the smallest AIC value from the power law distribution models which are verified as the target power law distribution model. Of course, in some embodiments, one power-law distribution model may also be randomly selected from the power-law distribution models that are verified as the target power-law distribution model, that is, any one of the first power-law distribution model and the second power-law distribution model may be randomly selected as the target power-law distribution model.
In some embodiments, the preset Information Criterion may also be a Bayesian Information Criterion (BIC) denoted as BIC ═ ln (k2) -2ln (L2), k2 being the number of parameters, L2 being the likelihood function. The verification of the power law distribution model by adopting the BIC information criterion comprises the following steps: taking the power law distribution model as a likelihood function of a BIC information criterion of the BIC information criterion; and when the BIC value is greater than the preset BIC threshold value, the verification result is passed. And when the power law distribution models which pass the verification are multiple, selecting the power law distribution model with the largest BIC value from the power law distribution models which pass the verification as a target power law distribution model, or randomly selecting one power law distribution model from the power law distribution models which pass the verification as the target power law distribution model.
S104: and when the verification is passed, predicting the WiFi hotspot by adopting a power law distribution model, and controlling and managing the WiFi hotspot according to a prediction result.
It can be understood that, according to the difference of the power law distribution model, the prediction result obtained by predicting the WiFi hotspot is also different. For example, when the target power-law distribution model is the first power-law distribution model or the third power-law distribution model, the prediction result is the measured use flow value, that is, the use flow size of the WiFi hotspot is preset; when the target power law distribution model is the second power law distribution model or the fourth power law distribution model, the prediction result is the predicted connection number, namely the connection quantity of the WiFi hotspot is predicted; the controlling and managing the WiFi hotspot according to the prediction result specifically includes controlling and managing at least one of a connection number and a traffic usage value of the WiFi hotspot, where the connection number and the traffic usage value may be a connection number and/or a traffic usage value of a terminal that is currently connected, or a connection number and/or a traffic usage value of a terminal that is not connected to the WiFi hotspot.
It should be noted that, the controlling and managing the number of connections of the WiFi hotspot may be to obtain a terminal priority of each terminal currently connected with the WiFi hotspot when the number of connections of the terminal currently connected with the WiFi hotspot is greater than a first threshold, and disconnect the connected terminals in sequence from high to low according to the terminal priority until the number of connections of the terminal is less than or equal to a second threshold; certainly, the priority of the terminal may be set according to the access duration of accessing the WiFi hotspot, for example, the priority of a long terminal is low when accessing, and the priority of a long terminal is high when accessing. The sizes of the first threshold and the second threshold can be flexibly adjusted according to the actual use condition. For example, a certain WiFi hotspot is predicted according to a second power law distribution model, the number of connections to be connected with the WiFi hotspot is predicted to be 60, the maximum number of connections of the WiFi hotspot is 100, the number of connections of the terminal currently connected with the WiFi hotspot is obtained to be 70, for maximum reasonable distribution, a second threshold value is set to be 50, when the number of connections of the terminal currently connected with the WiFi hotspot 70 is greater than the second threshold value, the terminal priorities of the 70 terminals are obtained, the terminal priorities are set according to access duration of each terminal, and the connected terminals are sequentially removed from low to high according to the terminal priorities until the number of connections accessed to the WiFi hotspot is less than or equal to a third threshold value 50. Of course, the control and management of the number of connections of the WiFi hotspot may also be performed by controlling the number of connections of the terminal subsequently accessed to the WiFi hotspot to be less than or equal to a third threshold according to the maximum number of bearer connections of the WiFi hotspot and the number of connections of the terminal currently connected to the WiFi hotspot. For example, a WiFi hotspot is predicted according to the second power law distribution model, the number of connections to be connected to the WiFi hotspot is predicted to be 80, the maximum number of connections carried by the WiFi hotspot is 120, the number of connections currently connected to the WiFi hotspot is 50, that is, the maximum number of connections that the WiFi hotspot can carry is 70, and the number of connections subsequently connected to the WiFi hotspot is controlled to be less than or equal to 70.
In this embodiment, the controlling and managing the traffic usage value of the WiFi hotspot may be to, when the traffic usage value of the terminal currently connected to the WiFi hotspot is greater than a fourth threshold, obtain terminal priorities of terminals currently connected to the WiFi hotspot, and disconnect the connected terminals in sequence from low to high according to the terminal priorities until the traffic usage value of the terminal is less than or equal to a fifth threshold; certainly, the priority of the terminal may be set according to the access duration of accessing the WiFi hotspot, for example, the priority of a long terminal is low when accessing, and the priority of a long terminal is high when accessing; it can be understood that the sizes of the fourth threshold and the fifth threshold can be flexibly adjusted according to actual requirements. For example, a WiFi hotspot is predicted according to a first power law distribution model, a traffic usage value of a terminal to be connected with the WiFi hotspot is predicted to be 10000M, a maximum bearer traffic usage value of the WiFi hotspot is 30000M, a traffic usage value of a terminal currently connected with the WiFi hotspot is obtained to be 25000M, for maximum reasonable distribution, a fourth threshold value is set to be 20000M, when the number of terminal connections 25000M currently connected with the WiFi hotspot is greater than the fourth threshold value 20000M, a terminal priority of each terminal currently connected with the WiFi hotspot is obtained, and connected terminals are disconnected in sequence from low to high according to the terminal priority until the traffic usage value of the terminal is less than or equal to a fifth threshold value 15000M.
Of course, the control and management of the connection number of the WiFi hotspot may also be to control the terminal traffic value of the subsequent access WiFi hotspot to be less than or equal to a sixth threshold according to the maximum bearer traffic value of the WiFi hotspot and the terminal traffic value currently connected with the WiFi hotspot. For example, a WiFi hotspot is predicted according to the first power law distribution model, the traffic value to be connected to the WiFi hotspot is predicted to be 22000M, the maximum bearer connection number of the WiFi hotspot is 30000M, the number of connections currently connected to the WiFi hotspot is 20000M, that is, the maximum traffic value that the WiFi hotspot can still bear is 10000M, and then the traffic value of the WiFi hotspot accessed subsequently is controlled to be 10000M. Of course, in other embodiments, after controlling the traffic value of the WiFi hotspot subsequently accessed to be equal to 10000M, the 10000M traffic value may be allocated according to the service type, for example, the upload traffic value is allocated to 4000M, the download traffic value is allocated to 6000M, or the traffic values of the network speed, the upload download speed, the transmission speed, and the like of each connection terminal are controlled not to exceed 100M. Flow distribution can also be performed on the flow value of each connection terminal according to the 10000M, for example, the 10000M is equally distributed to each connection terminal, or the flow value of each connection terminal is controlled not to exceed 200M, and the like.
The embodiment provides a WiFi hot spot flow control method, which includes the steps that at least one power law distribution model is established according to acquired flow use information, at least one power law distribution model is verified by adopting an AIC information criterion, an AIC value is compared with a preset threshold value, when the AIC value is smaller than the preset threshold value, the power law distribution model is verified to be passed, then one power law distribution model is selected as a target distribution model to predict WiFi hot spots, and when the prediction result is the predicted connection number, the connection number of the WiFi hot spots is controlled and managed; and when the prediction result is the predicted flow use value, controlling and managing the flow use value of the WiFi hotspot, knowing the use condition of the WiFi hotspot in advance according to the preset result, and further making a corresponding control management strategy, so that the management control of the WiFi hotspot is realized to the greatest extent, and the network planning and dynamic adjustment are effectively realized according to the management control of the WiFi hotspot.
Second embodiment
For better understanding of the present invention, this embodiment describes a WiFi hotspot flow control method with a specific example, as shown in fig. 3, fig. 3 is a detailed flow chart of a WiFi hotspot flow control method provided by a second embodiment of the present invention, where the WiFi hotspot flow control method includes:
s301: and acquiring the traffic use information of the target WiFi hotspot.
In this embodiment, the acquired traffic usage information of the WiFi hotspot includes a connection number of the WiFi hotspot, a traffic usage value of the WiFi hotspot, and a traffic usage duration of the WiFi hotspot.
S302: and establishing at least two power law distribution models according to the traffic use information.
Establishing a power law distribution model 1 by taking the connection number of the WiFi hotspots and the flow use duration of the WiFi hotspots as dependent variables and the flow use value of the WiFi hotspots as independent variables; setting a weight a for a connection number x1 connected with a WiFi hotspot, setting a weight b for a traffic use duration x2 of the WiFi hotspot, taking x1 a + x2 b as x of a power law distribution mathematical model, taking a traffic use value of the WiFi hotspot as y, substituting y into cx-r to obtain values of c and r, and further obtaining that the power law distribution model 1 is y1 ═ c (x1 a + x2 b) ^ -r.
Establishing a power law distribution model 2 by taking the flow use value of the WiFi hotspot and the flow use duration of the WiFi hotspot as dependent variables, taking the connection number of the WiFi hotspot as an independent variable, setting a weight c for the flow use value x3 of the WiFi hotspot, setting a weight b for the flow use duration x2 of the WiFi hotspot, taking x3 c + x2 b as x of the power law distribution mathematical model, taking the connection number of the WiFi hotspot as y, and substituting the y into the cx-r to obtain the values of c 'and r', so as to obtain that the power law distribution model 2 is y 2-c '(x 3 c + x2 b) from r'.
S303: and verifying at least two power law distribution models by adopting AIC information criterion.
In this embodiment, the AIC information criterion is expressed as AIC ═ 2k-2ln (L), k is the number of parameters, L is a likelihood function, the power law distribution model 1 (i.e., y1 ═ c (x1 ^ a + x2 ^ b) ^ r) is used as the likelihood function L of the AIC information criterion, the number of parameters 3 of the power law distribution model (i.e., x1, x2, y1) is used as k of the AIC information criterion, and then AIC1 of the power law distribution model is calculated. Similarly, the power law distribution model 2 (namely y2 ═ c '(x 3 ═ c + x2 ^ b) ^ -r') is used as the likelihood function L of the AIC information criterion, the number 3 of the parameters of the power law distribution model (namely x3, x2 and y2) is used as k of the AIC information criterion, and then the AIC2 of the power law distribution model is obtained through calculation; the AICs 1 and 2 were compared to preset thresholds.
S304: and judging whether at least two power law distribution models pass verification, if so, turning to S306, and if not, turning to S305.
When the AIC1 and the AIC2 are compared with a preset threshold value and are smaller than the preset threshold value, the two power law distribution models pass verification, and S306 is carried out; if the AIC1 or the AIC2 is smaller than the preset threshold, turning to S305; and if both the AIC1 and the AIC2 are larger than the preset threshold value, ending the process.
S305: and selecting the power law distribution model with the minimum AIC value from at least two power law distribution models as a target power law distribution model.
And when both the AIC1 and the AIC2 are smaller than a preset threshold value, comparing the AIC1 and the AIC2, and selecting the power law distribution model 1 corresponding to the AIC1 as a target power law distribution model when the AIC1 is smaller than the AIC 2.
S306: when only one power-law distribution model which passes the verification exists, the power-law distribution model is used as a target power-law distribution model.
If the AIC1 is larger than the preset threshold value and the AIC2 is smaller than the preset threshold value, the power law distribution model 2 corresponding to the AIC2 is selected as the target power law distribution model.
S307: and predicting the WiFi hot spot by adopting a target power law distribution model.
In this embodiment, the target power-law distribution model is a power-law distribution model 1, and the power-law distribution model 1 is adopted to predict traffic usage values of the WiFi hotspot, that is, how much traffic is used subsequently.
S308: and controlling and managing the WiFi hot spot according to the prediction result.
In order to reasonably distribute the flow value of the WiFi hotspot, a target WiFi hotspot is predicted according to a power law distribution model 1, the flow use value of a terminal to be connected with the WiFi hotspot is predicted to be 17000M, the maximum load-bearing flow use value of the WiFi hotspot is 30000M, the flow use value of the terminal currently connected with the WiFi hotspot is obtained to be 25000M, when the number of the terminal currently connected with the WiFi hotspot is larger than a fourth threshold value 20000M, the terminal priority of each terminal currently connected with the WiFi hotspot is obtained, the priority is determined from low to high according to the access duration of the terminal after accessing the WiFi hotspot, the connected terminals are disconnected according to the terminal priority from low to high in sequence until the flow use value of the terminal is equal to a fifth threshold value 15000M, at the moment, the flow use value of the terminal currently connected with the WiFi hotspot is 15000M, and the maximum load-bearing flow use value of the WiFi hotspot is 30000M, and controlling the traffic utilization value of the terminal subsequently accessed to the WiFi hotspot to be less than or equal to 15000M.
Certainly, after the current terminal traffic usage value connected with the WiFi hotspot and the subsequent terminal traffic usage value accessed to the WiFi hotspot are both controlled to be less than or equal to 15000M, a WiFi hotspot traffic value may be averagely allocated to each terminal, and then may be specifically allocated according to the number of terminal connections connecting the WiFi hotspot.
In this embodiment, for better understanding, a more specific example is used for explanation, two power law distribution models are established according to the obtained number of connections of WiFi hotspots, the traffic usage value of the WiFi hotspots, and the traffic usage duration of the WiFi hotspots, the two power law distribution models are verified by using the AIC information criterion, the AIC value is compared with a preset threshold, when the AIC values are both smaller than the preset threshold, the power law distribution model with the smallest AIC value is selected as a target distribution model to predict the WiFi hotspots, when the predicted result is the predicted traffic usage value, the traffic usage value of the WiFi hotspots is controlled and managed, the traffic value of the connected terminal is controlled to be smaller than or equal to a certain threshold, the traffic value of the terminal to be connected is controlled to be smaller than or equal to a certain threshold, dynamic distribution of the traffic value is realized, and reasonable traffic distribution in the greatest degree is achieved, and the method can meet the demand for traffic at the peak of people flow, the situation of poor signals is solved.
Third embodiment
The present embodiment further provides an apparatus, as shown in fig. 4, which includes a processor 401, a memory 402, and a communication bus 403, where:
the communication bus 403 is used for realizing connection communication between the processor 401 and the memory 402;
the processor 401 is configured to execute one or more programs stored in the memory 402 to implement the steps of the WiFi hotspot flow control method in the first and second embodiments described above.
In other embodiments, a control program may be further provided, where the control program may be configured to communicate with a router of a WiFi hotspot, and the control program may be configured on a terminal, or may be configured on a third-party device, such as a server, and implement the steps of the WiFi hotspot flow control method in the first embodiment and the WiFi hotspot flow control method in the second embodiment through the control program.
The present embodiment also provides a computer-readable storage medium, which stores one or more programs, where the one or more programs are executable by one or more processors to implement the steps of the WiFi hotspot flow control method in the first embodiment and/or the second embodiment.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A WiFi hotspot flow control method is characterized by comprising the following steps:
acquiring traffic use information of a target WiFi hotspot;
the traffic usage information includes: at least two of the connection number of the WiFi hotspots, the traffic use value of the WiFi hotspots and the traffic use duration of the WiFi hotspots;
establishing a corresponding power law distribution model according to the traffic use information;
verifying the power law distribution model by adopting a preset information verification criterion;
and when the verification is passed, predicting the WiFi hotspot by adopting the power law distribution model, and controlling and managing the WiFi hotspot according to a prediction result.
2. The WiFi hotspot traffic control method of claim 1, wherein the traffic usage information comprises at least one of a number of connections to the WiFi hotspot and a traffic usage value of the WiFi hotspot, a number of connections to the WiFi hotspot and a traffic usage duration of the WiFi hotspot, a traffic usage duration of the WiFi hotspot and a traffic usage value of the WiFi hotspot.
3. The WiFi hotspot flow control method of claim 2, wherein the establishing a power law distribution model according to the flow usage information comprises at least one of:
the connection number of the WiFi hotspot is used as an independent variable of a power law distribution mathematical model, and the flow use value of the WiFi hotspot is used as a dependent variable to obtain a first power law distribution model;
using the flow use duration of the WiFi hotspot as an independent variable of a power law distribution mathematical model, and using the connection number of the WiFi hotspot as a dependent variable to obtain a second power law distribution model;
taking the flow use duration of the WiFi hotspot as an independent variable of a power law distribution mathematical model, and taking the flow use value of the WiFi hotspot as a dependent variable to obtain a third power law distribution model;
and taking the flow use value of the WiFi hotspot as an independent variable of a power law distribution mathematical model, and taking the connection number of the WiFi hotspot as a dependent variable to obtain a fourth power law distribution model.
4. The WiFi hotspot traffic control method of claim 3, wherein the preset information verification criteria comprises AIC information criteria; when the power law distribution model comprises a model, the verifying the power law distribution model by adopting a preset information verification criterion comprises the following steps:
taking the power law distribution model as a likelihood function of the AIC information criterion, and taking the number of parameters of the power law distribution model as the number of parameters of the AIC information criterion to obtain an AIC value; and comparing the AIC value with a preset threshold value, and when the AIC value is smaller than the preset threshold value, taking the power law distribution model as a target power law distribution model.
5. The WiFi hotspot traffic control method of claim 3, wherein the preset information verification criteria comprises AIC information criteria; when the power law distribution model comprises at least two models, the verification of the power law distribution model by adopting a preset information verification criterion comprises the following steps:
taking the distribution of at least two power-law distribution models as a likelihood function of the AIC information criterion, taking the quantity distribution of parameters of the at least two power-law distribution models as the quantity of the parameters of the AIC information criterion, and obtaining at least two AIC values;
comparing the at least two AIC values with a preset threshold value, and when the at least two AIC values are both smaller than the preset threshold value, the verification result is passed;
selecting a power law distribution model with the minimum AIC value from the at least two power law distribution models as a target power law distribution model;
or the like, or, alternatively,
and randomly selecting one power law distribution model from the at least two power law distribution models as a target power law distribution model.
6. The WiFi hot spot flow control method of claim 4 or 5, wherein when a target power-law distribution model is a first power-law distribution model or a third power-law distribution model, the prediction result is a predicted flow usage value; when the target power-law distribution model is a second power-law distribution model or a fourth power-law distribution model, the prediction result is a prediction connection number;
the controlling and managing the WiFi hotspot according to the prediction result comprises the following steps:
and performing control management on at least one of the connection number and the traffic usage value of the WiFi hotspot.
7. The WiFi hotspot traffic control method of claim 6, wherein controlling and managing the number of connections of the WiFi hotspot comprises:
when the number of the current terminals connected with the WiFi hotspot is larger than a first threshold value, acquiring the terminal priority of each terminal currently connected with the WiFi hotspot, and sequentially disconnecting the connected terminals from low to high according to the terminal priority until the number of the terminal connections is smaller than or equal to a second threshold value;
and/or the first and/or second light sources,
and controlling the number of terminal connections subsequently accessed to the WiFi hotspot to be less than or equal to a third threshold value according to the maximum bearing connection number of the WiFi hotspot and the terminal connection number currently connected with the WiFi hotspot.
8. The WiFi hotspot traffic control method of claim 6, wherein controlling and managing the traffic usage value of the WiFi hotspot comprises:
when the current terminal flow usage value connected with the WiFi hotspot is larger than a fourth threshold value, acquiring the terminal priority of each terminal currently connected with the WiFi hotspot, and sequentially disconnecting the connected terminals from low to high according to the terminal priority until the terminal flow usage value is smaller than or equal to a fifth threshold value;
and/or the first and/or second light sources,
and controlling the flow use value of the terminal subsequently accessed to the WiFi hotspot to be smaller than or equal to a sixth threshold value according to the maximum load flow value of the WiFi hotspot and the current flow use value of the terminal connected with the WiFi hotspot.
9. A WiFi hotspot flow control device, the device comprising a processor, a memory, and a communication bus;
the communication bus is used for realizing connection communication between the processor and the memory;
the processor is configured to execute one or more programs stored in the memory to implement the steps of the WiFi hotspot flow control method of any of claims 1-8.
10. A computer readable storage medium, storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of the WiFi hotspot flow control method of any of claims 1-8.
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| US6885859B2 (en) * | 2002-09-12 | 2005-04-26 | Broadcom Corporation | Apparatus for controlling and monitoring a wireless hotspot through an interface with a cellular telephone network |
| CN102056180B (en) * | 2009-10-27 | 2013-12-18 | 华为技术有限公司 | Method and system for acquiring deployment scheme of wireless local area network (WLAN) access point (AP) |
| CN102333311B (en) * | 2011-10-27 | 2014-01-15 | 中国电信股份有限公司 | User access control method and system for wireless local area network |
| CN103024716B (en) * | 2012-11-27 | 2015-10-07 | 中兴通讯股份有限公司 | A kind of WiFi flow statistical method, device and system |
| CN104813702A (en) * | 2013-06-09 | 2015-07-29 | 华为技术有限公司 | Method for controlling wireless local area network flow and wireless local area network gateway |
| CN103856965A (en) * | 2014-03-24 | 2014-06-11 | 宁夏信友通信监理咨询有限责任公司 | WLAN (Wireless Local Area Network) comprehensive analysis system and method |
| KR102255958B1 (en) * | 2014-04-25 | 2021-05-25 | 삼성전자 주식회사 | Apparatus and method for controlling data traffic connections through wlan and cellular systems |
| CN107148032A (en) * | 2017-04-28 | 2017-09-08 | 维沃移动通信有限公司 | A kind of flow control methods and mobile terminal |
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