CN103246526A - Client pre-loading method and device - Google Patents
Client pre-loading method and device Download PDFInfo
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Abstract
The invention discloses a client pre-loading method and device. The client pre-loading method includes: obtaining use behavior characteristics of a user under various scenes in advance; obtaining a use behavior characteristic of the user corresponding to a current scene of the user, calculating a pre-loading required value corresponding to each operation of the user under the current scene according to a preset forecast strategy; obtaining loading data required by a switched scene which is a scene corresponding to operation in which the pre-loading required value exceeds a preset pre-loading required threshold; and monitoring scene switching of the user, guaranteeing that the switched scene is matched with a scene corresponding to the obtained loading data, and showing the obtained loading data. By means of the client pre-loading method and device, client loading time can be reduced.
Description
Technical field
The present invention relates to computer communication technology, particularly a kind of client prestrain method and client pre-load means.
Background technology
In present most client, need client to obtain corresponding loading data from server during loading, and because the restriction of network transfer speeds and the loading data volume that need obtain, client loads request of data from sending to server, to server response and return and load required data, may need to wait for tens milliseconds to several seconds, even tens seconds time, if the response time is long, will make that the load time is longer.
Generally speaking, client need be obtained from server and load the data majority when occurring in scene and switch, for example, for common website, it may be redirect between the page that scene is switched, and for rich client, it may be calling or switch between different scenes that scene is switched, or the switching (for example, the tab label switches, goes up nextpage switching etc.) of different little scenes in certain scene.When the user passes through the operation of client executing scene switching, need waiting for server to return new scene and play up required loading data, or wait for the loading data that need from buffer memory (cache) packing again, like this, if the user is before entering new scene, expend the long data load time, to make that the scene switch speed is slow, the page shows discontinuous, the user experiences may be subjected to bigger influence, thereby, the data load time when how to reduce the scene switching is the emphasis that present client loads research.
For present most of client, the general mode that when carrying out the scene blocked operation, triggers to server request of loading data that adopts, namely have only when the user determines to carry out the scene blocked operation, just play up to the server request and switch the needed loading data of back scene, though this mode can be obtained the loading data by scene switching demand, and can reduce the waste of the unnecessary network bandwidth, but when carrying out the scene switching at every turn, the user such as needs at the loading of to be switched back court scape desired data, can't take full advantage of the user browse switch before the prestrain of switching the back scene of time of scene, and because the load time is longer, its influence that user is experienced also becomes inevitable.
Based on above-mentioned technical matters, prior art has proposed the method for several client prestrains, is briefly described below.
One, as required or by the client prestrain mode of experience:
When the user browses web sites first, according to developer's experience, the scene that some users are comparatively commonly used has been carried out prestrain in client, like this, when the user enters these scenes, need not to obtain the loading data from server, thereby reduced period of reservation of number, improved the client loading velocity.But this method may need the data content of several default scenes is loaded together when loading first, has increased the time that starts client; Further, by the default scene that needs prestrain of developer's experience, be difficult to accomplish accurately to meet user's use habit; And this prestrain method also can't take full advantage of the time that the user browses current scene and carry out the prestrain that scene may appear in future.
Two, the client prestrain mode of picture rolling:
At present, much there is the client application of a large amount of image contents all to adopt the mode of picture rolling prestrain, for example, the microblogging picture presentation function of Tengxun's microblogging, Sina's microblogging.This method is for same Webpage, viewing area by the current browser of preferential Web page loading, and outside the viewing area, add picture or literal in the prestrain scope of a setting, to accelerate the subsequent load response speed of webpage, owing to added certain prestrain scope, when the user uses the slow scroll through pages of scroll bar, because the picture in the prestrain scope just has been loaded when the user browses current viewing area, thereby, the user can't discover because picture loads the delay sensation that produces, though this prestrain scheme realizes simple, but can only apply to the application under the single game scape, and not be suitable for complicated many scenes switching prestrains, make when many scenes are switched, still need pull required loading data from server, the load time is longer.
Summary of the invention
In view of this, fundamental purpose of the present invention is to propose a kind of client prestrain method, reduces the load time.
Another object of the present invention is to propose a kind of client pre-load means, reduce the load time.
For achieving the above object, the invention provides a kind of client prestrain method, this method comprises:
Obtain the usage behavior characteristic of user under each scene in advance;
Obtain user's usage behavior characteristic of user's current scene correspondence, calculate the corresponding prestrain requirements of each operation of user under the current scene according to the predicting strategy that sets in advance;
Obtain the required loading data of handoff scenario, described handoff scenario surpasses the scene of the operation correspondence of predefined prestrain demand threshold for the prestrain requirements;
The monitor user ' scene is switched, and the scene scene corresponding with the loading data of obtaining after determining to switch is complementary, and shows the loading data of obtaining.
Described statistics user usage behavior characteristic comprises:
Be sign with user, add up each user's usage behavior characteristic respectively; Or
Add up all users' usage behavior characteristic, obtain the mean value of all users' usage behavior characteristic, as user's usage behavior characteristic.
Described before obtaining user's usage behavior characteristic of user's current scene correspondence, further comprise:
Client to the user first the usage behavior characteristic sort, choose the scene of default number before the ordering, pull the required loading data of scene of this default number, form and load data set and storage;
Determine that current scene is the scene that the user browses first, from the loading data centralization of storing in advance, inquiry is obtained the loading data of current scene correspondence and is showed.
Described user's usage behavior characteristic comprises: time response and operating characteristic, wherein,
Described time response comprises: mean value and the mean square deviation of user's residence time in different scenes;
Described operating characteristic comprises that the user switches to the probability of another scene from a scene.
Described prestrain requirements according to each operation correspondence of user under the predicting strategy calculating current scene that sets in advance comprises:
By the mean value weight calculation function calculation user that the sets in advance mean value weight in the residence time of current scene, multiply each other with the mean value weight coefficient;
By the mean square deviation weight calculation function calculation user that the sets in advance mean square deviation weight in the residence time of current scene, multiply each other with the mean square deviation weight coefficient;
Calculate the probability right that user's executable operations under current scene switches to handoff scenario by the probability right computing function that sets in advance, with the probability right multiplication; Perhaps by the probability right computing function that sets in advance calculate the user under the current scene by the probability right of handoff scenario again of the scene after switching, multiply each other with the number of plies power of the prediction of probability right coefficient and decay factor; And
The long-pending addition of respectively multiplying each other is obtained the prestrain requirements of user's executable operations correspondence under the current scene.
After the step of described and probability right multiplication, further comprise:
After executable operations under the current scene, switch to the time that the pulls estimated value weight of the required loading data of handoff scenario correspondence by the time that the pulls estimated value weight calculation function calculation user who sets in advance, with pull time estimated value weight coefficient and multiply each other, and carry out the step of the long-pending addition that will respectively multiply each other;
After the step that the number of plies power of the prediction of described and probability right coefficient and decay factor multiplies each other, further comprise:
With the summation by way of the acquisition time estimated value of scene required loading data of current scene to last handoff scenario, multiply each other with the number of plies power of the prediction that pulls time estimated value weight coefficient and decay factor, and carry out the step of the long-pending addition that will respectively multiply each other.
The described required loading data of handoff scenario of obtaining comprise:
Inquiry is the loading data set of storage in advance, is up-to-date if having the required loading data of handoff scenario and this loading data, then obtains the required loading data of this handoff scenario from loading data centralization; Otherwise,
Send data acquisition request to server, pull the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold from server, and be stored in the loading data centralization; Or, sending the data preparation request to server, server receives the data preparation request, and the required loading data of handoff scenario are obtained in inquiry, and encapsulate loading data.
Further comprise:
Stab when upgrading threshold value with the difference of the timestamp that loads data greater than the time that sets in advance in the current time, client initiatively pulls corresponding loading data and the loading data of storage is upgraded to server.
Further comprise:
The scene scene corresponding with the loading data of obtaining after determine switching is not complementary, and the preloading data that interruption is being transmitted pulls required loading data of scene after the switching from server.
Further comprise:
Determine that the user withdraws from scene, according to the usage behavior characteristic of user under each scene of user's scene switching updated stored.
A kind of client pre-load means, this device comprises: usage behavior statistics of features module, prestrain requirements computing module, preloading data acquisition module and scene matching module, wherein,
Usage behavior statistics of features module is obtained the usage behavior characteristic of user under each scene;
Prestrain requirements computing module obtains user's usage behavior characteristic of user's current scene correspondence, calculates the corresponding prestrain requirements of each operation of user under the current scene according to the predicting strategy that sets in advance;
The preloading data acquisition module obtains the required loading data of handoff scenario, and described handoff scenario surpasses the scene of the operation correspondence of predefined prestrain demand threshold for the prestrain requirements;
The scene matching module, the monitor user ' scene is switched, and the scene scene corresponding with the loading data of obtaining after determining to switch is complementary, and shows the loading data of obtaining.
Described prestrain requirements computing module comprises: mean value weight calculation unit, mean square deviation weight calculation unit, probability right computing unit and prestrain requirements computing unit, wherein,
The mean value weight calculation unit is by the mean value weight calculation function calculation user that the sets in advance mean value weight in the residence time of current scene;
The mean square deviation weight calculation unit is by the mean square deviation weight calculation function calculation user that the sets in advance mean square deviation weight in the residence time of current scene;
The probability right computing unit calculates the probability right that user's executable operations under current scene switches to handoff scenario by the probability right computing function that sets in advance;
Prestrain requirements computing unit, mean value weight and mean value weight coefficient that the mean value weight calculation unit is calculated multiply each other, mean square deviation weight and mean square deviation weight coefficient that the mean square deviation weight calculation unit is calculated multiply each other, probability right and probability right multiplication that the probability right computing unit is calculated, and the long-pending addition that will respectively multiply each other.
Described scene matching module comprises: monitoring means, scene matching unit, loading data set unit and loading data display unit, wherein,
Monitoring means, the monitor user ' scene is switched, and exports the user's scene handover information that monitors to the scene matching unit;
The scene matching unit receives user's scene handover information, and the scene corresponding with the loading data that load the storage of data set unit is complementary, if coupling, to the handoff scenario information that loads data display unit output coupling;
Load the data display unit, according to the handoff scenario information that receives, obtain the required loading data of handoff scenario and displaying from loading the data set unit.
Described scene matching module further comprises: interrupt location and loading data pull unit, wherein,
The scene matching unit is further used for exporting unmatched handoff scenario information to interrupt location when the user's scene handover information that the receives scene corresponding with the loading data that load the storage of data set unit do not match;
Interrupt location, according to the handoff scenario information that receives, the preloading data that interruption is being transmitted sends a notification message to loading the data pull unit;
Load the data pull unit, receiving notice message pulls the required loading data of handoff scenario from server.
As seen from the above technical solutions, the usage behavior characteristic of user under each scene is added up and stored to a kind of client prestrain method and client pre-load means that the embodiment of the invention provides in advance; Obtain user's usage behavior characteristic of user's current scene correspondence, calculate the corresponding prestrain requirements of each operation of user under the current scene according to the predicting strategy that sets in advance; Obtain the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold; The monitor user ' scene is switched, and the scene scene corresponding with the loading data of obtaining after determining to switch is complementary, and shows the loading data of obtaining.Like this, by adding up and store the usage behavior characteristic of user under each scene in advance, usage behavior characteristic according to user under the current scene is predicted user's subsequent operation, and the required loading data of scene of obtaining prediction in the user browses the process of current scene are carried out prestrain, thereby when the user switches to the prediction scene, the load time of having reduced client.
Description of drawings
Fig. 1 is the statistics synoptic diagram of embodiment of the invention user usage behavior characteristic.
Fig. 2 is the client prestrain method overall procedure synoptic diagram of the embodiment of the invention.
Fig. 3 is the statistics synoptic diagram of embodiment of the invention time response.
Fig. 4 is embodiment of the invention scene operation characteristic synoptic diagram.
Fig. 5 is the client prestrain method idiographic flow synoptic diagram of the embodiment of the invention.
Fig. 6 obtains the required loading data flow synoptic diagram of handoff scenario for the embodiment of the invention.
Fig. 7 interrupts invalid prestrain schematic flow sheet for the embodiment of the invention.
Fig. 8 is the client pre-load means structural representation of the embodiment of the invention.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with the accompanying drawings and the specific embodiments.
Existing client prestrain method, no matter adopt as required or by experience prestrain mode or picture rolling prestrain mode, when many scenes are switched, client need pull required loading data from server, make that the loading required time is longer, the personalization that can not satisfy the user loads demand.In the practical application, for different scenes, each user's usage behavior is different, and namely the user may be different in the time that different scenes stop, and the probability that switches to different scenes from a scene also is different.In the embodiment of the invention, consider that the user is at user's usage behavior of different scenes, be that the user carries out the probability that different operating jumps to corresponding scene in the residence time (browsing time) of scene and in different scenes, a kind of client prestrain method based on statistical forecast is proposed, the user who obtains according to statistics is in average and the mean square deviation of the browsing time of different scenes, and the probability that jumps to corresponding scene at different scenes execution different operatings, predictive user is carried out a certain operation and is entered the probability of another scene under current scene, judge whether to utilize the user in the browsing time of current scene with this, the user carries out prestrain to entering next scene desired data, so that need not to wait for the time of obtaining data from server when entering next scene.
Institute it should be noted that, the described scene of the embodiment of the invention is switched, comprise: between the redirect between the page, different scene call or switching, scene in switching (for example, the tab label switches, goes up nextpage switching etc.), the switching of picture browsing and the redirect of link information etc. of different little scenes.
Fig. 1 is the statistics synoptic diagram of embodiment of the invention user usage behavior characteristic.Referring to Fig. 1, user's usage behavior characteristic comprises: time response and operating characteristic, and wherein, time response is represented the residence time with the user, operating characteristic represents from the probability that a scene switches to another scene that with the user each scene can corresponding Website page.
Through statistics, the user is under scene C1, and stop reaches the time of 20s for the content of browsing scene C1.Simultaneously, under scene C1, has the probability executable operations Q up to 60% respectively
1, jump to scene C2, have 20% probability executable operations Q from scene C1
2, jump to scene C3, have 10% probability executable operations Q from scene C1
3, from scene C1 jump to scene C4 and, 10% probability executable operations Q
4, from scene C1, withdraw from;
Under scene C2, the time that stops 10s is used for browsing the content of scene C2;
Under scene C3, the time that stops 5s is used for browsing the content of scene C3;
Under scene C4, the time that stops 2s is used for browsing the content of scene C4.
In the embodiment of the invention, consideration is when jumping to scene C2 from scene C1, can the needed loading data of scene C2 will be jumped to from scene C1, browse in the process of scene C1 the user, send data acquisition request by user end to server, the server response data is obtained request, and needed loading data (the needed loading data of scene C2) are transferred to client, or triggers needed loading data when encapsulation jumps to scene C2 from scene C1 from buffer memory.Like this, by this prestrain mode, when the user will enter scene C2 from scene C1, can need not to wait pending data to load the data of directly showing scene C2.
Fig. 2 is the client prestrain method overall procedure synoptic diagram of the embodiment of the invention.Referring to Fig. 2, this flow process comprises:
The usage behavior characteristic of user under each scene is added up and stored to step 201 in advance;
In this step, make the technical scheme of the embodiment of the invention can realize accurately predicting and make effective prestrain decision-making, at first need the user is added up in the usage behavior characteristic of client.
User's usage behavior characteristic comprises: time response and operating characteristic, wherein,
Time response mainly comprises: the mean value of user's residence time in different scenes
And meansquaredeviation.Wherein,
For the average characteristics of reflection user in the scene residence time, and σ is for the reflection user stability of the residence time when different time is visited Same Scene.Certainly, in the practical application, also can be according to the difference of using and the information type that can obtain, the content that corresponding change time response comprises.
Fig. 3 is the statistics synoptic diagram of embodiment of the invention time response.Referring to Fig. 3, obtain the scene C that the user enters
1～C
n, the statistics user enters scene C for the l time
iResidence time T
l, according to T
lObtain the user and enter scene C
iThe mean value of the residence time
And meansquaredeviation
i, wherein, n, l, i are natural number, 1≤i≤n,
In the formula,
M enters scene C for the user
iTotal degree.
Operating characteristic is used for the expression user and switches to the probability of another scene from a scene, comprises that mainly the user is at scene C
iUnder carried out operation Q
kThereby, switch to another scene C
jProbability, can be expressed as p (Q
k, C
j/ C
i), wherein, k, j is natural number, in the practical application, if the scene that the same operation of execution switches under same scene is constant, then from scene C
iSwitch to scene C
jProbability can be expressed as p (Q
k/ C
i)=p (C
j/ C
i).
When statistics user usage behavior characteristic, can adopt the statistical of the overall situation, namely the usage behavior characteristic with all users is objects of statistics, the statistics that obtains is applied to all users; Also can adopt personalized statistical, namely the usage behavior characteristic with unique user is objects of statistics, and the statistics that obtains is applied to user self; Can also adopt the statistical of both combinations, namely at first obtain effective overall user's usage behavior statistical property by the global statistics mode, and with the initialization value of this statistical property result as all users' usage behavior characteristic, start personalized statistical then, go to revise statistical property result as initialization value according to user's self usage behavior.That is to say that statistics user usage behavior characteristic comprises:
Be sign with user, add up each user's usage behavior characteristic respectively;
In this step, can be statistical unit with the client, each client be added up self-contained each user's usage behavior characteristic respectively, like this, need not consumption of network resources; Also can be after each client be added up self-contained each user's usage behavior characteristic respectively, each user's usage behavior characteristic is reported to server, server averages the same user's that each client reports usage behavior characteristic, usage behavior characteristic as this user, and be issued to each client and store, like this, need to consume certain Internet resources, but more can reflect user's individual demand by the usage behavior characteristic that server averages the user who obtains.
Or,
Add up all users' usage behavior characteristic, obtain the mean value of all users' usage behavior characteristic, as user's usage behavior characteristic.
In this step, after self-contained all users' of client statistics the usage behavior characteristic, the usage behavior characteristic that all users are total reports to server, server averages according to number of users total usage behavior characteristic that each client reports, as user's usage behavior characteristic, and be issued to each client and store.
Step 202 is obtained user's usage behavior characteristic of user's current scene correspondence, calculates the corresponding prestrain requirements of each operation of user under the current scene according to the predicting strategy that sets in advance;
In this step, if the scene that current scene is browsed first for the user before obtaining user's usage behavior characteristic of user's current scene correspondence, further comprises:
Determine that current scene is the scene that the user browses first, from the loading data centralization of storing in advance, the loading data of current scene correspondence are obtained in inquiry, and show.That is to say, after client obtains the usage behavior characteristic of user under each scene in statistics, to the user first the usage behavior characteristic sort, choose the scene of the preceding default number of ordering, pull the required loading data of scene of this default number, form and load data set and storage, after determining the scene that current scene is browsed first for the user, from the loading data centralization of storing in advance, inquiry is obtained the loading data of current scene correspondence and is showed.Like this, also can effectively reduce the load time of client.
When the user browses current scene, need determine whether and need carry out prestrain to the required data of the scene of the follow-up switching of user, namely carry out the prestrain demand and detect, in order to reduce the stand-by period of user when handoff scenario and the load time of client.
In the embodiment of the invention, carrying out the prestrain demand according to user's usage behavior characteristic detects, in this programme, satisfy the situation of prestrain demand, namely user's prestrain requirements comprises two aspects under the current scene that obtains according to the score policy calculation that sets in advance: be that the user wants long enough in the residence time of current scene on the one hand wherein, if the residence time is shorter, it is little to the minimizing effect of load time then to carry out prestrain; Be that the handoff predictions that to be switched scene is carried out is had bigger confidence level on the other hand, if the confidence level of handoff predictions is lower, can make that then the handoff scenario of the scene of actual switching and prediction is inconsistent, cause the unavailable of preloading data, and increased the unnecessary network traffic expense.
Want sufficiently long condition for the user in residence time of current scene, can be according to the mean value of residence time of the current scene of statistics
Meansquaredeviation determines, for the handoff predictions that to be switched scene is carried out the condition of bigger confidence level arranged, and can switch to the Probability p (Q of corresponding scene by execution different operating under the scene before deserving
k/ C
i) determine, like this, comprehensive
σ and p (Q
k/ C
i), whether the prestrain requirements that can calculate the user of the different operating that may carry out under current scene satisfies the prestrain demand.
In the embodiment of the invention, provide two kinds for the treatment of mechanisms of calculating the prestrain requirements, namely predicting strategy comprises: individual layer predicting strategy and multilayer predicting strategy.
1) individual layer predicting strategy
The individual layer predicting strategy refer to only consider the user under current scene residence time characteristic (mean value of the residence time and mean square deviation) and under current scene, carry out the prestrain requirements that probability that all operations switches to corresponding scene calculates user under the current scene.Under this mechanism, the scene that meets the prestrain demand must satisfy and can directly switch from current scene.
In the embodiment of the invention, adopt the linear weighted function method to calculate at scene C
iFollowing user's prestrain requirements certainly, also can adopt exponential weighting method or other nonlinear weight method to calculate, and linear weighted function method computing formula is as follows:
In the formula,
p
ExkBe that the user is at current scene C
iFollowing executable operations Q
kSwitch to scene C
jThe prestrain requirements;
w
σ(σ
i) user that obtains by mean square deviation weight calculation function calculation of expression is at current scene C
iThe mean square deviation weight of the residence time,
And w
σ(σ
i) can determine according to actual needs;
w
o(p (Q
k/ C
i) user that calculates by the probability right computing function of expression is at current scene C
iFollowing executable operations Q
kSwitch to scene C
jProbability right;
α, β, γ are corresponding mean value weight coefficient, mean square deviation weight coefficient and probability right coefficient, preferably, alpha+beta+γ=1 can be set.
In the practical application, it is also conceivable that will pull the scene (scene after the switching) that next time may occur loads the spent time estimated value of data as the foundation of calculating the prestrain demand, but it is given or set by the statistical conditions of previous operation to load the spent time estimated value experience of data, like this, formula (1) can be amended as follows:
In the formula,
W (L
j) expression is by pulling user that time estimated value weight calculation function calculation obtains at current scene C
iFollowing executable operations Q
kAfter will enter scene C
jThe time that the pulls estimated value weight of (switching the back scene) required loading data correspondence;
ξ is for pulling time estimated value weight coefficient accordingly.
L
jSize influence that the prestrain demand is calculated decide on concrete application.
2) multilayer predicting strategy
The multilayer predicting strategy refer to not only consider the user under current scene residence time characteristic and under current scene, carry out the probability (operating characteristic) that all operations switches to corresponding scene, time response and the operating characteristic of the scene (switching the back scene) that may occur of also looking to the future.Under this mechanism, the scene that meets the prestrain demand except can from current scene directly switch reach, comprised also that current scene can't directly arrive but the scene that may arrive by scene after switching.With time response and the operating characteristic of above-mentioned formula (1) and (2) consideration switching back scene, make amendment respectively, be converted into corresponding formula (3) and (4), as follows respectively:
In the formula,
P (C
j/ C
i) represent from current scene C
iSwitch to scene C again by the scene after switching
jProbability;
N represents the number of plies predicted;
λ is decay factor, and λ is introduced in 0＜λ＜1
nBe in order to weaken the possibility that deeper prediction scene is preloaded gradually;
Step 203 is obtained the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold;
In this step, obtain the required loading data of handoff scenario, described handoff scenario is the scene of prestrain requirements above the operation correspondence of predefined prestrain demand threshold, specifically, if:
p
Exk＞p
Th, then obtain the user at current scene C
iFollowing executable operations Q
kSwitch to scene C
jRequired loading data.
In the formula,
p
ThIt is predefined prestrain demand threshold.
When the corresponding prestrain requirements of arbitrary operation
Greater than p
ThThe time, think that it meets the demand of prestrain, p
ThBe worth more greatly, represent more strictly to the calculating of prestrain demand, the probability of carrying out prestrain is more low.
Obtaining the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold comprises:
Inquiry is the loading data set of storage in advance, is up-to-date if having the required loading data of handoff scenario and this loading data, then obtains the required loading data of this handoff scenario from loading data centralization; Otherwise,
Send data acquisition request to server, pull the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold from server, and be stored in the loading data centralization; Or, sending the data preparation request to server, server receives the data preparation request, and the required loading data of handoff scenario are obtained in inquiry, and the loading data are encapsulated to reduce as far as possible the application of transmission volume.
Determine to load whether data are up-to-date, can determine according to timestamp information and the current time stamp information of these loading data of storage, if the current time stabs and loads the difference of the timestamp of data and is not more than the time renewal threshold value that sets in advance, determine that then these loading data are up-to-date.In the practical application, can also stab when upgrading threshold value with the difference of the timestamp that loads data greater than the time that sets in advance in the current time, client initiatively pulls corresponding loading data and the loading data of storage is upgraded to server.
Fig. 4 is embodiment of the invention scene operation characteristic synoptic diagram.Referring to Fig. 4, suppose that the user enters into scene C1 after, have 60% probability respectively by executable operations Q
1Switch to scene C2, have 20% probability by executable operations Q
2Switch to scene C3, have 10% probability by executable operations Q
3Switch to scene C4, have 10% probability by executable operations Q
8Withdraw from; In scene C2, has 90% probability respectively by executable operations Q
4Switch to scene C5, have 10% probability by executable operations Q
5Switch to scene C6; In scene C6, has 50% probability respectively by executable operations Q
6Switch to scene C3, have 50% probability by executable operations Q
7Switch to scene C4.Like this, measurable user is under current scene C1, there is 54% probability can enter scene C5, has 6% probability can enter scene C6, have 20% probability can enter scene C3, there is 10% probability can enter scene C4, there is 10% probability to withdraw from, if α=β=0, predefined prestrain demand threshold are 50%, by adopting multilayer prediction (it is 2 that the maximum number of plies is set), after the user entered scene C1, scene C2 and C5 all can satisfy the prestrain demand so.
In this step, if the user is switched current scene by executable operations, then obtain the scene after the switching of executable operations correspondence, and judge whether the scene scene corresponding with the loading data of obtaining after switching is complementary, if coupling is showed the loading data that pull.
In the embodiment of the invention, the loading data of obtaining comprise: be stored in the loading data that load data centralization and the loading data that step 203 is obtained in advance.
As previously mentioned, in step 203, if send the data preparation request to server, show that then the loading data of obtaining comprise:
Send data acquisition request to server, server receives data acquisition request, the loading data that encapsulate is sent to client shows.
This step further comprises:
The scene scene corresponding with the loading data of obtaining after determining to switch is not complementary, the preloading data that interruption is being transmitted, from the preloading data that server pulls the required loading data of scene after the switching or the preloading data that interrupts transmitting and deletion have been finished, pull required loading data of scene after the switching from server.
Further, this method also comprises:
Step 205 determines that the user withdraws from scene, according to the usage behavior characteristic of user under each scene of user's scene switching updated stored.
In this step, when the user withdraws from scene, according to the user usage behavior characteristic of user under the corresponding scene that time response and the operating characteristic of each scene are distinguished updated stored, for example, recomputate mean value and the mean square deviation of the residence time.
Fig. 5 is the client prestrain method idiographic flow synoptic diagram of the embodiment of the invention.Referring to Fig. 5, this flow process comprises:
Step 501 enters new scene;
Step 502 is carried out the prestrain demand and is detected;
In this step, obtain user's usage behavior characteristic of new scene correspondence, according to the corresponding prestrain requirements of each operation of user under user's usage behavior property calculation new scene.
Step 503 judges whether to trigger prestrain, if, execution in step 504, otherwise, execution in step 521;
In this step, according to the prestrain demand that detection obtains, the corresponding prestrain requirements of each operation of user judges whether to satisfy predefined prestrain demand threshold under the new scene that namely calculates, if satisfy, triggers prestrain.
Step 504 starts prestrain;
Step 505 judges whether the user carries out predicted operation, if, execution in step 506, otherwise, execution in step 511;
In this step, the user can be arranged on whether carry out predicted operation when scene is switched, if carry out predicted operation, pulls the corresponding scene of predicted operation required loading data and storage from server in the user browses the process of current scene.
Step 506 is obtained the loading data that need from the loading data centralization of storage;
Step 507 enters next new scene, returns execution in step 502;
In this step, client is showed the loading data of obtaining according to user's operation to the user.
Further, client is obtained user's operation corresponding link information (scene), mate with the loading data corresponding link information of obtaining (scene), if the match is successful, the loading data of obtaining are showed to the user, if coupling is unsuccessful, then handle according to existing procedure, namely send request to server, pull the user and operate the required loading data of corresponding link information.
Step 511 starts and interrupts invalid prestrain;
In this step, if the user does not carry out predicted operation, i.e. the operation of the operation carried out of user and prediction is inconsistent, then needs the preloading data that interrupts transmitting, i.e. invalid prestrain.
Step 512 is obtained the loading data that need from server or buffer memory;
Step 513 enters next new scene, returns execution in step 502;
In this step, the corresponding scene of the new scene that enters and predicted operation is different.
Step 521, executable operations is carried out scene and is switched;
Step 522 enters next new scene, returns execution in step 502.
Be elaborated to obtaining the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold more below.
Fig. 6 obtains the required loading data flow synoptic diagram of handoff scenario for the embodiment of the invention.Referring to Fig. 6, this flow process comprises:
Step 601 is obtained a new task from prestrain task waiting list;
In this step, if exist a plurality of prestrain requirements to surpass predefined prestrain demand threshold, corresponding a plurality of handoff scenario then, a plurality of handoff scenario are formed prestrain task waiting lists, the corresponding new task of each handoff scenario.
Step 603 encapsulates and returns the loading data that need, execution in step 605;
Step 605 adds prestrain with the loading data of returning and finishes tabulation;
Step 606 judges whether prestrain task waiting list is empty, if, finish this flow process, otherwise, execution in step 601 returned.
Describe interrupting invalid prestrain below.
When the operation of carrying out as the user and the operation of prediction are inconsistent, before carried out prestrain data may to when the scene that advance into without any effect, when the user operates handoff scenario, two kinds of situations may occur: the data of a preceding prestrain are also being transmitted and the transmission of the data of a preceding prestrain is finished.For first kind of situation, if the data of a preceding prestrain are not carried out any intervention, client need be waited for and reloading after the data transmission of a preceding prestrain is finished when advancing into the needed loading data of scene.And for second kind of situation, though can be to not exerting an influence when the loading time-delay that advances into the scene desired data, but the data of a preceding prestrain may be brought two kinds of problems: taken certain memory headroom and comprised dirty data, dirty data refers to exist physically, but non-existent data in logic, for instance, in the prestrain process, front end has carried out insertion, deletion or has upgraded operation, make data in buffer change, but also be not written to the data in disk or the data file.
Fig. 7 interrupts invalid prestrain schematic flow sheet for the embodiment of the invention.Referring to Fig. 7, interrupt invalid prestrain and comprise: interrupt the data that prestrain has been finished in ongoing invalid prestrain and deletion, this flow process comprises:
Step 701 has judged whether ongoing prestrain task, if having, and execution in step 702, otherwise, execution in step 703;
Step 702 determines to carry out to need in the task of prestrain the prestrain task of interrupting, and the prestrain task that will need to interrupt is carried out and interrupted handling execution in step 703;
In this step, the prestrain task that needs to interrupt should satisfy following arbitrary condition:
Condition 1: the prestrain task residue load time that this need interrupt is long, has blocked the loading that is about to enter the scene desired data;
In the embodiment of the invention, can add initial time stamp by the task of all being begun prestrain, when needs interrupt this prestrain task, calculate this prestrain task load time, estimate the residue load time of this prestrain task according to the estimated value of this total load time of prestrain task, if the residue load time of estimation has surpassed predefined load time threshold value, then with this prestrain tasks interrupt, the load time threshold value can be set according to user's demand for experience of different application.
Condition 2: the prestrain task that this need interrupt needs the data possibility of prestrain lower just in loaded data for the scene that is about to enter.
In the embodiment of the invention, the scene that is about to enter as current scene, and is adopted above-mentioned prestrain demand computing formula to obtain this task just whether to surpass predefined prestrain demand threshold p in the prestrain demand weight of loaded data
ThIf do not have, then with this prestrain tasks interrupt.
In this step, finished the data of prestrain for some, do not impacted though can not load the data that are about to enter scene, may bring two kinds of problems: one has taken certain memory headroom; Its two, comprise dirty data, for example, in the prestrain process, front end has carried out insertion, deletion or has upgraded operation, makes the data of server end storage change, and the data of having finished prestrain are not carried out corresponding the variation.
Deletion needs the data of deletion to comprise:
In this step, if can then need not deletion by the dirty data of finishing prestrain being inserted accordingly, deletes or upgrading operation.
Step 2, whether the prestrain demand weight that adopts above-mentioned prestrain demand computing formula to obtain the data of finishing prestrain surpasses predefined load time threshold value, if surpass, then keeps data, if do not surpass, then execution in step 3;
Step 3 is assessed the current shared memory headroom of data of having finished prestrain and whether is surpassed tolerance limit (rule of thumb setting), if surpass, then deletes the data that all prestrain demand weights do not surpass predefined load time threshold value.
By as seen above-mentioned, the client prestrain method of the embodiment of the invention, by adding up and store the usage behavior characteristic of user under each scene in advance, usage behavior characteristic according to user under the current scene is predicted user's subsequent operation, and the required loading data of scene of obtaining prediction in the user browses the process of current scene are carried out prestrain, thereby when the user switches to the prediction scene, need not to obtain corresponding loading data from service area, reduce the load time of client, thereby improved user's experience; Further, carry out prestrain according to user's usage behavior characteristic, can accurately meet the user use habit, satisfy user's individual demand; And, can be applied to the prestrain under many scenes switchings.
Fig. 8 is the client pre-load means structural representation of the embodiment of the invention.Referring to Fig. 8, this device comprises: usage behavior statistics of features module, prestrain requirements computing module, preloading data acquisition module and scene matching module, wherein,
The usage behavior characteristic of user under each scene is added up and stored to usage behavior statistics of features module;
Prestrain requirements computing module obtains user's usage behavior characteristic of user's current scene correspondence, calculates the corresponding prestrain requirements of each operation of user under the current scene according to the predicting strategy that sets in advance;
The preloading data acquisition module obtains the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold;
The scene matching module, the monitor user ' scene is switched, and the scene scene corresponding with the loading data of obtaining after determining to switch is complementary, and shows the loading data of obtaining.
Prestrain requirements computing module comprises: mean value weight calculation unit, mean square deviation weight calculation unit, probability right computing unit and prestrain requirements computing unit (not shown), wherein,
The mean value weight calculation unit is by the mean value weight calculation function calculation user that the sets in advance mean value weight in the residence time of current scene;
The mean square deviation weight calculation unit is by the mean square deviation weight calculation function calculation user that the sets in advance mean square deviation weight in the residence time of current scene;
The probability right computing unit calculates the probability right that user's executable operations under current scene switches to handoff scenario by the probability right computing function that sets in advance;
Prestrain requirements computing unit, mean value weight and mean value weight coefficient that the mean value weight calculation unit is calculated multiply each other, mean square deviation weight and mean square deviation weight coefficient that the mean square deviation weight calculation unit is calculated multiply each other, probability right and probability right multiplication that the probability right computing unit is calculated, and the long-pending addition that will respectively multiply each other.
The scene matching module comprises: monitoring means, scene matching unit, loading data set unit, loading data display unit, interrupt location and loading data pull unit (not shown), wherein,
Monitoring means, the monitor user ' scene is switched, and exports the user's scene handover information that monitors to the scene matching unit;
The scene matching unit receives user's scene handover information, and the scene corresponding with the loading data that load the storage of data set unit is complementary, if coupling, to the handoff scenario information that loads data display unit output coupling, if do not match, export unmatched handoff scenario information to interrupt location;
Load the data display unit, according to the handoff scenario information that receives, obtain the required loading data of handoff scenario and displaying from loading the data set unit;
Interrupt location, according to the handoff scenario information that receives, the preloading data that interruption is being transmitted sends a notification message to loading the data pull unit;
Load the data pull unit, receiving notice message pulls the required loading data of handoff scenario from server.
The above is preferred embodiment of the present invention only, is not for limiting protection scope of the present invention.Within the spirit and principles in the present invention all, any modification of doing, be equal to and replace and improvement etc., all should be included within protection scope of the present invention.
Claims (14)
1. client prestrain method is characterized in that this method comprises:
Obtain the usage behavior characteristic of user under each scene in advance;
Obtain user's usage behavior characteristic of user's current scene correspondence, calculate the corresponding prestrain requirements of each operation of user under the current scene according to the predicting strategy that sets in advance;
Obtain the required loading data of handoff scenario, described handoff scenario surpasses the scene of the operation correspondence of predefined prestrain demand threshold for the prestrain requirements;
The monitor user ' scene is switched, and the scene scene corresponding with the loading data of obtaining after determining to switch is complementary, and shows the loading data of obtaining.
2. the method for claim 1 is characterized in that, described statistics user usage behavior characteristic comprises:
Be sign with user, add up each user's usage behavior characteristic respectively; Or
Add up all users' usage behavior characteristic, obtain the mean value of all users' usage behavior characteristic, as user's usage behavior characteristic.
3. the method for claim 1 is characterized in that, and is described before obtaining user's usage behavior characteristic of user's current scene correspondence, further comprises:
Client to the user first the usage behavior characteristic sort, choose the scene of default number before the ordering, pull the required loading data of scene of this default number, form and load data set and storage;
Determine that current scene is the scene that the user browses first, from the loading data centralization of storing in advance, inquiry is obtained the loading data of current scene correspondence and is showed.
4. the method for claim 1 is characterized in that, described user's usage behavior characteristic comprises: time response and operating characteristic, wherein,
Described time response comprises: mean value and the mean square deviation of user's residence time in different scenes;
Described operating characteristic comprises that the user switches to the probability of another scene from a scene.
5. method as claimed in claim 4 is characterized in that, described prestrain requirements according to each operation correspondence of user under the predicting strategy calculating current scene that sets in advance comprises:
By the mean value weight calculation function calculation user that the sets in advance mean value weight in the residence time of current scene, multiply each other with the mean value weight coefficient;
By the mean square deviation weight calculation function calculation user that the sets in advance mean square deviation weight in the residence time of current scene, multiply each other with the mean square deviation weight coefficient;
Calculate the probability right that user's executable operations under current scene switches to handoff scenario by the probability right computing function that sets in advance, with the probability right multiplication; Perhaps by the probability right computing function that sets in advance calculate the user under the current scene by the probability right of handoff scenario again of the scene after switching, multiply each other with the number of plies power of the prediction of probability right coefficient and decay factor; And
The long-pending addition of respectively multiplying each other is obtained the prestrain requirements of user's executable operations correspondence under the current scene.
6. method as claimed in claim 5 is characterized in that, after the step of described and probability right multiplication, further comprises:
After executable operations under the current scene, switch to the time that the pulls estimated value weight of the required loading data of handoff scenario correspondence by the time that the pulls estimated value weight calculation function calculation user who sets in advance, with pull time estimated value weight coefficient and multiply each other, and carry out the step of the long-pending addition that will respectively multiply each other;
After the step that the number of plies power of the prediction of described and probability right coefficient and decay factor multiplies each other, further comprise:
With the summation by way of the acquisition time estimated value of scene required loading data of current scene to last handoff scenario, multiply each other with the number of plies power of the prediction that pulls time estimated value weight coefficient and decay factor, and carry out the step of the long-pending addition that will respectively multiply each other.
7. as each described method of claim 1 to 6, it is characterized in that the described required loading data of handoff scenario of obtaining comprise:
Inquiry is the loading data set of storage in advance, is up-to-date if having the required loading data of handoff scenario and this loading data, then obtains the required loading data of this handoff scenario from loading data centralization; Otherwise,
Send data acquisition request to server, pull the required loading data of handoff scenario that the prestrain requirements surpasses the operation correspondence of predefined prestrain demand threshold from server, and be stored in the loading data centralization; Or, sending the data preparation request to server, server receives the data preparation request, and the required loading data of handoff scenario are obtained in inquiry, and encapsulate loading data.
8. method as claimed in claim 7 is characterized in that, further comprises:
Stab when upgrading threshold value with the difference of the timestamp that loads data greater than the time that sets in advance in the current time, client initiatively pulls corresponding loading data and the loading data of storage is upgraded to server.
9. method as claimed in claim 7 is characterized in that, further comprises:
The scene scene corresponding with the loading data of obtaining after determine switching is not complementary, and the preloading data that interruption is being transmitted pulls required loading data of scene after the switching from server.
10. method as claimed in claim 7 is characterized in that, further comprises:
Determine that the user withdraws from scene, according to the usage behavior characteristic of user under each scene of user's scene switching updated stored.
11. a client pre-load means is characterized in that, this device comprises: usage behavior statistics of features module, prestrain requirements computing module, preloading data acquisition module and scene matching module, wherein,
Usage behavior statistics of features module is obtained the usage behavior characteristic of user under each scene;
Prestrain requirements computing module obtains user's usage behavior characteristic of user's current scene correspondence, calculates the corresponding prestrain requirements of each operation of user under the current scene according to the predicting strategy that sets in advance;
The preloading data acquisition module obtains the required loading data of handoff scenario, and described handoff scenario surpasses the scene of the operation correspondence of predefined prestrain demand threshold for the prestrain requirements;
The scene matching module, the monitor user ' scene is switched, and the scene scene corresponding with the loading data of obtaining after determining to switch is complementary, and shows the loading data of obtaining.
12. device as claimed in claim 11 is characterized in that, described prestrain requirements computing module comprises: mean value weight calculation unit, mean square deviation weight calculation unit, probability right computing unit and prestrain requirements computing unit, wherein,
The mean value weight calculation unit is by the mean value weight calculation function calculation user that the sets in advance mean value weight in the residence time of current scene;
The mean square deviation weight calculation unit is by the mean square deviation weight calculation function calculation user that the sets in advance mean square deviation weight in the residence time of current scene;
The probability right computing unit calculates the probability right that user's executable operations under current scene switches to handoff scenario by the probability right computing function that sets in advance;
Prestrain requirements computing unit, mean value weight and mean value weight coefficient that the mean value weight calculation unit is calculated multiply each other, mean square deviation weight and mean square deviation weight coefficient that the mean square deviation weight calculation unit is calculated multiply each other, probability right and probability right multiplication that the probability right computing unit is calculated, and the long-pending addition that will respectively multiply each other.
13., it is characterized in that described scene matching module comprises as claim 11 or 12 described devices: monitoring means, scene matching unit, loading data set unit and loading data display unit, wherein,
Monitoring means, the monitor user ' scene is switched, and exports the user's scene handover information that monitors to the scene matching unit;
The scene matching unit receives user's scene handover information, and the scene corresponding with the loading data that load the storage of data set unit is complementary, if coupling, to the handoff scenario information that loads data display unit output coupling;
Load the data display unit, according to the handoff scenario information that receives, obtain the required loading data of handoff scenario and displaying from loading the data set unit.
14. device as claimed in claim 13 is characterized in that, described scene matching module further comprises: interrupt location and loading data pull unit, wherein,
The scene matching unit is further used for exporting unmatched handoff scenario information to interrupt location when the user's scene handover information that the receives scene corresponding with the loading data that load the storage of data set unit do not match;
Interrupt location, according to the handoff scenario information that receives, the preloading data that interruption is being transmitted sends a notification message to loading the data pull unit;
Load the data pull unit, receiving notice message pulls the required loading data of handoff scenario from server.
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