CN108196616B - Time synchronization method for detecting data of variable-diameter inertial navigation subsystem in pipeline - Google Patents
Time synchronization method for detecting data of variable-diameter inertial navigation subsystem in pipeline Download PDFInfo
- Publication number
- CN108196616B CN108196616B CN201711457704.1A CN201711457704A CN108196616B CN 108196616 B CN108196616 B CN 108196616B CN 201711457704 A CN201711457704 A CN 201711457704A CN 108196616 B CN108196616 B CN 108196616B
- Authority
- CN
- China
- Prior art keywords
- search
- subsystem
- data
- inertial navigation
- sampling points
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000005070 sampling Methods 0.000 claims abstract description 132
- 230000008569 process Effects 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims abstract description 9
- 230000003068 static effect Effects 0.000 claims abstract description 8
- 238000010845 search algorithm Methods 0.000 claims description 28
- 238000010586 diagram Methods 0.000 claims description 6
- 238000007689 inspection Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 230000007704 transition Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000013459 approach Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 230000010355 oscillation Effects 0.000 claims description 3
- 230000002776 aggregation Effects 0.000 claims description 2
- 238000004220 aggregation Methods 0.000 claims description 2
- 230000001360 synchronised effect Effects 0.000 abstract description 15
- 238000012937 correction Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 31
- 238000000917 particle-image velocimetry Methods 0.000 description 26
- 230000007547 defect Effects 0.000 description 4
- 238000005259 measurement Methods 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 241000282887 Suidae Species 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000010779 crude oil Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000013213 extrapolation Methods 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 238000005469 granulation Methods 0.000 description 1
- 230000003179 granulation Effects 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 230000001681 protective effect Effects 0.000 description 1
- 208000010586 pulmonary interstitial glycogenosis Diseases 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
- G06F1/04—Generating or distributing clock signals or signals derived directly therefrom
- G06F1/12—Synchronisation of different clock signals provided by a plurality of clock generators
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mechanical Engineering (AREA)
- Navigation (AREA)
Abstract
The invention discloses a synchronous operation process of PIG reducing and inertial navigation subsystem output data based on mileage data time. The process comprises the steps of firstly, graphing mileage data columns in a reducing and inertial navigation output data matrix, and taking the number of sampling points as a horizontal axis; then, respectively marking the number of sampling points of the mileage data from static to moving in a moment in two graphs by using a starting point searching method; further, the corresponding time of the mileage data from static to moving in each system clock is obtained; and finally, solving the difference value of the two clocks, and correcting the clocks of the variable diameter and inertial navigation subsystems to be at the same time to realize the synchronization of data. Compared with a method using all mileage data as a synchronization reference, the synchronization method can effectively solve the problem of synchronization failure caused by failure of the odometer or late-stage mileage data correction in the PIG movement process, and can perform synchronization operation on all output data of the two subsystems as long as the completeness of the mileage data of the service cylinder stage is ensured.
Description
Technical Field
The invention relates to a synchronization method for output data of an internal subsystem of a detection device in a pipeline, in particular to a reducing subsystem and an inertial navigation subsystem in the detection device in the pipeline, and the synchronization method can be used by a reducing/inertial navigation combined internal detection system of various pipeline pipe diameters.
Background
1. In-pipeline inspection
The pipeline internal detection device (PIG) can measure the defect or deformation of the pipeline in the motion process of the pipeline. The PIG may carry multiple detection subsystems, typically depending on the current task needs. Because each detection subsystem needs to be flexibly combined according to different tasks, each subsystem generally has independent data acquisition and storage capacity. Because the reducing device occupies a relatively small PIG space, the reducing device and the inertial navigation device are often installed in a cavity of the PIG and are considered as a whole in space, and one mileage datum can be shared.
Synchronization problem between PIG internal subsystems
When multiple detection subsystems need to be used together, the synchronous relation among data is very important. For example, when two subsystems of diameter-variable detection and inertial navigation positioning detection are mounted on the same PIG platform and online detection is performed simultaneously, theoretically, downloaded data should be highly synchronous, that is, when detection is finished, objects of inertial navigation positioning and diameter-variable detection must keep the same scale in time and space according to detected data. Specifically, if a variable diameter detector detects a certain pipe wall defect, at the same time, the inertial navigation positioning device should give the geographic coordinates corresponding to the defect.
However, the detection principle of the sensor is different, the corresponding detection device has different electrical structures, and the sampling rate, the signal format, the system clock and even the output data format are completely different. Therefore, it is difficult to determine the one-to-one correspondence relationship of the output data of the inertial navigation subsystem and the variable diameter subsystem in time.
This requires synchronizing the two batches of output data after both subsystems have completed outputting the data. The essence is to ensure that the two batches of output data use the same time scale on their respective time axes.
3. Limitations of existing synchronization methods
A previous approach to this problem is a method of directly using mileage data as a synchronization reference. The same path of odometer signals are respectively led into different detection subsystems to be used as a column of an output data matrix. The output data matrix means that each row of data comprises a plurality of data items (one of which is mileage), and is called a sampling point; all data of sampling points are from sampling of the detection subsystem at the same moment; by introducing parameters such as the sampling frequency and the signal format of the subsystem, the sampling time of the row sampling point under the subsystem clock can be calculated. Since each sampling point contains a mileage data item, the mileage data can be taken as the horizontal axis of coordinates when outputting the data. When different detection subsystems use common mileage data as a horizontal axis of a coordinate system, synchronization of output data of different subsystems is achieved.
The disadvantages of using the mileage data directly as the synchronization reference signal are as follows:
1) are prone to failure. The odometer operates outside the protective housing of the PIG, with a higher probability of data failure relative to other detection subsystems. Inside the oil and gas pipeline, the working environment is very bad, and the failure rate of the mechanical structure of the odometer is very high. Even if the odometer keeps a mechanical structure without deformation and damage in a severe environment of about one hundred hours, serious problems of slippage, blockage, failure of electrical components and the like of the odometer can be caused by high-viscosity crude oil, highly corrosive natural gas, defects of the inner wall of a pipeline and the like. Once the mileage data is partially or completely invalid, all synchronization operations cannot be performed.
2) The mileage correction causes the relevant synchronous data to be corrected again. After the measurement, the accuracy of the mileage data also needs to be corrected, for example, compensation calculation for the slippage of the odometer, or the like. However, the mileage data is used as synchronization reference data, and once the mileage data is corrected, the detection data of all the related subsystems are also corrected in synchronization. That is, before the synchronization operation of the two subsystems, in order to ensure that the output data inside the two subsystems and the current mileage output data are "bound" together, the "synchronization" operation inside the subsystem output data must be performed first, and the "synchronization" operation inside the subsystem may also be performed multiple times. This significantly increases the complexity of the system data processing and reduces the system reliability.
Disclosure of Invention
Object of the Invention
The invention aims to solve the problems of the mileage synchronization method of the variable diameter subsystem and the inertial navigation subsystem, so that the starting point time can still be calculated as a synchronization reference after the mileage data loses the data of the moving part after leaving the service barrel or after the mileage data is locally or globally corrected, and relatively high synchronization precision is ensured.
Technical scheme
A time synchronization method for detecting data of a variable diameter inertial navigation subsystem in a pipeline is characterized in that an odometer signal of a PIG system is simultaneously introduced into the variable diameter inertial navigation subsystem and the inertial navigation subsystem which are arranged in a cabin; in the stage of the serve barrel, mileage data is complete; in the process of power-on operation, respective system clocks of the two subsystems are at the same precision level; the method is characterized in that: the method comprises the following steps:
step one, original odometer data in a variable-diameter output data matrix and an inertial navigation output data matrix are respectively imaged and defined as an original odometer data graph of an inertial navigation subsystem and an original odometer data graph of a variable-diameter subsystem;
the formats of the output data of the inertial navigation subsystem and the variable diameter subsystem are similar, the output data are organized into a two-dimensional table form, each line of data comprises a plurality of data items, the original signal of the odometer is also one of the data items, and each line of data is called a sampling point; starting from normal data acquisition after power-on, self-inspection and starting, the sampling points are called as sampling point numbers according to a positive integer sequence arranged by time, and are defined as m, wherein m is a positive integer;
placing a first sampling point obtained after electrification on an original point of a coordinate axis, namely 0 point, and mapping a subsequent sampling point number m to a positive integer of a transverse axis, namely 1 to N sampling points; the original signal value of the odometer corresponding to each sampling point is taken as a longitudinal axis;
step two, defining the moment when the PIG is converted from a static state to a motion state in the ball serving barrel as a starting point, wherein the physical time of the starting points of the reducing subsystem and the inertial navigation subsystem is the same; starting point search algorithm is adopted for original odometer signals of the two subsystems, and sampling points of starting points are marked in an original odometer data graph of the inertial navigation subsystem and an original odometer data graph of the reducing subsystem respectively and are defined as m1 and m 2;
the starting point search algorithm is as follows:
1) determining a precision target search _ time of a search starting point algorithm, wherein the precision must be matched with a precision target of the whole synchronization algorithm, namely the precision target of the search algorithm is higher than the precision target of the whole synchronization algorithm by one order of magnitude, namely the error time search _ time of the search algorithm is one N times of the error time goal _ time of the synchronization algorithm;
2) determining a search step size step: defining the sampling frequency (the number of sampling points in one second) of a certain subsystem as sample _ times, and then the step length of searching is the precision target of the algorithm of the search starting point multiplied by the sampling frequency
step＝search_time×sample_times
In the searching process, when the sampling point number of the ith searching is miThe number m of sampling points of the i +1 th searchi+1Is provided with
mi+1＝mi+step
3) Defining a search space Ss：
SsThe interval is a set formed by sampling points, is a section of continuous interval on the abscissa of the mileage original data, and is defined by a starting point m _ start and an end point m _ end of the search;
definition of SsThe principle of (1) is as follows: ensuring that the target of searching, namely the starting point, is between m _ start and m _ end;
setting SsThe method comprises the following steps: observing the graph of the original mileage data, wherein the starting point is necessarily in a transition region from a straight region to an oscillation region, and the left boundary of the transition region is set as m _ start;
m _ start is the number of sampling points for starting search, m _ end is the number of sampling points for finishing search, and subsequent search calculation is carried out, wherein the maximum search frequency N can be determined at the momentsIs composed of
Ns＝(m_end-m_start)/step
Let i be the current search times, define the search space
Ss＝{mi∣m_start≤mi≤m_end,i＝1,2,…,Ns}
Wherein m isiThe sampling point number of the ith search is;
4) determining a standing interval S of the PIG in the pitching barrel; a standing interval, namely an aggregation interval of sampling points marked in an original signal diagram of the subsystem odometer, wherein the PIG is in a standing state and has no micro movement, and the sampling points on the boundary of the standing interval are respectively defined as ms0And ms1Then can define
S＝{(m,SV)∣ms0≤m≤ms1}
Wherein m is the number of sampling points in the standing interval, SVOriginal signals of the odometer corresponding to m;
the method for setting S is as follows: observing on a mileage original data graph, from an origin, an image quickly tends to be stable and enters a flat area, the area is continuously provided with a plurality of sampling points from the abscissa, in the flat area, the image only has a high-frequency noise signal, and large fluctuation of the ordinate does not occur, and the left landmark of the area is marked as the sampling point ms0And the right landmark is marked as the number m of sampling pointss1；
On the premise of ensuring that the PIG is in a static state, the S interval is as large as possible, and S in the standing state can be calculated more accurately in a statistical senseVI.e. all S in the rest interval SVIs defined as E (S)V)；
5) Setting parameters used in the search:
defining the number of target sampling points of the ith search as mi，mi∈Ss；
Two sets are defined that need to be used in the ith search: left set (S)l) And right set (S)r) Defining the number of sampling points contained in the left set as mlDefining the number of sampling points contained in the right set as mr(ii) a Two sets SlAnd SrThey are respectively the number m of sampling points of the current searchiIs at the center of miTwo sets of sampling points on the left and right sides, the number of elements in the two sets is set as the number of all sampling points in 1 second, namely sample _ times, which includes
Sl＝{(ml,SV)∣mi－sample_times≤ml≤mi,mi∈Ss}
Sr＝{(mr,SV)∣mi≤mr≤mi+sample_times,mi∈Ss}
Two sets all SVAre respectively defined as El(SV) And Er(SV) I.e. El(SV) Is SlAll S inVObserved value of (E)r(SV) Is SrAll S inVWhen m is an observed value ofiIs a starting point, El(SV) Inevitably approaching to E (S)V) And E isr(SV) Must be larger than E (S)V) (ii) a When m isiNot the starting point, El(SV) And Er(SV) Must also approach E (S)V) Or likewise greater than E (S)V)；
Two thresholds epsilon defining the present search for decisionlAnd εrThe principle of initial setting is epsilonlIs greater than 0 and as small as possible, andrthan epsilonlOne order of magnitude larger, i.e. epsilonrThan epsilonlM is larger than M times, and M is an integer from 2 to 10; if the initial setting search results in more than 1 starting point, then epsilon is reducedlAnd increase εrSearching again;
6) at SsIn the range from m _ start to m _ end, searching for the starting point, and performing NsCalculating secondary search; for the ith search, i is 1,2, …, NsWhen there is
∣El(SV)－E(SV)∣＜εlAnd Er(SV)－E(SV)＞εr
If true, then m is acceptediIs a starting point; when in use
∣El(mi)－E(SV)∣≥εlOr Er(mi)－E(SV)≤εr
If it is true, then m needs to be matchedi+1The number of sampling points continues to search for the (i + 1) th time, where mi+1＝mi+step；
7) If the above search is completed and only one starting point is obtained, the starting point search algorithm of the subsystem is ended if the whole SsWithin the range according to the current epsilonlAnd εrSearching for multiple starting points, the epsilon needs to be adjustedlAnd εrSearching again until one is left; the adjustment is made by reducing epsilonlOr increase εr(ii) a The epsilonlAnd εrThe method must be simultaneously used in the original data of the odometers of the two subsystems to respectively obtain starting points m1 and m 2;
step three, according to the sampling frequency sample _ times of the variable diameter subsystem and the inertial navigation subsystem, respectively solving the corresponding time t of each sampling point number m under the system clock of the variable diameter subsystem and the inertial navigation subsystem in the original signal diagram of the odometer, wherein the corresponding time t comprises the corresponding time t1 and t2 of m1 and m2 in the system clock of the variable diameter subsystem and the inertial navigation subsystem,
t＝m/sample_times
converting a horizontal axis of the original sampling point number m into a horizontal axis of the current subsystem clock t;
step four, solving the difference value delta t between t1 and t 2: t2-t1, wherein the Δ t is used as the difference value of two subsystem clocks;
fifthly, correcting the clocks of the two subsystems to be at the same time by utilizing delta t, and realizing the synchronization of the data of the two subsystems of diameter changing and inertial navigation;
when the variable-diameter subsystem clock is required to be used as standard time for subsequent processing, the corresponding time of all sampling points of the inertial navigation subsystem is required to be reduced by delta t; when the time of the inertial navigation subsystem is required to be standard time, delta t is required to be added to all time parameters of the variable-diameter subsystem.
Advantages and effects
Compared with the prior art, the invention has the beneficial effects that:
firstly, the problem that the synchronous operation cannot be carried out when the odometer fails is remarkably solved. The odometer body part is a mechanical structure, faults almost occur in the movement process after the ball serving barrel is separated, and the ball serving barrel stage is not always problematic. The invention only requires the integrity of the phase data of the ball serving barrel, namely m1 and m2 in the second step can be found, and the whole algorithm is not influenced subsequently. The impact of an odometer failure on subsequent synchronization operations is substantially eliminated.
Secondly, the problem that the previous synchronous operation result is invalid due to the correction of the odometer data is completely solved. The mileage data is corrected mainly aiming at the problems of slipping, data consistency and the like in the operation of the PIG, and the data of the standing stage of the ball dispensing tube cannot be influenced, so that the values of m1 and m2 in the step II cannot be influenced, and the subsequent synchronous operation cannot be influenced.
Drawings
FIG. 1 shows odometer raw data of an inertial navigation subsystem;
FIG. 2 is raw odometer data for the reducing subsystem;
FIG. 3 is inertial navigation subsystem odometer output data;
FIG. 4 is the odometer output data of the reducing subsystem;
FIG. 5 is mileage data output by diameter variation and inertial navigation at the same time scale;
FIG. 6 is a partial enlargement of the inertial navigation subsystem odometer near the starting point of the raw data.
Detailed Description
The invention is further described below with reference to the accompanying drawings:
FIG. 1 shows raw data of typical inertial navigation detection subsystem mileage, with the horizontal axis representing the number of sampling points, the vertical axis representing a mileage signal containing a large amount of noise, which is substantially a triangular wave level, and m1 on the horizontal axis representing the number of sampling points at the starting point of the inertial navigation subsystem in the tee.
Fig. 2 is raw data of mileage of a typical reducing detection subsystem, the horizontal axis is the number of sampling points, the vertical axis is a mileage signal containing a large amount of noise, the mileage signal is substantially a triangular wave level, and m2 is the number of sampling points of an activation point of a reducing subsystem in a serve barrel.
FIG. 3 is output data of a typical inertial navigation subsystem, with time on the horizontal axis and mileage on the vertical axis in meters. t1 is the time when the inertial navigation subsystem passes the starting point in the barrel, which is calculated by m1 under the inertial navigation subsystem clock standard.
FIG. 4 is output data of a typical reducing subsystem, with time on the horizontal axis and mileage on the vertical axis in meters. t2 is the time when the reducing subsystem passes the starting point in the ball serving barrel, which is calculated by m2 under the reducing subsystem clock standard.
Fig. 5 shows the mileage data output by the typical reducing detection subsystem and the inertial navigation detection subsystem in a coordinate system, and the horizontal axis represents time. t1 is the time when the inertial navigation subsystem passes the starting point in the barrel, which is calculated by m1 under the inertial navigation subsystem clock standard. t2 is the time when the variable-diameter subsystem passes through the starting point in the service barrel, which is calculated by m2 under the variable-diameter subsystem clock standard; and delta t is the synchronization error of the clocks of the two subsystems of the diameter changing and the inertial navigation.
Fig. 6 is a partial amplification of the inertial navigation subsystem odometer near the starting point of raw data, which is a signal near the starting point, and in this case, due to the existence of noise and other factors, a manual method cannot accurately locate the point m1, so that a starting point search algorithm needs to be used.
in-Pipeline Inspection Systems (PIGs) typically carry multiple inspection devices, each with independent data acquisition and storage capabilities. Due to the size limitation of the ball launching cylinder and the ball collecting cylinder, an inertial navigation positioning device (the core is an IMU system) and a reducing detection device are often used as two subsystems to carry PIG (particle image velocimetry) to enter a pipeline. When a plurality of detection devices need to be used together, the synchronous relation among the data is very important. However, since the respective sensor detection principles of the variable diameter system and the inertial navigation system are different, the corresponding electrical appliance structures are also different, the sampling rate, the signal format and the like are also completely different, and the data of different types of sensors at the same time are likely to be dislocated on a uniform time axis, so that the synchronous operation is required. The operation mainly takes the time data obtained by calculation as a synchronization basis, and the execution premise is as follows: an odometer signal of the PIG system is simultaneously introduced into the diameter-variable subsystem and the inertial navigation subsystem; the respective system times of the two subsystems are at the same level of accuracy.
A time synchronization method for detecting data of a variable diameter inertial navigation subsystem in a pipeline is characterized in that an odometer signal of a PIG system is simultaneously introduced into the variable diameter inertial navigation subsystem and the inertial navigation subsystem which are arranged in a cabin; in the stage of the serve barrel, mileage data is complete; in the process of power-on operation, respective system clocks of the two subsystems are at the same precision level; the method is characterized in that: the method comprises the following steps:
step one, original odometer data in a variable-diameter output data matrix and an inertial navigation output data matrix are respectively imaged and defined as an original odometer data graph (shown in figure 1) of an inertial navigation subsystem and an original odometer data graph (shown in figure 2) of a variable-diameter subsystem;
the formats of the output data of the inertial navigation subsystem and the variable diameter subsystem are similar, the output data are organized into a two-dimensional table form, each row of data comprises a plurality of data items, and the original signal (voltage value, unit V, defined as S in the following formula) of the odometerV) Also one, each row of data is called a sample point; starting from normal data acquisition after power-on, self-inspection and starting, the sampling points are called as sampling point numbers according to a positive integer sequence arranged by time, and are defined as m, wherein m is a positive integer;
placing a first sampling point obtained after electrification on an original point of a coordinate axis, namely 0 point, and mapping a subsequent sampling point number m to a positive integer of a transverse axis, namely 1 to N sampling points; the original signal value of the odometer corresponding to each sampling point is taken as a longitudinal axis;
the original signals of the odometer are substantially triangular waves, and need to be converted into mileage in meters according to a method provided by an odometer product specification when participating in subsequent data processing, such as the ordinate of fig. 3, 4 and 5;
and step two, defining the moment when the PIG is converted from the static state to the motion state in the ball serving barrel as a starting point, wherein the physical time of the starting points of the reducing subsystem and the inertial navigation subsystem is the same. Starting point search algorithm is adopted for original odometer signals of the two subsystems, and sampling points of starting points are marked in an original odometer data graph of the inertial navigation subsystem and an original odometer data graph of the reducing subsystem respectively and are defined as m1 and m 2;
the launch point search algorithm can solve the following problems: because the PIG is subjected to pressure in the ball serving barrel, before the PIG enters a sensible and observable motion state, a slow acceleration motion process actually exists, the process can last for tens of seconds at most, and the number of related sampling points is related to the sampling frequency of the two subsystems and reaches tens of thousands (as shown in figures 1 and 6). When the starting point is directly obtained by adopting a manual marking method, the number of sampling points of the starting point is sequentially judged in the original data graphs (such as fig. 1 and fig. 2) of the odometer of the inertial navigation subsystem and the diameter-variable subsystem, and an error ranging from 0.1 second to 10 seconds is generated. Two possible starting points a and B shown in fig. 6 are converted into time when the sampling frequency of the inertial navigation subsystem is 500Hz, and the phase difference can reach more than 5 seconds. The goal of the synchronous operation of the two subsystems is to expect that the difference of the output time of the system clocks of the two subsystems facing the same moment, namely the synchronous time precision, real time, is as small as possible. By carrying out inverse extrapolation on the target measurement precision of the detection engineering in the whole PIG, the time precision of the synchronous operation of the two subsystems needs to be lower than 0.1 second, so that the error of manual operation cannot be ignored. It is necessary to select a state tracking method executed by a machine to judge the data inflection points of the two graphs in the same scale, namely, to start the point search algorithm. By adopting the search algorithm, the accurate sampling point number of the starting point time of the two subsystems of the PIG can be obtained under the same scale, the data is important for the two subsystems, and the final time-based synchronous processing precision is substantially determined.
The starting point search algorithm must use the raw data of the inertial navigation subsystem and the variable diameter subsystem odometers (such as fig. 1 and fig. 2), and has the necessity. Otherwise, after the original data of the inertial navigation and diameter-variable odometer is subjected to denoising, granulation, softening, data conversion and the like, some information is lost, the data loss conditions of different subsystems are different, and the evaluation is difficult, so that the processed data cannot ensure that a more accurate starting point is obtained under the same scale, and the original data must be used.
The starting point search algorithm is as follows:
1) determining a precision target search _ time of a search starting point algorithm, wherein the precision must be matched with a precision target of the whole synchronization algorithm, namely the precision target of the search algorithm is higher than the precision target of the whole synchronization algorithm by one order of magnitude, namely the error time search _ time of the search algorithm is one N times of the error time goal _ time of the synchronization algorithm;
2) determining a search step size step: defining the sampling frequency (the number of sampling points in one second) of a certain subsystem as sample _ times, and then the step length of searching is the precision target of the algorithm of the search starting point multiplied by the sampling frequency
step＝search_time×sample_times
In the searching process, when the sampling point number of the ith searching is miThe number m of sampling points of the i +1 th searchi+1Is provided with
mi+1＝mi+step
3) FIG. 6 is an example of defining a search space Ss：
SsThe interval is a set formed by sampling points, is a section of continuous interval on the abscissa of the mileage original data, and is defined by a starting point m _ start and an end point m _ end of the search;
definition of SsThe principle of (1) is as follows: the target of the guaranteed search, i.e. the starting point, is between m _ start and m _ end.
Setting SsThe method comprises the following steps: observing the graph of the mileage original data, the starting point is necessarily in the transition region from the flat region to the oscillation region, the left boundary of the transition region is set as m _ start, for example, m _ start equals 195000, and the right boundary is set as m _ end, for example, m _ end equals 204000;
m _ start is the number of sampling points for starting search, m _ end is the number of sampling points for finishing search, and subsequent search calculation is carried out, wherein the maximum search frequency N can be determined at the momentsIs composed of
Ns＝(m_end-m_start)/step
Let i be the current search times, define the search space
Ss＝{mi∣m_start≤mi≤m_end,i＝1,2,…,Ns}
Wherein m isiThe sampling point number of the ith search is;
4) determining a standing interval S of the PIG in the pitching barrel; stationary intervals, i.e. marked in the original signal diagram of the subsystem odometer, PIG is in a standing state, the collection interval of the sampling points without micro movement defines the boundary sampling points of the standing interval as ms0And ms1Then can define
S＝{(m,SV)∣ms0≤m≤ms1}
Wherein m is the number of sampling points in the standing interval, SVOriginal signals of the odometer corresponding to m;
as shown in fig. 1 and 6, the method of setting S is as follows: observing on a mileage original data graph, from an origin, an image quickly tends to be stable and enters a flat area, the area is continuously provided with a plurality of sampling points from the abscissa, in the flat area, the image only has a high-frequency noise signal, and large fluctuation of the ordinate does not occur, and the left landmark of the area is marked as the sampling point ms0And the right landmark is marked as the number m of sampling pointss1E.g. ms0＝1000，ms1190000, the S region thus defined ensures that the period of time PIG is placed in the barrel in a stationary state, undisturbed by other factors;
on the premise of ensuring that the PIG is in a static state, the S interval is as large as possible, and S in the standing state can be calculated more accurately in a statistical senseVI.e. all S in the rest interval SVIs defined as E (S)V)；
5) Setting parameters used in the search:
defining the number of target sampling points of the ith search as mi，mi∈Ss；
Two sets are defined that need to be used in the ith search: left set (S)l) And right set (S)r) Defining the number of sampling points contained in the left set as mlDefining the number of sampling points contained in the right set as mr(ii) a Two sets SlAnd SrThey are respectively the number m of sampling points of the current searchiIs at the center of miTwo sets of sampling points on the left and right sides, the number of elements in the two sets is set as the number of all sampling points in 1 second, namely sample _ times, which includes
Sl＝{(ml,SV)∣mi－sample_times≤ml≤mi,mi∈Ss}
Sr＝{(mr,SV)∣mi≤mr≤mi+sample_times,mi∈Ss}
Two sets all SVAre respectively defined as El(SV) And Er(SV) I.e. El(SV) Is SlAll S inVObserved value of (E)r(SV) Is SrAll S inVWhen m is an observed value ofiIs a starting point, El(SV) Inevitably approaching to E (S)V) And E isr(SV) Must be larger than E (S)V) (ii) a When m isiNot the starting point, El(SV) And Er(SV) Must also approach E (S)V) Or likewise greater than E (S)V)；
When a certain search sampling point number mi195005, then SlThe ranges are expressed as follows: sl＝{(ml,SV)∣194505≤ml≤195005}；SrThe ranges are expressed as follows: sr＝{(mr,SV)∣195005≤mr≤195505}；
Two thresholds epsilon defining the present search for decisionlAnd εrThe principle of initial setting is epsilonlIs greater than 0 and as small as possible, andrthan epsilonlOne order of magnitude larger, i.e. epsilonrThan epsilonlM is larger than M times, and M is an integer from 2 to 10; if the initial setting search results in more than 1 starting point, then epsilon is reducedlAnd increase εrSearching again;
6) at SsIn the range from m _ start to m _ end, searching for the starting point, and performing NsAnd (5) calculating secondary search. For the ith search, i is 1,2, …, NsWhen there is
∣El(SV)－E(SV)∣＜εlAnd Er(SV)－E(SV)＞εr
If true, then m is acceptediIs a starting point; when in use
∣El(mi)－E(SV)∣≥εlOr Er(mi)－E(SV)≤εr
If it is true, then m needs to be matchedi+1The number of sampling points continues to search for the (i + 1) th time, where mi+1＝mi+step；
7) If the above search is completed and only one starting point is obtained, the starting point search algorithm of the subsystem is ended if the whole SsWithin the range according to the current epsilonlAnd εrSearching for multiple starting points, the epsilon needs to be adjustedlAnd εrSearching again until one is left; the adjustment is made by reducing epsilonlOr increase εr(ii) a The epsilonlAnd εrThe method must be simultaneously used in the original data of the odometers of the two subsystems to respectively obtain starting points m1 and m 2;
step three, according to the sampling frequency sample _ times of the variable diameter subsystem and the inertial navigation subsystem, respectively solving the corresponding time t of each sampling point number m under the respective system clock in the original signal diagram of the odometer of the variable diameter subsystem and the inertial navigation subsystem, wherein the corresponding time t comprises the corresponding time t1 and t2 of m1 and m2 in the respective system clock, and the horizontal coordinates are shown in fig. 3 and fig. 4;
t＝m/sample_times
converting a horizontal axis of the original sampling point number m into a horizontal axis of the current subsystem clock t;
step four, solving the difference value delta t between t1 and t 2: t2-t1, where Δ t is the difference between the two subsystem clocks, as shown in fig. 5;
fifthly, correcting the clocks of the two subsystems to be at the same time by utilizing delta t, and realizing the synchronization of the data of the two subsystems of diameter changing and inertial navigation;
when the variable-diameter subsystem clock is required to be used as standard time for subsequent processing, the corresponding time of all sampling points of the inertial navigation subsystem is required to be reduced by delta t; when the time of the inertial navigation subsystem is required to be standard time, delta t is required to be added to all time parameters of the variable-diameter subsystem.
An actual operation example:
the method comprises the following steps:
the first step is to obtain the original data of the mileage of the typical inertial navigation detection subsystem in the graph 1, wherein the horizontal axis is the number of sampling points, the vertical axis is a mileage signal containing a large amount of noise, the signal is substantially a triangular wave level, m1 on the horizontal axis is the number of sampling points of the starting point of the inertial navigation subsystem in the tee box, and the area S near the starting point is amplified in the graph 6;
the original data of the mileage of the typical reducing detection subsystem shown in the figure 2 is obtained in the first step, the horizontal axis is the number of sampling points, the vertical axis is a mileage signal containing a large amount of noise, the mileage signal is substantially a triangular wave level, and m2 is the number of sampling points of the starting point of the reducing subsystem in the barrel. It is apparent that the data of fig. 2 has the same waveform as the data of fig. 1, and is homologous data, but needs to be synchronized.
Step two:
the necessity of starting the point search algorithm:
fig. 6 shows signals in an S region near the start point, where a (the number of sampling points 199246) and B (the number of sampling points 202419) may be both start points, the sampling frequency is 500Hz, and the time difference between A, B is 6.346 seconds, so it is difficult to accurately define m1 points manually due to the existence of noise and other factors.
Start Point search Algorithm 1)
When the synchronization precision of the two subsystems, namely the real-time and the real-time, is required to reach 0.1 second, the precision search _ time of the starting point searching algorithm is required to be between 0.05 and 0.01 second;
start Point search Algorithm 2)
The sampling frequency is 500Hz, the search _ time is 0.01, step is 5,
start Point search Algorithm 3)
As shown in fig. 6, it is possible to set: 195000 for m _ start, 204000 for m _ end, Ns＝1800。
Start point search algorithm 4)
As shown in fig. 1, m can be determineds0＝1000，ms1＝190000，E(SV)＝1.772130
Start Point search Algorithm 5)
Take the data of FIG. 6 as an example, εlInitial set value is 0.001,. epsilonrThe initial set value was 0.01.
Start Point search Algorithm 6)
When the 1 st search is carried out, i is equal to 1, searching the number m of sampling points1＝195000，SlThe ranges are expressed as follows: sl＝{(ml,SV)∣194500≤mlLess than or equal to 195000}, to obtain El(SV)＝1.772128；SrThe ranges are expressed as follows: sr＝{(mr,SV)∣195000≤mrLess than or equal to 195500}, to obtain Er(SV) 1.772234. Does not satisfy Er(SV)－E(SV)＞εrAnd therefore do not accept m1195000 is the starting point.
When the 2 nd search, i.e. i is 2, searching the sampling point number m2＝195005，SlThe ranges are expressed as follows: sl＝{(ml,SV)∣194505≤mlLess than or equal to 195005}, to obtain El(SV)＝1.772127；SrThe ranges are expressed as follows: sr＝{(mr,SV)∣195005≤mrLess than or equal to 195505}, to obtain Er(SV) 1.772151. Does not satisfy Er(SV)－E(SV)＞εrAnd therefore do not accept m2195005 is the starting point.
When the 1640 th search is carried out, namely i equals 1640, searching the sampling point number m1640＝203195，SlThe ranges are expressed as follows: sl＝{(ml,SV)∣202695≤mlLess than or equal to 203195}, to obtain El(SV)＝1.772517；SrThe ranges are expressed as follows: sr＝{(mr,SV)∣203195≤mrLess than or equal to 203695}, to obtain Er(SV) 1.783518. Meeting the judgment condition, receiving m1640203195 is the starting point.
Start Point search Algorithm 7)
After 1800 searches are completed, the searched starting points reach 22, not only the starting points are found, and epsilon is modifiedrThe value is 0.03, and the search is repeated to obtain m1724＝203615。SlThe ranges are expressed as follows: sl＝{(ml,SV)∣203115≤mlLess than or equal to 203615}, to obtain El(SV)＝1.772802；SrThe ranges are expressed as follows: sr＝{(mr,SV)∣203615≤mrLess than or equal to 204115}, to obtain Er(SV) 1.803518. Meeting the judgment condition, receiving m1724203615 is the only starting point of the inertial navigation subsystem, i.e. m1 is 203615.
A similar procedure gave m2 ═ 106710.
Step three:
and step three, obtaining a graph 3 which is output data of a typical inertial navigation subsystem, wherein the horizontal axis is time, and the vertical axis is mileage and unit meter. t1 is the calculated time 407.23 seconds of the inertial navigation subsystem passing the starting point in the ball serving barrel under the inertial navigation subsystem clock standard by m 1.
And step three, obtaining figure 4, which is output data of the typical reducing subsystem, wherein the horizontal axis is time, and the vertical axis is mileage and unit meter. t2 is the time 213.42 seconds of the starting point of the reducing subsystem in the ball serving barrel, which is calculated by m2 under the reducing subsystem clock standard.
As can be seen from fig. 3 and 4, since the raw data is filtered, many pieces of mileage data in the low speed range "disappear", and if the starting point is obtained at this time, the starting point "moves", and the moving distance is not uniform, so that the starting point must be searched using the raw data.
Step four:
and step four, obtaining a graph 5, and putting mileage data output by the typical reducing detection subsystem and the inertial navigation detection subsystem into a coordinate system, wherein delta t is a synchronous error of clocks of the reducing subsystem and the inertial navigation subsystem, and the obtained delta t is 210.08 seconds.
Step five:
with the variable diameter clock as a standard, 210.08 seconds are subtracted from the time corresponding to all inertial navigation output data (including but not limited to mileage data).
Claims (1)
1. A time synchronization method for detecting data of a variable diameter inertial navigation subsystem in a pipeline is characterized in that an odometer signal of a PIG system is simultaneously introduced into the variable diameter inertial navigation subsystem and the inertial navigation subsystem which are arranged in a cabin; in the stage of the serve barrel, mileage data is complete; in the process of power-on operation, respective system clocks of the two subsystems are at the same precision level; the method is characterized in that: the method comprises the following steps:
step one, original odometer data in a variable-diameter output data matrix and an inertial navigation output data matrix are respectively imaged and defined as an original odometer data graph of an inertial navigation subsystem and an original odometer data graph of a variable-diameter subsystem;
the formats of the output data of the inertial navigation subsystem and the variable diameter subsystem are similar, the output data are organized into a two-dimensional table form, each line of data comprises a plurality of data items, the original signal of the odometer is also one of the data items, and each line of data is called a sampling point; starting from normal data acquisition after power-on, self-inspection and starting, the sampling points are called as sampling point numbers according to a positive integer sequence arranged by time, and are defined as m, wherein m is a positive integer;
placing a first sampling point obtained after electrification on an original point of a coordinate axis, namely 0 point, and mapping a subsequent sampling point number m to a positive integer of a transverse axis, namely 1 to N sampling points; the original signal value of the odometer corresponding to each sampling point is taken as a longitudinal axis;
step two, defining the moment when the PIG is converted from a static state to a motion state in the ball serving barrel as a starting point, wherein the physical time of the starting points of the reducing subsystem and the inertial navigation subsystem is the same; starting point search algorithm is adopted for original odometer signals of the two subsystems, and sampling points of starting points are marked in an original odometer data graph of the inertial navigation subsystem and an original odometer data graph of the reducing subsystem respectively and are defined as m1 and m 2;
the starting point search algorithm is as follows:
1) determining a precision target search _ time of a search starting point algorithm, wherein the precision must be matched with a precision target of the whole synchronization algorithm, namely the precision target of the search algorithm is higher than the precision target of the whole synchronization algorithm by one order of magnitude, namely the error time search _ time of the search algorithm is one N times of the error time goal _ time of the synchronization algorithm;
2) determining a search step size step: defining the sampling frequency (the number of sampling points in one second) of a certain subsystem as sample _ times, and then the step length of searching is the precision target of the algorithm of the search starting point multiplied by the sampling frequency
step＝search_time×sample_times
In the searching process, when the sampling point number of the ith searching is miThe number m of sampling points of the i +1 th searchi+1Is provided with
mi+1＝mi+step
3) Defining a search space Ss：
SsThe interval is a set formed by sampling points, is a section of continuous interval on the abscissa of the mileage original data, and is defined by a starting point m _ start and an end point m _ end of the search;
definition of SsThe principle of (1) is as follows: ensuring that the target of searching, namely the starting point, is between m _ start and m _ end;
setting SsThe method comprises the following steps: observing the graph of the original mileage data, wherein the starting point is necessarily in a transition region from a straight region to an oscillation region, and the left boundary of the transition region is set as m _ start;
m _ start is the number of sampling points for starting search, m _ end is the number of sampling points for finishing search, and subsequent search calculation is carried out, wherein the maximum search frequency N can be determined at the momentsIs composed of
Ns＝(m_end－m_start)/step
Let i be the current search times, define the search space
Ss＝{mi∣m_start≤mi≤m_end,i＝1,2,…,Ns}
Wherein m isiThe sampling point number of the ith search is;
4) determining a standing interval S of the PIG in the pitching barrel; a standing interval, namely an aggregation interval of sampling points marked in an original signal diagram of the subsystem odometer, wherein the PIG is in a standing state and has no micro movement, and the sampling points on the boundary of the standing interval are respectively defined as ms0And ms1Then can define
S＝{(m,SV)∣ms0≤m≤ms1}
Wherein m is the number of sampling points in the standing interval, SVOriginal signals of the odometer corresponding to m;
the method for setting S is as follows: observing on a mileage original data graph, from an origin, an image quickly tends to be stable and enters a flat area, the area is continuously provided with a plurality of sampling points from the abscissa, in the flat area, the image only has a high-frequency noise signal, and large fluctuation of the ordinate does not occur, and the left landmark of the area is marked as the sampling point ms0And the right landmark is marked as the number m of sampling pointss1；
On the premise of ensuring that the PIG is in a static state, the S interval is as large as possible, and S in the standing state can be calculated more accurately in a statistical senseVI.e. all S in the rest interval SVIs defined as E (S)V)；
5) Setting parameters used in the search:
defining the number of target sampling points of the ith search as mi，mi∈Ss；
Two sets are defined that need to be used in the ith search: left set (S)l) And right set (S)r) Defining the number of sampling points contained in the left set as mlDefining the number of sampling points contained in the right set as mr(ii) a Two sets SlAnd SrThey are respectively the number m of sampling points of the current searchiIs at the center of miTwo sets of sampling points on the left and right sides, the number of elements in the two sets is set as the number of all sampling points in 1 second, namely sample _ times, which includes
Sl＝{(ml,SV)∣mi－sample_times≤ml≤mi,mi∈Ss}
Sr＝{(mr,SV)∣mi≤mr≤mi+sample_times,mi∈Ss}
Two sets all SVAre respectively defined as El(SV) And Er(SV) I.e. El(SV) Is SlAll S inVObserved value of (E)r(SV) Is SrAll S inVWhen m is an observed value ofiIs a starting point, El(SV) Inevitably approaching to E (S)V) And E isr(SV) Must be larger than E (S)V) (ii) a When m isiNot the starting point, El(SV) And Er(SV) Must also approach E (S)V) Or likewise greater than E (S)V)；
Two thresholds epsilon defining the present search for decisionlAnd εrThe principle of initial setting is epsilonlIs greater than 0 and as small as possible, andrthan epsilonlOne order of magnitude larger, i.e. epsilonrThan epsilonlM is larger than M times, and M is an integer from 2 to 10; if the initial setting search results in more than 1 starting point, then epsilon is reducedlAnd increase εrSearching again;
6) at SsIn the range from m _ start to m _ end, searching for the starting point, and performing NsCalculating secondary search; for the ith search, i is 1,2, …, NsWhen there is
∣El(SV)－E(SV)∣＜εlAnd Er(SV)－E(SV)＞εr
If true, then m is acceptediIs a starting point; when in use
∣El(mi)－E(SV)∣≥εlOr Er(mi)－E(SV)≤εr
If it is true, then m needs to be matchedi+1SamplingThe point number continues to search for the (i + 1) th time, where mi+1＝mi+step；
7) If the above search is completed and only one starting point is obtained, the starting point search algorithm of the subsystem is ended if the whole SsWithin the range according to the current epsilonlAnd εrSearching for multiple starting points, the epsilon needs to be adjustedlAnd εrSearching again until one is left; the adjustment is made by reducing epsilonlOr increase εr(ii) a The epsilonlAnd εrThe method must be simultaneously used in the original data of the odometers of the two subsystems to respectively obtain starting points m1 and m 2;
step three, according to the sampling frequency sample _ times of the variable diameter subsystem and the inertial navigation subsystem, respectively solving the corresponding time t of each sampling point number m under the system clock of each subsystem in the original signal diagram of the odometer of the variable diameter subsystem and the inertial navigation subsystem, wherein the corresponding time t comprises the corresponding time t1 and t2 of m1 and m2 in the system clock of each subsystem;
t＝m/sample_times
converting a horizontal axis of the original sampling point number m into a horizontal axis of the current subsystem clock t;
step four, solving the difference value delta t between t1 and t 2: t2-t1, wherein the Δ t is used as the difference value of two subsystem clocks;
fifthly, correcting the clocks of the two subsystems to be at the same time by utilizing delta t, and realizing the synchronization of the data of the two subsystems of diameter changing and inertial navigation;
when the variable-diameter subsystem clock is required to be used as standard time for subsequent processing, the corresponding time of all sampling points of the inertial navigation subsystem is required to be reduced by delta t; when the time of the inertial navigation subsystem is required to be standard time, delta t is required to be added to all time parameters of the variable-diameter subsystem.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711457704.1A CN108196616B (en) | 2017-12-28 | 2017-12-28 | Time synchronization method for detecting data of variable-diameter inertial navigation subsystem in pipeline |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711457704.1A CN108196616B (en) | 2017-12-28 | 2017-12-28 | Time synchronization method for detecting data of variable-diameter inertial navigation subsystem in pipeline |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108196616A CN108196616A (en) | 2018-06-22 |
| CN108196616B true CN108196616B (en) | 2021-01-08 |
Family
ID=62585144
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711457704.1A Expired - Fee Related CN108196616B (en) | 2017-12-28 | 2017-12-28 | Time synchronization method for detecting data of variable-diameter inertial navigation subsystem in pipeline |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108196616B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1604096A1 (en) * | 2003-02-24 | 2005-12-14 | Nederlandse Organisatie voor toegepast-natuurwetenschappelijk Onderzoek TNO | Method for determining a position of an object |
| CN101281115A (en) * | 2008-04-15 | 2008-10-08 | 天津市嘉信技术工程公司 | Correlation analysis recognition method of detection signal in closed spacing corrosion |
| US8001858B2 (en) * | 2007-01-19 | 2011-08-23 | Cogen William | Pipeline inspection apparatus and method using radio frequency identification and inertial navigation |
| CN102563361A (en) * | 2012-01-18 | 2012-07-11 | 中国石油大学(华东) | Device and method for detecting and positioning leakage of gas transmission pipeline based on conventional data and sound wave signals |
| CN105549057A (en) * | 2015-12-07 | 2016-05-04 | 韩厚增 | Inertial auxiliary GPS/BDS fusion large-scale measurement device and method for quickly measuring land parcel |
| CN106499957A (en) * | 2016-12-28 | 2017-03-15 | 沈阳工业大学 | A kind of pressure wave signal generator and in-pipeline detector real-time tracking localization method |
-
2017
- 2017-12-28 CN CN201711457704.1A patent/CN108196616B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1604096A1 (en) * | 2003-02-24 | 2005-12-14 | Nederlandse Organisatie voor toegepast-natuurwetenschappelijk Onderzoek TNO | Method for determining a position of an object |
| US8001858B2 (en) * | 2007-01-19 | 2011-08-23 | Cogen William | Pipeline inspection apparatus and method using radio frequency identification and inertial navigation |
| CN101281115A (en) * | 2008-04-15 | 2008-10-08 | 天津市嘉信技术工程公司 | Correlation analysis recognition method of detection signal in closed spacing corrosion |
| CN102563361A (en) * | 2012-01-18 | 2012-07-11 | 中国石油大学(华东) | Device and method for detecting and positioning leakage of gas transmission pipeline based on conventional data and sound wave signals |
| CN105549057A (en) * | 2015-12-07 | 2016-05-04 | 韩厚增 | Inertial auxiliary GPS/BDS fusion large-scale measurement device and method for quickly measuring land parcel |
| CN106499957A (en) * | 2016-12-28 | 2017-03-15 | 沈阳工业大学 | A kind of pressure wave signal generator and in-pipeline detector real-time tracking localization method |
Non-Patent Citations (1)
| Title |
|---|
| 《管道惯性测绘原始数据迭代去噪算法》;靳鹏;《无损检测》;20160310;第38卷(第3期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108196616A (en) | 2018-06-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107390250B (en) | A kind of positioning survey attitude positioning method based on inertial navigation system and double antenna GPS | |
| CN110906875B (en) | Visual processing method for aperture measurement | |
| CN109791128B (en) | Method and system for measuring and inspecting pipes | |
| CN110531390A (en) | Doppler's integration method combines ionospheric residual method detection and repairs cycle slip | |
| CN103335648B (en) | A kind of autonomous method for recognising star map | |
| CN110058222B (en) | Double-layer particle filter tracking-before-detection method based on sensor selection | |
| CN110823216B (en) | Adaptive accelerometer temperature compensation method for inertial navigation | |
| CN103792155B (en) | Based on the bit wear Forecasting Methodology of inertia measurement parameter | |
| CN110738275B (en) | UT-PHD-based multi-sensor sequential fusion tracking method | |
| CN110261052A (en) | Using power hammer excitation and photogrammetric Modal Analysis of Structures system and method | |
| CN106489068B (en) | Measured value analytical equipment and measured value analysis method | |
| CN106813570B (en) | The identification of elongated cylindrical object dimensional and localization method based on line-structured light scanning | |
| CN104075694A (en) | Real-time high speed posture measuring system and method | |
| JP2014240266A (en) | Sensor drift amount estimation device and program | |
| CN108196616B (en) | Time synchronization method for detecting data of variable-diameter inertial navigation subsystem in pipeline | |
| CN113916306B (en) | Pipeline defect detection and positioning method based on multi-sensing information fusion | |
| CN108267782B (en) | Earth's surface absolute displacement test macro and method based on machine vision and numeric value analysis | |
| CN108768605A (en) | Online synchronization method for detecting magnetic flux leakage and inertial navigation subsystem data in pipeline | |
| CN109470269B (en) | Calibration method, calibration equipment and calibration system for space target measuring mechanism | |
| CN108121001A (en) | The quasi-static scene positioning accuracy optimization method differentiated based on fixed solution continuity | |
| CN112378376A (en) | Seabed deformation combined monitoring method based on sensing array and inclinometer | |
| CN116524116A (en) | Drainage pipeline three-dimensional model construction system with multi-sensor data fusion function | |
| CN110888142A (en) | Spacecraft hidden target point measuring method based on MEMS laser radar measuring technology | |
| CN112036073A (en) | 3D printing part measurement result correction method | |
| CN108106634A (en) | Star sensor internal parameter calibration method for direct star observation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210108 |