Online fast processing method for real-time data based on edge computing

An online fast processing method for real-time data based on edge computing, comprising the following specific steps:

• installing an acceleration sensor on a cantilever beam-liked structure, the acceleration sensor to collect and monitor acceleration data on the cantilever beam-liked structure;
• acquiring the acceleration data of the cantilever beam-liked structure by the acceleration sensor to calculate an integrated speed based on an edge device, wherein an acquired original acceleration data of the cantilever beam-liked structure is a=(a1i, a2i, . . . , ani)T, wherein i represents the ith monitoring time point, and n represents the number of monitoring points;
• assuming that the interval between two monitoring time points is Δt, the sampling frequency of the corresponding sensor is 1/Δt, and the speed calculation of the corresponding monitoring point is expressed as follows:
• vji=(aji-1+aji)×Δt/2 (1)
• wherein aji-1 represents the acceleration corresponding to the jth monitoring point at the (i−1)th time point, and aji, represents the acceleration value corresponding to the jth monitoring point at the ith time point, wherein j∈[1,n], and vji represents the integrated speed corresponding to the jth monitoring point at the ith time point;
• after calculating the integrated speed on the edge device, judging whether the number of accelerations currently collected at the same monitoring point reaches ¼ of the sampling frequency, i.e., whether the number of acceleration sample points collected at the same monitoring point is more than ¼ of the number of sampling frequencies;
• if the number of accelerations currently collected is less than ¼ of the sampling frequency value, integrating the integrated speed again to acquire a corresponding displacement on the edge device, and the displacement currently obtained is the final ideal displacement value of the cantilever beam-liked structure;
• if the number of collected accelerations is more than ¼ of the sampling frequency value, dynamically de-noising the integrated speed to reduce the influence of the drift on the integrating speed;
• the whole de-noising process is expressed as follows:
• Zji=α·Zji-1+(1−α)·vji (2)
• wherein Zji represents the trend value contained in the current integrating speed, i represent the ith moment, and j represents the corresponding jth monitoring point;
• Zji-1 represents the drift trend value corresponding to the previous time point, and when i=2, the value of Zji-1 is only the integrating speed of the previous time point, i.e., the initial point;
• vji represents the integrating speed of the jth monitoring point at the ith time point;
• α is the weight value corresponding to Zji-1;
• if the sampling frequency of the acceleration sensor is f the sampling interval between two adjacent time points is 1/f and the corresponding weight value is expressed as follows:
• α
• =
• 1
• /
• 4
• 1
• /
• 4
• +
• 1
• /
• f
• (
• 3
• )
• according to formula (2), the drift trend value at the current monitoring point not only depends on the trend value corresponding to the previous time point, but also depends on the speed value obtained by integration at the current time point;
• according to formula (3), when the value of α is close to 1, the drifting trend corresponding to the current time point is greatly affected by the drifting trend of the previous time point, and the current integrating speed value only gives the drifting trend of the previous time point a step size;
• and the de-noised speed corresponding to the current time point is expressed as follows:
• v
• ⌢
• j
• i
• =
• v
• j
• i
• -
• Z
• j
• i
• =
• v
• j
• i
• -
• α
• ·
• Z
• j
• i
• -
• 1
• -
• (
• 1
• -
• α
• )
• ·
• v
• j
• i
• =
• α
• ·
• (
• v
• j
• i
• -
• Z
• j
• i
• -
• 1
• )
• (
• 4
• )
• wherein {circumflex over (v)}ji represents a de-noised ideal speed;
• obtaining corresponding displacement of the cantilever beam-liked structure, comprising integrating the de-noised ideal speed with formula (4), wherein the integration process is as follows:
• sji=({circumflex over (v)}ji-1+{circumflex over (v)}ji)×Δt/2 (5)
• wherein sji represents the displacement corresponding to the ith monitoring time point, and j represents the reference sign of the monitoring point;
• filtering data online with regard to real-time integration results;
• and a Butterworth filter is adopted, the principle of which is expressed as follows:
• ❘
• "\[LeftBracketingBar]"
• H
• ⁡
• (
• w
• )
• ❘
• "\[RightBracketingBar]"
• 2
• =
• 1
• -
• 1
• 1
• +
• (
• w
• w
• c
• )
• 2
• ⁢
• m
• =
• 1
• -
• 1
• 1
• +
• ε
• 2
• (
• w
• w
• p
• )
• 2
• ⁢
• m
• (
• 6
• )
• wherein wc represents the cut-off frequency corresponding to the filter, m represents the order of the corresponding filter, and wp represents the pass-band edge frequency;
• when the corresponding frequency w is more than the cut-off frequency, that is w>wc, the corresponding
• (
• w
• w
• c
• )
• 2
• ⁢
• m
• value is close to infinity, and the |H(w)|2 value is closer to 1, which indicates that all the signals pass, when the corresponding signal frequency is lower than the cut-off frequency, that is w≤wc, the corresponding
• (
• w
• w
• c
• )
• 2
• ⁢
• m
• value is close to 0, and |H(w)|2 is close to 0;
• at this moment, the corresponding signal will be cut off, which is 0;
• and the process is expressed as follows:
• ❘
• "\[LeftBracketingBar]"
• H
• ⁡
• (
• w
• )
• ❘
• "\[RightBracketingBar]"
• 2
• =
• {
• 0
• w
• ≤
• w
• c
• 1
• w
• >
• w
• c
• (
• 7
• )
• the larger the filter order of n is, the smoother the filtered value is, and the closer the filter is to an ideal filter;
• the filter order adopted is set to 8, the cut-off frequency is set to 0.012×w, and
• adopting a high-pass filter to de-noise the integrated displacement on the edge device, when the frequency is higher than the set cut-off frequency, the displacement obtained by integration can be regarded as the final ideal value, and when the frequency corresponding to the displacement point is lower than the cutoff frequency, the value corresponding to this part is flattened;
• and the de-noised integral displacement is expressed as follows:
• ŝji=sji−Nji (8)
• wherein sji represents the result obtained by the corresponding direct integration in formula (5), Nji represents the noise value obtained by high-pass filter, and ŝji represents the corresponding ideal integral displacement value after de-noising;
• embedding the corresponding ideal integral displacement value after de-noising into the edge device to describe the motion state of the cantilever beam-liked structure so as to realize dynamic fast processing of online signals.