CN110413510A - A kind of data processing method, device and equipment - Google Patents
A kind of data processing method, device and equipment Download PDFInfo
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Abstract
This application discloses a kind of data processing method, device and equipment, the method includes performance parameters and smoothness prediction model based on each first terminal, prediction can be carried out to the fluency for running application program on each first terminal, obtain the prediction smoothness parameter of each first terminal；Wherein, smooth prediction model is obtained to the performance parameter of each second terminal in the second quantity second terminal and its smooth parameter progress data process of fitting treatment of actual measurement；Generate the smooth parameter data set of full dose terminal.It can get comprehensive automatic test data using technical solution provided by the present application, reduce testing cost and error probability, improve testing efficiency.
Description
Technical field
The present invention relates to field of communication technology more particularly to a kind of data processing methods, device and equipment.
Background technique
With the fast development of terminal applies, the renewal speed of the application program of terminal is getting faster.If the application of terminal
When program needs to realize a new function, it usually needs carry out functional test to the new function.
In the prior art, usually by tester to the people for carrying out the function from a large amount of type terminals purchased on the market
Work functional test.There are at least one following defects for this functional test mode: leading due to needing to purchase a large amount of type terminals
Cause functional test at high cost；Due to for manual testing and there are higher risk of error；It, can not and since test is time-consuming larger
Meet application program publication demand etc. immediately.
Summary of the invention
The present invention provides a kind of data processing method, device and equipment, may be implemented to can get comprehensive automation survey
Data are tried, testing cost and error probability is reduced, improves testing efficiency.
On the one hand, the present invention provides a kind of data processing methods, comprising:
Obtain the performance parameter of each first terminal in the first quantity first terminal；
Performance parameter based on each first terminal and smooth prediction model, to being run on each first terminal
The fluency of application program can be carried out prediction, obtain the prediction smoothness parameter of each first terminal；Wherein, described smooth pre-
Surveying model is the performance parameter to the second quantity second terminal and its surveys obtained by smooth parameter progress data process of fitting treatment
's；The smooth parameter of the actual measurement is based on test case corresponding with the attribute information of the application program to each second terminal
It is obtained to carry out smooth performance test；
The performance parameter of attribute information, each first terminal based on the application program and its smooth ginseng of prediction
The performance parameter of several and each second terminal and its smooth parameter of actual measurement, generate the smooth parameter data set of full dose terminal；
Wherein, the full dose terminal includes the first quantity first terminal and the second quantity second terminal.
On the other hand, the present invention also provides a kind of data processing methods, comprising:
It receives the trigger action to destination application that target terminal is sent to request, the trigger action request includes institute
State the performance parameter of target terminal and the attribute information of the destination application；
The attribute information of the performance parameter of the target terminal and the destination application is input to smooth prediction mould
Type can be carried out prediction to the fluency for running the destination application on the target terminal, obtain the target terminal
Target smoothness parameter；
The target smoothness parameter is sent to the target terminal, so that the target terminal is based on the target smoothness
The operating status of destination application described in state modulator；
Wherein, the smooth prediction model be to the performance parameter of each second terminal in the second quantity second terminal and
It is obtained to carry out data process of fitting treatment for the smooth parameter of flow measurement in fact；The smooth parameter of the actual measurement is based on the attribute with application program
The corresponding test case of information tests each second terminal.
On the other hand, the present invention also provides a kind of data processing equipments, comprising:
Parameter acquisition module, for obtaining the performance parameter of each first terminal in the first quantity first terminal；
Parameter prediction module, for performance parameter and smoothness prediction model based on each first terminal, to described
The fluency that application program is run on each first terminal can be carried out prediction, obtain the prediction smoothness ginseng of each first terminal
Number；Wherein, the smooth prediction model is to the performance parameter of each second terminal in the second quantity second terminal and in fact
It is obtained that the smooth parameter of flow measurement carries out data process of fitting treatment；The smooth parameter of the actual measurement is based on the attribute with the application program
The corresponding test case of information tests each second terminal；
Dataset generation module, the performance for attribute information, each first terminal based on the application program
Parameter and its performance parameter and its actual measurement smoothness parameter for predicting smooth parameter and each second terminal, generate full dose
The smooth parameter data set of terminal；
Wherein, the full dose terminal includes the first quantity first terminal and the second quantity second terminal.
On the other hand, the present invention also provides a kind of data processing equipments, comprising:
Request receiving module, the trigger action to destination application for receiving target terminal transmission is requested, described
Trigger action request includes the performance parameter of the target terminal and the attribute information of the destination application；
Parameter determination module, for by the attribute information of the performance parameter of the target terminal and the destination application
It is input to smooth prediction model, prediction can be carried out to the fluency for running the destination application on the target terminal, obtain
To the target smoothness parameter of the target terminal；
Parameter sending module, for the target smoothness parameter to be sent to the target terminal, so that the target is whole
The operating status of end group destination application described in the target smoothness state modulator；
Wherein, the smooth prediction model be to the performance parameter of each second terminal in the second quantity second terminal and
It is obtained to carry out data process of fitting treatment for the smooth parameter of flow measurement in fact；The smooth parameter of the actual measurement is based on the attribute with application program
The corresponding test case of information tests each second terminal.
On the other hand, the present invention also provides a kind of data processing equipment, the equipment includes processor and memory, described
At least one instruction, at least a Duan Chengxu, code set or instruction set are stored in memory, described at least one instructs, is described
An at least Duan Chengxu, the code set or instruction set are loaded by the processor and are executed to realize the number such as any description above
According to processing method.
A kind of data processing method, device and equipment provided by the present application, at least have the following technical effect that
The embodiment of the present invention is established smooth pre- according to the smooth parameter of actual measurement of second terminal and the performance parameter of second terminal
Model is surveyed, and prediction can be carried out to the fluency of first terminal based on the smoothness prediction model, obtains predicting smooth parameter, later
The smooth parameter data set for generating full dose terminal, without buy a large amount of terminal device to be tested can be obtained it is comprehensive from
Dynamicization test data, reduces testing cost and error probability, improves testing efficiency, can meet application program and issue need immediately
It asks.In addition, establishing smooth prediction model according to smooth parameter is surveyed, it can more reflect the true fluency of terminal operating application program
Energy；And the corresponding target smoothness parameter of target terminal can be inquired from smoothness supplemental characteristic concentration, it is smooth pre- so as to reduce
Calculation amount is surveyed, and then can quickly prejudge the corresponding smooth performance of target terminal, improves user experience.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology and advantage, below will be to implementation
Example or attached drawing needed to be used in the description of the prior art are briefly described, it should be apparent that, the accompanying drawings in the following description is only
It is only some embodiments of the present invention, for those of ordinary skill in the art, without creative efforts,
It can also be obtained according to these attached drawings other attached drawings.
Fig. 1 is a kind of flow diagram of data processing method provided in an embodiment of the present invention.
Fig. 2 is a kind of flow diagram for establishing smooth prediction model provided in an embodiment of the present invention.
Fig. 3 is the part flow diagram of another data processing method provided in an embodiment of the present invention.
Fig. 4 is a kind of schematic diagram of application scenarios provided in an embodiment of the present invention.
Fig. 5 is the flow diagram of another data processing method provided in an embodiment of the present invention.
Fig. 6 is a kind of block diagram of data processing equipment provided in an embodiment of the present invention.
Fig. 7 is the block diagram of another data processing equipment provided in an embodiment of the present invention.
Fig. 8 is a kind of the hard of equipment for realizing method provided by the embodiment of the present invention provided in an embodiment of the present invention
Part structural schematic diagram.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work
It encloses.
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to embodiment party of the present invention
Formula is described in further detail.
A kind of specific embodiment of data processing method of the present invention introduced below, Fig. 1 is provided in an embodiment of the present invention one
The flow diagram of kind of data processing method, present description provides the method operating procedure as described in embodiment or flow chart,
It but based on routine or may include more or less operating procedure without creative labor.The step of being enumerated in embodiment
Sequence is only one of numerous step execution sequence mode, does not represent and unique executes sequence.System in practice or
When server product executes, can be executed according to embodiment or the execution of method shown in the drawings sequence or parallel (such as simultaneously
The environment of row processor or multiple threads).It is specific as shown in Figure 1, the method may include:
S101: the performance parameter of each first terminal in the first quantity first terminal is obtained.
In embodiments of the present invention, first terminal may include smart phone, tablet computer, laptop, desktop
The equipment of the types such as brain, digital assistants, intelligent wearable device.In one embodiment, first terminal can be to have pacified
Dress or a certain application program application program or destination application of new function to be tested (for example, with) that will be installed
Terminal device.The quantity of first terminal is the first quantity, preferably several, the performance of each first terminal of first quantity
Parameter is different.Exemplary, if first terminal is smart phone, first quantity can include but is not limited to be several
Hundred even thousands of.
In embodiments of the present invention, the performance parameter of first terminal may include CPU (Central Processing
Unit, central processing unit) quantity, cpu frequency, GPU (Graphics Processing Unit, graphics processor) frequency,
One of GPU quantity, memory size (original memory, actual memory), terminal models, screen size and screen resolution are more
Kind.
It in embodiments of the present invention, can be from each when needing the new function to a certain application program to test
The performance parameter of request first terminal in one terminal, can also be from the performance parameter server for being stored with all first terminals
The middle performance parameter for obtaining the first terminal.
S103: performance parameter and smooth prediction model based on each first terminal, to each first terminal
The fluency of upper operation application program can be carried out prediction, obtain the prediction smoothness parameter of each first terminal.
In embodiments of the present invention, smooth prediction model is the property to each second terminal in the second quantity second terminal
It is obtained that energy parameter and its smooth parameter of actual measurement carry out data process of fitting treatment.Second terminal also may include smart phone, put down
The equipment such as plate computer, laptop, desktop computer, digital assistants, intelligent wearable device.Second terminal preferably and first
The different terminal device of the performance parameter of terminal.Specifically, second terminal can be the terminal device of existing second quantity,
Second quantity is preferably several, and the performance parameter of each second terminal is different.It is exemplary, if second terminal is intelligence
Mobile phone, then second quantity can include but is not limited to be 50~300.
In one embodiment, the quantity of the quantity and second terminal of first terminal can be associated.It is exemplary, second terminal
For the terminal device of the existing material object for actual measurement, first terminal be for predict and can not no material object terminal device.If
When a certain application program is tested in test, the available institute for being mounted with application program correlation app in the market
There is the performance parameter of user terminal, removes the performance parameter of all second terminals from the performance parameter of all user terminals
The performance parameter of other terminals is the performance parameter of first terminal.Certainly, the first quantity and the second quantity can bases
Specific rule is configured.Another exemplary, settable second quantity is the 1/2~1/100 of the first quantity.
In embodiments of the present invention, surveying smooth parameter is based on test corresponding with the attribute information of the application program
It is obtained that use-case carries out smooth performance test to each second terminal.The smooth parameter of the actual measurement is for characterizing second
The data of the smooth performance of terminal operating application program.
In embodiments of the present invention, application program can be application program or target application with new function to be tested
Program.It is exemplary, the application program can for wechat small routine, wechat, microblogging, QQ, browser, games, shopping program,
The general programs such as video program.The attribute information of application program may include that Program Type information is (such as big game class, small-sized
Game class, short video class, long video class, chat class and shopping class etc.), program identification information etc..
In embodiments of the present invention, test case is associated with the attribute information of application program.It is exemplary, if application program
For trivial games class, then test case can be and match test program code corresponding to the trivial games class game or test foot
This.Specifically, the test program code or test script can be it is changeless, i.e., all trivial games class application programs are equal
It can unify with the test program code fixed or test script.Certainly, in other embodiments, the test program code or survey
Training originally can carry out suitability adjustment according to the attribute information of other applications.
In certain embodiments, which is to each second terminal in the second quantity second terminal
Performance parameter and its survey smooth parameter to carry out data process of fitting treatment obtained.It include the property of terminal in smooth prediction model
The mapping relations of the attribute information of energy parameter, the smooth parameter of terminal and application program.By by the performance parameter of first terminal
Be input in the smoothness prediction model, that is, realize on the first terminal run application program fluency can be carried out it is pre-
It surveys, obtains the prediction smoothness parameter of the smooth performance for running the application program for characterizing the first terminal.
S105: the performance parameter of attribute information, each first terminal based on the application program and its pre- flow measurement
The smooth parameter of the performance parameter and its actual measurement of smooth parameter and each second terminal, generates the smooth parameter of full dose terminal
Data set.
In embodiments of the present invention, full dose terminal may include that the first quantity first terminal and the second quantity second are whole
End.Full dose terminal refers to the terminal device of all models in the market or is mounted with that whole terminals of the application-specific are set
It is standby.The smoothness parameter data set is corresponding with the attribute information of the performance parameter of target terminal and destination application for determination
Target smoothness parameter so that operation of the target terminal based on destination application described in the target smoothness state modulator
State.
It is smooth by the performance parameter of second terminal and its smooth parameter of actual measurement, the performance parameter of second terminal and its prediction
Parameter, and the attribute information of corresponding application program is combined, generate the smooth parameter data set of full dose terminal.Stream generated
Smooth supplemental characteristic concentrates measured data and prediction data including all terminals.The smoothness parameter data set can be according to field class
Type or data attribute establish database or the smoothness parameter data set establishes index storage table with easy-to-look-up.The stream
Smooth parameter data set can store at least one background server, in the local disk of interdependent node or cloud.
The embodiment of the present invention is established smooth pre- according to the performance parameter of the smooth parameter of the actual measurement of second terminal and second terminal
Model is surveyed, and prediction can be carried out to the fluency of first terminal based on the smoothness prediction model, obtains predicting smooth parameter, later
The smooth parameter data set for generating full dose terminal, without buy a large amount of terminal device to be tested can be obtained it is comprehensive from
Dynamicization test data, reduces testing cost and error probability, improves testing efficiency, can meet application program and issue need immediately
It asks.In addition, establishing smooth prediction model according to smooth parameter is surveyed, it can more reflect the true fluency of terminal operating application program
Energy；And the corresponding target smoothness parameter of target terminal can be inquired from smoothness supplemental characteristic concentration, to reduce smooth prediction
Calculation amount, so can the corresponding smooth performance of rapid feedback target terminal as a result, improving user experience.
In one embodiment, the data processing method can also include establishing smooth prediction model.Fig. 2 is this hair
A kind of flow diagram for foundation smoothness prediction model that bright embodiment provides.Specifically as shown in Fig. 2, the foundation is smooth pre-
Surveying model can be realized by following steps:
S201: the performance parameter of each second terminal in the second quantity second terminal is obtained.
In embodiments of the present invention, second terminal can be the terminal device of existing second quantity, second quantity
A preferably several, the performance parameter of each second terminal is different.That is, corresponding second number of the second quantity second terminal
Measure performance parameter.
In practical applications, second terminal can be smart phone, and second quantity can include but is not limited to be 50
~300, it may be, for example, 100,150,200 etc..
S203: test case corresponding with the attribute information of the application program is obtained.
Test case is associated with the attribute information of application program, to match different types of application program at the terminal
Test.The test program code of test event that is that test case is write by tester and matching application program (to be tested)
Or test script.It is exemplary, the test event may include the application scenarios of application program, application program running environment and answer
It is at least one with the relevant configuration information (such as shared memory ratio, storage location etc.) of program, by parsing the test event simultaneously
Conversion generates corresponding test case.
In certain embodiments, if application program is trivial games class and tests the application program in the stream of terminal operating
Smooth performance, then test case can be and match test program code or test script corresponding to the trivial games class game.By
Substantially being in operation application program will be using the application on pattern drafting to terminal display screen, so by testing the application program
Picture can reflect the application program in the operation fluency energy of the terminal in the rendering smoothness performance of terminal page.It is real one
Apply in example, if the classification of application program is identical, the test program code or test script can be it is changeless, i.e., it is all
Trivial games class application program can be unified with fixed test program code or test script, such as the rendering of trivial games class picture
Test script.Smooth performance test is carried out to same class application program by using fixed test case, is surveyed so as to reduce
Example on probation writes the development time.
In embodiments of the present invention, test corresponding with the attribute information of the application program can be obtained from local disk
Use-case can also obtain the test case from other servers or cloud.
S205: survey can be carried out to the fluency for running application program in each second terminal using the test case
Examination, obtains the actual measurement smoothness parameter of each second terminal.
It in embodiments of the present invention, can be after establishing the communication connection of second terminal and test equipment, by every
The test case is run in a second terminal to carry out smooth performance test, to obtain the actual measurement smoothness ginseng of each second terminal
Number.The smoothness parameter of surveying is the data for characterizing the smooth performance of second terminal operation application program.
In one embodiment, the test case may include that institute's rendered object is gradually increased on terminal interface
The information of number of objects and/or object sophistication levels.
Wherein, the object may include smart (such as cartoon animals such as Little Bear, doggie), personage, plant, animal, object
Product, picture, photo etc..The object sophistication levels are used for complexity of the description object in drafting or rendering.It is exemplary, three
The sophistication levels of dimensional object are higher than the sophistication levels of two-dimensional object；The top number or lines of model corresponding to object are more
Complexity is higher when then drawing or render, and corresponding object sophistication levels are also higher.
Correspondingly, it is described using the test case in each second terminal run application program smooth performance
It is tested, obtains the actual measurement smoothness parameter of each second terminal, comprising:
S2051: actual frame per second and corresponding institute's rendered object during monitoring each second terminal rendered object
Number of objects and/or object sophistication levels.
Frame per second (Frame per Second, FPS) is display frame number per second.Each second terminal is in rendered object process
In, since the number of objects and/or object sophistication levels of rendered object are gradually increasing, in present frame rendered object, going through
All objects drawn under history frame are ceaselessly moving, and so as the time of rendered object is increasingly longer, draw pair
The number of objects and/or object sophistication levels of elephant are also higher and higher, and current picture frame per second will change with the time is drawn.Pass through
The currently practical frame per second and corresponding drawn number of objects and/or object sophistication levels of monitoring rendered object, it is available
Related data, and then establish the actual frame per second of each second terminal and the linear pass of number of objects (and/or object sophistication levels)
System.
In practical applications, in the case where the object drawn is smart, if test case includes on terminal interface
Gradually increase the information for drawing smart smart quantity.At this point, drawing smart currently practical frame per second and correspondence by obtaining
Draw smart smart quantity, establish the actual frame per second of each second terminal and the linear relationship of smart quantity later.If
Test case includes that the information for drawing smart smart quantity and smart sophistication levels is gradually increased on terminal interface.This
When, smart currently practical frame per second and the corresponding smart quantity for drawing spirit and smart sophistication levels are drawn by obtaining,
Smart quantity and smart sophistication levels are obtained into test data through conversion rear weight processing (such as weighted sum etc.) later, later
Establish the actual frame per second of each second terminal and the linear relationship of test data.
S2053: determine that the actual frame per second of each second terminal is equal to preset frame rate when institute rendered object most
Blob quantity and/or highest object sophistication levels.
In embodiments of the present invention, the actual frame per second of each second terminal of foundation and largest object quantity and/
Or after the linear relationship of highest object sophistication levels, the largest object quantity under preset frame rate can be determined according to the linear relationship
And/or highest object sophistication levels.
In certain embodiments, not frame corresponding to Caton when the preset frame rate can be terminal rendered object
Rate.
For example, if the application program is game class, the preset frame rate is equal to 60 frames.If the application program
For video class, then the preset frame rate is equal to 30 frames.At this point, the most objects drawn in the case where actual frame per second is by 60 or 30 frames
Quantity be the largest object quantity；And/or the most complex object drawn in the case where actual frame per second is by 60 or 30 frames is
The highest object sophistication levels.
Exemplary, when application program is game class, if depicting 5000 spirits, FPS is able to maintain in 60 frames；If
5100 spirits are drawn, FPS is just reduced to 55 frames；It is 5000 spirits that then determining FPS, which is the largest object quantity under 60 frames,.When
So, can also be according to the linear relationship of frame per second and the smart quantity of drafting, calculating FPS is the largest object quantity under 60 frames.
It should be noted that the preset frame rate is not limited to as 60 frames or 30 frames.The improvement or equipment shown with terminal
Improvement, preset frame rate can do suitability adjustment, such as settable preset frame rate is the value greater than 60 frames or 30 frames.
S2055: using the largest object quantity and/or the highest object sophistication levels as each second terminal
Actual measurement smoothness parameter.
In certain embodiments, using the largest object quantity or the highest object sophistication levels as described every
The actual measurement smoothness parameter of a second terminal；Or by the largest object quantity or the highest object sophistication levels after converting
Weighting handles to obtain test data, later using the test data as the actual measurement smoothness parameter of each second terminal.
S207: using machine learning algorithm, performance parameter to each second terminal and corresponding described each the
The actual measurement smoothness parameter of two terminals carries out data process of fitting treatment, obtains the smooth prediction model.
In one embodiment, machine learning algorithm may include decision Tree algorithms, neural network algorithm etc..Decision Tree algorithms
It is a kind of method for approaching discrete function value.The decision Tree algorithms may include that gradient promotes decision tree (Gradient
Boosting Decision Tree, GBDT) algorithm, random forests algorithm etc..
In embodiments of the present invention, which is GBDT algorithm.GBDT algorithm is a kind of decision tree of iteration
Algorithm, the algorithm are made of more decision trees, and the conclusion of all trees has added up as final prediction result.GBDT algorithm uses
Multiple learners respectively learn training set, and final model is the synthesis to above-mentioned multiple learners, most important
Two methods are Bagging algorithm and Boosting algorithm.Certainly, in other embodiments, which can also wrap
Include other learning algorithms.
In embodiments of the present invention, described to use machine learning algorithm, to the performance parameter of each second terminal and
The actual measurement smoothness parameter of corresponding each second terminal carries out data process of fitting treatment, obtains the smooth prediction model, can
To include:
S2071: the performance parameter based on each second terminal is ranked up the second terminal of the second quantity.
The performance parameter of second terminal may include CPU quantity, cpu frequency, GPU frequency, GPU quantity, memory size, end
Hold one of model, screen size and screen resolution or a variety of.
Exemplary, the performance parameter of second terminal includes CPU quantity (such as monokaryon, double-core, four cores etc.), cpu frequency (example
Such as 1.4Ghz, 1.7Ghz, 2Ghz), memory size (such as 2G, 4G, 8G etc.).According to the performance parameter of second terminal to each
Second terminal carry out properties scoring, such as four core CPU be denoted as 4 points, double-core CPU be denoted as 2 points, monokaryon CPU be denoted as 0 point etc.；
Later according to the score summation of properties, each second terminal is ranked up, the second terminal set after being sorted.
S2073: it is grouped, is obtained according to second terminal of the default rule of classification to second quantity after sequence
Third quantity terminal set.
The default rule of classification can be configured and adjust according to the actual situation, which may include
Number of packet (such as 3 groups, 4 groups or multiple groups can be divided into), each group of ratio (such as ratio in 3:4:3), every group of score
Range etc..According to preset rule of classification, the second terminal of second quantity after sequence is grouped, third is obtained
Quantity terminal set.It is exemplary, the second quantity second terminal can be divided into high, medium and low three kinds different configuration class
Group obtains 3 terminal set.
S2075: the second terminal in each terminal set is divided into training set and test set.
It in one embodiment, can be according to certain division proportion (such as 85:15,90:10,98:2 etc.) to each terminal
First terminal in set is divided, and respectively corresponds to obtain training set and test set.
S2077: smooth prediction training is carried out to default machine learning model based on the training set and the test set, is obtained
To the smooth prediction model.
Training set is used for for the default machine learning model of training, test set to the default machine learning model after training
(such as GBDT model) is tested, with Optimized model parameter；Using the model after optimization as the smooth prediction model.
The embodiment of the present invention trains stream by being grouped multiple second terminals to the terminal set after grouping later
Smooth prediction model.By the way that terminal to be divided to the group of different class, detailed-oriented data fitting can be carried out to every group of terminal respectively, thus
Improve the accuracy of data fitting accuracy and obtained smooth prediction model.
The specific embodiment of another data processing method of the present invention introduced below, Fig. 3 is provided in an embodiment of the present invention
Another part flow diagram of data processing method.It is specific as shown in figure 3, generate smooth parameter data set it
Afterwards, the method can also include:
S301: receiving the trigger action to destination application that target terminal is sent and request, the trigger action request
The attribute information of performance parameter and the destination application including the target terminal.
In embodiments of the present invention, user has carried out the trigger action event (example to destination application in target terminal
Such as click, sliding, selection), generate the trigger action request.The trigger action request may include to target application journey
The starting of sequence is requested, load new function is requested etc..The performance parameter of target terminal may include CPU quantity, cpu frequency, GPU frequency
One of rate, GPU quantity, memory size, terminal models, screen size and screen resolution are a variety of.Destination application
Attribute information may include Program Type information (such as big game class, small game class, short video class, long video class, chat
Its class and shopping class etc.), program identification information etc..
S303: the performance parameter and the target application of inquiry with the target terminal are concentrated from the smooth supplemental characteristic
The corresponding target smoothness parameter of the attribute information of program.
According to the attribute information of the performance parameter of target terminal and the destination application, concentrated from smooth supplemental characteristic
Inquire corresponding target smoothness parameter.If inquire there are it is corresponding as a result, if return to target from the inquiry to target terminal it is smooth
Parameter.If do not inquire it is corresponding as a result, if can directly return query result be " sky " or " being not present " prompt information；
The attribute information of the performance parameter of the target terminal and destination application can be input in smooth prediction model and be predicted,
To export corresponding target smoothness parameter.
S305: the target smoothness parameter of inquiry is sent to the target terminal, so that the target terminal is based on
The operating status of destination application described in the target smoothness state modulator.
In embodiments of the present invention, the operating status includes starting state.
Correspondingly, described be sent to the target terminal for the target smoothness parameter of inquiry, so that the target is whole
The operating status of end group destination application described in the target smoothness state modulator, comprising:
The target smoothness parameter of inquiry is sent to the target terminal, so that the target terminal is based on the mesh
It marks smooth parameter and judges whether the destination application meets entry condition, and make the target terminal according to judging result
Control the starting state of the destination application.
In one embodiment, if destination application is game class, target terminal obtains the target smoothness parameter (example
If spirit quantity maximum under 60FPS is 4500), target terminal judge the target smoothness parameter be unsatisfactory for entry condition (such as
Maximum spirit quantity is not less than 5500 under 60FPS), then target terminal is controlled according to the judging result for being unsatisfactory for entry condition
The destination application does not start, and the prompt of such as " terminal device can not open the program " is sent to user.
Fig. 4 is a kind of schematic diagram of application scenarios provided in an embodiment of the present invention.
" XX fighting landlord " as shown in figure 4, include multiple small routines in the wechat app of target terminal, such as in Fig. 4,
" XX is jumped ", " XX Tanks Evolution ", " XX reading seniority among brothers and sisters " etc..If " jump " trivial games therein are wechat trivial games programs
Newly-increased function, user like to try " XX is jumped " trivial games, and by click " XX is jumped " icon, into band, " XX jumps one
The homepage of jump " pattern, target terminal sends trigger action request to background server later, carries in trigger action request
The attribute information of trivial games program and the performance parameter of target terminal, target terminal receives the mesh of background server feedback later
The query result of smooth parameter is marked, if target terminal detects the target smoothness parameter and is unsatisfactory for starting " XX is jumped " trivial games
Condition (target smoothness parameter lower than starting threshold value), then do not start the trivial games, and show that " current equipment system wouldn't be supported
The prompt page of trivial games program ".
The embodiment of the present invention, can be corresponding near searching target terminal by the smooth supplemental characteristic concentration in full dose terminal
Target smoothness parameter, target terminal can prejudge the operation fluency of destination application according to the target smoothness parameter later, if
Judge that the operation fluency of destination application is bad, the case where the Caton that can deposit cash, then judges that the destination application is not met
Entry condition, and then provide the prompt failing to open accordingly.So, it is possible to reduce smooth predicted calculation amount quickly prejudges mesh
The smooth performance for marking application program avoids the problems such as opening Caton brought by the destination application or not being adapted to, and improves and uses
Family experience sense.
The specific embodiment of another data processing method of the present invention introduced below, Fig. 5 is provided in an embodiment of the present invention
The flow diagram of another data processing method, present description provides the methods as described in embodiment or flow chart to operate step
It suddenly, but based on routine or may include more or less operating procedure without creative labor.It is enumerated in embodiment
Sequence of steps is only one of numerous step execution sequence mode, does not represent and unique executes sequence.In practice be
When system or server product or terminal execute, it can execute according to embodiment or method shown in the drawings sequence or hold parallel
Row (such as environment of parallel processor or multiple threads).It is specific as shown in figure 5, the method may include:
S501: receiving the trigger action to destination application that target terminal is sent and request, the trigger action request
The attribute information of performance parameter and the destination application including the target terminal.
S503: the attribute information of the performance parameter of the target terminal and the destination application is input to smooth pre-
Model is surveyed, prediction can be carried out to the fluency for running the destination application on the target terminal, it is whole to obtain the target
The target smoothness parameter at end.
Wherein, the smooth prediction model be to the performance parameter of each second terminal in the second quantity second terminal and
It is obtained to carry out data process of fitting treatment for the smooth parameter of flow measurement in fact；The smooth parameter of the actual measurement is based on the attribute with application program
The corresponding test case of information tests each second terminal.
S505: being sent to the target terminal for the target smoothness parameter, so that the target terminal is based on the mesh
Mark the operating status of destination application described in smooth state modulator.
The particular content and details of step S501~S505 in the embodiment of the present invention can be found in above-described embodiment, in order to keep away
Exempt to repeat, details are not described herein.
The embodiment of the present invention surveys smooth parameter by obtaining in second terminal using test case, so can more reflect
The true smooth performance of terminal operating application program.Due to the performance of actual measurement smooth parameter and second terminal according to second terminal
Parameter establishes smooth prediction model, and can be carried out prediction to the fluency of target terminal based on the smoothness prediction model, directly
To target smoothness parameter.In this way, not needing to buy a large amount of terminal device to be tested, testing cost is substantially reduced；And it can reduce
Artificial to test brought test error, test result accuracy is higher.In addition, also shortening test time-consuming, can meet using journey
Sequence issues demand immediately.
Following is apparatus of the present invention embodiment, can be used for executing embodiment of the present invention method.For apparatus of the present invention reality
Undisclosed details and beneficial effect in example are applied, embodiment of the present invention method is please referred to.
Referring to FIG. 6, it illustrates a kind of block diagrams of data processing equipment provided in an embodiment of the present invention.The device has
Realize that the function in above method example, the function it is real can also to be executed corresponding software by hardware realization by hardware
It is existing.The data processing equipment 60 may include:
Parameter acquisition module 61, for obtaining the performance parameter of each first terminal in the first quantity first terminal；
Parameter prediction module 62, for performance parameter and smoothness prediction model based on each first terminal, to institute
The fluency for stating operation application program on each first terminal can be carried out prediction, and the prediction for obtaining each first terminal is smooth
Parameter；Wherein, the smooth prediction model be to the performance parameter of each second terminal in the second quantity second terminal and its
It is obtained to survey smooth parameter progress data process of fitting treatment；The smooth parameter of the actual measurement is based on the category with the application program
The property corresponding test case of information tests each second terminal；
Dataset generation module 63, the performance ginseng for attribute information, the first terminal based on the application program
Number and its performance parameter and its actual measurement smoothness parameter for predicting smooth parameter and the second terminal, generate full dose terminal
Smooth parameter data set；
Wherein, the full dose terminal includes the first quantity first terminal and the second quantity second terminal.
In some embodiments, described device 60 can also include model building module 64, the model building module 64
Include:
First acquisition unit 641, for obtaining the performance parameter of each second terminal in the second quantity second terminal；
Second acquisition unit 642, for obtaining test case corresponding with the attribute information of the application program；
Parameter determination unit 643, for using the test case to running application program in each second terminal
Fluency can be carried out test, obtain the actual measurement smoothness parameter of each second terminal；
Model foundation unit 644, for using machine learning algorithm, performance parameter to each second terminal and right
The actual measurement smoothness parameter for each second terminal answered carries out data process of fitting treatment, obtains the smooth prediction model.
In one embodiment, the parameter determination unit 643 may include:
Detection sub-unit, for monitoring actual frame per second and corresponding institute during each second terminal rendered object
The number of objects and/or object sophistication levels of rendered object；
First determines subelement, for determining that the actual frame per second of each second terminal is equal to preset frame rate when institute
The largest object quantity and/or highest object sophistication levels of rendered object；
Second determines subelement, for using the largest object quantity and/or the highest object sophistication levels as institute
State the actual measurement smoothness parameter of each second terminal.
In one embodiment, the model foundation unit 644 may include:
Sorting subunit, for the performance parameter based on each second terminal, to the second terminal of the second quantity
It is ranked up；
It is grouped subelement, for carrying out according to second terminal of the default rule of classification to second quantity after sequence
Grouping, obtains third quantity terminal set；
Subelement is divided, for the second terminal in each terminal set to be divided into training set and test set；
Model foundation subelement, for being flowed based on the training set and the test set to default machine learning model
Smooth prediction training obtains the smooth prediction model.
In one embodiment, described device 60 can also include:
Request receiving module 65, the trigger action to destination application for receiving target terminal transmission are requested, institute
Stating trigger action request includes the performance parameter of the target terminal and the attribute information of the destination application；
Enquiry module 66, for concentrating performance parameter and the institute of inquiry and the target terminal from the smooth supplemental characteristic
State the corresponding target smoothness parameter of attribute information of destination application；
Sending module 67, for the target smoothness parameter of inquiry to be sent to the target terminal, so that the mesh
Mark operating status of the terminal based on destination application described in the target smoothness state modulator.
In one embodiment, the operating status includes starting state.Correspondingly, the sending module can be used for:
The target smoothness parameter of inquiry is sent to the target terminal, so that the target terminal is based on the mesh
It marks smooth parameter and judges whether the destination application meets entry condition, and make the target terminal according to judging result
Control the starting state of the destination application.
Referring to FIG. 7, it illustrates the block diagrams of another data processing equipment provided in an embodiment of the present invention.Device tool
There is the function of realizing in above method example, the function can also be executed corresponding software by hardware realization by hardware
It realizes.The data processing equipment 70 may include:
Request receiving module 71, the trigger action to destination application for receiving target terminal transmission are requested, institute
Stating trigger action request includes the performance parameter of the target terminal and the attribute information of the destination application；
Parameter determination module 72, for believing the attribute of the performance parameter of the target terminal and the destination application
Breath is input to smooth prediction model, can be carried out prediction to the fluency for running the destination application on the target terminal,
Obtain the target smoothness parameter of the target terminal；
Parameter sending module 73, for the target smoothness parameter to be sent to the target terminal, so that the target
Operating status of the terminal based on destination application described in the target smoothness state modulator；
Wherein, the smooth prediction model be to the performance parameter of each second terminal in the second quantity second terminal and
It is obtained to carry out data process of fitting treatment for the smooth parameter of flow measurement in fact；The smooth parameter of the actual measurement is based on the attribute with application program
The corresponding test case of information tests each second terminal.
The embodiment of the invention provides a kind of data processing equipment, the equipment includes processor and memory, described to deposit
Be stored at least one instruction, at least a Duan Chengxu, code set or instruction set in reservoir, at least one instruction, it is described extremely
A few Duan Chengxu, the code set or instruction set are loaded by the processor and are executed to realize as above method embodiment is mentioned
The data processing method of confession.
Memory can be used for storing software program and module, and processor is stored in the software program of memory by operation
And module, thereby executing various function application and data processing.Memory can mainly include storing program area and storage number
According to area, wherein storing program area can application program needed for storage program area, function etc.；Storage data area can store basis
The equipment uses created data etc..In addition, memory may include high-speed random access memory, can also include
Nonvolatile memory, for example, at least a disk memory, flush memory device or other volatile solid-state parts.Phase
Ying Di, memory can also include Memory Controller, to provide access of the processor to memory.
Further, Fig. 8 shows a kind of hardware knot of equipment for realizing method provided by the embodiment of the present invention
Structure schematic diagram, the equipment can be terminal, mobile terminal or other equipment, the equipment may also participate in composition or
Include device provided by the embodiment of the present invention.As shown in figure 8, terminal 10 may include one or more (adopts in figure
With 102a, 102b ... ..., 102n is shown) processor 102 (processor 102 can include but is not limited to Micro-processor MCV or
The processing unit of programmable logic device FPGA etc.), memory 104 for storing data and the biography for communication function
Defeated device 106.It in addition to this, can also include: display, input/output interface (I/O interface), universal serial bus
(USB) port (a port that can be used as in the port of I/O interface is included), network interface, power supply and/or camera.This
Field those of ordinary skill is appreciated that structure shown in Fig. 8 is only to illustrate, and does not cause to the structure of above-mentioned electronic device
It limits.For example, terminal 10 may also include than shown in Fig. 8 more perhaps less component or have with shown in Fig. 8
Different configurations.
It is to be noted that said one or multiple processors 102 and/or other data processing circuits lead to herein
Can often " data processing circuit " be referred to as.The data processing circuit all or part of can be presented as software, hardware, firmware
Or any other combination.In addition, data processing circuit for single independent processing module or all or part of can be integrated to meter
In any one in other elements in calculation machine terminal 10 (or mobile device).As involved in the embodiment of the present application,
The data processing circuit controls (such as the selection for the variable resistance end path connecting with interface) as a kind of processor.
Memory 104 can be used for storing the software program and module of application software, as described in the embodiment of the present invention
Corresponding program instruction/the data storage device of method, the software program that processor 102 is stored in memory 104 by operation
And module realizes a kind of above-mentioned Processing with Neural Network method thereby executing various function application and data processing.It deposits
Reservoir 104 may include high speed random access memory, may also include nonvolatile memory, as one or more magnetic storage fills
It sets, flash memory or other non-volatile solid state memories.In some instances, memory 104 can further comprise relative to place
The remotely located memory of device 102 is managed, these remote memories can pass through network connection to terminal 10.Above-mentioned network
Example include but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Transmitting device 106 is used to that data to be received or sent via a network.Above-mentioned network specific example may include
The wireless network that the communication providers of terminal 10 provide.In an example, transmitting device 106 includes a network
Adapter (Network Interface Controller, NIC), can be connected by base station with other network equipments so as to
It is communicated with internet.In an example, transmitting device 106 can be radio frequency (Radio Frequency, RF) module,
It is used to wirelessly be communicated with internet.
Display can such as touch-screen type liquid crystal display (LCD), the liquid crystal display aloow user with
The user interface of terminal 10 (or mobile device) interacts.
It should be understood that embodiments of the present invention sequencing is for illustration only, do not represent the advantages or disadvantages of the embodiments.
And above-mentioned this specification specific embodiment is described.Other embodiments are within the scope of the appended claims.One
In a little situations, the movement recorded in detail in the claims or step can be executed according to the sequence being different from embodiment and
Still desired result may be implemented.In addition, process depicted in the drawing not necessarily requires the particular order shown or company
Continuous sequence is just able to achieve desired result.In some embodiments, multitasking and parallel processing it is also possible or
It may be advantageous.
The embodiments are all described in a progressive manner for each of this specification, same and similar portion between each embodiment
Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for device and
For server example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring to side
The part of method embodiment illustrates.
Those of ordinary skill in the art will appreciate that realizing that all or part of the steps of above-described embodiment can pass through hardware
It completes, relevant hardware can also be instructed to complete by program, the program can store in a kind of computer-readable
In storage medium, storage medium mentioned above can be read-only memory, disk or CD etc..
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of data processing method characterized by comprising
Obtain the performance parameter of each first terminal in the first quantity first terminal；
Performance parameter and smooth prediction model based on each first terminal, are applied to running on each first terminal
The fluency of program can be carried out prediction, obtain the prediction smoothness parameter of each first terminal；Wherein, the smooth prediction mould
Type is to the performance parameter of each second terminal in the second quantity second terminal and its to survey smooth parameter progress data fitting
It handles obtained；The smooth parameter of the actual measurement is based on test case corresponding with the attribute information of the application program to institute
It is obtained to state each smooth performance test of second terminal progress；
The performance parameter of attribute information, each first terminal based on the application program and its predict smooth parameter, with
And the performance parameter and its smooth parameter of actual measurement of each second terminal, generate the smooth parameter data set of full dose terminal；
Wherein, the full dose terminal includes the first quantity first terminal and the second quantity second terminal.
2. the method according to claim 1, wherein the smoothness prediction model is through the following steps that realize:
Obtain the performance parameter of each second terminal in the second quantity second terminal；
Obtain test case corresponding with the attribute information of the application program；
Test can be carried out to the fluency for running application program in each second terminal using the test case, obtain institute
State the actual measurement smoothness parameter of each second terminal；
Using machine learning algorithm, the reality of performance parameter and corresponding each second terminal to each second terminal
The smooth parameter of flow measurement carries out data process of fitting treatment, obtains the smooth prediction model.
3. according to the method described in claim 2, it is characterized in that, the test case includes gradually increasing on terminal interface
The number of objects of institute's rendered object and/or the information of object sophistication levels；
Correspondingly, described can be carried out the fluency for running application program in each second terminal using the test case
Test, obtains the actual measurement smoothness parameter of each second terminal, comprising:
Monitor the number of objects of the actual frame per second and corresponding institute's rendered object during each second terminal rendered object
And/or object sophistication levels；
Determine that the actual frame per second of each second terminal is equal to the largest object quantity of preset frame rate when institute rendered object
And/or highest object sophistication levels；
The largest object quantity and/or the highest object sophistication levels is smooth as the actual measurement of each second terminal
Parameter.
4. according to the method described in claim 2, it is characterized in that, described use machine learning algorithm, to described each second
The actual measurement smoothness parameter of the performance parameter of terminal and corresponding each second terminal carries out data process of fitting treatment, obtains described
Smooth prediction model, comprising:
Based on the performance parameter of each second terminal, the second terminal of the second quantity is ranked up；
It is grouped according to second terminal of the default rule of classification to second quantity after sequence, obtains third quantity
Terminal set；
Second terminal in each terminal set is divided into training set and test set；
Smooth prediction training is carried out to default machine learning model based on the training set and the test set, obtains the smoothness
Prediction model.
5. method according to claim 1 to 4, which is characterized in that the method also includes:
It receives the trigger action to destination application that target terminal is sent to request, the trigger action request includes the mesh
Mark the performance parameter of terminal and the attribute information of the destination application；
Inquiry and the performance parameter of the target terminal and the category of the destination application are concentrated from the smooth supplemental characteristic
The property corresponding target smoothness parameter of information；
The target smoothness parameter of inquiry is sent to the target terminal, so that the target terminal is based on the target stream
The operating status of destination application described in smooth state modulator.
6. according to the method described in claim 5, it is characterized in that, the operating status includes starting state；
Correspondingly, described be sent to the target terminal for the target smoothness parameter of inquiry, so that the target terminal base
The operating status of the destination application described in the target smoothness state modulator, comprising:
The target smoothness parameter of inquiry is sent to the target terminal, so that the target terminal is based on the target stream
Smooth parameter judges whether the destination application meets entry condition, and controls the target terminal according to judging result
The starting state of the destination application.
7. a kind of data processing method characterized by comprising
It receives the trigger action to destination application that target terminal is sent to request, the trigger action request includes the mesh
Mark the performance parameter of terminal and the attribute information of the destination application；
The attribute information of the performance parameter of the target terminal and the destination application is input to smooth prediction model, it is right
The fluency that the destination application is run on the target terminal can be carried out prediction, obtain the target stream of the target terminal
Smooth parameter；
The target smoothness parameter is sent to the target terminal, so that the target terminal is based on the target smoothness parameter
Control the operating status of the destination application；
Wherein, the smooth prediction model is to the performance parameter of each second terminal in the second quantity second terminal and in fact
It is obtained that the smooth parameter of flow measurement carries out data process of fitting treatment；The smooth parameter of the actual measurement is based on the attribute information with application program
Corresponding test case tests each second terminal.
8. a kind of data processing equipment characterized by comprising
Parameter acquisition module, for obtaining the performance parameter of each first terminal in the first quantity first terminal；
Parameter prediction module, for performance parameter and smoothness prediction model based on each first terminal, to described each
The fluency that application program is run on first terminal can be carried out prediction, obtain the prediction smoothness parameter of each first terminal；
Wherein, the smooth prediction model is to the performance parameter of each second terminal in the second quantity second terminal and its actual measurement stream
It is obtained that smooth parameter carries out data process of fitting treatment；The smooth parameter of the actual measurement is based on the attribute information with the application program
Corresponding test case tests each second terminal；
Dataset generation module, the performance parameter for attribute information, each first terminal based on the application program
And its predict the performance parameter of smooth parameter and each second terminal and its survey smooth parameter, generate full dose terminal
Smooth parameter data set；
Wherein, the full dose terminal includes the first quantity first terminal and the second quantity second terminal.
9. a kind of data processing equipment characterized by comprising
Request receiving module, the trigger action to destination application for receiving target terminal transmission are requested, the triggering
Operation requests include the performance parameter of the target terminal and the attribute information of the destination application；
Parameter determination module, for inputting the attribute information of the performance parameter of the target terminal and the destination application
To smooth prediction model, prediction can be carried out to the fluency for running the destination application on the target terminal, obtain institute
State the target smoothness parameter of target terminal；
Parameter sending module, for the target smoothness parameter to be sent to the target terminal, so that the target terminal base
The operating status of the destination application described in the target smoothness state modulator；
Wherein, the smooth prediction model is to the performance parameter of each second terminal in the second quantity second terminal and in fact
It is obtained that the smooth parameter of flow measurement carries out data process of fitting treatment；The smooth parameter of the actual measurement is based on the attribute information with application program
Corresponding test case tests each second terminal.
10. a kind of data processing equipment, which is characterized in that the equipment includes processor and memory, is deposited in the memory
Contain at least one instruction, at least a Duan Chengxu, code set or instruction set, at least one instruction, an at least Duan Cheng
Sequence, the code set or instruction set are loaded by the processor and are executed to realize the data as described in claim 1 to 6 is any
Processing method or data processing method as claimed in claim 7.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112286808A (en) * | 2020-10-29 | 2021-01-29 | 北京字节跳动网络技术有限公司 | Application program testing method and device, electronic equipment and medium |
| CN112835641A (en) * | 2021-02-02 | 2021-05-25 | 上海臣星软件技术有限公司 | Configuration method and device for application program running function |
| CN113769387A (en) * | 2021-09-18 | 2021-12-10 | 网易（杭州）网络有限公司 | Game graphic parameter configuration method and device and terminal equipment |
| CN114064513A (en) * | 2022-01-12 | 2022-02-18 | 北京新氧科技有限公司 | Page fluency performance detection method, device, equipment and system |
| CN114827646A (en) * | 2022-03-23 | 2022-07-29 | 百果园技术(新加坡)有限公司 | Method, device and equipment for preloading live broadcast room in video stream and storage medium |
| CN115333941A (en) * | 2022-01-28 | 2022-11-11 | 花瓣云科技有限公司 | Method for acquiring application running condition and related equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105308941A (en) * | 2014-05-16 | 2016-02-03 | 华为技术有限公司 | Method and apparatus for resource configuration |
| CN106294902A (en) * | 2015-05-28 | 2017-01-04 | 阿里巴巴集团控股有限公司 | Method, device and the electronic equipment of prediction mobile applications page performance |
| CN108334440A (en) * | 2017-01-19 | 2018-07-27 | 阿里巴巴集团控股有限公司 | A kind of processing method and processing device, client obtaining application performance test result |
| US20180253372A1 (en) * | 2017-03-01 | 2018-09-06 | International Business Machines Corporation | Performance test of software products with reduced duration |
| CN108984369A (en) * | 2018-07-13 | 2018-12-11 | 厦门美图移动科技有限公司 | Caton prediction method and device and mobile terminal |
| CN109697090A (en) * | 2018-12-27 | 2019-04-30 | Oppo广东移动通信有限公司 | A kind of method, terminal device and the storage medium of controlling terminal equipment |
| CN109814933A (en) * | 2019-01-29 | 2019-05-28 | 腾讯科技（深圳）有限公司 | A kind of business data processing method and device |
-
2019
- 2019-06-28 CN CN201910576170.7A patent/CN110413510B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105308941A (en) * | 2014-05-16 | 2016-02-03 | 华为技术有限公司 | Method and apparatus for resource configuration |
| CN106294902A (en) * | 2015-05-28 | 2017-01-04 | 阿里巴巴集团控股有限公司 | Method, device and the electronic equipment of prediction mobile applications page performance |
| CN108334440A (en) * | 2017-01-19 | 2018-07-27 | 阿里巴巴集团控股有限公司 | A kind of processing method and processing device, client obtaining application performance test result |
| US20180253372A1 (en) * | 2017-03-01 | 2018-09-06 | International Business Machines Corporation | Performance test of software products with reduced duration |
| CN108984369A (en) * | 2018-07-13 | 2018-12-11 | 厦门美图移动科技有限公司 | Caton prediction method and device and mobile terminal |
| CN109697090A (en) * | 2018-12-27 | 2019-04-30 | Oppo广东移动通信有限公司 | A kind of method, terminal device and the storage medium of controlling terminal equipment |
| CN109814933A (en) * | 2019-01-29 | 2019-05-28 | 腾讯科技（深圳）有限公司 | A kind of business data processing method and device |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112286808A (en) * | 2020-10-29 | 2021-01-29 | 北京字节跳动网络技术有限公司 | Application program testing method and device, electronic equipment and medium |
| CN112286808B (en) * | 2020-10-29 | 2023-08-11 | 抖音视界有限公司 | Application program testing method and device, electronic equipment and medium |
| CN112835641A (en) * | 2021-02-02 | 2021-05-25 | 上海臣星软件技术有限公司 | Configuration method and device for application program running function |
| CN113769387A (en) * | 2021-09-18 | 2021-12-10 | 网易（杭州）网络有限公司 | Game graphic parameter configuration method and device and terminal equipment |
| CN114064513A (en) * | 2022-01-12 | 2022-02-18 | 北京新氧科技有限公司 | Page fluency performance detection method, device, equipment and system |
| CN115333941A (en) * | 2022-01-28 | 2022-11-11 | 花瓣云科技有限公司 | Method for acquiring application running condition and related equipment |
| CN115333941B (en) * | 2022-01-28 | 2023-08-22 | 花瓣云科技有限公司 | Method for acquiring application running condition and related equipment |
| CN114827646A (en) * | 2022-03-23 | 2022-07-29 | 百果园技术(新加坡)有限公司 | Method, device and equipment for preloading live broadcast room in video stream and storage medium |
| CN114827646B (en) * | 2022-03-23 | 2023-12-12 | 百果园技术(新加坡)有限公司 | Method, device, equipment and storage medium for preloading live broadcasting room in video stream |
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