CN117906566A - GNSS and MEMS fusion deformation monitoring method and system - Google Patents
GNSS and MEMS fusion deformation monitoring method and system Download PDFInfo
- Publication number
- CN117906566A CN117906566A CN202410111050.0A CN202410111050A CN117906566A CN 117906566 A CN117906566 A CN 117906566A CN 202410111050 A CN202410111050 A CN 202410111050A CN 117906566 A CN117906566 A CN 117906566A
- Authority
- CN
- China
- Prior art keywords
- deformation
- acceleration
- change information
- real
- state
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 70
- 238000012544 monitoring process Methods 0.000 title claims abstract description 48
- 230000004927 fusion Effects 0.000 title claims abstract description 20
- 230000008859 change Effects 0.000 claims abstract description 90
- 230000001133 acceleration Effects 0.000 claims abstract description 83
- 230000003068 static effect Effects 0.000 claims abstract description 44
- 238000001514 detection method Methods 0.000 claims abstract description 36
- 238000004364 calculation method Methods 0.000 claims abstract description 25
- 238000001914 filtration Methods 0.000 claims abstract description 24
- 230000002159 abnormal effect Effects 0.000 claims abstract description 15
- 230000008569 process Effects 0.000 claims abstract description 13
- 238000004422 calculation algorithm Methods 0.000 claims description 20
- 239000011159 matrix material Substances 0.000 claims description 14
- 230000005856 abnormality Effects 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 10
- 239000013598 vector Substances 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- 230000003321 amplification Effects 0.000 claims description 4
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 4
- 230000004044 response Effects 0.000 abstract description 17
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 238000006073 displacement reaction Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000001360 synchronised effect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Landscapes
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention provides a deformation monitoring method and system for fusion of GNSS and MEMS, wherein the method comprises the following steps: detecting the state of a monitoring body based on the MEMS accelerometer, wherein the state detection comprises abnormal detection of the acceleration state and detection of inclination angle change of the acceleration state; determining a reporting time period under the condition that the state of the monitoring body is detected and known to change; performing inter-epoch differential positioning calculation according to adjacent epochs in the reporting period to obtain inter-epoch coordinate change information corresponding to the adjacent epochs; calculating average RTK change information according to RTK data corresponding to the reporting time period, and carrying out joint judgment according to the RTK change information and inter-epoch coordinate change information to determine deformation information; and adjusting the process noise of the real-time static filter according to the deformation information and executing real-time static filtering, so that the accuracy, reliability and response speed of deformation monitoring are improved.
Description
Technical Field
The invention relates to the technical field of satellite navigation positioning, in particular to a GNSS and MEMS fusion deformation monitoring method, system, equipment and medium.
Background
In the traditional satellite navigation positioning technology, the millimeter-level positioning result of each epoch is obtained through a real-time static filtering positioning algorithm, so that deformation conditions monitored by deformation are responded, better reliability can be ensured when an observation environment is better, for example, by setting larger epoch noise, the requirement on monitoring precision of 2.5mm in level and 5mm in height can be met due to the fact that the observation environment is better, and rapid (within one hour) response can be realized when a monitoring body deforms.
However, the actual monitoring environment is generally very complex, for example, the cycle slip of the observed value is more, the data integrity rate is lower, the observed noise and the multipath are larger, and the smaller noise can ensure the high precision of the filtering calculation. However, the method is less in noise and too much in dependence on historical observation information, so that deformation response is delayed, and the service requirement of monitoring quick response is difficult to meet.
When the combination of the inter-epoch difference and the RTK is used for judging deformation, the deformation is also limited by an observation environment, so that deformation response is not timely, and conditions such as leakage detection or false detection occur; when triggering based on the threshold value of the accelerometer, outputting a real-time RTK (real-time kinematic) calculation result, wherein the triggering of the acceleration value is easy to misjudge, the accuracy of the output RTK calculation result is not high, and the overall current technical main problems are as follows:
(1) Under severe environment, differential positioning precision among epochs is not high, and reliability is insufficient to reduce deformation response performance:
In a severe environment, the number of the total satellites is small, zhou Tiaobian are large, the geometric configuration of the available satellites is poor, the accuracy of inter-epoch coordinate difference information calculated by inter-epoch differential positioning is low, and the method can be judged to be unreliable when the detection fails. At this time, the real-time static filter cannot adjust the coordinate state parameter information in the filter according to the calculated inter-epoch coordinate difference information. When there is deformation in practice, the deformation response is slow due to the fact that the real-time static filter is not corrected quickly.
(2) When the external slow deformation and the instantaneous deformation are smaller, the prior method can leak detection deformation:
when the monitored body changes slowly, the real-time static filter does not necessarily respond to the coordinate change of the monitored body in time based on preset noise, and at the moment, the coordinate difference information calculated through inter-epoch differential positioning cannot capture the integral deformation, because the inter-epoch differential positioning can only calculate the instantaneous deformation among the epochs, and cannot capture the slow deformation of a plurality of epochs.
In addition, when the deformation is slow, the collected acceleration does not change too much, and at the moment, the deformation cannot be sensed by triggering the acceleration threshold, so that deformation leakage detection is caused.
(3) MEMS accelerometer sensing algorithms are not rigorous and lack GNSS positioning checkings:
The existing sensing algorithm based on the MEMS accelerometer mainly relies on the real-time acceleration value and the preset acceleration threshold value to simply compare for multiple times to sense deformation, the method relies on the acceleration threshold value input from outside, and when external noise is large, such as wind and rain, the RTK calculation result is easy to report for multiple times, and the calculation accuracy is reduced due to frequent output. I.e., the result output by the acceleration state determination algorithm, lacks further checking.
Disclosure of Invention
The embodiment of the invention provides a deformation monitoring method, a system, equipment and a medium for fusion of GNSS and MEMS, which are used for solving the problems of the related technology, and the technical scheme is as follows:
In a first aspect, an embodiment of the present invention provides a deformation monitoring method for fusion of GNSS and MEMS, including:
Detecting the state of a monitoring body based on the MEMS accelerometer, including abnormal detection of the acceleration state and inclination change detection of the acceleration state; determining a reporting time period under the condition that the state of the monitoring body is detected and known to change;
Performing inter-epoch differential positioning calculation according to adjacent epochs in the reporting period to obtain inter-epoch coordinate change information corresponding to the adjacent epochs;
Calculating average RTK change information according to RTK data corresponding to the reporting time period, and carrying out joint judgment according to the RTK change information and inter-epoch coordinate change information to determine deformation information;
And adjusting the process noise of the real-time static filter according to the deformation information and executing real-time static filtering.
In one embodiment, the method for detecting the abnormal acceleration state comprises the following steps:
Creating an abnormality determination window, and adding the acceleration data detected through stationarity to the abnormality determination window until the abnormality determination window is successfully initialized;
calculating a difference value between the real-time acceleration acquired in real time and the acceleration average value of the abnormality judgment window, and obtaining overrun statistical data by judging whether the difference value exceeds the limit;
and judging whether the acceleration state is abnormal or not according to the overrun statistical data.
In one embodiment, a method of stationarity detection includes:
collecting stable acceleration in a stable state, and evaluating intrinsic noise of the stable acceleration based on a wavelet analysis method; determining a threshold interval according to the intrinsic noise;
and if the real-time acceleration is within the threshold value interval, detecting through stationarity.
In one embodiment, a method for detecting a change in inclination of an acceleration state includes:
amplifying the observed noise of the real-time acceleration which is not passed by the stability detection to obtain the acceleration data after noise amplification;
filtering and denoising the acceleration data after noise amplification to obtain a filtered acceleration value;
and calculating inclination angle change data according to the filtered acceleration value and the acceleration reference value, and judging that the inclination angle change is abnormal when the inclination angle change data exceeds a threshold value.
In one embodiment, the method for calculating inter-epoch coordinate change information includes:
The inter-station single difference observation equation of two adjacent epochs t 1、t2 carries out secondary difference calculation, and the residual error of the finishing satellite p is as follows:
wherein, The parameters are the parameter coefficients of three coordinate components respectively, and the variation of the clock difference of the delta T receiver;
And carrying out iterative calculation on residual vectors calculated by a plurality of satellite error equations based on a least square algorithm, and estimating to obtain inter-epoch coordinate change information of adjacent epochs.
In one embodiment, a method of joint determination includes:
when the RTK change information is larger than a first preset value, determining that the RTK is deformed;
When the inter-epoch coordinate change information has a sequence larger than a second preset value, and the number of times larger than the second preset value is more than the preset number of times, determining that the deformation exists;
And when the RTK change information is larger than a third preset value and the inter-epoch coordinate change information has a sequence larger than the third preset value, determining that the RTK change information is deformed.
In one embodiment, the method for adjusting the process noise is:
under the condition that deformation is judged, converting deformation information in a reporting period into horizontal change information and elevation change information based on the deformation information, and if the deformation information is larger than a fourth preset value, directly resetting the real-time static filter; if the state parameter precision matrix is smaller than the fourth preset value, converting the state parameter precision matrix into horizontal noise and elevation noise, and adding the horizontal noise and the elevation noise into the real-time static filter to obtain a new state parameter precision matrix, so that the updated real-time static filter is obtained.
In a second aspect, an embodiment of the present invention provides a deformation monitoring system for fusion of GNSS and MEMS, and a deformation monitoring method for fusion of GNSS and MEMS is performed as described above.
In a third aspect, an embodiment of the present invention provides an electronic device, including: memory and a processor. Wherein the memory and the processor are in communication with each other via an internal connection, the memory is configured to store instructions, the processor is configured to execute the instructions stored by the memory, and when the processor executes the instructions stored by the memory, the processor is configured to perform the method of any one of the embodiments of the above aspects.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program, the method of any one of the above embodiments being performed when the computer program is run on a computer.
The advantages or beneficial effects in the technical scheme at least comprise:
The invention aims to provide a deformation monitoring method based on a GNSS combined MEMS accelerometer, which is characterized in that along with the continuous development of a GNSS receiver and an MEMS technology, a low-cost and low-power-consumption MEMS acceleration sensor can be integrated on the GNSS, the sensor can sense the state change of a monitoring body based on the accelerometer so as to transmit the information to a real-time static filter, when the monitoring body deforms at any speed, the deformation information which is calculated by combining the MEMS acceleration state monitoring information and the GNSS combined and reversely is fused, and then the process noise of the real-time static Kalman filter is flexibly adjusted, so that early warning is performed in time, and the accuracy, the reliability and the response speed of deformation monitoring are improved.
The foregoing summary is for the purpose of the specification only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will become apparent by reference to the drawings and the following detailed description.
Drawings
In the drawings, the same reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily drawn to scale. It is appreciated that these drawings depict only some embodiments according to the disclosure and are not therefore to be considered limiting of its scope.
FIG. 1 is a schematic diagram of an overall flow of a GNSS and MEMS integrated deformation monitoring method according to the present invention;
FIG. 2 is a schematic diagram of an accelerometer-based receiver state detection flow according to the present invention;
FIG. 3 is a schematic diagram of a deformation response algorithm based on accelerometer and GNSS fusion in accordance with the present invention;
fig. 4 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Hereinafter, only certain exemplary embodiments are briefly described. As will be recognized by those of skill in the pertinent art, the described embodiments may be modified in various different ways without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.
Example 1
The embodiment provides a deformation monitoring method of fusion of a GNSS and an MEMS, along with continuous development of a GNSS receiver and an MEMS technology, a MEMS acceleration sensor with low cost and low power consumption can be integrated on the GNSS, and the sensor can sense a monitoring body based on an accelerometer, namely, the state change of the receiver, so that the information is transferred to a real-time static filter, and the accuracy, reliability and response speed of deformation monitoring are improved.
As shown in fig. 1, the deformation monitoring includes the following steps:
Step S100: detecting the state of the monitoring body based on the MEMS accelerometer, and determining a reporting time period under the condition that the state of the monitoring body is detected to change; the reporting time period is a time period for monitoring the change of the body state; the state detection comprises acceleration state anomaly detection and acceleration state inclination change detection;
Step S200: performing inter-epoch differential positioning calculation according to adjacent epochs in the reporting period to obtain inter-epoch coordinate change information corresponding to the adjacent epochs; calculating average RTK change information according to RTK data corresponding to the reporting time period;
step S300: carrying out joint judgment according to RTK change information and inter-epoch coordinate change information to determine deformation information;
step S400: and adjusting the process noise of the real-time static filter according to the deformation information to obtain an updated real-time static filter, and executing real-time static filtering through the updated real-time static filter.
The state detection is mainly used for detecting whether the monitoring body deforms at any speed or not, and the method mainly comprises acceleration state abnormality detection and acceleration state inclination angle change detection. Specifically:
referring to fig. 2, the principle of abnormal acceleration state detection is mainly as follows:
Step S101: acceleration intrinsic noise calibration: collecting accelerometer high-frequency data in a stable state, wherein the data collection frequency corresponding to the high-frequency data is generally more than 50 Hz; evaluating intrinsic noise of the accelerometer high frequency data based on a wavelet analysis method; wavelet analysis refers to the use of an oscillating waveform of finite length or fast decay, which becomes the parent wavelet, to represent the signal, equivalent to identifying local variations in the accelerometer high frequency data to determine the intrinsic noise.
Step S102: an acceleration stability window is created, initialized and updated: the window length of the created acceleration stability window can be set to be 10, high-frequency acceleration data can be acquired in real time, wherein the acceleration data can be real-time triaxial acceleration data, if the acceleration data meets the threshold value interval of three times of intrinsic noise, the stability window is detected and counted through stability, and otherwise, the stability window is not counted.
Step S103: an acceleration abnormality determination window is created, initialized and updated: the window length of the established acceleration abnormity judgment window can be set to be the acceleration acquisition frequency, and when the acceleration stability window is successfully initialized (namely, window data are completely updated), the data detected by the stability are added into the abnormity judgment window until the initialization is successful.
Step S104: acceleration abnormality detection determination: judging whether the real-time acceleration is abnormal or not by taking the acceleration of the abnormal judging window as a reference, making a difference between the real-time acceleration value and the average value of the acceleration window to obtain a difference value between the real-time acceleration value and the average value of the acceleration window, judging whether the difference value is within 3 times of standard deviation of the window to judge whether the error exceeds the limit, evaluating the error to exceed the limit if the difference value exceeds the 3 times of standard deviation of the window, starting counting if the error exceeds the limit, evaluating the number of times of overrun in a preset time, such as the number of times of overrun in 1s, and considering that the acceleration state is abnormal if the integral proportion exceeds 50%.
The method for detecting the inclination angle change of the acceleration state comprises the following steps:
Step S105: executing the steps S101 to S103 to create a corresponding window and initialize and update the window;
Step S106: step S103, after the window is initialized successfully, the triaxial acceleration mean value information of the window at the moment is recorded and is used as a reference for calculating the inclination angle information;
Step S107: kalman filtering denoising: carrying out kalman filtering on current acceleration data, wherein noise in state updating is output based on the state of a stationarity window, if stationarity passes, observation noise is intrinsic noise, and if stationarity does not pass, the observation noise is amplified by 100 times, so that the kalman filtering can quickly reflect the actual change of acceleration while denoising;
step S108: calculating a real-time dip angle: and calculating the inclination angle change by using the filtered acceleration value and the acceleration reference value and a method for calculating the interior angle based on the vector product, and judging the deformation if the inclination angle exceeds a threshold value.
As shown in fig. 3, when the state transmission change of the MEMS accelerometer is monitored, encryption coordinated calculation is performed, for example, an original one-time filtering solution is output in one hour, coordinated encryption is performed for one-time filtering solution in five minutes, and the sampling frequency of the observed data is synchronously increased from 0.1HZ to 1HZ in the encryption period.
Marking a time period when the state of the accelerometer changes as a reporting time period, and executing the differential stability judgment between the RTK and the epoch in the reporting time period:
Step S201: and in the reporting period, continuously making an inter-epoch differential positioning algorithm by adjacent epochs in the reporting period to obtain inter-epoch coordinate change information, wherein the inter-epoch coordinate change information comprises an inter-epoch coordinate change sequence.
The method mainly comprises the steps of obtaining inter-epoch coordinate transformation information, constructing an error observation equation according to inter-station difference and inter-epoch difference modes, and constructing a double-difference observation equation in a similar process; specifically:
Performing secondary differential calculation on the single difference observation equation between two adjacent epochs t 1、t2, and finishing the residual error of the satellite p as follows:
wherein, The parameters are the parameter coefficients of three coordinate components respectively, and the variation of the clock difference of the delta T receiver;
Carrying out iterative calculation on residual vectors calculated by a plurality of satellite error equations based on a least square algorithm, and estimating to obtain inter-epoch coordinate change information of adjacent epochs; the least square algorithm iteratively estimates a final parameter vector through a formula, wherein the formula is x= (A TPA)-1AT PL;
wherein A is based on a plurality of observation satellites The constructed design matrix, P is a weight matrix designed based on the altitude angle function, and L is a residual vector calculated by a plurality of satellite error equations.
Step S202: based on the RTK resolving sequence and the inter-epoch coordinate change sequence in the time period, the joint judgment is carried out, and the specific method is as follows:
The RTK calculation in the reporting period can acquire dynamic coordinate information in the period, and the coordinate information is sensitive to the position. And selecting a stable RTK resolving sequence with the latest time in the period and the prior reference coordinate information to make difference, and obtaining average RTK change information. The prior reference coordinate information is the coordinate result of the last resolving period.
Step S300: and carrying out joint judgment based on the RTK change information and the inter-epoch coordinate change information, wherein the judgment rule is as follows:
When the RTK change information is larger than a first preset value, determining that the RTK is deformed; if the RTK change information is detected to be larger than 0.05m, determining that the RTK change information is deformed;
When the inter-epoch coordinate change information has a sequence larger than a second preset value, and the number of times larger than the second preset value is more than the preset number of times, determining that the deformation exists; if the coordinate change sequence between the epochs is detected to exist for more than 2 times and is more than 0.03m, the deformation is judged;
When the RTK change information is larger than a third preset value and the inter-epoch coordinate change sequence is larger than the third preset value, determining that the RTK change information is deformed; if the RTK change information is detected to be larger than 0.03m and the inter-epoch coordinate change sequence is detected to be larger than 0.03m, determining that the RTK change information is deformed;
And the other cases are suspected deformation, namely deformation response is carried out only by adding noise between encryption and calculation periods, namely noise with the level delta h of 1E-4m and the level delta Gao Cheng v of 0.2mm is added into the coordinate parameter state precision matrix.
Step S400: and adjusting the process noise of the real-time static filter according to the deformation information to obtain the updated real-time static filter.
Specifically, the method for adjusting the process noise comprises the following steps:
Under the condition that deformation is judged, based on deformation information in a reporting period, converting the deformation information into horizontal change information dh and elevation change information dv, if the deformation information is larger than a fourth preset value, if the deformation information is larger than 0.05m, directly resetting the real-time static filter, and realizing quick response; if the deformation information is smaller than the fourth preset value, converting the deformation information into horizontal noise and elevation noise if the deformation information is smaller than 0.05m, and adding the horizontal noise and the elevation noise into a real-time static filter to obtain a new state parameter precision matrix. The main steps of the noise adding are as follows:
Qnew＝Qold+Qu
δh＝(round(dh/0.05)+1)*1E-3
δv＝(round(dv/0.05)+1)*1E-3；
Wherein Q new is the coordinate parameter precision matrix after the noise is added, Q old is the coordinate parameter precision matrix before the noise is added, Q u is the whole noise, and the whole noise is mainly composed of Q pos coordinate noise submatrices, Q N and noise submatrices composed of the horizontal direction and the elevation direction, and the coordinate parameter precision matrix can be converted into Q pos through matrix conversion.
And (3) adding noise to obtain an updated real-time static filter, and carrying out state updating and observation updating on the system state by using the updated real-time static filter. The real-time static filtering mainly comprises two main modules, namely state updating and observation updating, and in the real-time static filtering process, the real-time static filtering can be performed through the constructed error observation equation and the state observation equation.
The method has the following beneficial effects:
(1) The method introduces a MEMS accelerometer rapid sensing algorithm, and senses external deformation information based on accurate judgment of an acceleration abnormal value and rapid calculation of an inclination angle by self-adaptive Kalman filtering. And through the accurate state sensing of the MEMS accelerometer, reliable deformation signals are provided for GNSS real-time static filtering. The algorithm module is not limited by the observation environment, deformation size, baseline length and the like. The method can provide reliable external deformation input signals for GNSS static filtering under the conditions of severe service, fewer satellites and small deformation value.
In the prior art, deformation is responded according to GNSS data, when the environment is severe, the observation environment is fewer, the satellite geometric configuration is poor, the deformation response is slow, the actual deformation is difficult to detect when the inclination angle changes, and the MEMS accelerometer sensing algorithm is only detected according to a real-time threshold value, so that false alarm is easy to reduce the resolving precision.
(2) And the MEMS accelerometer is fast linked and the GNSS deformation value is jointly detected, so that the deformation response reliability is improved. According to the method, when the MEMS accelerometer accurately senses external deformation, the GNSS can encrypt and collect observation data, and meanwhile, the GNSS enters an encryption resolving mode. At this time, the actual deformation is detected by an inter-epoch differential positioning algorithm and an RTK dynamic algorithm based on the GNSS data in the cryptoperiod. On the premise of being based on MEMS accelerometer signals, the method is further combined with GNSS to detect deformation, if the GNSS detection deformation is small, the outside environment is possibly false alarm, noise is removed only through encryption and calculation, and if the outside environment has suspected deformation or determined large deformation, the filter is correspondingly updated based on deformation values.
In the prior art, when deformation is sensed by MEMS acceleration threshold detection, the mode of RTK resolving or the mode of direct noise adding is switched, deformation detection is lack, deformation false detection is easily caused, and the real-time static filtering resolving precision is reduced.
(3) Accurate noise adding based on deformation value information. According to the method, after the MEMS accelerometer and the GNSS jointly detect the deformation value, the deformation value is converted into corresponding plane displacement and elevation displacement based on calculation, the plane displacement and the elevation displacement are accurately converted into noise through displacement, and the quick response of the deformation can be guaranteed while the resolving precision is guaranteed.
In the prior art, when the MEMS acceleration threshold detects deformation, the coordinate result of the RTK resolving engine is directly output, the real-time static main filter is reset or noise is added, the error detection is easy, and the deformation response precision is low.
Example two
The present embodiment provides a deformation monitoring system for fusion of GNSS and MEMS, which performs the deformation monitoring method of the first embodiment. Specifically, the system includes:
The state detection module is used for detecting the state of the monitoring body based on the MEMS accelerometer, and determining a reporting time period under the condition that the state of the monitoring body is detected and known to change; the reporting time period is a time period for monitoring the change of the body state; the state detection comprises acceleration state anomaly detection and acceleration state inclination change detection;
The deformation analysis module is used for carrying out inter-epoch differential positioning calculation according to adjacent epochs in the reporting period to obtain inter-epoch coordinate change information corresponding to the adjacent epochs; calculating average RTK change information according to RTK data corresponding to the reporting time period; carrying out joint judgment according to RTK change information and inter-epoch coordinate change information to determine deformation information;
and the noise adding filtering module is used for adjusting the process noise of the real-time static filter according to the deformation information to obtain an updated real-time static filter, and executing the real-time static filtering through the updated real-time static filter.
The system is based on a fusion mode of an accurate state judgment algorithm of the MEMS accelerometer and a GNSS real-time static Kalman filtering algorithm, and can well solve the problems of less satellite data, more rough differences, larger accelerometer noise, deformation false detection and the like in a severe observation environment; in the MEMS accelerometer sensing algorithm, through the calibrated accelerometer intrinsic noise, the algorithm can detect the abnormal acceleration state according to the self noise characteristic of the accelerometer, and meanwhile, an adaptive kalman filtering model is built according to the intrinsic noise of the accelerometer, so that the change inclination angle under the condition of slow deformation can be rapidly obtained. When the accurate perception acceleration state changes, the GNSS can carry out encryption acquisition and encryption calculation at the moment, and the real-time static filter can carry out accurate filter updating based on deformation information of the temporary RTK calculation engine and the inter-epoch differential positioning algorithm combined inverse calculation, so that deformation response performance is further improved.
The functions of each module in the system of the embodiment of the present invention may be referred to the corresponding descriptions in the above method, and will not be repeated here.
Example III
Fig. 4 shows a block diagram of an electronic device according to an embodiment of the invention. As shown in fig. 4, the electronic device includes: memory 100 and processor 200, and memory 100 stores a computer program executable on processor 200. The processor 200, when executing the computer program, implements the deformation monitoring method of the GNSS and MEMS fusion in the above embodiment. The number of memory 100 and processors 200 may be one or more.
The electronic device further includes:
The communication interface 300 is used for communicating with external equipment and performing data interaction transmission.
If the memory 100, the processor 200, and the communication interface 300 are implemented independently, the memory 100, the processor 200, and the communication interface 300 may be connected to each other and perform communication with each other through buses. The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 100, the processor 200, and the communication interface 300 are integrated on a chip, the memory 100, the processor 200, and the communication interface 300 may communicate with each other through internal interfaces.
The embodiment of the invention provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the method provided in the embodiment of the invention.
The embodiment of the invention also provides a chip, which comprises a processor and is used for calling the instructions stored in the memory from the memory and running the instructions stored in the memory, so that the communication equipment provided with the chip executes the method provided by the embodiment of the invention.
The embodiment of the invention also provides a chip, which comprises: the input interface, the output interface, the processor and the memory are connected through an internal connection path, the processor is used for executing codes in the memory, and when the codes are executed, the processor is used for executing the method provided by the embodiment of the invention.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processor, digital signal processor (DIGITAL SIGNAL processing, DSP), application Specific Integrated Circuit (ASIC), field programmable gate array (fieldprogrammablegate array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. A general purpose processor may be a microprocessor or any conventional processor or the like. It is noted that the processor may be a processor supporting an advanced reduced instruction set machine (ADVANCED RISC MACHINES, ARM) architecture.
Further, optionally, the memory may include a read-only memory and a random access memory, and may further include a nonvolatile random access memory. The memory may be volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The nonvolatile memory may include a read-only memory (ROM), a Programmable ROM (PROM), an erasable programmable ROM (erasable PROM), an electrically erasable programmable EPROM (EEPROM), or a flash memory, among others. Volatile memory can include random access memory (random access memory, RAM), which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available. For example, static random access memory (STATIC RAM, SRAM), dynamic random access memory (dynamic random access memory, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate synchronous dynamic random access memory (double DATA DATE SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCHLINK DRAM, SLDRAM), and direct memory bus random access memory (direct rambus RAM, DR RAM).
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with the present invention are fully or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. Computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that various changes and substitutions are possible within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A method for deformation monitoring by fusion of GNSS and MEMS, comprising:
Detecting the state of a monitoring body based on the MEMS accelerometer, including abnormal detection of the acceleration state and inclination change detection of the acceleration state; determining a reporting time period under the condition that the state of the monitoring body is detected and known to change;
performing inter-epoch differential positioning calculation according to adjacent epochs in the reporting period to obtain inter-epoch coordinate change information corresponding to the adjacent epochs;
Calculating average RTK change information according to RTK data corresponding to the reporting time period, and carrying out joint judgment according to the RTK change information and the inter-epoch coordinate change information to determine deformation information;
And adjusting the process noise of the real-time static filter according to the deformation information and executing real-time static filtering.
2. The method for monitoring deformation by fusion of GNSS and MEMS according to claim 1, wherein the method for detecting the abnormal acceleration state is as follows:
creating an abnormality determination window, and adding acceleration data detected through stationarity to the abnormality determination window until the abnormality determination window is successfully initialized;
calculating a difference value between the real-time acceleration acquired in real time and the acceleration average value of the abnormality judgment window, and judging whether the difference value exceeds the limit or not to obtain overrun statistical data;
and judging whether the acceleration state is abnormal or not according to the overrun statistical data.
3. The method of claim 2, wherein the method of stationarity detection comprises:
Collecting stable acceleration in a stable state, and evaluating intrinsic noise of the stable acceleration based on a wavelet analysis method; determining a threshold interval according to the intrinsic noise;
And if the real-time acceleration is within the threshold value interval, detecting through stationarity.
4. The method for GNSS and MEMS fusion deformation monitoring according to claim 1, wherein the method for acceleration state tilt angle change detection comprises:
amplifying the observed noise of the real-time acceleration which is not passed by the stability detection to obtain the acceleration data after noise amplification;
filtering and denoising the acceleration data after noise amplification to obtain a filtered acceleration value;
and calculating inclination angle change data according to the filtered acceleration value and the acceleration reference value, and judging that the inclination angle change is abnormal when the inclination angle change data exceeds a threshold value.
5. The method for monitoring deformation of a GNSS and MEMS fusion according to claim 1, wherein the method for calculating the inter-epoch coordinate change information is as follows:
The inter-station single difference observation equation of two adjacent epochs t 1、t2 carries out secondary difference calculation, and the residual error of the finishing satellite p is as follows:
wherein, The parameters are the parameter coefficients of three coordinate components respectively, and the variation of the clock difference of the delta T receiver;
And carrying out iterative calculation on residual vectors calculated by a plurality of satellite error equations based on a least square algorithm, and estimating to obtain inter-epoch coordinate change information of adjacent epochs.
6. The method of claim 1, wherein the method of joint determination comprises:
When the RTK change information is larger than a first preset value, determining that the RTK change information is deformed;
when the inter-epoch coordinate change information has a sequence larger than a second preset value and the number of times larger than the second preset value is more than the preset number of times, determining that the deformation exists;
and when the RTK change information is larger than a third preset value and the inter-epoch coordinate change information has a sequence larger than the third preset value, determining that the RTK change information is deformed.
7. The method for monitoring deformation of a GNSS and MEMS fusion according to claim 1, wherein the method for adjusting process noise is:
Under the condition that deformation is judged, based on the deformation information converted to horizontal change information and elevation change information in the reporting period, if the deformation information is larger than a fourth preset value, directly resetting the real-time static filter; if the state parameter precision matrix is smaller than the fourth preset value, converting the state parameter precision matrix into horizontal noise and elevation noise, and adding the horizontal noise and the elevation noise into the real-time static filter to obtain a new state parameter precision matrix, so that the updated real-time static filter is obtained.
8. A GNSS and MEMS integrated deformation monitoring system, wherein the GNSS and MEMS integrated deformation monitoring method according to any of claims 1 to 7 is performed.
9. An electronic device, comprising: a processor and a memory, the memory storing instructions that are loaded and executed by the processor to implement the GNSS and MEMS fusion deformation monitoring method according to any of claims 1 to 7.
10. A computer readable storage medium, wherein a computer program is stored in the computer readable storage medium, and when the computer program is executed by a processor, the deformation monitoring method of the fusion of GNSS and MEMS according to any of claims 1 to 7 is implemented.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410111050.0A CN117906566A (en) | 2024-01-25 | 2024-01-25 | GNSS and MEMS fusion deformation monitoring method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410111050.0A CN117906566A (en) | 2024-01-25 | 2024-01-25 | GNSS and MEMS fusion deformation monitoring method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117906566A true CN117906566A (en) | 2024-04-19 |
Family
ID=90686832
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410111050.0A Pending CN117906566A (en) | 2024-01-25 | 2024-01-25 | GNSS and MEMS fusion deformation monitoring method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117906566A (en) |
-
2024
- 2024-01-25 CN CN202410111050.0A patent/CN117906566A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2899547B1 (en) | Method and apparatus for determining acceleration of vehicle | |
| US10330494B2 (en) | Method to determine a pressure noise metric for fall detection systems | |
| EP3591354B1 (en) | Axle-load measuring apparatus and axle-load measuring method | |
| CN108290537A (en) | Collision for traffic events diagnoses | |
| US20190072947A1 (en) | Method of predicting plant data and apparatus using the same | |
| CN210863662U (en) | Formaldehyde concentration detection device and air purifier | |
| CN106053724A (en) | Cloud computing-based gas sensor precision compensating method and device | |
| CN107787406A (en) | blade load sensing system for wind turbine | |
| EP3542131A1 (en) | Self-calibrating inertial measurement system and method | |
| CN111736194A (en) | Combined inertial navigation system and navigation data processing method | |
| US20220221485A1 (en) | Speed and Acceleration Calculation and Measurement Method, Device, and Application Based on Regularization Algorithms | |
| US20210240991A1 (en) | Information processing method, information processing device, non-transitory computer-readable recording medium recording information processing program, and information processing system | |
| CN117906566A (en) | GNSS and MEMS fusion deformation monitoring method and system | |
| CN115291253B (en) | Vehicle positioning integrity monitoring method and system based on residual error detection | |
| CN109808641A (en) | Anti-theft alarm method and device, electronic equipment and readable storage medium storing program for executing | |
| CN111024986B (en) | Ultrasonic anemorumbometer data calibration method, device and equipment and storage medium | |
| US20170248629A1 (en) | Method for operating a micromechanical z-accelerometer | |
| CN116991115A (en) | Method, device, equipment and medium for monitoring state of main shaft of numerical control machine tool | |
| CN115096309A (en) | Fusion positioning method and device, electronic equipment and storage medium | |
| CN109758703A (en) | A kind of error correction systems and method for fire-fighting scene of a fire pressure-altitude sensor | |
| CN112212908B (en) | Intelligent sensor and intelligent method thereof | |
| CN114608654A (en) | Anti-seismic support safety early warning method and system based on multi-sensor network | |
| CN110728007B (en) | Dynamic fault diagnosis method based on model features | |
| CN113077628A (en) | Algorithm of composite geomagnetic vehicle detector | |
| CN112781556A (en) | Well lid transaction monitoring method and device based on multi-data fusion filtering |
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 |