CN116599908A - Internet private line speed regulation method and device, electronic equipment and medium - Google Patents
Internet private line speed regulation method and device, electronic equipment and medium Download PDFInfo
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Abstract
The application provides an internet private line speed regulation method, an internet private line speed regulation device, electronic equipment and a medium. The method comprises the following steps: acquiring line information of a current internet private line, wherein the line information comprises time information, bandwidth utilization rate and network quality parameters at the current moment; inputting the line information of the internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a model obtained by training in advance; and carrying out special line speed regulation on the internet special line according to the bandwidth utilization rate of the internet special line at the next moment and the bandwidth utilization rate threshold of the internet special line at the next moment. The method solves the problem that the bandwidth resources cannot be fully utilized in the traditional internet private line service.
Description
Technical Field
The present application relates to communications technologies, and in particular, to a method and apparatus for internet private line speed regulation, an electronic device, and a medium.
Background
In recent years, with the continuous appearance of novel technologies such as 5G, the traditional home-wide service tends to be saturated, the service carried by the traditional metropolitan area network has new changes, the internet private line service is gradually raised, and new requirements are also put forward. The internet private line service refers to a service of accessing a client network and equipment to the internet through a special link to provide real-time on-line internet service with various rates for clients.
The traditional internet private line provides a private line with fixed bandwidth for users, but the quality of the line is often deteriorated in the period of busy service, and the user experience is affected; the line is idle on holidays or at specific time, increasing the cost of ordering the dedicated line. Therefore, the conventional internet private line service has the problem that bandwidth resources cannot be fully utilized.
Disclosure of Invention
The application provides an internet private line speed regulation method, an internet private line speed regulation device, electronic equipment and a medium, which are used for solving the problem that the bandwidth resource cannot be fully utilized in the traditional internet private line service.
In one aspect, the application provides an internet private line speed regulation method, which comprises the following steps:
acquiring line information of a current internet private line, wherein the line information comprises time information, bandwidth utilization rate and network quality parameters at the current moment;
inputting the line information of the current internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a model obtained by training in advance;
and carrying out special line speed regulation on the internet special line according to the bandwidth utilization rate of the internet special line at the next moment and the bandwidth utilization rate threshold of the internet special line at the next moment.
In one possible implementation, the method further includes:
collecting line information according to a preset period in preset data collection time as characteristic parameters of model training, wherein the characteristic parameters comprise time information, bandwidth utilization rate and network quality parameters;
performing single-heat coding on the time information in the characteristic parameters, and splicing the time information with the corresponding characteristic parameters to obtain a model training vector;
establishing an initial model, wherein the initial model is a time sequence-based cyclic neural network model and comprises a feature extraction layer and a full connection layer;
and inputting the model training vector into the initial model, and carrying out model training on the initial model by using a loss function of a mean square error to obtain the prediction model.
In one possible implementation manner, the collecting the line information according to the predetermined period includes:
receiving line information reported by a metropolitan area network controller;
for a plurality of the line information received in each of the predetermined periods, line information in the predetermined period is obtained by averaging.
In one possible implementation manner, the inputting the line information of the current internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model includes:
Performing single-heat coding on time information in the line information of the current internet private line, and splicing the time information with the bandwidth utilization rate and the network quality parameter at the current moment to obtain a model input vector;
inputting the model input vector into a feature extraction layer of the prediction model to obtain an output feature vector corresponding to the current moment;
and inputting the feature vector into a full-connection layer of the prediction model to carry out regression prediction, so as to obtain the bandwidth utilization rate of the next moment of output.
In one possible implementation manner, the performing dedicated line speed adjustment on the internet dedicated line according to the bandwidth utilization of the internet dedicated line at the next moment and the bandwidth utilization threshold of the internet dedicated line at the next moment includes:
if the bandwidth utilization rate of the internet private line at the next moment is higher than the threshold upper limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth speed-up;
if the bandwidth utilization rate of the internet private line at the next moment is lower than the threshold lower limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth deceleration;
and if the bandwidth utilization rate of the internet private line at the next moment is between the upper limit and the lower limit of the bandwidth utilization rate threshold of the internet private line at the next moment, maintaining the current bandwidth.
On the other hand, the application provides an internet private line speed regulating device, which comprises: the system comprises an acquisition module, a network quality parameter acquisition module and a network quality parameter acquisition module, wherein the acquisition module is used for acquiring line information of a current internet private line, and the line information comprises time information, bandwidth utilization rate and the network quality parameter at the current moment;
the prediction module is used for inputting the line information of the current internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a model obtained by training in advance;
and the speed regulation module is used for carrying out special line speed regulation on the internet special line according to the bandwidth utilization rate of the internet special line at the next moment and the bandwidth utilization rate threshold of the internet special line at the next moment.
In one possible implementation, the apparatus further includes:
the training module is used for collecting line information according to a preset period in preset data collection time and taking the line information as a characteristic parameter of model training, wherein the characteristic parameter comprises time information, bandwidth utilization rate and network quality parameters;
performing single-heat coding on the time information in the characteristic parameters, and splicing the time information with the corresponding characteristic parameters to obtain a model training vector;
Establishing an initial model, wherein the initial model is a time sequence-based cyclic neural network model and comprises a feature extraction layer and a full connection layer;
and inputting the model training vector into the initial model, and carrying out model training on the initial model by using a loss function of a mean square error to obtain the prediction model.
In one possible implementation manner, the training module is specifically configured to:
receiving line information reported by a metropolitan area network controller;
for a plurality of the line information received in each of the predetermined periods, line information in the predetermined period is obtained by averaging.
In one possible implementation manner, the speed regulation module is specifically configured to:
performing single-heat coding on time information in the line information of the current internet private line, and splicing the time information with the bandwidth utilization rate and the network quality parameter at the current moment to obtain a model input vector;
inputting the model input vector into a feature extraction layer of the prediction model to obtain an output feature vector corresponding to the current moment;
and inputting the feature vector into a full-connection layer of the prediction model to carry out regression prediction, so as to obtain the bandwidth utilization rate of the next moment of output.
In one possible implementation manner, the speed regulation module is specifically configured to:
if the bandwidth utilization rate of the internet private line at the next moment is higher than the threshold upper limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth speed-up;
if the bandwidth utilization rate of the internet private line at the next moment is lower than the threshold lower limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth deceleration;
and if the bandwidth utilization rate of the internet private line at the next moment is between the upper limit and the lower limit of the bandwidth utilization rate threshold of the internet private line at the next moment, maintaining the current bandwidth.
In yet another aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes computer-executable instructions stored in the memory to implement the method as described above.
In yet another aspect, the application provides a computer-readable storage medium having stored therein computer-executable instructions for performing the method as described above when executed by a processor.
According to the internet private line speed regulation method, the device, the electronic equipment and the medium, the bandwidth utilization rate of the user internet private line at the next moment is predicted by using the regression prediction model based on the cyclic neural network, and the internet private line is subjected to private line speed regulation according to the predicted bandwidth utilization rate of the internet private line at the next moment and the corresponding bandwidth utilization rate threshold value, so that the requirements of user business on speed rising in busy hours and speed reducing in idle hours are met. According to the scheme provided by the application, the AI model is used for predicting the flow of the user line and carrying out intelligent speed regulation of the internet private line, so that dynamic speed regulation according to the service requirement of the client side is realized, the line quality deterioration in the busy service period is avoided, the line is idle in the idle service period, the bandwidth resource is fully utilized, and the user experience is effectively improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic flow chart illustrating an internet private line speed regulation method according to a first embodiment of the present application;
FIG. 2 is a schematic flow chart of obtaining a prediction model according to a first embodiment of the present application;
FIG. 3 is a diagram schematically showing an exemplary structure of a prediction model according to a first embodiment of the present application;
fig. 4 is a schematic flow chart illustrating speed regulation by a user using an internet private line according to a first embodiment of the present application;
fig. 5 is a schematic structural diagram of an internet private line speed regulation device according to a second embodiment of the present application;
fig. 6 is a schematic structural diagram of an internet dedicated line speed regulation electronic device according to a third embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
The modules in the present application refer to functional modules or logic modules. It may be in the form of software, the functions of which are implemented by the execution of program code by a processor; or may be in hardware. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
In recent years, with the continuous appearance of novel technologies such as 5G, the traditional home-wide service tends to be saturated, the service carried by the traditional metropolitan area network has new changes, the internet private line service is gradually raised, and new requirements are also put forward. The internet private line service refers to a service of accessing a client network and equipment to the internet through a special link to provide real-time on-line internet service with various rates for clients.
The traditional internet private line provides a private line with fixed bandwidth for users, but the quality of the line is often deteriorated in the period of busy service, and the user experience is affected; the line is idle on holidays or at specific time, increasing the cost of ordering the dedicated line.
Therefore, the special line speed regulation can better meet the demands of clients according to the traffic use condition of the clients, and with the development of artificial intelligence technology, the prediction of user behavior based on AI is increasingly integrated into the daily life of people. For enterprise users of the internet private line, the traffic use condition of the users in a specific time period often has a certain regularity, and the possibility is provided for training a traffic prediction model of the users.
The technical scheme of the application is illustrated in the following specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Example 1
Fig. 1 is a flow chart of an internet private line speed regulation method according to an embodiment of the application. As shown in fig. 1, the internet private line speed regulation method provided in this embodiment may include:
s101, acquiring line information of a current internet private line, wherein the line information comprises time information, bandwidth utilization rate and network quality parameters at the current moment;
s102, inputting line information of the internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a model obtained by training in advance;
s103, carrying out special line speed regulation on the internet special line according to the bandwidth utilization rate of the internet special line at the next moment and the bandwidth utilization rate threshold of the internet special line at the next moment.
In practical application, the execution main body of the embodiment may be an internet dedicated line speed regulation device, and the device may be implemented by a computer program, for example, application software and the like; alternatively, the computer program may be implemented as a medium storing a related computer program, for example, a usb disk, a cloud disk, or the like; still alternatively, it may be implemented by a physical device, e.g., a chip, a server, etc., integrated with or installed with the relevant computer program.
Through carrying out investigation to internet private line user, can find that the line of user internet private line is busy and the line is idle often has certain time law, for example dining enterprise 11 every day: 00-14:00, 17:00-20: the 00 lines are busy, the bandwidth utilization rate is high, the user network quality is poor, the rest time lines are idle, and the user network quality is good. Therefore, the business busy and idle of a specific enterprise is regular, so that the rule can be learned through a deep learning model, and the flow prediction of the line is realized. In view of this, in this embodiment, the AI model is introduced to predict the traffic of the subscriber line, so as to determine the busy hour and the idle hour of the subscriber line, and then intelligent speed regulation is performed according to the correlation result.
Specifically, the internet private line speed regulating device obtains line information of the current internet private line, wherein the line information includes time information, bandwidth utilization rate and network quality parameters at the current moment, for example, the line information may include time, week, bandwidth utilization rate and quality of service (Quality of Service, qoS) (may include time delay, jitter, packet loss, etc.), and may be selected according to practical application conditions, which is not limited herein. Inputting the obtained line information of the current internet private line into a prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a time sequence-based recurrent neural network regression prediction model and is trained in advance.
For example, assuming that a restaurant enterprise orders an internet special line speed regulation product proposed by the scheme, the internet special line speed regulation device automatically collects line information of the internet special line of the enterprise, and inputs the line information at the current moment into a trained prediction model to obtain predicted line information at the next moment. For example, the current line information is about 10 points on Saturday, the bandwidth utilization rate is about 70%, the prediction model is input, the line information at the next time is about 11 points on Saturday, the bandwidth utilization rate is 100%, and the preset upper limit of the bandwidth utilization rate threshold is 75%, which indicates that the line will enter a busy state at the next time, and special line speed-up is performed.
In practical applications, there may be various ways to obtain the prediction model, and fig. 2 is a schematic flow chart of obtaining the prediction model according to an embodiment of the present application, and in one example, the method further includes:
s201, collecting line information according to a preset period in preset data collection time as characteristic parameters of model training, wherein the characteristic parameters comprise time information, bandwidth utilization rate and network quality parameters;
s202, performing single-heat coding on time information in the characteristic parameters, and splicing the time information with the corresponding characteristic parameters to obtain a model training vector;
S203, an initial model is established, wherein the initial model is a time sequence-based cyclic neural network model and comprises a feature extraction layer and a full connection layer;
s204, inputting the model training vector into the initial model, and performing model training on the initial model by using a loss function of a mean square error to obtain the prediction model.
Specifically, the scheme uses a time-series-based recurrent neural network model as an initial model, wherein the initial model comprises a feature extraction layer for feature extraction and a fully connected layer for regression prediction. The data acquisition time and the period are preset by a user, and the internet special line speed regulating device acquires line information according to the preset period in the preset data acquisition time so as to acquire the historical line information of the user as a characteristic parameter of model training. The characteristic parameters include, but are not limited to, time information (time, week), bandwidth utilization, network quality parameters QoS (delay, jitter, packet loss), and can be selected according to the needs of practical application. Because the time information (time and week) is discrete value and the data volume is moderate, unique coding is carried out on the time information, the value of the discrete feature is expanded to European space, and the feature analysis can be better carried out. And splicing the processed time information with other characteristic parameters to obtain a model training vector for learning a user flow using rule by the model. The model training vector is input into an initial model, and the initial model is trained by using a loss function of mean square error to obtain a prediction model in order to improve the accuracy of regression prediction.
In one example, the inputting the line information of the current internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model includes:
performing single-heat coding on time information in the line information of the current internet private line, and splicing the time information with the bandwidth utilization rate and the network quality parameter at the current moment to obtain a model input vector;
inputting the model input vector into a feature extraction layer of the prediction model to obtain an output feature vector corresponding to the current moment;
and inputting the feature vector into a full-connection layer of the prediction model to carry out regression prediction, so as to obtain the bandwidth utilization rate of the next moment of output.
Specifically, the model input vector is obtained according to the line information at the current moment, and the process of obtaining the model input vector is similar to that of the model training vector, and is not described herein. After the model input vector is input into the prediction model, the feature extraction layer performs feature extraction on the model input vector to obtain a feature vector corresponding to the current moment output by the feature extraction layer, wherein the feature vector corresponding to the current moment contains line feature information and historical feature information of the current moment. And inputting the feature vector corresponding to the current moment into the full-connection layer, and obtaining the predicted bandwidth utilization rate of the next moment output by the full-connection layer through regression prediction.
Fig. 3 is a diagram illustrating a structure of a prediction model according to an embodiment of the present application. As shown in fig. 3, it is assumed that line information is obtained by collecting states of lines used by users every one hour, including: time (0-23), week (1-7), bandwidth utilization of the user, qoS of the user (delay, jitter, packet loss). And carrying out characteristic processing on the line information at different moments: one-hot encoding (One-hot) is used for characterizing the time and day two parameters, and the user's bandwidth utilization and QoS values are data values so that no mapping is required. Splicing the processed line information at different moments to obtain different informationVector X corresponding to time i 。
X i ＝Concat[Onehot(F i ):Onehot(F week ):F Bi-1 :F QoSi-1 ]
The next time to be predicted is denoted by time t, and the current time corresponding to the next time is denoted by time t-1. i=t, then the model input vector X t The calculation formula is as follows:
X t ＝Concat[Onehot(F t ):Onehot(F week ):F Bt-1 :F QoSt-1 ]
similarly, according to the historical line information, a model training vector X can be obtained 0 ，X 1 ，…，X t-1 。
Since the bandwidth utilization of the user at the next moment needs to be predicted, a time-series cyclic neural network model is selected for modeling, wherein the cyclic neural network unit selects a gating cyclic unit (Gate Recurrent Unit, GRU). Since the final task is to perform regression prediction, the model is trained using a Loss function of Mean Square Error (MSE), the calculation formula is as follows:
Loss＝MSE(o t ,F Bt )＝(o i -F Bi ) 2 /n,,i＝1,2,3…n
Input vector X through model t And training the obtained hidden state vector h corresponding to the current moment t-1 To obtain a feature vector h containing current input information and historical feature information t Predicting corresponding feature vector h of next time t t The calculation formula is as follows:
h t ＝GRU(h t-1 ,X t )
obtaining a characteristic vector h t Then, regression prediction output is carried out through the fully connected neural network, and the bandwidth utilization rate o of the next moment t is predicted t The calculation formula is as follows:
o t ＝MLP(h t )
wherein the MLP is a multi-layered fully connected neural network.
Finally, the AI model designed by the method is used for completing the prediction of the bandwidth utilization rate of the user at the next moment according to the historical line flow information of the user and the network state information at the current moment.
In one example, the collecting the line information according to the predetermined period includes:
receiving line information reported by a metropolitan area network controller;
for a plurality of the line information received in each of the predetermined periods, line information in the predetermined period is obtained by averaging.
The traditional internet private line carries through the IP metropolitan area network and the IPRAN network of the ground city, each ground city is provided with one IP metropolitan area network and one IPRAN, and if the national customer speed regulation is to be realized, the network management systems of all provinces are needed to be respectively connected, so that the difficulty of the speed regulation task is increased. In order to meet the intelligent speed regulation requirement of the Internet private line user, the intelligent metropolitan area network is adopted to bear the Internet private line. The intelligent metropolitan area network is a converged network of an IP metropolitan area network and an IPRAN in the local city, and the networks in all the local cities are converged into one intelligent metropolitan area network. If the speed regulation is to be realized, only the intelligent metropolitan area network SDN controller needs to be docked, so that the complexity of the intelligent speed regulation task docking is reduced; the intelligent metropolitan area network is a comprehensive SDN network, all device configurations can be issued through an intelligent metropolitan area network SDN controller, and minute-level configuration issuing can be completed. Therefore, the internet private line in the intelligent metropolitan area network has the condition of issuing the user non-perception speed regulation task.
Specifically, the collected line information is reported to an intelligent metropolitan area network SDN controller for storage and processing, and the intelligent metropolitan area network SDN controller reports the processed user line information to an internet private line speed regulating device. In each preset period, the internet special line speed regulating device can receive the line information for a plurality of times, and then average value is taken as the line information in the period. For example, the internet private line speed regulating device collects the state of the line used by the user every one hour, and the minimum granularity of the line state reported by the intelligent metropolitan area network controller is 15 minutes, so that the internet private line speed regulating device takes the average value of the four received controller report messages in each period as the line information in each hour.
In practical application, the speed regulation manner of the private line may be multiple, in an example, the performing the private line speed regulation on the internet private line according to the bandwidth utilization of the internet private line at the next moment and the bandwidth utilization threshold of the internet private line at the next moment includes:
if the bandwidth utilization rate of the internet private line at the next moment is higher than the threshold upper limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth speed-up;
If the bandwidth utilization rate of the internet private line at the next moment is lower than the threshold lower limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth deceleration;
and if the bandwidth utilization rate of the internet private line at the next moment is between the upper limit and the lower limit of the bandwidth utilization rate threshold of the internet private line at the next moment, maintaining the current bandwidth.
Specifically, the user may preset a set speed regulation rule, including: and when the speed regulation threshold is triggered, the speed regulation bandwidths for speed increase and speed reduction are carried out. Judging the relation between the bandwidth utilization rate and the upper and lower threshold values of the internet private line at the next moment according to the predicted bandwidth utilization rate of the internet private line at the next moment, if the bandwidth utilization rate is higher than the preset upper threshold value, indicating that the next moment is a busy service period, and increasing the bandwidth to the bandwidth set in the speed regulation rule; if the bandwidth utilization rate is lower than the preset threshold lower limit, indicating that the next moment is a service idle period, and slowing down the bandwidth to the bandwidth set in the speed regulation rule, wherein the line is in an idle state; if the bandwidth utilization rate is between the upper and lower limits of the preset threshold, the next time line is indicated to be good in service condition, and the original bandwidth is kept unchanged.
For example, assume that the user sets the bandwidth utilization threshold to 70% upper and 30% lower, the up-speed bandwidth to 150% of the original bandwidth, and the down-speed bandwidth to 50% of the original bandwidth. If the bandwidth utilization rate of the next time line of the user is predicted to be 90%, the bandwidth is adjusted to 150% of the original bandwidth; predicting that the bandwidth utilization rate of the next time line of the user is 20%, and adjusting the bandwidth to be 50% of the original bandwidth; and predicting that the bandwidth utilization rate of the next time line of the user is 50%, and keeping the original bandwidth unchanged.
Fig. 4 is a schematic flow chart of speed regulation by using internet private line for a user according to an embodiment of the present application. As shown in fig. 4, the user logs in from the internet private line selected for use by the service system and selects intelligent speed regulation, sets the time (minimum 15 days) for collecting data and the speed regulation rule (the upper and lower limits of the bandwidth utilization threshold for speed increase and decrease, and the speed regulation bandwidth for speed increase and speed decrease when the speed regulation threshold is triggered). And starting an intelligent training mode, and the internet private line speed regulating device acquires the reported private line state information of the user within the preset time and sends the obtained characteristic information to the AI model for training. And loading the current latest training model, collecting the historical internet private line state information of the user on the same day, sending the information into an AI model, and predicting the bandwidth utilization rate of the user at the next moment. If the predicted bandwidth utilization rate is higher than the upper threshold limit designed by the user, controlling automatic issuing of speed-up configuration, wherein the speed-up bandwidth is the bandwidth set in the user rule; if the predicted bandwidth utilization rate is lower than the threshold lower limit designed by the user, controlling automatic downlink speed-down configuration, wherein the speed-down bandwidth is preset by the user; and if the predicted bandwidth is between the upper and lower thresholds set by the user, keeping the original bandwidth unchanged.
In the internet private line speed regulation method provided by the embodiment, the bandwidth utilization rate of the user internet private line at the next moment is predicted by using the regression prediction model based on the cyclic neural network, and the private line speed regulation is performed on the internet private line according to the predicted bandwidth utilization rate of the internet private line at the next moment and the corresponding bandwidth utilization rate threshold value, so that the requirements of user business on speed rising in busy hours and speed reducing in idle hours are met. According to the scheme provided by the application, the AI model is used for predicting the flow of the user line and carrying out intelligent speed regulation of the internet private line, so that dynamic speed regulation according to the service requirement of the client side is realized, the line quality deterioration in the busy service period is avoided, the line is idle in the idle service period, the bandwidth resource is fully utilized, and the user experience is effectively improved.
Example two
Fig. 5 is a schematic structural diagram of an internet dedicated line speed regulating device according to an embodiment of the present application. As shown in fig. 5, the internet private line speed regulating device provided in this embodiment may include:
the acquiring module 51 is configured to acquire line information of a current internet private line, where the line information includes time information, bandwidth utilization and network quality parameters at a current moment;
The prediction module 52 is configured to input line information of the dedicated internet line to a time sequence-based prediction model, and obtain a bandwidth utilization rate of the dedicated internet line at a next moment output by the prediction model; the prediction model is a model obtained by training in advance;
and the speed regulating module 53 is configured to regulate the speed of the dedicated internet line according to the bandwidth utilization of the dedicated internet line at the next moment and the bandwidth utilization threshold of the dedicated internet line at the next moment.
In practical application, the internet private line speed regulating device can be realized by a computer program, for example, application software and the like; alternatively, the computer program may be implemented as a medium storing a related computer program, for example, a usb disk, a cloud disk, or the like; still alternatively, it may be implemented by a physical device, e.g., a chip, a server, etc., integrated with or installed with the relevant computer program.
Through carrying out investigation to internet private line user, can find that the line of user internet private line is busy and the line is idle often has certain time law, for example dining enterprise 11 every day: 00-14:00, 17:00-20: the 00 lines are busy, the bandwidth utilization rate is high, the user network quality is poor, the rest time lines are idle, and the user network quality is good. Therefore, the business busy and idle of a specific enterprise is regular, so that the rule can be learned through a deep learning model, and the flow prediction of the line is realized. In view of this, in this embodiment, the AI model is introduced to predict the traffic of the subscriber line, so as to determine the busy hour and the idle hour of the subscriber line, and then intelligent speed regulation is performed according to the correlation result.
Specifically, the internet private line speed regulating device obtains line information of the current internet private line, wherein the line information includes time information, bandwidth utilization rate and network quality parameters at the current moment, for example, the line information may include time, week, bandwidth utilization rate and quality of service (Quality of Service, qoS) (may include time delay, jitter, packet loss, etc.), and may be selected according to practical application conditions, which is not limited herein. Inputting the obtained line information of the current internet private line into a prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a time sequence-based recurrent neural network regression prediction model and is trained in advance.
In practical applications, the selection of the prediction model may be various, and in one example, the apparatus further includes:
the training module is used for collecting line information according to a preset period in preset data collection time and taking the line information as a characteristic parameter of model training, wherein the characteristic parameter comprises time information, bandwidth utilization rate and network quality parameters;
performing single-heat coding on the time information in the characteristic parameters, and splicing the time information with the corresponding characteristic parameters to obtain a model training vector;
Establishing an initial model, wherein the initial model is a time sequence-based cyclic neural network model and comprises a feature extraction layer and a full connection layer;
and inputting the model training vector into the initial model, and carrying out model training on the initial model by using a loss function of a mean square error to obtain the prediction model.
Specifically, the scheme uses a time-series-based recurrent neural network model as an initial model, wherein the initial model comprises a feature extraction layer for feature extraction and a fully connected layer for regression prediction. The data acquisition time and the period are preset by a user, and the internet special line speed regulating device acquires line information according to the preset period in the preset data acquisition time so as to acquire the historical line information of the user as a characteristic parameter of model training. The characteristic parameters include, but are not limited to, time information (time, week), bandwidth utilization, network quality parameters QoS (delay, jitter, packet loss), and can be selected according to the needs of practical application. Because the time information (time and week) is discrete value and the data volume is moderate, unique coding is carried out on the time information, the value of the discrete feature is expanded to European space, and the feature analysis can be better carried out. And splicing the processed time information with other characteristic parameters to obtain a model training vector for learning a user flow using rule by the model. The model training vector is input into an initial model, and the initial model is trained by using a loss function of mean square error to obtain a prediction model in order to improve the accuracy of regression prediction.
In one example, the prediction module 52 is specifically configured to:
performing single-heat coding on time information in the line information of the current internet private line, and splicing the time information with the bandwidth utilization rate and the network quality parameter at the current moment to obtain a model input vector;
inputting the model input vector into a feature extraction layer of the prediction model to obtain an output feature vector corresponding to the current moment;
and inputting the feature vector into a full-connection layer of the prediction model to carry out regression prediction, so as to obtain the bandwidth utilization rate of the next moment of output.
Specifically, the model input vector is obtained according to the line information at the current moment, and the process of obtaining the model input vector is similar to that of the model training vector, and is not described herein. After the model input vector is input into the prediction model, the feature extraction layer performs feature extraction on the model input vector to obtain a feature vector corresponding to the current moment output by the feature extraction layer, wherein the feature vector corresponding to the current moment contains line feature information and historical feature information of the current moment. And inputting the feature vector corresponding to the current moment into the full-connection layer, and obtaining the predicted bandwidth utilization rate of the next moment output by the full-connection layer through regression prediction.
In one example, the training module is specifically configured to:
receiving line information reported by a metropolitan area network controller;
for a plurality of the line information received in each of the predetermined periods, line information in the predetermined period is obtained by averaging.
The traditional internet private line carries through the IP metropolitan area network and the IPRAN network of the ground city, each ground city is provided with one IP metropolitan area network and one IPRAN, and if the national customer speed regulation is to be realized, the network management systems of all provinces are needed to be respectively connected, so that the difficulty of the speed regulation task is increased. In order to meet the intelligent speed regulation requirement of the Internet private line user, the intelligent metropolitan area network is adopted to bear the Internet private line. The intelligent metropolitan area network is a converged network of an IP metropolitan area network and an IPRAN in the local city, and the networks in all the local cities are converged into one intelligent metropolitan area network. If the speed regulation is to be realized, only the intelligent metropolitan area network SDN controller needs to be docked, so that the complexity of the intelligent speed regulation task docking is reduced; the intelligent metropolitan area network is a comprehensive SDN network, all device configurations can be issued through an intelligent metropolitan area network SDN controller, and minute-level configuration issuing can be completed. Therefore, the internet private line in the intelligent metropolitan area network has the condition of issuing the user non-perception speed regulation task.
Specifically, the collected line information is reported to an intelligent metropolitan area network SDN controller for storage and processing, and the intelligent metropolitan area network SDN controller reports the processed user line information to an internet private line speed regulating device. In each preset period, the internet special line speed regulating device can receive the line information for a plurality of times, and then average value is taken as the line information in the period.
In practical applications, there may be various ways of speed regulation of the dedicated line, and in one example, the speed regulation module 53 is specifically configured to:
if the bandwidth utilization rate of the internet private line at the next moment is higher than the threshold upper limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth speed-up;
if the bandwidth utilization rate of the internet private line at the next moment is lower than the threshold lower limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth deceleration;
and if the bandwidth utilization rate of the internet private line at the next moment is between the upper limit and the lower limit of the bandwidth utilization rate threshold of the internet private line at the next moment, maintaining the current bandwidth.
Specifically, the user may preset a set speed regulation rule, including: and when the speed regulation threshold is triggered, the speed regulation bandwidths for speed increase and speed reduction are carried out. Judging the relation between the bandwidth utilization rate and the upper and lower threshold values of the internet private line at the next moment according to the predicted bandwidth utilization rate of the internet private line at the next moment, if the bandwidth utilization rate is higher than the preset upper threshold value, indicating that the next moment is a busy service period, and increasing the bandwidth to the bandwidth set in the speed regulation rule; if the bandwidth utilization rate is lower than the preset threshold lower limit, indicating that the next moment is a service idle period, and slowing down the bandwidth to the bandwidth set in the speed regulation rule, wherein the line is in an idle state; if the bandwidth utilization rate is between the upper and lower limits of the preset threshold, the next time line is indicated to be good in service condition, and the original bandwidth is kept unchanged.
In the internet private line speed regulating device provided by the embodiment, the bandwidth utilization rate of the user internet private line at the next moment is predicted by using the regression prediction model based on the cyclic neural network, and the internet private line is subjected to private line speed regulation according to the predicted bandwidth utilization rate of the internet private line at the next moment and the corresponding bandwidth utilization rate threshold value, so that the requirements of user business on speed rising in busy hours and speed reducing in idle hours are met. According to the scheme provided by the application, the AI model is used for predicting the flow of the user line and carrying out intelligent speed regulation of the internet private line, so that dynamic speed regulation according to the service requirement of the client side is realized, the line quality deterioration in the busy service period is avoided, the line is idle in the idle service period, the bandwidth resource is fully utilized, and the user experience is effectively improved.
Example III
Fig. 6 is a schematic structural diagram of an electronic device provided in an embodiment of the disclosure, as shown in fig. 6, where the electronic device includes:
a processor 291, the electronic device further comprising a memory 292; a communication interface (Communication Interface) 293 and bus 294 may also be included. The processor 291, the memory 292, and the communication interface 293 may communicate with each other via the bus 294. Communication interface 293 may be used for information transfer. The processor 291 may call logic instructions in the memory 292 to perform the methods of the above-described embodiments.
Further, the logic instructions in memory 292 described above may be implemented in the form of software functional units and stored in a computer-readable storage medium when sold or used as a stand-alone product.
The memory 292 is a computer-readable storage medium that may be used to store a software program, a computer-executable program, and program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 291 executes functional applications and data processing by running software programs, instructions and modules stored in the memory 292, i.e., implements the methods of the method embodiments described above.
Memory 292 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the terminal device, etc. Further, memory 292 may include high-speed random access memory, and may also include non-volatile memory.
The disclosed embodiments provide a non-transitory computer readable storage medium having stored therein computer-executable instructions that, when executed by a processor, are configured to implement the method of the previous embodiments.
Example IV
The disclosed embodiments provide a computer program product comprising a computer program which, when executed by a processor, implements the method provided by any of the embodiments of the disclosure described above.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (12)
1. The internet private line speed regulation method is characterized by comprising the following steps of:
acquiring line information of a current internet private line, wherein the line information comprises time information, bandwidth utilization rate and network quality parameters at the current moment;
Inputting the line information of the current internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a model obtained by training in advance;
and carrying out special line speed regulation on the internet special line according to the bandwidth utilization rate of the internet special line at the next moment and the bandwidth utilization rate threshold of the internet special line at the next moment.
2. The method according to claim 1, wherein the method further comprises:
collecting line information according to a preset period in preset data collection time as characteristic parameters of model training, wherein the characteristic parameters comprise time information, bandwidth utilization rate and network quality parameters;
performing single-heat coding on the time information in the characteristic parameters, and splicing the time information with the corresponding characteristic parameters to obtain a model training vector;
establishing an initial model, wherein the initial model is a time sequence-based cyclic neural network model and comprises a feature extraction layer and a full connection layer;
and inputting the model training vector into the initial model, and carrying out model training on the initial model by using a loss function of a mean square error to obtain the prediction model.
3. The method of claim 2, wherein the collecting the line information at a predetermined period comprises:
receiving line information reported by a metropolitan area network controller;
for a plurality of the line information received in each of the predetermined periods, line information in the predetermined period is obtained by averaging.
4. The method according to claim 2, wherein the inputting the line information of the current internet private line into the time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model includes:
performing single-heat coding on time information in the line information of the current internet private line, and splicing the time information with the bandwidth utilization rate and the network quality parameter at the current moment to obtain a model input vector;
inputting the model input vector into a feature extraction layer of the prediction model to obtain an output feature vector corresponding to the current moment;
and inputting the feature vector into a full-connection layer of the prediction model to carry out regression prediction, so as to obtain the bandwidth utilization rate of the next moment of output.
5. The method according to any one of claims 1-4, wherein said dedicated line speed adjusting the dedicated line according to the bandwidth utilization of the dedicated line at the next moment and the bandwidth utilization threshold of the dedicated line at the next moment includes:
If the bandwidth utilization rate of the internet private line at the next moment is higher than the threshold upper limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth speed-up;
if the bandwidth utilization rate of the internet private line at the next moment is lower than the threshold lower limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth deceleration;
and if the bandwidth utilization rate of the internet private line at the next moment is between the upper limit and the lower limit of the bandwidth utilization rate threshold of the internet private line at the next moment, maintaining the current bandwidth.
6. An internet private line speed regulation device, which is characterized by comprising:
the system comprises an acquisition module, a network quality parameter acquisition module and a network quality parameter acquisition module, wherein the acquisition module is used for acquiring line information of a current internet private line, and the line information comprises time information, bandwidth utilization rate and the network quality parameter at the current moment;
the prediction module is used for inputting the line information of the current internet private line into a time sequence-based prediction model to obtain the bandwidth utilization rate of the internet private line at the next moment output by the prediction model; the prediction model is a model obtained by training in advance;
and the speed regulation module is used for carrying out special line speed regulation on the internet special line according to the bandwidth utilization rate of the internet special line at the next moment and the bandwidth utilization rate threshold of the internet special line at the next moment.
7. The apparatus of claim 6, wherein the apparatus further comprises:
the training module is used for collecting line information according to a preset period in preset data collection time and taking the line information as a characteristic parameter of model training, wherein the characteristic parameter comprises time information, bandwidth utilization rate and network quality parameters;
performing single-heat coding on the time information in the characteristic parameters, and splicing the time information with the corresponding characteristic parameters to obtain a model training vector;
establishing an initial model, wherein the initial model is a time sequence-based cyclic neural network model and comprises a feature extraction layer and a full connection layer;
and inputting the model training vector into the initial model, and carrying out model training on the initial model by using a loss function of a mean square error to obtain the prediction model.
8. The device according to claim 7, wherein the training module is specifically configured to:
receiving line information reported by a metropolitan area network controller;
for a plurality of the line information received in each of the predetermined periods, line information in the predetermined period is obtained by averaging.
9. The device according to claim 7, characterized in that said speed regulation module is in particular adapted to:
Performing single-heat coding on time information in the line information of the current internet private line, and splicing the time information with the bandwidth utilization rate and the network quality parameter at the current moment to obtain a model input vector;
inputting the model input vector into a feature extraction layer of the prediction model to obtain an output feature vector corresponding to the current moment;
and inputting the feature vector into a full-connection layer of the prediction model to carry out regression prediction, so as to obtain the bandwidth utilization rate of the next moment of output.
10. The device according to any one of claims 6 to 9, wherein the speed regulation module is in particular configured to:
if the bandwidth utilization rate of the internet private line at the next moment is higher than the threshold upper limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth speed-up;
if the bandwidth utilization rate of the internet private line at the next moment is lower than the threshold lower limit of the bandwidth utilization rate of the internet private line at the next moment, executing bandwidth deceleration;
and if the bandwidth utilization rate of the internet private line at the next moment is between the upper limit and the lower limit of the bandwidth utilization rate threshold of the internet private line at the next moment, maintaining the current bandwidth.
11. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1-5.
12. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-5.
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