CN108921229A - Data reconstruction method and device - Google Patents
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Abstract
The present invention provides a kind of data reconstruction method and device.The method is applied to the server communicated to connect with wireless access point AP, is stored with multiple prediction models in server.The method includes：Judge whether lack AP data between current AP data and the upper AP data once received according to the current time stamp in the AP data being currently received and the historical time stamp in the last AP data received, wherein, the AP data that the current AP data and the last time receive are sent by same AP；When being, target prediction model corresponding with the AP data of missing is selected from multiple prediction models；It is predicted according to AP data of the target prediction model to missing, to restore the AP data of missing.As a result, when lacking AP data, it can be predicted by target prediction model corresponding with the AP data of missing, so that the AP data to missing are restored.
Description
Technical field
The present invention relates to fields of communication technology, in particular to a kind of data reconstruction method and device.
Background technique
In WIFI network, AP (Access Point, wireless access point) can send AP data to server.Due to certain
Reason will lead to the AP data that server does not receive AP transmission, if the AP data of missing are more, according only to having received and saved
The obtained statistical report of AP data cannot react truth.
Summary of the invention
In order to overcome above-mentioned deficiency in the prior art, the embodiment of the present invention is designed to provide a kind of data recovery side
Method and device can be predicted when lacking AP data by target prediction model corresponding with the AP data of missing, from
And the AP data of missing are restored.
The embodiment of the present invention provides a kind of data reconstruction method, applied to the service communicated to connect with wireless access point AP
Device is stored with multiple prediction models in the server, the method includes：
According to the current time stamp in the AP data being currently received and the history in the last AP data received
Time stab judges whether lack AP data between current AP data and the upper AP data once received, wherein described to work as
The AP data that preceding AP data and the last time receive are sent by same AP；
When determining to lack AP data between the current AP data and the upper AP data once received, from described
Target prediction model corresponding with the AP data of missing is selected in multiple prediction models；
It is predicted according to AP data of the target prediction model to the missing, to restore the AP number of the missing
According to.
Optionally, in embodiments of the present invention, the basis is currently received in AP data current time stamp and
The historical time stamp in AP data that last time receives judge current AP data and the upper AP data once received it
Between the step of whether lacking AP data include：
Calculate the historical time stamp and prefixed time interval and value；
Judge whether the current time stamp is greater than described and value；
When the current time stamp is greater than described and value, determine in current AP data and the upper AP number once received
AP data are lacked between.
Optionally, in embodiments of the present invention, the corresponding predicted time section of each prediction model, it is described from the multiple pre-
Surveying the step of target prediction model corresponding with the AP data of missing is selected in model includes：
According to the current time stamp and the prefixed time interval or the historical time stamp and it is described default when
Between interval calculation obtain the missing time stamp in the AP data of the missing；
According to the missing time stamp and the corresponding predicted time section of each prediction model from the multiple prediction model
In select prediction model corresponding with the target prediction period where the missing time stamp as the target prediction mould
Type.
Optionally, in embodiments of the present invention, the also corresponding data type of prediction of each prediction model, it is described according to
Missing time stamp and the corresponding predicted time section of each prediction model are selected and the missing from the multiple prediction model
Target prediction period corresponding prediction model where time stab includes as the step of target prediction model：
The class of the AP data of the missing is determined according to the AP data that the current AP data or the last time receive
Type；
It is described lack that data type of prediction is selected from the prediction model that predicted time section is the target prediction period
The prediction model of the type of the AP data of mistake is as the target prediction model.
Optionally, in embodiments of the present invention, the current time stamp in the AP data that the basis is currently received
And the historical time stamp in the last AP data received judges in current AP data and the upper AP data once received
Between before the step of whether lacking AP data, the method also includes：
It is established according to the history AP data of storage and stores the multiple prediction model.
The embodiment of the present invention also provides a kind of Data Recapture Unit, applied to the server communicated to connect with AP, the clothes
Multiple prediction models are stored in business device, described device includes：
Judgment module, for according to the current time stamp and the last AP received in the AP data being currently received
Historical time stamp in data judges whether lack AP data between current AP data and the upper AP data once received,
Wherein, the AP data that the current AP data and the last time receive are sent by same AP；
Model selection module, for determining to lack between the current AP data and the upper AP data once received
When losing AP data, target prediction model corresponding with the AP data of missing is selected from the multiple prediction model；
Data recovery module, for being predicted according to AP data of the target prediction model to the missing, with extensive
The AP data of the multiple missing.
Optionally, in embodiments of the present invention, when the judgment module is according to current in the AP data being currently received
Between historical time stamp in stamp and the last AP data received judge once to receive in current AP data with upper
The mode that AP data whether are lacked between AP data includes：
Calculate the historical time stamp and prefixed time interval and value；
Judge whether the current time stamp is greater than described and value；
When the current time stamp is greater than described and value, determine in current AP data and the upper AP number once received
AP data are lacked between.
Optionally, in embodiments of the present invention, the corresponding predicted time section of each prediction model, the Model selection module
It is selected from the multiple prediction model and includes with the mode of the corresponding target prediction model of AP data of missing：
According to the current time stamp and the prefixed time interval or the historical time stamp and it is described default when
Between interval calculation obtain the missing time stamp in the AP data of the missing；
According to the missing time stamp and the corresponding predicted time section of each prediction model from the multiple prediction model
In select prediction model corresponding with the target prediction period where the missing time stamp as the target prediction mould
Type.
Optionally, in embodiments of the present invention, the also corresponding data type of prediction of each prediction model, the model selection
Module is selected from the multiple prediction model according to the missing time stamp and the corresponding predicted time section of each prediction model
Prediction model corresponding with the target prediction period where the missing time stamp is as the target prediction model out
Mode includes：
The class of the AP data of the missing is determined according to the AP data that the current AP data or the last time receive
Type；
It is described lack that data type of prediction is selected from the prediction model that predicted time section is the target prediction period
The prediction model of the type of the AP data of mistake is as the target prediction model.
Optionally, in embodiments of the present invention, described device further includes：
Model building module is established for the history AP data according to storage and stores the multiple prediction model.
In terms of existing technologies, the invention has the advantages that：
The embodiment of the present invention provides a kind of data reconstruction method and device.The method is applied to the clothes communicated to connect with AP
It is engaged in device, is stored with multiple prediction models in the server.The server receives the AP data sent by AP, and carries out to it
It saves.The server is after receiving AP data, according to the current time stamp and upper one in the AP data being currently received
History stamp in the secondary AP data received judges whether lack between current AP data and the upper AP data once received
Lose AP data.Wherein, the AP data that the current AP data and the last time receive are sent by same AP.Described in judgement
When lacking AP data between the AP data that current AP data and the last time receive, selected from the multiple prediction model
Target prediction model corresponding with the AP data of missing.Then according to the target prediction model to the AP data of the missing into
Row prediction, to restore the AP data of the missing.It, can be by corresponding with the AP data of missing as a result, when lacking AP data
Target prediction model predicted, so that the AP data to missing are restored.
For enable invention above objects, features, and advantages be clearer and more comprehensible, present pre-ferred embodiments are cited below particularly, and
Cooperate appended attached drawing, is described in detail below.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the block diagram of server provided in an embodiment of the present invention.
Fig. 2 is one of the flow diagram of data reconstruction method provided in an embodiment of the present invention.
Fig. 3 is the flow diagram for the sub-step that step S120 includes in Fig. 2.
Fig. 4 is the flow diagram for the sub-step that step S130 includes in Fig. 2.
Fig. 5 is the flow diagram for the sub-step that sub-step S132 includes in Fig. 4.
Fig. 6 is the two of the flow diagram of data reconstruction method provided in an embodiment of the present invention.
Fig. 7 is one of the block diagram of Data Recapture Unit provided in an embodiment of the present invention.
Fig. 8 is the two of the block diagram of Data Recapture Unit provided in an embodiment of the present invention.
Icon：100- server；110- memory；120- storage control；130- processor；200- data restore dress
It sets；210- model building module；220- judgment module；230- Model selection module；240- data recovery module.
Specific embodiment
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
It should be noted that：Similar label and letter indicate similar terms in following attached drawing, therefore, once a certain Xiang Yi
It is defined in a attached drawing, does not then need that it is further defined and explained in subsequent attached drawing.Meanwhile of the invention
In description, term " first ", " second " etc. are only used for distinguishing description, are not understood to indicate or imply relative importance.
With reference to the accompanying drawing, it elaborates to some embodiments of the present invention.In the absence of conflict, following
Feature in embodiment and embodiment can be combined with each other.
Fig. 1 is please referred to, Fig. 1 is the block diagram of server 100 provided in an embodiment of the present invention.The server 100
It is communicated to connect with multiple AP, for receiving the AP data sent by each AP, and in the database by the AP received storage, with
Continue after an action of the bowels and is analyzed according to all AP data in database.The server 100 can be separate server, be also possible to
The cluster server being made of multiple servers.The server 100 may include：Memory 110, storage control 120 and
Processor 130.
It is directly or indirectly electrically connected between the memory 110, storage control 120 and each element of processor 130,
To realize the transmission or interaction of data.For example, these elements can pass through one or more communication bus or signal wire between each other
It realizes and is electrically connected.It is stored with the database for saving AP data in memory 110, is multiple for predicting the prediction of AP data
Model and Data Recapture Unit 200, the Data Recapture Unit 200 includes at least one can be with software or firmware (firmware)
Form be stored in the software function module in the memory 110.The processor 130 is stored in memory by operation
Software program and module in 110 are answered such as the Data Recapture Unit 200 in the embodiment of the present invention thereby executing various functions
With and data processing, i.e., realization the embodiment of the present invention in data reconstruction method.
Wherein, the memory 110 may be, but not limited to, random access memory (Random Access
Memory, RAM), read-only memory (Read Only Memory, ROM), programmable read only memory (Programmable
Read-Only Memory, PROM), erasable read-only memory (Erasable Programmable Read-Only
Memory, EPROM), electricallyerasable ROM (EEROM) (Electric Erasable Programmable Read-Only
Memory, EEPROM) etc..Wherein, memory 110 is for storing program, the processor 130 after receiving and executing instruction,
Execute described program.The processor 130 and other possible components can control the access of memory 110 in the storage
It is carried out under the control of device 120.
The processor 130 may be a kind of IC chip, the processing capacity with signal.Above-mentioned processor
130 can be general processor, including central processing unit (Central Processing Unit, CPU), network processing unit
(Network Processor, NP) etc..It can also be digital signal processor (DSP), specific integrated circuit (ASIC), scene
Programmable gate array (FPGA) either other programmable logic device, discrete gate or transistor logic, discrete hardware group
Part.It may be implemented or execute disclosed each method, step and the logic diagram in the embodiment of the present invention.General processor can be with
It is that microprocessor or the processor are also possible to any conventional processor etc..
It is appreciated that structure shown in FIG. 1 is only to illustrate, server 100 may also include than shown in Fig. 1 more or more
Few component, or with the configuration different from shown in Fig. 1.Each component shown in Fig. 1 can use hardware, software or its group
It closes and realizes.
Referring to figure 2., Fig. 2 is one of the flow diagram of data reconstruction method provided in an embodiment of the present invention.The side
Method is applied to the server 100 communicated to connect at least one AP, is stored in the server 100 multiple for predicting AP number
According to prediction model.The detailed process of data reconstruction method is described in detail below.
Step S120, according in the AP data being currently received current time stamp and the last AP data received
In historical time stamp judge whether lack AP data between current AP data and the upper AP data once received.
Referring to figure 3., Fig. 3 is the flow diagram for the sub-step that step S120 includes in Fig. 2.Step S120 may include
Sub-step S121, sub-step S122 and sub-step S123.
Sub-step S121, calculate the historical time stamp and prefixed time interval and value.
Sub-step S122, judges whether the current time stamp is greater than described and value.
Sub-step S123 determines when the current time stamp is greater than described and value in current AP data and upper primary
AP data are lacked between the AP data received.
In the present embodiment, the AP data that the current AP data and the last time receive are sent by same AP.Institute
State server 100 according in the AP data sent by same AP that receive of last time historical time stamp and it is described default when
Between interval calculation obtain one and value, then remember this and value and the current time stamp in the AP data that are currently received into row ratio
Compared with.Wherein, each AP sends AP data to server 100 according to the prefixed time interval, i.e., under normal circumstances, same AP is sent
Two AP data between time interval be the prefixed time interval.
If the current time stamp no more than should and value, indicate current AP data that the server 100 receives with
The time interval between AP data that last time receives is normal, the AP data and upper one that the server 100 is currently received
There is no missing data between the secondary AP data received.It should and be worth if the current time stamp is greater than, indicate the server
Time interval between the 100 current AP data received and the last AP data received is abnormal, and server 100 is described
There are AP data to be not received by before current AP data, that is to say, that current AP data and the last AP data received it
Between lack AP data.
If not lacking AP data between the AP data that the current AP data and the last time receive, do not need into
Row data are restored.If lacking AP data, the present embodiment between the AP data that the current AP data and the last time receive
Then follow the steps S130.
Step S130 selects target prediction model corresponding with the AP data of missing from the multiple prediction model.
Referring to figure 4., Fig. 4 is the flow diagram for the sub-step that step S130 includes in Fig. 2.Each prediction model is corresponding
One predicted time section.Step S130 may include sub-step S131 and sub-step S132.
Sub-step S131, according to the current time stamp and the prefixed time interval or the historical time stamp with
The missing time stamp in the AP data of the missing is calculated in the prefixed time interval.
Sub-step S132, according to the missing time stamp and the corresponding predicted time section of each prediction model from described more
It is selected in a prediction model described in prediction model conduct corresponding with the target prediction period where the missing time stamp
Target prediction model.
It in the present embodiment, can be by the difference of the calculating current time stamp and the prefixed time interval, to obtain
To the time stab in the AP data of the missing, i.e. missing time stamp.It can also be by calculating the historical time stamp
With the prefixed time interval and value, to obtain the missing time stamp.Of course, it should be understood that can also according to institute
State other AP data that current time stamp, prefixed time interval, historical time stamp, database include etc. by other means
Obtain the missing time stamp.
Missing time stamp predicted time section corresponding with each prediction model is compared, the missing is obtained
The target prediction period where time stab, then using predicted time section be the target prediction time prediction model as
The target prediction model.
Further, referring to figure 5., Fig. 5 is the flow diagram for the sub-step that sub-step S132 includes in Fig. 4.Each
The also corresponding data type of prediction of prediction model, sub-step S132 may include sub-step S1321 and sub-step S1322.
Sub-step S1321 determines the missing according to the AP data that the current AP data or the last time receive
AP data type.
Sub-step S1322 selects data prediction from the prediction model that predicted time section is the target prediction period
Type is the prediction model of the type of the AP data of the missing as the target prediction model.
The server 100 can be directly using the prediction model that predicted time section is the target prediction time as described in
Target prediction model.It can also be institute by predicted time section in the case where each prediction model corresponding data type of prediction
The prediction model of target prediction time is stated as suspected target prediction model.Then by the type of the AP data of the missing and often
The data type of prediction of a suspected target prediction model is compared, and obtains number to search from the suspected target prediction model
It is predicted that type is the prediction model of the type of the AP data of the missing, the target prediction model is obtained as a result,.
Step S140 is predicted according to AP data of the target prediction model to the missing, is lacked with restoring described
The AP data of mistake.
In the present embodiment, it can estimate the AP data of the missing according to the target prediction model, and by estimated value
AP data as the missing.After the AP data for obtaining the missing, save it in database, for further dividing
Analysis uses in other respects.
In an embodiment of the present embodiment, the server 100 can obtain described more from other equipment processing
A prediction model.
Optionally, if the server 100 selects in the database for storing AP data two to be sequentially received
The AP data sent by same AP, and judged between the two AP data according to the time stab in two AP data of selection
Whether AP data are lacked, at this point, the AP data first received in two AP data of selection are to receive the last time
Data, after the AP data that receive be the AP data being currently received.Then judge whether to lack AP number in the manner described above
According to, and the AP data that recovery lacks when lacking AP data.
Fig. 6 is please referred to, Fig. 6 is the two of the flow diagram of data reconstruction method provided in an embodiment of the present invention.In step
Before S120, the method can also include step S110.
Step S110 is established according to the history AP data of storage and is stored the multiple prediction model
In the present embodiment, the server 100 can according to the AP data stored in database by analysis, it is trained
It is saved to the multiple prediction model, and to the multiple prediction model.Optionally, the multiple prediction model is being obtained
Afterwards, the server 100 can also establish new prediction model according to subsequently received AP data or to established prediction
Model is updated.
Data reconstruction method is illustrated below.
Assuming that the received AP data of the server 100 are data on flows, the server 100 is stored with multiple flows
Prediction model, the corresponding predicted time section of each flux prediction model are different.For example, the corresponding flow of 8. -22 point of Monday is pre-
Surveying model is：Q=1843.366+405.776X, wherein X is the terminal quantity communicated with AP, and Q is WLAN mouthfuls of total flows；Monday
7 points of 23 points-morning corresponding process prediction models are：Q=3.444+19.583X.If 13 points of missing AP data of Monday, will so that
Report that chart is discontinuous, the statistical data inaccuracy of daily handling capacity, it is therefore desirable to restore the AP data of missing.Due to missing
The time stab of AP data is 13 points of Monday, and 13 points of terminal quantities being connected on the AP of Monday are 20, according to 8 points of Monday-
22 points of corresponding flux prediction model Q=1843.366+405.776X can estimate total flow value in the AP data of the missing
9959Kbytes.Thus the AP data of missing have been restored.
Further, if the received AP data of the server 100 are data on flows, the server 100 is stored with
Multiple prediction models such as flux prediction model and terminal quantity prediction model then can first select flow from multiple prediction models
Prediction model selects target flow prediction model further according to the time stab of the AP data of missing from flux prediction model.
Fig. 7 is please referred to, Fig. 7 is one of the block diagram of Data Recapture Unit 200 provided in an embodiment of the present invention.It is described
Data Recapture Unit 200 is applied to the server 100 communicated to connect with AP, is stored with multiple prediction moulds in the server 100
Type.The Data Recapture Unit 200 may include judgment module 220, Model selection module 230 and data recovery module 240.
Judgment module 220, for according in the AP data that are currently received current time stamp and the last time receive
AP data in historical time stamp judge whether lack AP between current AP data and the upper AP data once received
Data, wherein the AP data that the current AP data and the last time receive are sent by same AP.
In the present embodiment, the judgment module 220 according in the AP data being currently received current time stamp and
The historical time stamp in AP data that last time receives judge current AP data and the upper AP data once received it
Between whether lack the modes of AP data and include：
Calculate the historical time stamp and prefixed time interval and value；
Judge whether the current time stamp is greater than described and value；
When the current time stamp is greater than described and value, determine in current AP data and the upper AP number once received
AP data are lacked between.
In the present embodiment, the judgment module 220 is used to execute the step S120 in Fig. 2, about the judgment module
220 specific descriptions are referred to the description of step S120 in Fig. 2.
Model selection module 230, for determine the current AP data and the upper AP data once received it
Between when lacking AP data, selected from the multiple prediction model and the corresponding target prediction model of AP data that lacks.
Optionally, the corresponding predicted time section of each prediction model, the Model selection module 230 is from the multiple prediction
It is selected in model and includes with the mode of the corresponding target prediction model of AP data of missing：
According to the current time stamp and the prefixed time interval or the historical time stamp and it is described default when
Between interval calculation obtain the missing time stamp in the AP data of the missing；
According to the missing time stamp and the corresponding predicted time section of each prediction model from the multiple prediction model
In select prediction model corresponding with the target prediction period where the missing time stamp as the target prediction mould
Type.
Further, the also corresponding data type of prediction of each prediction model, the Model selection module 230 is according to described
Missing time stamp and the corresponding predicted time section of each prediction model are selected and the missing from the multiple prediction model
Target prediction period corresponding prediction model where time stab includes as the mode of the target prediction model：
The class of the AP data of the missing is determined according to the AP data that the current AP data or the last time receive
Type；
It is described lack that data type of prediction is selected from the prediction model that predicted time section is the target prediction period
The prediction model of the type of the AP data of mistake is as the target prediction model.
In the present embodiment, the Model selection module 230 is used to execute the step S130 in Fig. 2, about the model
The specific descriptions of selecting module 230 are referred to the description of step S130 in Fig. 2.
Data recovery module 240, for being predicted according to AP data of the target prediction model to the missing, with
Restore the AP data of the missing.
In the present embodiment, the data recovery module 240 is used to execute the step S140 in Fig. 2, about the data
The specific descriptions of recovery module 240 are referred to the description of step S140 in Fig. 2.
Fig. 8 is please referred to, Fig. 8 is the two of the block diagram of Data Recapture Unit 200 provided in an embodiment of the present invention.It is described
Data Recapture Unit 200 can also include model building module 210.
Model building module 210 is established for the history AP data according to storage and stores the multiple prediction model.
In the present embodiment, the model building module 210 is used to execute the step S110 in Fig. 6, about the model
The specific descriptions for establishing module 210 are referred to the description of step S110 in Fig. 6.
In conclusion the embodiment of the present invention provides a kind of data reconstruction method and device.The method is applied to logical with AP
Believe the server of connection, is stored with multiple prediction models in the server.The server receives the AP data sent by AP,
And it is saved.The server is after receiving AP data, according to the current time in the AP data being currently received
History stamp in stamp and the last AP data received judges in current AP data and the upper AP data once received
Between whether lack AP data.Wherein, the AP data that the current AP data and the last time receive are sent by same AP.
When determining to lack AP data between the current AP data and the upper AP data once received, from the multiple prediction
Target prediction model corresponding with the AP data of missing is selected in model.Then according to the target prediction model to the missing
AP data predicted, to restore the AP data of the missing.As a result, when lacking AP data, can by with missing
The corresponding target prediction model of AP data is predicted, so that the AP data to missing are restored.
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of data reconstruction method, which is characterized in that applied to the server communicated to connect with wireless access point AP, the clothes
Multiple prediction models are stored in business device, the method includes：
According to the current time stamp in the AP data being currently received and the historical time in the last AP data received
Stamp judges whether lack AP data between current AP data and the upper AP data once received, wherein the current AP
The AP data that data and the last time receive are sent by same AP；
When determining to lack AP data between the current AP data and the upper AP data once received, from the multiple
Target prediction model corresponding with the AP data of missing is selected in prediction model；
It is predicted according to AP data of the target prediction model to the missing, to restore the AP data of the missing.
2. the method according to claim 1, wherein when current in the AP data that the basis is currently received
Between historical time stamp in stamp and the last AP data received judge once to receive in current AP data with upper
The step of AP data whether are lacked between AP data include：
Calculate the historical time stamp and prefixed time interval and value；
Judge whether the current time stamp is greater than described and value；
When the current time stamp is greater than described and value, determine current AP data and the upper AP data once received it
Between lack AP data.
3. according to the method described in claim 2, it is characterized in that, each prediction model correspond to a predicted time section, it is described from
The step of corresponding target prediction model of AP data with missing is selected in the multiple prediction model include：
According between the current time stamp and the prefixed time interval or the historical time stamp and the preset time
Every the missing time stamp in the AP data that the missing is calculated；
It is selected from the multiple prediction model according to the missing time stamp and the corresponding predicted time section of each prediction model
Prediction model corresponding with the target prediction period where the missing time stamp is as the target prediction model out.
4. according to the method described in claim 3, it is characterized in that, the also corresponding data type of prediction of each prediction model, institute
It states and is selected from the multiple prediction model according to the missing time stamp and the corresponding predicted time section of each prediction model
Step of the prediction model corresponding with the target prediction period where the missing time stamp as the target prediction model
Suddenly include：
The type of the AP data of the missing is determined according to the AP data that the current AP data or the last time receive；
Selecting data type of prediction from the prediction model that predicted time section is the target prediction period is the missing
The prediction model of the type of AP data is as the target prediction model.
5. the method according to claim 1, wherein current in the AP data that the basis is currently received
Historical time stamp in time stab and the last AP data received judges once to receive in current AP data with upper
AP data between before the step of whether lacking AP data, the method also includes：
It is established according to the history AP data of storage and stores the multiple prediction model.
6. a kind of Data Recapture Unit, which is characterized in that applied to the server communicated to connect with AP, stored in the server
There are multiple prediction models, described device includes：
Judgment module, for according in the AP data that are currently received current time stamp and the last AP data received
In historical time stamp judge whether lack AP data between current AP data and the upper AP data once received,
In, the AP data that the current AP data and the last time receive are sent by same AP；
Model selection module, for determining to lack AP between the current AP data and the upper AP data once received
When data, target prediction model corresponding with the AP data of missing is selected from the multiple prediction model；
Data recovery module, for being predicted according to AP data of the target prediction model to the missing, to restore
State the AP data of missing.
7. device according to claim 6, which is characterized in that the judgment module is according in the AP data being currently received
Current time stamp and the last AP data received in historical time stamp judge current AP data and it is upper once
The mode that AP data whether are lacked between the AP data received includes：
Calculate the historical time stamp and prefixed time interval and value；
Judge whether the current time stamp is greater than described and value；
When the current time stamp is greater than described and value, determine current AP data and the upper AP data once received it
Between lack AP data.
8. device according to claim 7, which is characterized in that the corresponding predicted time section of each prediction model, the mould
Type selecting module is selected from the multiple prediction model includes with the mode of the corresponding target prediction model of AP data of missing：
According between the current time stamp and the prefixed time interval or the historical time stamp and the preset time
Every the missing time stamp in the AP data that the missing is calculated；
It is selected from the multiple prediction model according to the missing time stamp and the corresponding predicted time section of each prediction model
Prediction model corresponding with the target prediction period where the missing time stamp is as the target prediction model out.
9. device according to claim 8, which is characterized in that the also corresponding data type of prediction of each prediction model, institute
Model selection module is stated according to the missing time stamp and the corresponding predicted time section of each prediction model from the multiple pre-
It surveys in model and selects prediction model corresponding with the target prediction period where the missing time stamp as the target
The mode of prediction model includes：
The type of the AP data of the missing is determined according to the AP data that the current AP data or the last time receive；
Selecting data type of prediction from the prediction model that predicted time section is the target prediction period is the missing
The prediction model of the type of AP data is as the target prediction model.
10. device according to claim 6, which is characterized in that described device further includes：
Model building module is established for the history AP data according to storage and stores the multiple prediction model.
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