CN114301562A - Wireless network time synchronization period self-adaptive method and system - Google Patents
Wireless network time synchronization period self-adaptive method and system Download PDFInfo
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Abstract
The invention relates to the technical field of network time synchronization, and discloses a period self-adaptive method and a period self-adaptive system for wireless network time synchronization, wherein the method comprises the following steps: s1, time synchronization: the method comprises the steps that all nodes in a network are synchronized by an applied time synchronization algorithm, timestamps corresponding to all nodes are obtained, and instantaneous errors obtained by clock offset estimation among the nodes are used as local synchronization error estimation values; s2, synchronization error prediction: obtaining a convergence probability estimated value by utilizing the local synchronization error estimated value; s3, re-synchronization period adjustment: and predicting the convergence condition of the time synchronization by using the estimated value of the convergence probability, adjusting the resynchronization cycle according to the convergence condition, and returning to the step 1. The invention solves the problems of incapability of adaptively adjusting the future synchronization period, poor adaptability, poor robustness, high network communication overhead and the like in the prior art.
Description
Technical Field
The invention relates to the technical field of network time synchronization, in particular to a period self-adaptive method and a period self-adaptive system for wireless network time synchronization.
Background
Time synchronization is a fundamental support technology for wireless sensor networks. In the application of the wireless sensor network, the data collected by the sensor nodes has no meaning if no space and time information exists. Accurate time synchronization is the basis of the technologies of realizing the operation and positioning of the self protocol of the sensor network, multi-sensor data fusion, tracking of a moving target, a protocol based on the accurate time synchronization, an energy-saving mechanism based on a sleep/interception mode and the like.
According to published papers at home and abroad, authorized related patent information and related protocols or standards such as NTP (network Time protocol), IEEE standard 1588v2, WIA-PA, ISA100.11a and WirelessHART, the intellectual property oriented to large-scale wireless network Time synchronization mainly focuses on the aspects of Time information exchange, parameter estimation, implementation scheme and the like of a Time synchronization algorithm. And no published documents exist for a period self-adaptive method of a dynamic network time synchronization algorithm.
The prior art comprises the following steps: the method has the problems of incapability of adaptively adjusting the future synchronization period, poor adaptability, poor robustness, high communication overhead of the network and the like.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a period self-adaption method and a period self-adaption system for wireless network time synchronization, and solves the problems that the prior art cannot self-adaptively adjust the future synchronization period, has poor adaptability, poor robustness, high communication overhead of a network and the like.
The technical scheme adopted by the invention for solving the problems is as follows:
a period self-adaptive method for wireless network time synchronization comprises the following steps:
s1, time synchronization: the method comprises the steps that all nodes in a network are synchronized by an applied time synchronization algorithm, timestamps corresponding to all nodes are obtained, and instantaneous errors obtained by clock offset estimation among the nodes are used as local synchronization error estimation values;
s2, synchronization error prediction: obtaining a convergence probability estimated value by utilizing the local synchronization error estimated value;
s3, re-synchronization period adjustment: and predicting the convergence condition of the time synchronization by using the estimated value of the convergence probability, adjusting the resynchronization cycle according to the convergence condition, and returning to the step 1.
As a preferable technical solution, in step S1, the neighboring node v is setiAnd vjEach having a corresponding time stamp Li[k]And Lj[k]Then the synchronization error estimate El[k]Is derived from the following formula:
El[k]＝Li[k]-Lj[k]。
as a preferred technical solution, the step S2 includes the following steps:
s21, real-time convergence detection: according to the estimated value E of local synchronous errorl[k]And calculating to obtain a convergence state value out [ k ]]And convergence probability estimate Pc[k]；
The convergence state value out [ k ] is calculated as:
where μ denotes the mean error, σ denotes the standard deviation of error, EmaxRepresents the maximum allowable error, a1、b1Are all integers which are more than 1, and xi is the element (0, 1);
s22, synchronous error characteristic estimation: using the converged state value out [ k ]]For estimator parameter EmaxReal-time updating is performed and then returns to step S1.
As a preferred technical solution, the step S3 includes the following steps:
s31, convergence decision: according to the convergence probability estimated value Pc[k]And comparing the current value with a preset convergence threshold value to perform convergence judgment: if convergence probability estimate Pc[k]If the convergence threshold value is larger than the convergence threshold value, the convergence judgment is true, otherwise, the convergence judgment is false;
s32, cycle adjustment: if the convergence judgment is false, shortening the resynchronization cycle; if the convergence decision is true, the resynchronization cycle is extended.
As a preferable technical solution, the step S3 further includes the following steps before the step S31:
s30, EWMA filtering: for the convergence probability estimation value P obtained in step S21c[k]Performing EWMA filtering, and estimating the convergence probability value P after filteringc[k]Is input to step S31.
As a preferred technical solution, the step S3 further includes the following steps:
and S33, recording the re-synchronization period after the period adjustment obtained in the step S32 and the estimated value of the local synchronization error under the re-synchronization period into a sample table.
As a preferred technical solution, the step S3 further includes the following steps:
s34, recording the environmental temperature of the step S32 in a sample table.
A wireless network time synchronization period self-adaptive system is based on the wireless network time synchronization period self-adaptive method and comprises a time synchronization unit, a synchronization error prediction unit and a resynchronization period adjustment unit which are electrically connected in sequence, wherein the resynchronization period adjustment unit is also electrically connected with the time synchronization unit; wherein the content of the first and second substances,
a time synchronization unit: synchronizing all nodes in the network by using an applied time synchronization algorithm to obtain timestamps corresponding to all nodes, and taking an instantaneous error obtained by estimating clock offset between the nodes as a local synchronization error estimated value;
a synchronization error prediction unit: obtaining a convergence probability estimated value by using the local synchronization error estimated value;
a resynchronization cycle adjustment unit: the method is used for predicting the convergence condition of time synchronization by using the estimated value of the convergence probability, adjusting the resynchronization cycle according to the convergence condition and inputting the resynchronization cycle into the time synchronization unit.
As a preferred technical scheme, the synchronous error prediction unit comprises a real-time convergence detection module and a synchronous error characteristic estimation module, the time synchronization unit, the real-time convergence detection module, the synchronous error characteristic estimation module and the resynchronization cycle adjustment unit are electrically connected in sequence, wherein,
a real-time convergence detection module: for estimating the local synchronization error El[k]And calculating to obtain a convergence state value out [ k ]]And convergence probability estimate Pc[k]；
The convergence state value out [ k ] is calculated as:
where μ denotes the mean error, σ denotes the standard deviation of error, EmaxRepresents the maximum allowable error, a1、b1Are all integers which are more than 1, and xi is the element (0, 1);
a synchronization error feature estimation module: for utilizing the convergence status value out [ k ]]For estimator parameter EmaxPerforming real-time update, and then updating EmaxAnd inputting the real-time convergence detection module.
As a preferred technical scheme, the resynchronization cycle adjusting unit comprises a convergence judging module and a cycle adjusting module, wherein the synchronization error predicting unit, the convergence judging module, the cycle adjusting module and the time synchronizing unit are electrically connected in sequence,
s31, convergence judging module: for estimating the value P according to the convergence probabilityc[k]And comparing the current value with a preset convergence threshold value to perform convergence judgment: if convergence probability estimate Pc[k]If the convergence threshold value is larger than the convergence threshold value, the convergence judgment is true, otherwise, the convergence judgment is false;
s32, the period adjustment module: the method is used for adjusting the resynchronization period, and if the convergence judgment is false, the resynchronization period is shortened; if the convergence decision is true, the resynchronization cycle is extended.
Compared with the prior art, the invention has the following beneficial effects:
(1) according to the method, error prediction can be obtained according to the time stamp in a dynamic network, and the convergence probability is obtained by an error prediction module to adjust the future synchronization period, so that the time synchronization of the wireless sensor network node realizes self-adaptive long-term survival under the condition of ensuring convergence;
(2) the method can directly utilize the clock offset estimation value in the time synchronization algorithm as the synchronization error estimation, does not need additional communication overhead to obtain the timestamp of the node, does not need independent calculation, and does not cause interference and influence on the adopted time synchronization algorithm; the invention can be very easily embedded into a time synchronization algorithm of practical application, and has extremely excellent expansibility;
(3) according to the invention, short-term fluctuation is eliminated by the obtained convergence probability through EWMA filtering, and the method is more stable, more real and more effective;
(4) if the system is suddenly attacked or unexpected, the network is restarted, and the most suitable synchronization period can be quickly found according to the sample table, so that the system is quickly recovered to be stable and synchronous, and the service life of the system is prolonged;
(5) the invention can adapt to the change of temperature.
Drawings
Fig. 1 is a schematic diagram illustrating steps of a periodic adaptive method for time synchronization of a wireless network according to the present invention;
fig. 2 is a schematic structural diagram of a periodic adaptive system for wireless network time synchronization according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited to these examples.
Example 1
As shown in fig. 1 and fig. 2, a method for periodic adaptation of time synchronization of a wireless network includes the following steps:
s1, time synchronization: the method comprises the steps that all nodes in a network are synchronized by an applied time synchronization algorithm, timestamps corresponding to all nodes are obtained, and instantaneous errors obtained by clock offset estimation among the nodes are used as local synchronization error estimation values;
s2, synchronization error prediction: obtaining a convergence probability estimated value by utilizing the local synchronization error estimated value;
s3, re-synchronization period adjustment: and predicting the convergence condition of the time synchronization by using the estimated value of the convergence probability, adjusting the resynchronization cycle according to the convergence condition, and returning to the step 1.
The invention can obtain error prediction according to the time stamp in a dynamic network, and the error prediction module obtains the convergence probability to adjust the future synchronization period, so that the time synchronization of the wireless sensor network node realizes self-adaptive long-term survival under the condition of ensuring convergence. The method can directly utilize the clock offset estimation value in the time synchronization algorithm as the synchronization error estimation, does not need additional communication overhead to obtain the timestamp of the node, does not need independent calculation, and does not cause interference and influence on the adopted time synchronization algorithm; the invention can be very easily embedded into a time synchronization algorithm in practical application, and has extremely excellent expansibility.
As a preferable technical solution, in step S1, the neighboring node v is setiAnd vjEach having a corresponding time stamp Li[k]And Lj[k]Then the synchronization error estimate El[k]Is derived from the following formula:
El[k]＝Li[k]-Lj[k]。
this facilitates obtaining a local synchronization error estimate.
As a preferred technical solution, the step S2 includes the following steps:
s21, real-time convergence detection: according to the estimated value E of local synchronous errorl[k]And calculating to obtain a convergence state value out [ k ]]And convergence probability estimate Pc[k]；
The convergence state value out [ k ] is calculated as:
where μ denotes the mean error, σ denotes the standard deviation of error, EmaxRepresents the maximum allowable error, a1、b1Are all integers which are more than 1, and xi is the element (0, 1);
s22, synchronous error characteristic estimation: using the converged state value out [ k ]]For estimator parameter EmaxReal-time updating is performed and then returns to step S1.
This facilitates obtaining the convergence probability estimate.
As a preferred technical solution, the step S3 includes the following steps:
s31, convergence decision: according to the convergence probability estimated value Pc[k]And comparing the current value with a preset convergence threshold value to perform convergence judgment: if convergence probability estimate Pc[k]If the convergence threshold value is larger than the convergence threshold value, the convergence judgment is true, otherwise, the convergence judgment is false;
s32, cycle adjustment: if the convergence judgment is false, shortening the resynchronization cycle; if the convergence decision is true, the resynchronization cycle is extended.
This facilitates the adjustment of the resynchronization period according to the convergence situation.
As a preferable technical solution, the step S3 further includes the following steps before the step S31:
s30, EWMA filtering: for the convergence probability estimation value P obtained in step S21c[k]Performing EWMA filtering, and estimating the convergence probability value P after filteringc[k]Is input to step S31.
Short-term fluctuation is eliminated through the EWMA filtering, and the method is more stable, real and effective.
As a preferred technical solution, the step S3 further includes the following steps:
and S33, recording the re-synchronization period after the period adjustment obtained in the step S32 and the estimated value of the local synchronization error under the re-synchronization period into a sample table.
If the system is suddenly attacked or unexpected, the network is restarted, and the most suitable synchronization period can be quickly found according to the sample table, so that the system is quickly recovered to be stable and synchronous, and the service life of the system is prolonged.
As a preferred technical solution, the step S3 further includes the following steps:
s34, recording the environmental temperature of the step S32 in a sample table.
This allows the invention to accommodate temperature variations.
Example 2
As shown in fig. 1 and fig. 2, as a further optimization of embodiment 1, this embodiment includes all the technical features of embodiment 1, and in addition, this embodiment further includes the following technical features:
a wireless network time synchronization period self-adaptive system is based on the wireless network time synchronization period self-adaptive method and comprises a time synchronization unit, a synchronization error prediction unit and a resynchronization period adjustment unit which are electrically connected in sequence, wherein the resynchronization period adjustment unit is also electrically connected with the time synchronization unit; wherein the content of the first and second substances,
a time synchronization unit: synchronizing all nodes in the network by using an applied time synchronization algorithm to obtain timestamps corresponding to all nodes, and taking an instantaneous error obtained by estimating clock offset between the nodes as a local synchronization error estimated value;
a synchronization error prediction unit: obtaining a convergence probability estimated value by using the local synchronization error estimated value;
a resynchronization cycle adjustment unit: the method is used for predicting the convergence condition of time synchronization by using the estimated value of the convergence probability, adjusting the resynchronization cycle according to the convergence condition and inputting the resynchronization cycle into the time synchronization unit.
The invention can obtain error prediction according to the time stamp in a dynamic network, and the error prediction module obtains the convergence probability to adjust the future synchronization period, so that the time synchronization of the wireless sensor network node realizes self-adaptive long-term survival under the condition of ensuring convergence. The method can directly utilize the clock offset estimation value in the time synchronization algorithm as the synchronization error estimation, does not need additional communication overhead to obtain the timestamp of the node, does not need independent calculation, and does not cause interference and influence on the adopted time synchronization algorithm; the invention can be very easily embedded into a time synchronization algorithm in practical application, and has extremely excellent expansibility.
As a preferred technical scheme, the synchronous error prediction unit comprises a real-time convergence detection module and a synchronous error characteristic estimation module, the time synchronization unit, the real-time convergence detection module, the synchronous error characteristic estimation module and the resynchronization cycle adjustment unit are electrically connected in sequence, wherein,
a real-time convergence detection module: for estimating the local synchronization error El[k]And calculating to obtain a convergence state value out [ k ]]And convergence probability estimate Pc[k]；
The convergence state value out [ k ] is calculated as:
where μ denotes the mean error, σ denotes the standard deviation of error, EmaxRepresents the maximum allowable error, a1、b1Are all integers which are more than 1, and xi is the element (0, 1);
a synchronization error feature estimation module: for utilizing the convergence status value out [ k ]]For estimator parameter EmaxPerforming real-time update, and then updating EmaxAnd inputting the real-time convergence detection module.
This facilitates obtaining the convergence probability estimate.
As a preferred technical scheme, the resynchronization cycle adjusting unit comprises a convergence judging module and a cycle adjusting module, wherein the synchronization error predicting unit, the convergence judging module, the cycle adjusting module and the time synchronizing unit are electrically connected in sequence,
s31, convergence judging module: for estimating the value P according to the convergence probabilityc[k]And comparing the current value with a preset convergence threshold value to perform convergence judgment: if convergence probability estimate Pc[k]If the convergence threshold value is larger than the convergence threshold value, the convergence judgment is true, otherwise, the convergence judgment is false;
s32, the period adjustment module: the method is used for adjusting the resynchronization period, and if the convergence judgment is false, the resynchronization period is shortened; if the convergence decision is true, the resynchronization cycle is extended.
This facilitates the adjustment of the resynchronization period according to the convergence situation.
Example 3
As shown in fig. 1 and 2, the present embodiment includes all the technical features of the embodiments 1 and 2, and provides a more detailed implementation manner based on the embodiments 1 and 2.
The invention relates to a period self-adaptive system for wireless network time synchronization, which mainly comprises a time synchronization unit, a synchronization error prediction unit and a resynchronization period adjustment unit. The period real-time self-adaption can obtain error prediction in a dynamic network according to a time stamp, and a convergence probability is obtained by an error prediction module to adjust a future synchronization period, so that the time synchronization of the wireless sensor network node realizes the self-adaption long-term survival under the condition of ensuring convergence.
(1) A time synchronization unit;
the time synchronization unit comprises two modules of synchronization and parameter estimation, firstly, all nodes in the network are synchronized according to a time synchronization algorithm actually applied by the system, and the synchronization module can obtain time stamps corresponding to all nodes, so that in the parameter estimation module, an instantaneous error obtained by estimating clock offset between the nodes is used as a synchronization error estimation value.
Let neighboring node viAnd vjEach having a corresponding time stamp Li[k]And Lj[k]Then the synchronization error estimate El[k]Is derived from the following formula:
El[k]＝Li[k]-Lj[k]。
the clock offset estimation is an indispensable part of all relevant protocols or standards of the time synchronization algorithm, and the invention can directly utilize the clock offset estimation value in the time synchronization algorithm as the synchronization error estimation value El[k]. Therefore, the invention does not need additional communication overhead to obtain the time stamp of the node and does not need to separately calculate El[k]Meanwhile, the adopted time synchronization algorithm cannot be interfered and influenced. These advantages enable the present invention to be very easily embedded in a time synchronization algorithm for practical use, with extremely excellent extensibility.
(2) A synchronization error prediction unit;
in a dynamic network, there are various unknown situations, and even if the effects that the same algorithm can achieve in different scenes and scales are different, the adaptive capacity of the time synchronization technology in coping with different situations is extremely important. Compared with the prior static stateThe period self-adaptive algorithms under the network are different, the method estimates the convergence probability of the system by adopting the ARCE real-time convergence estimation instead of estimating the future specific error, can estimate the convergence state of the system in real time, is more flexible and can adapt to the changeful network conditions. The invention mainly uses a real-time convergence detection module and a synchronous error characteristic estimation module in the ARCE. The input of the part is a local synchronization error E output by the time synchronization unitl[k]The output is the convergence probability estimate Pc[k]。
A real-time convergence detection module;
the key of this part is to use a three-segment function with input E to detect the convergence status of the system in real timel[k]The output is out [ k ]]. The local synchronization error determines the interval of the convergence state through the function, and the convergence state value is obtained. Then the final convergence probability P is obtained by a convergence probability calculation unitc[k]. The principle of this section is given by the following equation.
Where μ denotes the mean error, σ denotes the standard deviation of error, EmaxRepresents the maximum allowable error, a1、b1All integers are more than 1, xi is an element (0,1), and the setting of xi is designed according to specific situations.
A synchronous error characteristic estimation module;
according to the output out k of the real-time convergence detection module]In this section, the error characteristics μ, σ may be calculated to account for the estimator parameters E in the convergence detection modulemaxAnd performing real-time updating, and forming a small feedback in the part to realize self-regulation of the system.
(3) A resynchronization cycle adjusting unit;
the resynchronization period adjusting unit comprises a convergence judgment logic unit, a period adjusting unit and a sample table unit. The whole unit predicts the convergence situation of time synchronization according to the convergence probability estimation sent by the synchronization error prediction unit, takes the convergence situation as a regulation period, and generates a corresponding sample to be included in a sample table after the convergence situation is stable.
A convergence judgment logic unit;
the convergence probability derived from the error prediction module is input to a buffer where the observed variation can be derived, with reference to P in subsequent cycle adjustmentsc[k]The variation of (c) modifies the cycle length. The probability of convergence in the buffer needs to be processed with an Exponentially Weighted Moving Average (EWMA) filter, the principle of which is given by the following equation.
Pt＝αYt-1+(1-α)×Pt-1,t>2，
The larger the alpha value is, the faster the proportion occupied by the historical data is reduced, and the larger the filtering window size is along with the time lapse, namely, the longer the system uses the periodic real-time self-adaptive method described in the invention, the closer the obtained predicted value is to the true value.
The larger the value is, the faster the proportion of the historical data is reduced, and the larger the size of the filtering window gradually becomes along with the time lapse, namely, the longer the system uses the periodic real-time adaptive method described in the invention, the closer the obtained predicted value is to the true value.
And judging whether the system reaches convergence by using a convergence threshold preset by a convergence judgment unit according to the convergence probability value obtained by filtering, and outputting the convergence judgment to be true when the convergence probability is greater than the threshold, otherwise, outputting the convergence judgment to be false.
A period adjusting unit;
this section needs to decide the variation of the period with reference to the output of the convergence decision and the data variation in the convergence probability buffer. Firstly, if the convergence decision is false, it is proved that the system can not achieve convergence under the period, therefore, the period and P need to be determined according toc[k]The length of the period is adjusted according to the relationship, and the length of the resynchronization period is shortened according to the convergence probability and the corresponding module of the period. If the convergence decision output is true, then P needs to be combined while the resynchronization period is extended according to the modulecThe data in the buffer judges whether the new period is effective, if the data in the buffer is in a gradually increasing trend, the period adjustment is effective, and if the data in the buffer is in a gradually increasing trend, the period adjustment is effectiveFluctuation of the convergence threshold value indicates that the period regulation has not reached the optimal state, and P needs to be addedcThe depth of the buffer and parameters such as the data variation trend in the buffer can be shown, such as the mean value mu and the standard deviation sigma, so as to adjust the period together, and the period is made as large as possible under the condition of ensuring convergence. Wherein Pc is the final output convergence probability estimate of the synchronization error prediction model, and P is a single Pc[k]Enter into PcIn a buffer, PcThe buffer stores the P obtained each timec[k]Value for subsequent use.
PcThe values in the buffer are gradually accumulated along with the operation of the system, and the more the data is, the more the period regulation can be judged to be effective, so that the system resynchronization period in the optimal state is obtained, therefore, the longer the service time is, the better the effect is, and the self-adaptive long-term survival of the network can be realized.
And thirdly, a sample table.
The sample table can be obtained through experiments or self-learning, and real-time updating is supported. After a stable convergence probability is obtained through convergence judgment, the obtained resynchronization period is recorded as a sample, and is recorded into a sample table together with the synchronization error under the period. After the system runs through a long-term periodic adaptive algorithm, a large amount of learning experience can be accumulated. Under the condition, if the system is suddenly attacked or unexpected, the network is restarted, and the most suitable synchronization period can be quickly found according to the sample table, so that the system is quickly recovered to be stable and synchronized, and the service life of the system is prolonged.
In addition, the current temperature can be recorded in the sample table, and indoor and outdoor are distinguished, so that the system applying the periodic real-time self-adaptive algorithm can adapt to the change of the temperature.
As described above, the present invention can be preferably realized.
All features disclosed in all embodiments in this specification, or all methods or process steps implicitly disclosed, may be combined and/or expanded, or substituted, in any way, except for mutually exclusive features and/or steps.
The foregoing is only a preferred embodiment of the present invention, and the present invention is not limited thereto in any way, and any simple modification, equivalent replacement and improvement made to the above embodiment within the spirit and principle of the present invention still fall within the protection scope of the present invention.
Claims (10)
1. A period self-adaptive method for wireless network time synchronization is characterized by comprising the following steps:
s1, time synchronization: the method comprises the steps that all nodes in a network are synchronized by an applied time synchronization algorithm, timestamps corresponding to all nodes are obtained, and instantaneous errors obtained by clock offset estimation among the nodes are used as local synchronization error estimation values;
s2, synchronization error prediction: obtaining a convergence probability estimated value by utilizing the local synchronization error estimated value;
s3, re-synchronization period adjustment: and predicting the convergence condition of the time synchronization by using the estimated value of the convergence probability, adjusting the resynchronization cycle according to the convergence condition, and returning to the step 1.
2. The method of claim 1, wherein in step S1, the neighboring node v is setiAnd vjEach having a corresponding time stamp Li[k]And Lj[k]Then the synchronization error estimate El[k]Is derived from the following formula:
El[k]＝Li[k]-Lj[k]。
3. the periodic adaptation method for time synchronization of wireless networks as claimed in claim 2, wherein the step S2 comprises the steps of:
s21, real-time convergence detection: according to the estimated value E of local synchronous errorl[k]And calculating to obtain a convergence state value out [ k ]]And convergence probability estimate Pc[k]；
The convergence state value out [ k ] is calculated as:
where μ denotes the mean error, σ denotes the standard deviation of error, EmaxRepresents the maximum allowable error, a1、b1Are all integers which are more than 1, and xi is the element (0, 1);
s22, synchronous error characteristic estimation: using the converged state value out [ k ]]For estimator parameter EmaxReal-time updating is performed and then returns to step S1.
4. The periodic adaptation method for time synchronization of wireless networks as claimed in claim 3, wherein the step S3 comprises the steps of:
s31, convergence decision: according to the convergence probability estimated value Pc[k]And comparing the current value with a preset convergence threshold value to perform convergence judgment: if convergence probability estimate Pc[k]If the convergence threshold value is larger than the convergence threshold value, the convergence judgment is true, otherwise, the convergence judgment is false;
s32, cycle adjustment: if the convergence judgment is false, shortening the resynchronization cycle; if the convergence decision is true, the resynchronization cycle is extended.
5. The periodic adaptation method for wireless network time synchronization of claim 4, wherein the step S3 further comprises the following steps before the step S31:
s30, EWMA filtering: for the convergence probability estimation value P obtained in step S21c[k]Performing EWMA filtering, and estimating the convergence probability value P after filteringc[k]Is input to step S31.
6. The periodic adaptation method for time synchronization of wireless networks according to claim 4 or 5, wherein the step S3 further comprises the steps of:
and S33, recording the re-synchronization period after the period adjustment obtained in the step S32 and the estimated value of the local synchronization error under the re-synchronization period into a sample table.
7. The periodic adaptation method for time synchronization of wireless networks as claimed in claim 6, wherein the step S3 further comprises the steps of:
s34, recording the environmental temperature of the step S32 in a sample table.
8. A wireless network time synchronization period self-adaptive system is characterized in that the wireless network time synchronization period self-adaptive method based on any one of claims 1 to 7 comprises a time synchronization unit, a synchronization error prediction unit and a resynchronization period adjustment unit which are electrically connected in sequence, wherein the resynchronization period adjustment unit is also electrically connected with the time synchronization unit; wherein the content of the first and second substances,
a time synchronization unit: synchronizing all nodes in the network by using an applied time synchronization algorithm to obtain timestamps corresponding to all nodes, and taking an instantaneous error obtained by estimating clock offset between the nodes as a local synchronization error estimated value;
a synchronization error prediction unit: obtaining a convergence probability estimated value by using the local synchronization error estimated value;
a resynchronization cycle adjustment unit: the method is used for predicting the convergence condition of time synchronization by using the estimated value of the convergence probability, adjusting the resynchronization cycle according to the convergence condition and inputting the resynchronization cycle into the time synchronization unit.
9. The wireless network time-synchronized period adaptive system according to claim 8, wherein the synchronization error prediction unit comprises a real-time convergence detection module and a synchronization error feature estimation module, the time synchronization unit, the real-time convergence detection module, the synchronization error feature estimation module, and the resynchronization period adjustment unit are electrically connected in sequence, wherein,
a real-time convergence detection module: for estimating the local synchronization error El[k]And calculating to obtain a convergence state value out [ k ]]And convergence probability estimate Pc[k]；
The convergence state value out [ k ] is calculated as:
where μ denotes the mean error, σ denotes the standard deviation of error, EmaxRepresents the maximum allowable error, a1、b1Are all integers which are more than 1, and xi is the element (0, 1);
a synchronization error feature estimation module: for utilizing the convergence status value out [ k ]]For estimator parameter EmaxPerforming real-time update, and then updating EmaxAnd inputting the real-time convergence detection module.
10. The wireless network time synchronization cycle adaptive system according to claim 9, wherein the resynchronization cycle adjusting unit comprises a convergence judging module and a cycle adjusting module, the synchronization error predicting unit, the convergence judging module, the cycle adjusting module and the time synchronizing unit are electrically connected in sequence, wherein,
s31, convergence judging module: for estimating the value P according to the convergence probabilityc[k]And comparing the current value with a preset convergence threshold value to perform convergence judgment: if convergence probability estimate Pc[k]If the convergence threshold value is larger than the convergence threshold value, the convergence judgment is true, otherwise, the convergence judgment is false;
s32, the period adjustment module: the method is used for adjusting the resynchronization period, and if the convergence judgment is false, the resynchronization period is shortened; if the convergence decision is true, the resynchronization cycle is extended.
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