Note: Descriptions are shown in the official language in which they were submitted.
METHOD, APPARATUS, ELECTRONIC DEVICE, AND COMPUTER-READABLE
MEDIUM FOR TRAFFIC CONTROL
BACKGROUND OF THE INVENTION
Technical Field
[0001] The present invention relates to the technical field of computers, and
more particularly
to a method, an apparatus, an electronic device, and a computer-readable
medium for
traffic control.
Description of Related Art
[0002] With the rapid development of network technologies, applications based
on networks
keep increasing in both number and complexity. These applications continuously
devour
network resources, leading to network congestion. For solving network
congestion,
network traffic control has become a hot topic. Network traffic control is
measures to
control network data traffic using software or hardware. To implement it, the
first step is
to build up a traffic control strategy, and then control traffic according to
this strategy. In
the prior art, common traffic control strategies may depend on manual
maintenance by
operations and maintenance personnel or may involve using a linear prediction
model to
make linear prediction of network traffic so as to realize dynamic monitoring
of network
traffic.
[0003] In the known traffic control strategies as describe previously, manual
maintenance is less
flexible and has relatively low traffic usage. While the practice of using a
linear prediction
model to set a traffic control strategy is more flexible than manual
maintenance, a linear
prediction has inherent errors, which can even aggravate when used in complex
scenes,
leading to waste of resources.
SUMMARY OF THE INVENTION
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[0004] The present invention provides a method, an apparatus, an electronic
device, and a
computer-readable medium for traffic control, which can precisely predict
traffic
situations and implement traffic control schemes specific to the predicted
situations.
[0005] The present invention provides the following schemes.
[0006] In a first aspect, the present invention provides a method for traffic
control, which
comprises:
[0007] collecting request data of a target application;
[0008] predicting a traffic according to the request data using a traffic
prediction model, so as to
obtain a prediction result related to a target cycle, in which the traffic
prediction model is
constructed based on a neural network model;
[0009] generating a traffic control strategy according to the prediction
result related to the target
cycle; and
[0010] controlling the traffic of the target application in the target cycle
according to the traffic
control strategy.
[0011] Further, the step of predicting a traffic according to the request data
using a traffic
prediction model, so as to obtain a prediction result related to a target
cycle comprises:
[0012] entering the request data to said traffic prediction models of
different prediction cycles
for traffic prediction, so as to obtain the prediction results related to at
least two different
cycles; and
[0013] analyzing the prediction results related to the different cycles, so as
to obtain the
prediction result related to the target cycle.
[0014] Further, the step of analyzing the prediction results related to the
different cycles, so as to
obtain the prediction result related to the target cycle comprises:
[0015] using the prediction result of a short said cycle to correct the
prediction result of a long
said cycle; and
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[0016] taking the corrected prediction result of the longer cycle as the
prediction result of the
target cycle.
[0017] Further, the step of analyzing the prediction results related to the
different cycles, so as to
obtain the prediction result related to the target cycle comprises:
[0018] performing fit analysis on the prediction results related to the
different cycles, so as to
obtain the prediction result related to the target cycle.
[0019] Further, the step of analyzing the prediction results related to the
different cycles, so as to
obtain the prediction result related to the target cycle comprises:
[0020] calculating a mean of the prediction results related to the different
cycles; and
[0021] taking the mean of the prediction results related to the different
cycles as the prediction
result of the target cycle.
[0022] Further, the traffic prediction model is constructed through:
[0023] acquiring a sample training set for traffic prediction, which includes
input samples and
result samples;
[0024] inputting the input sample to the initialized neural network model, so
as to obtain a
prediction result set;
[0025] comparing the prediction result set to the result samples, so as to
obtain prediction errors;
[0026] adjusting the neural network model according to the prediction errors;
[0027] using the adjusted neural network model to repeatedly make prediction
based on the input
samples; and
[0028] comparing the prediction errors to standard error conditions, and when
the prediction
errors satisfy the standard error conditions, determining that the neural
network model
corresponding to the prediction errors is the traffic prediction model.
[0029] Further, the step of adjusting the neural network model according to
the prediction errors
comprises:
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[0030] correcting a weight of every synapse in the neural network model
according to the
prediction errors using a backpropagation algorithm.
[0031] In a second aspect, the present invention provides an apparatus for
traffic control, which
comprises:
[0032] a data-collecting unit, for collecting request data of a target
application;
[0033] a traffic-predicting unit, for predicting a traffic according to the
request data using a traffic
prediction model, so as to obtain a traffic prediction result related to a
target cycle, in
which the traffic prediction model is constructed based on a neural network
model;
[0034] a strategy-generating unit, for generating traffic control strategy
according to the traffic
prediction result related to the target cycle; and
[0035] a traffic-controlling unit, for controlling the traffic of the target
application in the target
cycle according to the traffic control strategy.
[0036] Further, the traffic-predicting unit comprises:
[0037] a prediction module, entering the request data to said traffic
prediction models of different
prediction cycles for traffic prediction, so as to obtain the prediction
results related to at
least two different cycles; and
[0038] an analysis module, for analyzing the prediction results related to the
different cycles, so
as to obtain the prediction result related to the target cycle.
[0039] Further, the analysis module is specifically for:
[0040] using the prediction result of a short said cycle to correct the
prediction result of a long
said cycle; and
[0041] taking the corrected prediction result of the longer cycle as the
prediction result of the
target cycle.
[0042] Further, the analysis module is specifically for:
[0043] performing fit analysis on the prediction results related to the
different cycles, so as to
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obtain the prediction result related to the target cycle.
[0044] Further, the analysis module is specifically for:
[0045] calculating a mean of the prediction results related to the different
cycles; and
[0046] taking the mean of the prediction results related to the different
cycles as the prediction
result of the target cycle.
[0047] Further, the apparatus further comprises a traffic prediction model
training module, for:
[0048] acquiring a sample training set for traffic prediction, which includes
input samples and
result samples;
[0049] inputting the input sample to the initialized neural network model, so
as to obtain a
prediction result set;
[0050] comparing the prediction result set to the result samples, so as to
obtain prediction errors;
[0051] adjusting the neural network model according to the prediction errors;
[0052] using the adjusted neural network model to repeatedly make prediction
based on the input
samples; and
[0053] comparing the prediction errors to standard error conditions, and when
the prediction
errors satisfy the standard error conditions, determining that the neural
network model
corresponding to the prediction errors is the traffic prediction model.
[0054] Further, the step of adjusting the neural network model according to
the prediction errors
comprises:
[0055] correcting a weight of every synapse in the neural network model
according to the
prediction errors using a backpropagation algorithm.
[0056] In a third aspect, the present invention provides an electronic device,
which comprises:
[0057] one or more processors; and
[0058] a memory associated with the one or more processors, the memory storing
program
instructions, the program instructions, when read and executed by the one or
more
Date Recue/Date Received 2022-03-08
processors, executing any of the methods of the first aspect.
[0059] In a fourth aspect, the present invention provides a computer-readable
medium, storing
therein a computer program, wherein the program, when executed by a processor,
implements any of the methods of the first aspect.
[0060] The embodiments of the present invention provide the following
technical effects:
[0061] 1. In the embodiment of the present invention, the neural network model
provides precise
traffic prediction, so that traffic monitoring strategies specific to
prediction results can be
generated, thereby facilitating reasonable distribution of the traffic, and
enabling warning
of potential traffic bursts to be given according to prediction results;
[0062] 2. The technical scheme of the embodiment of the present invention
performs analysis
according to the traffic prediction results related to different prediction
cycles so as to
obtain a prediction result related to a target cycle, which is more precise
than the
prediction result obtained using the known linear prediction method; and
[0063] 3. The technical scheme of the embodiment of the present invention
continuously trains
and adjusts the traffic prediction model according to prediction errors,
thereby
eliminating the defects of the conventional methods about inability to predict
sudden
incidents.
[0064] Of course, it is not necessary to achieve all of the foregoing
advantages for
implementation of any product of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0065] To better illustrate the technical schemes as disclosed in the
embodiments of the present
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Date Recue/Date Received 2022-03-08
invention, accompanying drawings referred in the description of the
embodiments below
are introduced briefly. It is apparent that the accompanying drawings as
recited in the
following description merely provide a part of possible embodiments of the
present
invention, and people of ordinary skill in the art would be able to obtain
more drawings
according to those provided herein without paying creative efforts, wherein:
[0066] FIG. 1 is a flowchart of a method according to one embodiment of the
present invention;
[0067] FIG. 2 is a structural diagram of an apparatus according to one
embodiment of the present
invention; and
[0068] FIG. 3 is a structural diagram of a computer system according to one
embodiment of the
present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0069] The following description will be made with reference to the
accompanying drawings of
embodiments of the present invention and detail the technical schemes of the
embodiments with clarity and completeness. It is obvious that the described
embodiments
are merely a part of all possible embodiments of the present invention, but
not all of them.
Any other embodiment devised by a person of ordinary skill in the art based on
the
embodiments of the present invention shall be encompassed in the scope of the
present
invention.
[0070] In embodiments of the present invention, technical schemes based on
neural network
model prediction traffic and preforming traffic control according to
prediction results are
provided, which solve the problems of the prior-art traffic control schemes
about inferior
flexibility and low traffic usage caused by manual maintenance and about error-
incurred
waste of resources as seen in use of a linear prediction model for traffic
prediction.
[0071] Some embodiments of the present invention will be described below to
explain these
schemes.
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[0072] Embodiment 1
[0073] Referring to FIG. 1, a traffic control method specifically may comprise
the following
steps.
[0074] Si is about collecting request data of a target application.
[0075] The request data from the interface of the target application are
collected in a real-time
manner and used to determine the traffic situation of the target application.
The request
data from the interface of the target application may include but are not
limited to the
following eight forms:
[0076] Get, make a request to a specific resource (request specific page
information, and return
the entity body);
[0077] Post, submit data to the specified resource for processing request
(Submit form, Upload
files), it may also lead to the establishment of new resources or the
modification of
existing resources;
[0078] Put, upload the latest content to the specified resource location (the
content of the
specified document is replaced by the data transmitted from the client to the
server);
[0079] Head, with the server get request a consistent response, the response
body will not return,
get the original information contained in the small message header (and get
the request is
similar to, there is no specific content in the response returned, for getting
headers);
[0080] Delete, Request server delete request-URL Resources marked *(ask the
server to delete
the page);
[0081] Trace, Display requests received by the server, for testing and
diagnosis;
[0082] Opions, Returns the server's support for a specific resource HTML
Request method or
web Server send * Test server function (Allow clients to view server
performance); and
[0083] Connec, proxy server in HTTP/1.1 protocols changes connection to a
pipe.
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[0084] S2 is about using the traffic prediction model to perform traffic
prediction according to
the request data, so as to obtain a prediction result related to a target
cycle. The traffic
prediction model is constructed on the basis of a neural network model.
[0085] Different from the prior art solutions, the embodiment of the present
invention uses a
traffic prediction model constructed on the basis of a network model to
perform traffic
prediction. Traffic prediction is the guide to generation or selection of
traffic control
strategies. Preciseness of traffic prediction results has direct relationship
with the
correctness of generation or selection of a traffic control strategies,
thereby influencing
performance of traffic control. The traffic prediction result may reflect a
specific traffic
value range, or may alternatively be a variation trend of traffic in the
target cycle.
[0086] In one embodiment, prediction is performed through:
[0087] S21: entering the request data to said traffic prediction models of
different prediction
cycles for traffic prediction, so as to obtain the prediction results related
to at least two
different cycles; and
[0088] S22: analyzing the prediction results related to the different cycles,
so as to obtain the
prediction result related to the target cycle.
[0089] The traffic prediction models for different prediction cycles are
plural traffic prediction
models trained respectively using request data collected in different cycles
as training
samples, such as short cycle traffic prediction models, middle cycle traffic
prediction
models, and long cycle traffic prediction models. Correspondingly, the
obtained
prediction results include short cycle prediction results, middle cycle
prediction results,
and long cycle prediction results.
[0090] The prediction results related to different cycles obtained as
described previously are then
analyzed so as to obtain a prediction result related to a target cycle. This
is mainly about
analyzing the prediction results related to cycle of different lengths
comprehensively, so
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as to ascertain the traffic situation that is related to a cycle as long as
possible, and
conforms, as much as possible, to the actual traffic situation of the target
application in a
cycle as long as possible. Specifically, the foregoing analysis may be as
described in any
one or more of the following embodiments.
[0091] In one embodiment, the step of analyzing the prediction results related
to the different
cycles, so as to obtain the prediction result related to the target cycle
comprises:
[0092] using the prediction result of a short said cycle to correct the
prediction result of a long
said cycle; and
[0093] taking the corrected prediction result of the longer cycle as the
prediction result of the
target cycle.
[0094] The short cycles and the long cycles are identified by sorting the
cycles by length. In the
present embodiment, the step of using the prediction result of a short said
cycle to correct
the prediction result of a long said cycle comprises:
[0095] (1) correcting the prediction result related to the longest cycle
according to the prediction
results related to the other, short cycles; and
[0096] (2) grouping the prediction results of the cycles, and correcting the
prediction results
related to the relatively long cycles according to the prediction results
related to the
relatively short cycles in each group, and then obtaining the prediction
result related to
the target cycle according to the prediction results related to the relatively
long cycles in
each group (particularly, mean calculation, for example).
[0097] Specifically, the foregoing correction may be realized using the ratio
between the
prediction results of the short cycles and the long cycles to correct the
prediction results
of the long cycles, or by setting weights for different cycles. The embodiment
of the
present invention puts no limitation on how the correction is realized.
Date Recue/Date Received 2022-03-08
[0098] In one embodiment, the step of analyzing the prediction results related
to the different
cycles, so as to obtain the prediction result related to the target cycle
comprises:
[0099] performing fit analysis on the prediction results related to the
different cycles, so as to
obtain the prediction result related to the target cycle.
[0100] The fit analysis mainly refers to determining a target function
according to the discrete
prediction results of different cycles by means of adjusting the factor. The
target function
is the one having the smallest difference with the discrete prediction results
of the
different cycles.
[0101] In one embodiment, the step of analyzing the prediction results related
to the different
cycles, so as to obtain the prediction result related to the target cycle
comprises:
[0102] calculating a mean of the prediction results related to the different
cycles; and
[0103] taking the mean of the prediction results related to the different
cycles as the prediction
result of the target cycle.
[0104] The foregoing mean calculation mainly refers to calculating the
arithmetic mean or
weighted mean of the prediction results related to different cycles.
[0105] It is to be noted that, in addition to analysis of the prediction
results of different cycles as
describe above, other approaches may be used to obtain the prediction result
of the target
cycle, including calculating the median of the prediction results of different
cycles, and
taking the median as the prediction result of the target cycle. The median-
based approach
is preferably to be used in cases where the number of cycles is large and the
differences
among different cycles are small.
[0106] In one embodiment, the traffic prediction model is constructed through:
[0107] S21 ': acquiring a sample training set for traffic prediction, which
includes input samples
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and result samples;
[0108] S22': inputting the input sample to the initialized neural network
model, so as to obtain a
prediction result set;
[0109] S23': comparing the prediction result set to the result samples, so as
to obtain prediction
errors;
[0110] S24': based on prediction errors adjusting neural network model;
[0111] S25': using the adjusted neural network model to repeatedly make
prediction based on
the input samples; and
[0112] S26': comparing the prediction errors to standard error conditions, and
when the
prediction errors satisfy the standard error conditions, determining that the
neural network
model corresponding to the prediction errors is the traffic prediction model.
[0113] In S22', the initialized neural network model has an initialized
weight. In S24', the
synapse weights of the neural network model are corrected according to
prediction errors
using a backpropagation algorithm. Thereby, the traffic prediction model
constructed as
described in the embodiment of the present invention has the ability of
adaptive learning,
and can continuously adjusting synapse weights by comparing the prediction
errors and
standard errors, thereby eliminating the defects of the conventional methods
about
inability to predict sudden incidents.
[0114] S3: generating a traffic control strategy according to the prediction
result of the target
cycle.
[0115] The traffic control strategy may be generated according to the
prediction result, or may
alternatively be selected from pre-loaded ones that correspond to different
prediction
results.
[0116] S4 is about controlling the traffic of the target application in the
target cycle according to
the traffic control strategy.
[0117] As described above, with the relatively precise traffic prediction
result, a targeted traffic
control strategy may be generated, thereby achieving effective traffic control
for the target
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application.
[0118] Corresponding to the method for traffic control as described above, the
embodiment of
the present invention further provides a traffic control apparatus, Referring
to FIG. 2, it
comprises the following components.
[0119] A data collecting unit 201 is for collecting request data of a target
application.
[0120] The data collecting unit acquires request data of a target application
through the interface
of the target application. The request data may include various forms, and the
embodiment of the present invention puts no limitation thereon.
[0121] A traffic-predicting unit 202 is for suing the traffic prediction model
to perform traffic
prediction according to request data, so as to obtain a traffic prediction
result related to a
target cycle, traffic prediction model based on neural network model.
[0122] In one embodiment, the traffic-predicting unit 202 specifically
comprises: traffic
prediction models related to different prediction cycles, and specifically
comprises:
[0123] a prediction module, for entering the request data to said traffic
prediction models of
different prediction cycles for traffic prediction, so as to obtain the
prediction results
related to at least two different cycles; and
[0124] an analysis module, for analyzing the prediction results related to the
different cycles, so
as to obtain the prediction result related to the target cycle.
[0125] In one embodiment, the analysis module is specifically for:
[0126] using the prediction result of a short said cycle to correct the
prediction result of a long
said cycle; and
[0127] taking the corrected prediction result of the longer cycle as the
prediction result of the
target cycle.
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[0128] The short cycles and the long cycles are identified by sorting the
cycles by length. In the
present embodiment, the step of using the prediction result of a short said
cycle to correct
the prediction result of a long said cycle comprises:
[0129] (1) correcting the prediction result related to the longest cycle
according to the prediction
results related to the other, short cycles; and
[0130] (2) grouping the prediction results of the cycles, and correcting the
prediction results
related to the relatively long cycles according to the prediction results
related to the
relatively short cycles in each group, and then obtaining the prediction
result related to
the target cycle according to the prediction results related to the relatively
long cycles in
each group (particularly, mean calculation, for example).
[0131] In one embodiment, the analysis module is specifically for:
[0132] performing fit analysis on the prediction results related to the
different cycles, so as to
obtain the prediction result related to the target cycle.
[0133] In one embodiment, the analysis module is specifically for:
[0134] calculating a mean of the prediction results related to the different
cycles; and
[0135] taking the mean of the prediction results related to the different
cycles as the prediction
result of the target cycle.
[0136] The mean calculation mainly refers to calculating the arithmetic mean
or weighted mean
of the prediction results related to different cycles.
[0137] In one embodiment, the apparatus of the embodiment of the present
invention further
comprises a traffic prediction model training module, for:
[0138] acquiring a sample training set for traffic prediction, which includes
input samples and
result samples;
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[0139] inputting the input sample to the initialized neural network model, so
as to obtain a
prediction result set;
[0140] comparing the prediction result set to the result samples, so as to
obtain prediction errors;
[0141] adjusting the neural network model according to the prediction errors;
[0142] using the adjusted neural network model to repeatedly make prediction
based on the input
samples; and
[0143] comparing the prediction errors to standard error conditions, and when
the prediction
errors satisfy the standard error conditions, determining that the neural
network model
corresponding to the prediction errors is the traffic prediction model.
[0144] The step of adjusting the neural network model based on prediction
errors comprises:
correcting the synapse weights of the neural network model according to
prediction errors
using a backpropagation algorithm.
[0145] A strategy generating unit 203 is for generating traffic control
strategy according to the
traffic prediction result relate to the target cycle.
[0146] A traffic control unit 204 is for controlling the traffic of the target
application in the target
cycle according to the traffic control strategy.
[0147] Additionally, an embodiment of the present invention further provides
an electronic
device, which comprises:
[0148] one or more processors; and
[0149] a memory associated with the one or more processors, the memory storing
program
instructions, the program instructions, when read and executed by the one or
more
processors, executing the method for traffic control as provided in the
embodiment of the
present invention.
[0150] FIG. 3 illustratively depicts a structure of the computer system. It
may specifically include
Date Recue/Date Received 2022-03-08
a processor 310, a video display adapter 311, a disk driver 312, an I/O port
313, a network
port 314, and a memory 320. The processor 310, the video display adapter 311,
the disk
driver 312, the I/O port 313, the network port 314, and the memory 320 may be
communicatively connected through a communication bus 330.
[0151] Therein, the processor 310 may be implemented using a common Central
Processing Unit
(CPU), a microprocessor, an Application Specific Integrated Circuit (ASIC), or
one or
more integrated circuits for executing relevant programs to realize the
technical scheme
provided by the present invention.
[0152] The memory 320 may be realized using a ROM (Read Only Memory), a RAM
(Random Access Memory), a static storage device, a dynamic storage device or
any
analog. The memory 320 may store for an operating system 321 that controls
operation
of the computer system 300, and a basic input/output system (BIOS) for
controlling low-
level operations of the computer system 300. In addition, it may further store
a web
browser 323, a data storage management system 324, and an icon and font
processing
system 325. The icon and font processing system 325 may be an application
enabling
operations of the foregoing various steps of the embodiments of the present
invention. In
any case, when the technical scheme provided by present invention is realized
using
software or firmware, the related program codes are stored in the memory 320
for the
processor 310 to call and execute.
[0153] The I/O port 313 is for connecting an I/O module for allowing data
input and output. The
input/output module may be built in the apparatus as a component (not shown),
or may
be set externally and connected to the apparatus so as to provides
corresponding functions.
Therein, the input device may include a keyboard, a mouse, a touch panel, a
microphone,
various sensors or more. The output device may include a display, an
amplifier, a vibrator,
an indicator or more.
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[0154] The network port 314 is for connecting a communication module (not
shown) to allow
communication between the disclosed apparatus and external devices. Therein,
the
communication module may enable communication either in a wired manner (such
as
through a USB, a network line, etc.) or in a wireless way (such as through a
mobile
network, WIFI, Bluetooth, etc.).
[0155] The bus 330 comprises a channel allowing information transmission among
the
components of the device (i.e., the processor 310, the video display adapter
311, the disk
driver 312, the I/O port 313, the network port 314, and the memory 320).
[0156] Moreover, the computer system 300 may further obtain information about
specific
collection conditions from a virtual resources object collection condition
information
database for its condition determination or the like.
[0157] It is to be noted that while the apparatus in the depicted embodiment
works merely by
virtue of the processor 310, the video display adapter 311, the disk driver
312, the I/O
port 313, the network port 314, the memory 320, and the bus 330, in practical
implementations, the apparatus may further include additional components
required for
its desired purposes. Moreover, people skilled in the art would appreciate
that the
disclosed apparatus may only include the minimal number of components for
realizing
the scheme of the present invention instead of having all these depicted
components.
[0158] Especially, according to the embodiment of the present invention, the
process described
previously with reference to the flowchart may be realized as a computer
software
program. For example, an embodiment of the present invention comprises a
computer
program product, which includes a computer program carried by a computer-
readable
medium. The computer program includes program codes for executing the method
illustrated in the flowchart. In such an embodiment, the computer program may
be
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download and installed by a communication apparatus through a network, or may
be
installed from a memory, or may be installed from a ROM. When executed by a
processor,
the computer program executes the functions defined in the method of the
embodiment
of the present invention as described above.
[0159] It is to be noted that the computer-readable medium of the embodiment
of the present
invention may be a computer-readable signal medium or a computer-readable
storage
medium or any combination of the two. The computer-readable storage medium may
be,
but is not limited to, an electrical, magnetic, optical, electromagnetic,
infrared, or
semiconductor system, apparatus, or device, or any combination thereof. Morre
particular
examples of the computer-readable storage medium may include but are not
limited to
electric connection having one or more leads, a portable computer disk, a hard
drive, a
random-access memory (RAM), a read-only memory (ROM), an electrically erasable
programmable read-only memory (EPROM or flash), an optical fiber, a compact
disc
read-only memory (CD-ROM), an optical memory, a magnetic memory, or any
suitable
combination thereof. In embodiments of the present invention, the computer-
readable
storage medium may be any tangible medium that contains or stores a program.
The
program may be used by or with an instruction-executing system, an apparatus
or a device.
In embodiments of the present invention, the computer-readable signal medium
may
comprise data signals that are propagated in the baseband or propagated as a
part of carrier
waves, and carry computer-readable program codes. The propagatable data
signals may
be in various forms including, but not limited to, electromagnetic signals,
optical signals,
or any suitable combination thereof. The computer-readable signal medium may
alternatively be any computer-readable medium other than a computer-readable
storage
medium. The computer-readable signal medium may transmit, propagate, or send a
program that is to be used by or with an instruction-executing system, an
apparatus or a
device. The program codes contained in the computer-readable medium may be
transmitted through any suitable medium, including but not limited to: a power
cord, an
optical cable, RF (Radio Frequency), or any suitable combination thereof.
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Date Recue/Date Received 2022-03-08
[0160] The computer-readable medium may be contained in the aforementioned
server, or may
alternatively exist separately instead of being installed in the server. The
computer-
readable medium carries one or more programs. When executed by the server, the
one or
more programs make the server: in response to its detection result indicating
that the
accessory mode of a terminal is not activated, acquire the frame rate of the
application on
the terminal; when the frame rate satisfies screen-off conditions, determine
whether a user
is acquiring the screen information of the terminal; and in response to its
determination
result indicating that the user is not acquiring the screen information of the
terminal,
control the screen to enter the screen-off mode.
[0161] The computer program codes for executing operations of the embodiment
of the present
invention may be written in one or more program design languages or a
combination
thereof. The program design languages may include object-orientated program
design
languages, such as Java, Smalltalk, C++, and may further include conventional
procedural program design languages, such as "C" or the like. The program
codes may
be completely executed on a user computer, partially executed on a user
computer,
executed as an independent software package, partially executed on a user
computer and
partially executed on a remote computer, or completely executed on a remote
computer
or server. Where a remote computer is involved, the remote computer may be
connected
to the user computer through any kind of networks, such as a local area
network (LAN)
or a wide area network (WAN), or may be connected to an external computer
(such as by
using an Internet service provider through the Internet).
[0162] The embodiments of the present invention provide the following
technical effects:
[0163] 1. In the embodiment of the present invention, the neural network model
provides precise
traffic prediction, so that traffic monitoring strategies specific to
prediction results can be
generated, thereby facilitating reasonable distribution of the traffic, and
enabling warning
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Date Recue/Date Received 2022-03-08
of potential traffic bursts to be given according to prediction results;
[0164] 2. The technical scheme of the embodiment of the present invention
performs analysis
according to the traffic prediction results related to different prediction
cycles so as to
obtain a prediction result related to a target cycle, which is more precise
than the
prediction result obtained using the known linear prediction method; and
[0165] 3. The technical scheme of the embodiment of the present invention
continuously trains
and adjusts the traffic prediction model according to prediction errors,
thereby
eliminating the defects of the conventional methods about inability to predict
sudden
incidents.
[0166] The embodiments disclosed herein are described in a progressive sense,
and therefore a
part in one embodiment may having its details complemented by the description
for its
counter parts in other embodiments. Every embodiment is such described that it
only
emphasizes what differentiates it from the other embodiments. Particularly,
for a system
or an embodiment directed to a system, the subject matter may be described in
a
simplified way as more details may be learned from the embodiment about its
relevant
method. The system and system embodiment disclosed herein are merely
illustrative, in
which a unit described as a separated part may be or may be not physically
separated, and
a part shown as a unit may be or may be not a physical unit, meaning that it
may be
located at one site or alternatively be distributed across multiple units in a
network. The
purpose of an embodiment may be realized using all or some of the
described/shown
modules according to practical needs. People skilled in the art would
understand and
implement the present invention without paying creative efforts.
[0167] While some preferred embodiments of the present invention have been
described, it is
appreciated that, people skilled in the art in light of the basic creative
concepts may
change and modify theses embodiments in many ways. Thus, the appended claims
are
Date Recue/Date Received 2022-03-08
intended to be interpreted as being inclusive of theses preferred embodiments
and all
possible changes and modifications falling within the scope of the present
invention.
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