CN110274537A - Can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel - Google Patents
Can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel Download PDFInfo
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- CN110274537A CN110274537A CN201910658104.4A CN201910658104A CN110274537A CN 110274537 A CN110274537 A CN 110274537A CN 201910658104 A CN201910658104 A CN 201910658104A CN 110274537 A CN110274537 A CN 110274537A
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- 230000001360 synchronised effect Effects 0.000 title claims abstract description 21
- 230000002159 abnormal effect Effects 0.000 claims abstract description 14
- 238000004891 communication Methods 0.000 claims abstract description 14
- 238000001914 filtration Methods 0.000 claims abstract description 13
- 230000006870 function Effects 0.000 claims abstract description 9
- 230000008030 elimination Effects 0.000 claims abstract description 4
- 238000003379 elimination reaction Methods 0.000 claims abstract description 4
- 230000003993 interaction Effects 0.000 claims description 4
- 238000013500 data storage Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims 1
- 238000000034 method Methods 0.000 abstract description 7
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 230000008569 process Effects 0.000 abstract description 6
- 238000001514 detection method Methods 0.000 abstract description 3
- 230000034184 interaction with host Effects 0.000 abstract 1
- 238000013024 troubleshooting Methods 0.000 abstract 1
- 230000004044 response Effects 0.000 description 8
- 238000012545 processing Methods 0.000 description 6
- 230000004888 barrier function Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
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- 238000012423 maintenance Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B7/00—Measuring arrangements characterised by the use of electric or magnetic techniques
- G01B7/16—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge
- G01B7/18—Measuring arrangements characterised by the use of electric or magnetic techniques for measuring the deformation in a solid, e.g. by resistance strain gauge using change in resistance
Abstract
The present invention relates to structure real time monitoring fields, synchronism detection and monitoring application are strained for structure multiple spot, be related to it is a kind of can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, including resistance strain sensor, RC low-pass filter, multi-channel synchronous analog-digital converter, microprocessor, communication module, power module, FLASH and distributed memory.The microprocessor completes FIR data filtering, data feature values are extracted, superthreshold is alarmed online, the cooperated computing function of abnormal data elimination, and realizes the interaction with host computer by communication module.The present invention reduces the calculating pressure of server by the way that partial arithmetic process, part store function is distributed on intelligence sensor, reduces the demand to network, avoid calculating because of server pressure it is excessive caused by time delay, lead to troubleshooting not in time.
Description
Technical field
The present invention relates to structure real time monitoring field, for the dynamic strain sensor of structure monitoring, particular for
Structure multiple spot strains synchronism detection and monitoring application, is related to a kind of with distributed storage, the analysis of online cooperated computing and superthreshold
It is worth the synchronous dynamic strain sensor of intelligent multi-channel of warning function.
Background technique
Traditional structural healthy monitoring system, the main data acquisition of sensor node and transfer function, all numbers
According to processing and analyze by local monitor center or the completion of remote monitoring center server.When an a large amount of dynamic of system access
When sensor, mass data enters server simultaneously, and the network and data processing load of monitoring center will weigh very much, this can be to pre-
Alert actual effect produces serious influence, and possible some simple configuration states will postpone for a long time find extremely and report
It is alert, cause structure operation and maintenance personnel can not timely emergency disposal configuration state unusual condition.It can it is therefore desirable to develop one kind
The intelligence sensor of cooperated computing carries out necessary pretreatment to the data of acquisition, and filters out redundant data, can not only mitigate
The data processing and storage burden of monitoring center also make to alarm and dispose much sooner.
Summary of the invention
In order to solve the above technical problems, straining synchronism detection and monitoring application, present invention offer particular for structure multiple spot
It is a kind of can Cooperative Analysis the synchronous dynamic strain sensor of intelligent multi-channel, have distributed storage, the analysis of online cooperated computing and
Superthreshold warning function.
The technical solution adopted by the present invention is that: it is a kind of can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, packet
Include resistance strain sensor, RC low-pass filter, multi-channel synchronous analog-digital converter, microprocessor, communication module, power supply mould
Block, FLASH and distributed memory, the output of the resistance strain sensor are connected to the input of RC low-pass filter, and RC is low
The output of bandpass filter is connected to the input of multi-channel synchronous analog-digital converter, and the output of the analog-digital converter is connected to micro- place
The input of device is managed, the microprocessor is connected with communication module, FLASH and distributed memory, the communication module and monitoring
Center PC is connected by wired or wireless mode, and the power module is the power supply of other circuit units.
The microprocessor completes the collaboration processing function of acquisition data, and realizes the friendship with host computer by communication module
Mutually.
Preferably, the memory space of the distributed storage configuration 16GB or more can be with when big failure occurs in network
Being stored on intelligence sensor for initial data long period, sensor stability is promoted, data backup can also be played the role of,
When loss of data or damage can upload again.
The FLASH memory is used for the operation program and characteristic value of storage system, and characteristic value mainly includes mean value, greatly
Value, minimum.
The interaction of the microprocessor and host computer, including request real time data, network posture test request.
Specifically, the cooperated computing of the microprocessor includes FIR data filtering, data feature values are extracted, superthreshold exists
Report from a liner police, abnormal data elimination.
The FIR filtering carries out signal condition to collected strain signal and FIR is filtered, the cutoff frequency of filtering
It is customized according to institute's geodesic structure, so that reducing high-frequency signal is aliased into low-frequency noise, improves the accuracy of signal.
In view of structural strain data characteristics, the Upper threshold and lower threshold of structural strain are not to close in symmetrical about mean value
System.For a channel Ai, if its mean value, which compares visiting for threshold value, is limited to Mi, Xiamen is limited to Ni, and super threshold counter Li(is initial
Value is 0), not super threshold counter Ki(initial value is that 0), ephemeral data storage address is ADi.Below with the characteristic value of channel Ai
For calculating, illustrate the workflow of microprocessor cooperated computing and superstructure response alarm waveform extracting:
S1: do not stop to acquire real time data, carry out multichannel FIR parallel filtering；
S2: n initial data carry out mean value computation, and store in characteristic value to the space FLASH；
S3: the (n+1)th data point starts to be weighted mean value computation, and each new point weight is 1/n forever；
S4: new data point and feature value difference are calculated, and is compared with two threshold values:
(1) if the absolute value of this difference is not above threshold value:
1. data are directly stored on temporary memory space ADi if Li=0；
2. if 0 < Li < Q(Q is the continuous abnormal data points of starting alarm), reject the exception before the new data point
Data (are considered as pulse interference signal or acnode abnormal signal), and Li is reset, by new data point storage to temporary memory space
On ADi；
3. judge whether Ki reaches half of collection period number if Li >=Q, if do not reached, Ki=Ki+1, by new data
In point storage to temporary memory space ADi；If reached, then it is assumed that half period before first super threshold value arrives last one
The data segment of super threshold value second half of the cycle is a complete alarm waveform, this alarm waveform will be from interim storage address
ADi is moved to fixed data memory space, updates Min-max, and Li is reset, by new data point storage to interim storage
On the ADi of space；
(2) if the absolute value of new data point and characteristic value difference is more than threshold value Ni or Mi, super threshold count device
Li=Li+1, not super threshold counter Ki is reset, then is stored the data on temporary memory space ADi.
Textural anomaly response data differentiates:
When Ai obtains a complete alarm waveform, micro process will be the maximum of waveform in each channel at the same time section
It is compared, it can be determined which channel obtains maximum structural response, and the neighbouring relations and power installed according to multiple channels
Loading characteristic is learned, judges whether the sequence between maximum is normal, when maximum sequence is abnormal, or difference is larger, it is believed that
Stress relationship between channel is destroyed, and structure is abnormal response.Calculated result is uploaded to monitoring center by micro process, and
The Wave data in each channel in this period is all stored into fixed data memory.
The invention has the advantages that part store function is distributed on intelligence sensor by partial arithmetic process,
Reduce the calculating pressure of server, reduce demand to network, avoid calculating because of server pressure it is excessive caused by time delay, cause therefore
Barrier processing is not in time.
Detailed description of the invention
Fig. 1 be the present invention can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel structure chart.
Fig. 2 is the workflow of single channel microprocessor cooperated computing.
Specific embodiment
Below in conjunction with attached drawing, the present invention will be described in detail.
The present invention can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel structure chart as shown in Figure 1, including electricity
Hinder strain transducer, RC low-pass filter, multi-channel synchronous analog-digital converter, microprocessor, communication module, power module,
FLASH and distributed memory, the output of the resistance strain sensor are connected to the input of RC low-pass filter, RC low pass filtered
The output of wave device is connected to the input of multi-channel synchronous analog-digital converter, and the output of the analog-digital converter is connected to microprocessor
Input, the microprocessor is connected with communication module, FLASH and distributed memory, the communication module and monitoring center
PC is connected by wired or wireless mode, and the power module is microprocessor power supply.
The microprocessor completes the collaboration processing function of acquisition data, and realizes the friendship with host computer by communication module
Mutually.
Preferably, the memory space of the distributed storage configuration 16GB or more can be with when big failure occurs in network
Being stored on intelligence sensor for initial data long period, sensor stability is promoted, data backup can also be played the role of,
When loss of data or damage can upload again.
The FLASH memory is used for the operation program and characteristic value of storage system, and characteristic value mainly includes mean value, greatly
Value, minimum.
The interaction of the microprocessor and host computer, including request real time data, network posture test request.
Specifically, the cooperated computing of the microprocessor includes FIR data filtering, data feature values are extracted, superthreshold exists
Report from a liner police, abnormal data elimination.
The FIR filtering carries out signal condition to collected strain signal and FIR is filtered, the cutoff frequency of filtering
It is customized according to institute's geodesic structure, so that reducing high-frequency signal is aliased into low-frequency noise, improves the accuracy of signal.
In the present embodiment, 8 strained channels are shared, analog-digital converter uses 24 bit synchronization, 8 channel modulus converter.If
It is fixed:
8 channel numbers A1, A2, A3, A4, A5, A6, A7, A8；
8 channel mean values compare threshold value Upper threshold M1, M2, M3, M4, M5, M6, M7, M8；
8 channel mean values compare threshold value Lower Threshold N1, N2, N3, N4, N5, N6, N7, N8；
The super upper threshold in 8 channels and super lower threshold counter: L1, L2, L3, L4, L5, L6, L7, L8；
The not super threshold counter in 8 channels: K1, K2, K3, K4, K5, K6, K7, K8；
8 channels ephemeral data storage address AD1, AD2, AD3, AD4, AD5, AD6, AD7, AD8；
The initial value of counter is 0.
In view of structural strain data characteristics, therefore it is in pair that the Upper threshold of structural strain and lower threshold are not about mean value
Title relationship.Below by taking the characteristic value of first channel A1 calculates as an example, illustrate microprocessor cooperated computing and superstructure response report
The workflow of alert waveform extracting:
S1: do not stop to acquire real time data, carry out 8 channel FIR parallel filterings；
S2: n initial data carry out mean value computation, and store in characteristic value to the space FLASH；
S3: the (n+1)th data point starts to be weighted mean value computation, and each new point weight is 1/n forever；
S4: new data point and feature value difference are calculated, and is compared with two threshold values:
(1) if the absolute value of this difference is not above threshold value:
1. data are directly stored on temporary memory space AD1 if L1=0；
2. if 0 < L1 < Q(Q is the continuous abnormal data points of starting alarm), reject the exception before the new data point
Data (are considered as pulse interference signal or acnode abnormal signal), and L1 is reset, by new data point storage to temporary memory space
On AD1；
3. judge whether K1 reaches half of collection period number if L1 >=Q, if do not reached, K1=K1+1, by new data
In point storage to temporary memory space AD1；If reached, then it is assumed that half period before first super threshold value arrives last one
The data segment of super threshold value second half of the cycle is a complete alarm waveform, this alarm waveform will be from interim storage address
AD1 is moved to fixed data memory space, updates Min-max, and L1 is reset, by new data point storage to interim storage
On the AD1 of space；
(2) if the absolute value of new data point and characteristic value difference is more than threshold value N1 or M1, super threshold count device
L1=L1+1, not super threshold counter K1 is reset, then is stored the data on temporary memory space AD1.
Textural anomaly response data differentiates:
When A1 obtains a complete alarm waveform, micro process will be in A2, A3, A4, A5, A6, A7, A8 at the same time section
The maximum of waveform is compared, it can be determined which channel obtains maximum structural response, and the phase installed according to 8 channels
Adjacent relationship and mechanics loading characteristic judge whether the sequence between maximum normal, when maximum sort it is abnormal, or difference compared with
When big, it is believed that the stress relationship between 8 channels is destroyed, and structure is abnormal response.Micro process uploads calculated result
It is all stored into fixed data memory to monitoring center, and the Wave data in each channel in this period.
The present invention is not limited to the above embodiments, made any to above embodiment aobvious of those skilled in the art and
The improvement or change being clear to, all protection scope without departing from design of the invention and appended claims.
Claims (6)
1. one kind can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, which is characterized in that sensed including resistance-strain
Device, RC low-pass filter, multi-channel synchronous analog-digital converter, microprocessor, communication module, power module, FLASH and distribution
Memory, the output of the resistance strain sensor are connected to the input of RC low-pass filter, and the output of RC low-pass filter connects
It is connected to the input of multi-channel synchronous analog-digital converter, the output of the analog-digital converter is connected to the input of microprocessor, described
Microprocessor is connected with communication module, FLASH and distributed memory, the communication module and monitoring center PC by wired or
Wirelessly it is connected, the power module is the power supply of other circuit units；The microprocessor completes FIR data filtering, number
Alarm online according to characteristics extraction, superthreshold, the cooperated computing function of abnormal data elimination, and by communication module realize with it is upper
The interaction of position machine.
2. it is according to claim 1 can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, which is characterized in that institute
The memory space for stating distributed storage configuration 16GB or more, when big failure occurs in network, when can initial data is longer
Between be stored on intelligence sensor, promoted sensor stability, data backup can also be played the role of, when loss of data or damage
It can upload again.
3. it is according to claim 1 can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, which is characterized in that institute
Operation program and characteristic value of the FLASH memory for storage system are stated, characteristic value mainly includes mean value, maximum, minimum.
4. it is according to claim 1 can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, which is characterized in that institute
State the interaction of microprocessor and host computer, including request real time data, network posture test request.
5. it is according to claim 1 can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, which is characterized in that institute
It states FIR filtering signal condition and FIR is carried out to collected strain signal and be filtered, the cutoff frequency of filtering is tied according to surveying
Structure is customized, so that reducing high-frequency signal is aliased into low-frequency noise, improves the accuracy of signal.
6. it is according to claim 1 can cooperated computing the synchronous dynamic strain sensor of intelligent multi-channel, which is characterized in that institute
The workflow for stating microprocessor cooperated computing is:
For a channel Ai, if its mean value, which compares visiting for threshold value, is limited to Mi, Xiamen is limited to Ni, at the beginning of super threshold counter Li(
Initial value is that 0), not super threshold counter Ki(initial value is that 0), ephemeral data storage address is ADi:
S1: do not stop to acquire real time data, carry out multichannel FIR parallel filtering；
S2: n initial data carry out mean value computation, and store in characteristic value to the space FLASH；
S3: the (n+1)th data point starts to be weighted mean value computation, and each new point weight is 1/n forever；
S4: new data point and feature value difference are calculated, and is compared with two threshold values:
(1) if the absolute value of this difference is not above threshold value:
1. data are directly stored on temporary memory space ADi if Li=0；
2. if 0 < Li < Q(Q is the continuous abnormal data points of starting alarm), reject the exception before the new data point
Data (are considered as pulse interference signal or acnode abnormal signal), and Li is reset, by new data point storage to temporary memory space
On ADi；
3. judge whether Ki reaches half of collection period number if Li >=Q, if do not reached, Ki=Ki+1, by new data
In point storage to temporary memory space ADi；If reached, then it is assumed that half period before first super threshold value arrives last one
The data segment of super threshold value second half of the cycle is a complete alarm waveform, this alarm waveform will be from interim storage address
ADi is moved to fixed data memory space, updates Min-max, and Li is reset, by new data point storage to interim storage
On the ADi of space；
(2) if the absolute value of new data point and characteristic value difference is more than threshold value Ni or Mi, super threshold count device
Li=Li+1, not super threshold counter Ki is reset, then is stored the data on temporary memory space ADi.
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