GNSS and MEMS fusion-based deformation monitoring method and system

Abstract

The application provides a GNSS and MEMS fusion deformation monitoring method and system, and the method comprises the following steps: detecting the state of a monitoring body based on a MEMS accelerometer, wherein the state detection comprises acceleration state anomaly detection and acceleration state inclination change detection; determining an additional reporting period when it is detected that the state of the monitoring body changes; performing inter-epoch differential positioning calculation on adjacent epochs in the additional 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 additional reporting period; performing joint determination according to the RTK change information and the inter-epoch coordinate change information to determine deformation information; and adjusting the process noise of a real-time static filter according to the deformation information and performing real-time static filtering, so that the accuracy, reliability and response speed of deformation monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and positioning technology, and in particular to a deformation monitoring method, system, device and medium that integrates GNSS and MEMS. Background Technology

[0002] In traditional satellite navigation and positioning technology, millimeter-level positioning results for each epoch are obtained through real-time static filtering positioning algorithms to respond to deformation monitoring. When the observation environment is good, good reliability can be guaranteed. For example, by setting a large epoch noise, the monitoring accuracy requirement of 2.5 mm horizontal and 5 mm vertical can be guaranteed even with a large epoch noise due to the good observation environment. When the monitored object deforms, it can also respond quickly (within one hour).

[0003] However, actual monitoring environments are generally extremely complex, with many cycle slips in observations, low data integrity, and significant observation noise and multipath propagation. In such cases, lower noise levels are necessary to ensure high accuracy in filtering and solving. However, lower noise levels rely too heavily on historical observation information, leading to deformation response delays and making it difficult to meet the operational requirements for rapid monitoring response.

[0004] When deformation is determined by combining inter-epoch difference analysis and RTK, it is also limited by the observation environment, resulting in untimely deformation response and missed or false detections. When real-time RTK calculation results are output based on accelerometer threshold triggering, acceleration value triggering is prone to misjudgment, and the accuracy of the output RTK calculation results is not high. Overall, the main problems of the current technology are as follows:

[0005] (1) In harsh environments, the differential positioning accuracy between epochs is not high, and the reliability is insufficient, which reduces the deformation response performance:

[0006] In harsh environments, the number of observed satellites is small, cycle slips are frequent, and the geometry of available satellites is poor. The accuracy of the inter-epoch coordinate difference information calculated by inter-epoch differential positioning is low, and it will be deemed unreliable if the check fails. At this time, the real-time static filter cannot adjust the coordinate state parameters in the filter based on the calculated inter-epoch coordinate difference information. When actual deformation occurs, the lack of rapid correction of the real-time static filter leads to a slow deformation response.

[0007] (2) When slow deformation or small instantaneous deformation occurs in the external environment, existing methods will miss the deformation:

[0008] When the monitored object changes slowly, the real-time static filter, based on the preset noise, may not be able to respond to the coordinate changes of the monitored object in a timely manner. At this time, the coordinate difference information calculated by the inter-epoch differential positioning cannot capture the overall deformation, because the inter-epoch differential positioning can only calculate the instantaneous deformation between epochs and cannot capture such slow deformation over multiple epochs.

[0009] In addition, during slow deformation, the collected acceleration does not change much. At this time, the deformation cannot be detected by triggering the acceleration threshold, which will lead to the deformation being missed.

[0010] (3) The MEMS accelerometer sensing algorithm is not rigorous and lacks GNSS positioning verification:

[0011] Existing sensing algorithms based on MEMS accelerometers mainly rely on simple, repeated comparisons between real-time acceleration values ​​and preset acceleration thresholds to detect deformation. This method depends on an externally input acceleration threshold, and is prone to multiple false alarms when there is significant external noise, such as during windy or rainy weather. Frequent output of RTK calculation results will lead to a decrease in calculation accuracy. In other words, the output of the acceleration state determination algorithm lacks further verification. Summary of the Invention

[0012] This invention provides a deformation monitoring method, system, device, and medium that integrates GNSS and MEMS technologies to address existing problems in related technologies. The technical solution is as follows:

[0013] In a first aspect, embodiments of the present invention provide a deformation monitoring method integrating GNSS and MEMS, comprising:

[0014] The state of the monitored object is detected using MEMS accelerometers, including detection of acceleration anomalies and changes in acceleration tilt angle; when a change in the state of the monitored object is detected, the reporting period is determined.

[0015] Based on the differential positioning calculation between adjacent epochs within the reporting period, the coordinate change information between adjacent epochs is obtained;

[0016] The average RTK change information is calculated based on the RTK data corresponding to the reporting period. The deformation information is determined by combining the RTK change information and the coordinate change information between epochs.

[0017] Adjust the process noise of the real-time static filter based on deformation information and perform real-time static filtering.

[0018] In one implementation, the method for detecting acceleration state anomalies is as follows:

[0019] Create an anomaly detection window and add the acceleration data that has passed the stationarity test to the anomaly detection window until the anomaly detection window is successfully initialized;

[0020] Calculate the difference between the real-time acceleration obtained from real-time acquisition and the average acceleration value in the anomaly detection window, and obtain the out-of-limit statistics by judging whether the difference exceeds the limit;

[0021] Determine whether an abnormality has occurred in the acceleration status based on the out-of-limit statistical data.

[0022] In one implementation, the method for stationarity detection includes:

[0023] The stationary acceleration under steady-state conditions is collected, and the intrinsic noise of the stationary acceleration is evaluated based on wavelet analysis; the threshold range is determined based on the intrinsic noise.

[0024] If the real-time acceleration is within the threshold range, then the stationarity test is passed.

[0025] In one embodiment, the method for detecting changes in acceleration state tilt angle includes:

[0026] The noise of the real-time acceleration that fails the stationarity test is amplified to obtain the noise-amplified acceleration data.

[0027] The amplified acceleration data is filtered to remove noise, resulting in the filtered acceleration value.

[0028] The tilt angle change data is calculated based on the filtered acceleration value and the acceleration reference value. If the tilt angle change data exceeds the threshold, it is determined that the tilt angle change is abnormal.

[0029] In one implementation method, the coordinate change information between epochs is calculated as follows:

[0030] The residuals of satellite p are calculated by performing a quadratic difference calculation on the inter-station single-difference observation equations for two adjacent epochs t1 and t2, and then rearranging the results:

[0031]

[0032] in, These are the parameter coefficients of the three coordinate components, and the change in the difference of the receiver clock error ΔT;

[0033] The residual vectors calculated from the error equations of multiple satellites are iteratively calculated using the least squares algorithm to estimate the coordinate changes between adjacent epochs.

[0034] In one implementation, the joint determination method includes:

[0035] When the RTK change information is greater than the first preset value, it is determined to be a deformation.

[0036] If there is a sequence of coordinate changes between epochs that is greater than the second preset value, and the number of times it is greater than the second preset value is more than the preset number, then it is determined to be a deformation;

[0037] If the RTK change information is greater than the third preset value and there is a sequence of coordinate change information between epochs that is greater than the third preset value, it is determined to be a deformation.

[0038] In one implementation, the method for adjusting process noise is as follows:

[0039] If deformation is detected, the deformation information during the reporting period is converted into horizontal and vertical change information. If the deformation information is greater than the fourth preset value, the real-time static filter is directly reset; if it is less than the fourth preset value, it is converted into horizontal and vertical noise and added to the real-time static filter to obtain a new state parameter accuracy matrix, thus obtaining an updated real-time static filter.

[0040] Secondly, embodiments of the present invention provide a deformation monitoring system that integrates GNSS and MEMS, and performs the deformation monitoring method that integrates GNSS and MEMS as described above.

[0041] Thirdly, embodiments of the present invention provide an electronic device comprising a memory and a processor. The memory and the processor communicate with each other via an internal connection path. The memory stores instructions, and the processor executes the instructions stored in the memory. When the processor executes the instructions stored in the memory, it causes the processor to perform the method described in any of the above embodiments.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium that stores a computer program, wherein when the computer program is run on a computer, the methods in any of the embodiments described above are executed.

[0043] The advantages or beneficial effects of the above technical solutions include at least the following:

[0044] The purpose of this invention is to propose a deformation monitoring method based on GNSS and MEMS accelerometers. With the continuous development of GNSS receivers and MEMS technology, relatively low-cost and low-power MEMS accelerometers can be integrated into GNSS receivers. This sensor can sense the state changes of the monitored object based on the accelerometer and transmit the information to a real-time static filter. When the monitored object undergoes deformation at any speed, the process noise of the real-time static Kalman filter is flexibly adjusted after fusing the deformation information calculated by the MEMS acceleration state monitoring information and the GNSS joint back-calculation, thereby providing timely early warning and improving the accuracy, reliability, and response speed of deformation monitoring.

[0045] The above overview is for illustrative purposes 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 invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0046] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in the invention and should not be construed as limiting the scope of the invention.

[0047] Figure 1 This is a schematic diagram of the overall process of the deformation monitoring method integrating GNSS and MEMS of the present invention;

[0048] Figure 2 This is a schematic diagram of the receiver state detection process based on the accelerometer of the present invention;

[0049] Figure 3 This is a schematic diagram of the deformation response algorithm based on accelerometer and GNSS fusion of the present invention;

[0050] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0051] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0052] Example 1

[0053] This embodiment provides a deformation monitoring method that integrates GNSS and MEMS. With the continuous development of GNSS receivers and MEMS technology, a relatively low-cost and low-power MEMS accelerometer sensor can be integrated into GNSS receivers. This sensor can sense the state changes of the monitored object, i.e., the receiver, based on the accelerometer, and then transmit this information to a real-time static filter to improve the accuracy, reliability, and response speed of deformation monitoring.

[0054] like Figure 1 As shown, deformation monitoring includes the following specific steps:

[0055] Step S100: Detect the state of the monitored object based on the MEMS accelerometer, and determine the reporting period when the state of the monitored object changes; the reporting period is the period during which the state of the monitored object changes; the state detection includes acceleration state anomaly detection and acceleration state tilt angle change detection.

[0056] Step S200: Perform differential positioning calculation between adjacent epochs within the reporting period to obtain the coordinate change information between adjacent epochs; calculate the average RTK change information based on the RTK data corresponding to the reporting period.

[0057] Step S300: Determine deformation information by jointly judging the RTK change information and the inter-epoch coordinate change information;

[0058] Step S400: Adjust the process noise of the real-time static filter according to the deformation information to obtain the updated real-time static filter, and perform real-time static filtering through the updated real-time static filter.

[0059] Among them, state detection is mainly used to detect whether the monitored body undergoes deformation at arbitrary speeds. Its methods mainly include acceleration state anomaly detection and acceleration state tilt angle change detection. Specifically:

[0060] Combination Figure 2 As shown, the principle of acceleration state anomaly detection is mainly as follows:

[0061] Step S101: Accelerometer intrinsic noise calibration: Acquire high-frequency accelerometer data under steady-state conditions, wherein the data acquisition frequency corresponding to the high-frequency data is generally above 50Hz; evaluate the intrinsic noise of the high-frequency accelerometer data based on wavelet analysis; wherein, wavelet analysis refers to using finite-length or rapidly decaying oscillating waveforms, known as mother wavelets, to represent the signal, which is equivalent to identifying local changes in the high-frequency accelerometer data to determine the intrinsic noise.

[0062] Step S102: Create an acceleration stationary window and initialize and update it: The window length of the created acceleration stationary window can be set to 10. High-frequency acceleration data is collected in real time. The acceleration data can be real-time triaxial acceleration data. If the acceleration data is within the threshold range of three times the intrinsic noise, it passes the stationarity detection and is included in the stationarity window; otherwise, it is not included.

[0063] Step S103: Create an acceleration anomaly judgment window and initialize and update it: The window length of the created acceleration anomaly judgment window can be set to the acceleration acquisition frequency. After the acceleration stability window is successfully initialized (i.e. all window data is updated), the data that passes the stability test is added to the anomaly judgment window until the initialization is successful.

[0064] Step S104: Acceleration Anomaly Detection and Judgment: Based on the acceleration in the anomaly judgment window, determine whether there is an anomaly in the real-time acceleration. Subtract the real-time acceleration value from the mean value of the acceleration window to obtain the difference between the two. Determine whether the difference is within 3 times the standard deviation of the window to determine whether the error exceeds the limit. If it exceeds 3 times the standard deviation of the window, the error is evaluated as exceeding the limit. If it exceeds the limit, start counting and evaluate the number of times the limit is exceeded within a preset time, such as the number of times the limit is exceeded within 1 second. If the overall proportion exceeds 50%, the acceleration state is considered to be abnormal.

[0065] The method for detecting changes in acceleration state tilt angle is as follows:

[0066] Step S105: Execute steps S101 to S103 to create the corresponding window and initialize and update it;

[0067] Step S106: After the window is successfully initialized in step S103, record the mean value of the three-axis acceleration of the window at this moment as the reference for calculating the tilt angle information;

[0068] Step S107: Kalman filtering for noise reduction: The current acceleration data is subjected to Kalman filtering. In the state update, the noise is output based on the state of the stationarity window. If the stationarity is passed, the observed noise is intrinsic noise. If the stationarity is not passed, the observed noise will be amplified by 100 times. This allows Kalman filtering to achieve noise reduction while quickly reflecting the actual changes in acceleration.

[0069] Step S108: Real-time tilt angle calculation: The filtered acceleration value and the acceleration reference value are used to calculate the tilt angle change based on the vector product method. If the tilt angle exceeds the threshold, it is determined to be a deformation.

[0070] like Figure 3 As shown, when a change in the state transmission of the MEMS accelerometer is detected, encrypted linkage calculation is performed. For example, if the filtered solution is originally output once an hour, the linked encryption will output the filtered solution once every five minutes, and the sampling frequency of the observation data will be increased from 0.1HZ to 1HZ during the encryption period.

[0071] The time period during which the accelerometer state changes is marked as the reporting period, and the RTK and epoch differential stability determination is performed within the reporting period:

[0072] Step S201: During the reporting period, the inter-epoch differential positioning algorithm is continuously performed on adjacent epochs within the reporting period to obtain the inter-epoch coordinate change information, which includes the inter-epoch coordinate change sequence.

[0073] The acquisition of coordinate transformation information between epochs is mainly based on the difference between stations and the difference between epochs to construct error observation equations, similar to the process of constructing double-difference observation equations; specifically:

[0074] For the inter-station single-difference observation equations of two adjacent epochs t1 and t2, a second-order difference calculation is performed, and the residual of satellite p is rearranged as follows:

[0075]

[0076] in, These are the parameter coefficients of the three coordinate components, and the change in the difference of the receiver clock error ΔT;

[0077] The residual vectors calculated from the error equations of multiple satellites are iteratively calculated using the least squares algorithm to estimate the coordinate changes between adjacent epochs. Specifically, the least squares algorithm iteratively estimates the final parameter vector using the formula x = (A... T PA) -1 A T PL;

[0078] Among them, A is based on multiple observation satellites. The constructed design matrix consists of P, a weight matrix designed based on the elevation angle function, and L, a residual vector calculated from the error equations of multiple satellites.

[0079] Step S202: Joint determination is performed based on the RTK solution sequence within the time period and the coordinate change sequence between epochs. The specific method is as follows:

[0080] By performing RTK calculations within a reporting period, dynamic coordinate information can be obtained for that period. This coordinate information is highly sensitive to location. The average RTK change information is obtained by subtracting the most recent stable RTK calculation sequence within that period from the prior reference coordinate information. The prior reference coordinate information is the coordinate result from the previous calculation period.

[0081] Step S300: Perform a joint determination based on RTK change information and inter-epoch coordinate change information. The determination rules are as follows:

[0082] Deformation is determined when the RTK change information is greater than a first preset value; for example, deformation is determined when the detected RTK change information is greater than 0.05m.

[0083] If there is a sequence of coordinate changes between epochs that is greater than the second preset value, and the number of times it is greater than the second preset value is more than a preset number, then it is determined to be deformation; if more than two instances of coordinate changes between epochs are detected that are greater than 0.03m, then it is determined to be deformation.

[0084] If the RTK change information is greater than the third preset value and there is a situation in the inter-epoch coordinate change sequence that is greater than the third preset value, it is determined to be deformation; for example, if the detected RTK change information is greater than 0.03m and there is a situation in the inter-epoch coordinate change sequence that is greater than 0.03m, it is determined to be deformation.

[0085] Other cases are suspected deformations, and the deformation response is only obtained by adding noise during the encrypted calculation period, that is, by adding a horizontal δ to the coordinate parameter state accuracy matrix. h It is 1E-4m, with an elevation δ v The noise level is 0.2mm.

[0086] Step S400: Adjust the process noise of the real-time static filter according to the deformation information to obtain the updated real-time static filter.

[0087] Specifically, the method for adjusting process noise is as follows:

[0088] In cases where deformation is detected, the deformation information within the reporting period is converted into horizontal change information (dh) and elevation change information (dv). If the deformation information is greater than a fourth preset value (e.g., greater than 0.05m), the real-time static filter is directly reset for rapid response. If it is less than the fourth preset value (e.g., less than 0.05m), it is converted into horizontal and elevation noise and added to the real-time static filter to obtain a new state parameter accuracy matrix. The main steps of noise addition are as follows:

[0089] Q new =Q old +Q u

[0090]

[0091]

[0092]

[0093] δ h =(round(dh / 0.05)+1)*1E-3

[0094] δ v = (round(dv / 0.05)+1)*1E-3;

[0095] Q above new Q is the precision matrix of the coordinate parameters after adding noise. old Q is the coordinate parameter precision matrix before adding noise. u The overall noise is mainly composed of Q. pos Composed of coordinate noise submatrices, Q N The noise submatrix, consisting of the horizontal and vertical directions, can be transformed into Q through matrix transformation. pos .

[0096] After adding noise, an updated real-time static filter is obtained. This updated real-time static filter is then used to update the system state and observations. Real-time static filtering mainly consists of two modules: state update and observation update. During the real-time static filtering process, real-time static filtering can be performed by constructing the error observation equation and the state observation equation.

[0097] The beneficial effects of this method are as follows:

[0098] (1) This method introduces a MEMS accelerometer-based rapid sensing algorithm. Based on the accurate determination of acceleration anomalies and the rapid calculation of tilt angle using adaptive Kalman filtering, it senses external deformation information. Through the precise state sensing of the MEMS accelerometer, reliable deformation signals are provided for real-time static filtering of GNSS. This algorithm module is not limited by the observation environment, deformation magnitude, baseline length, etc. It can provide reliable external deformation input signals for GNSS static filtering even in harsh conditions and when the number of satellites is small and the deformation value is not large.

[0099] Existing technologies respond to deformation based on GNSS data. However, when the environment is harsh, the observation environment is limited, the satellite geometry is poor, and the deformation response is slow, resulting in changes in tilt angle, it is difficult to detect the actual deformation. Furthermore, the MEMS accelerometer sensing algorithm only detects based on real-time thresholds, which is prone to false alarms and reduces the accuracy of the calculation.

[0100] (2) Rapid linkage between MEMS accelerometer and joint detection of GNSS deformation values ​​improves the reliability of deformation response. In this method, when the MEMS accelerometer accurately senses external deformation, GNSS will encrypt and collect observation data, and simultaneously enter encrypted processing mode. At this time, based on the GNSS data within the encrypted period, the actual deformation magnitude is detected using an inter-epoch differential positioning algorithm and an RTK dynamic algorithm. That is, based on the MEMS accelerometer signal, deformation is further jointly detected by GNSS. If the GNSS detected deformation is small, indicating a possible false alarm, noise is only added through encrypted processing. If there is suspected deformation or a confirmed large deformation, the filter is updated accordingly based on the deformation value.

[0101] Existing technologies switch to RTK calculation or add noise directly when MEMS acceleration threshold detection detects deformation. The lack of deformation check can easily lead to false deformation detection and reduce the accuracy of real-time static filtering calculation.

[0102] (3) Precise noise addition based on deformation value information. After the deformation value is detected by MEMS accelerometer and GNSS, the calculated deformation value is converted into the corresponding planar displacement and elevation displacement. The displacement is accurately converted into noise, which ensures the accuracy of the solution and the fast response of the deformation.

[0103] Existing technologies directly output the coordinate results of the RTK solution engine when the MEMS acceleration threshold detects deformation. However, the real-time static main filter is easily misdetected or noise is added, and the deformation response accuracy is low.

[0104] Example 2

[0105] This embodiment provides a deformation monitoring system integrating GNSS and MEMS, which executes the deformation monitoring method of Embodiment 1. Specifically, the system includes:

[0106] The state detection module uses a MEMS accelerometer to detect the state of the monitored object. When a change in the state of the monitored object is detected, the reporting period is determined. The reporting period is the time when the state of the monitored object changes. The state detection includes acceleration state anomaly detection and acceleration state tilt angle change detection.

[0107] The deformation analysis module is used to perform differential positioning calculations between adjacent epochs within the reporting period to obtain coordinate change information between adjacent epochs; calculate the average RTK change information based on the RTK data corresponding to the reporting period; and perform joint judgment based on the RTK change information and the coordinate change information between epochs to determine the deformation information.

[0108] The noise-adding and filtering module is used to adjust the process noise of the real-time static filter based on the deformation information, so as to obtain an updated real-time static filter, and then perform real-time static filtering through the updated real-time static filter.

[0109] This system, based on a fusion of a MEMS accelerometer precise state determination algorithm and a GNSS real-time static Kalman filter algorithm, effectively addresses challenges such as limited satellite data, numerous gross errors, high accelerometer noise, and deformation false detections under harsh observation environments. In the MEMS accelerometer sensing algorithm, the calibrated accelerometer intrinsic noise is used to detect acceleration anomalies. An adaptive Kalman filter model is established based on this intrinsic noise to quickly calculate the tilt angle under slow deformation. During precise sensing of acceleration state changes, GNSS performs encrypted data acquisition and computation. The real-time static filter is updated precisely based on deformation information jointly calculated by a temporary RTK computation engine and an inter-epoch differential positioning algorithm, further improving deformation response performance.

[0110] The functions of each module in the system of this invention can be found in the corresponding descriptions in the above methods, and will not be repeated here.

[0111] Example 3

[0112] Figure 4 A structural block diagram of an electronic device according to an embodiment of the present invention is shown. Figure 4 As shown, the electronic device includes a memory 100 and a processor 200. The memory 100 stores a computer program that can run on the processor 200. When the processor 200 executes the computer program, it implements the deformation monitoring method of GNSS and MEMS fusion in the above embodiments. The number of memories 100 and processors 200 can be one or more.

[0113] The electronic device also includes:

[0114] The communication interface 300 is used to communicate with external devices and perform data exchange and transmission.

[0115] If the memory 100, processor 200, and communication interface 300 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0116] Optionally, in a specific implementation, if the memory 100, processor 200, and communication interface 300 are integrated on a single chip, then the memory 100, processor 200, and communication interface 300 can communicate with each other through an internal interface.

[0117] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method provided in this invention.

[0118] This invention also provides a chip, which includes a processor for calling and executing instructions stored in a memory, causing a communication device on which the chip is installed to perform the method provided in this invention.

[0119] This invention also provides a chip, including: an input interface, an output interface, a processor, and a memory. The input interface, output interface, processor, and memory are connected through an internal connection path. The processor is used to execute code in the memory. When the code is executed, the processor is used to execute the method provided in this invention.

[0120] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting the Advanced Reduced Instruction Set Computing (RISC) machine (ARM) architecture.

[0121] Further, optionally, the aforementioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0122] In the above embodiments, implementation can be achieved, in whole or in part, by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another.

[0123] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0124] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0125] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A deformation monitoring method integrating GNSS and MEMS, characterized in that, include: The state of the monitored object is detected based on MEMS accelerometers, including the detection of acceleration state anomalies and the detection of acceleration state tilt angle changes; The additional reporting period is determined when the status of the monitored object changes upon detection. Based on the adjacent epochs within the reporting period, the differential positioning calculation between epochs is performed to obtain the coordinate change information between adjacent epochs; The average RTK change information is calculated based on the RTK data corresponding to the reporting period. Deformation information is determined by jointly judging the RTK change information and the inter-epoch coordinate change information. The joint judgment method includes: determining deformation when the RTK change information is greater than a first preset value; determining deformation when there is a sequence of inter-epoch coordinate change information greater than a second preset value, and the number of times the sequence is greater than the second preset value exceeds a preset number; and determining deformation when the RTK change information is greater than a third preset value and there is a sequence of inter-epoch coordinate change information greater than the third preset value. The real-time static filter is adjusted based on the deformation information, and real-time static filtering is performed. The adjustment method for the process noise is as follows: when deformation is determined, the deformation information within the reporting period is converted into horizontal and vertical change information. If the deformation information is greater than a fourth preset value, the real-time static filter is directly reset; if it is less than the fourth preset value, it is converted into horizontal and vertical noise and added to the real-time static filter to obtain a new state parameter accuracy matrix, thus obtaining an updated real-time static filter.

2. The deformation monitoring method integrating GNSS and MEMS according to claim 1, characterized in that, The method for detecting acceleration state anomalies is as follows: Create an anomaly detection window and add the acceleration data that has passed the stationarity test to the anomaly detection window until the anomaly detection window is successfully initialized. Calculate the difference between the real-time acceleration obtained from real-time acquisition and the average acceleration value in the anomaly detection window, and obtain the out-of-limit statistics by determining whether the difference exceeds the limit; The acceleration status is determined based on the out-of-limit statistical data.

3. The deformation monitoring method integrating GNSS and MEMS according to claim 2, characterized in that, The method for stationarity detection includes: The stationary acceleration under steady-state conditions is collected, and the intrinsic noise of the stationary acceleration is evaluated based on wavelet analysis; a threshold range is determined based on the intrinsic noise. If the real-time acceleration is within the threshold range, then the stationarity test is passed.

4. The deformation monitoring method integrating GNSS and MEMS according to claim 1, characterized in that, The method for detecting changes in the tilt angle of the acceleration state includes: The noise of the real-time acceleration that fails the stationarity test is amplified to obtain the noise-amplified acceleration data. The amplified acceleration data is filtered to remove noise, resulting in the filtered acceleration value. The tilt angle change data is calculated based on the filtered acceleration value and the acceleration reference value. If the tilt angle change data exceeds the threshold, it is determined that the tilt angle change is abnormal.

5. The deformation monitoring method integrating GNSS and MEMS according to claim 1, characterized in that, The method for calculating the coordinate change information between epochs is as follows: two adjacent epochs , The inter-station single-difference observation equations are used for quadratic difference calculations, and satellite data is processed. The residual is: ； in, , , These are the parameter coefficients for the three coordinate components. The change in the receiver clock bias; The residual vectors calculated from the error equations of multiple satellites are iteratively calculated using the least squares algorithm to estimate the coordinate changes between adjacent epochs.

6. A deformation monitoring system integrating GNSS and MEMS, characterized in that, Perform the deformation monitoring method that integrates GNSS and MEMS as described in any one of claims 1 to 5.

7. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores instructions that are loaded and executed by the processor to implement the deformation monitoring method for GNSS and MEMS fusion as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the deformation monitoring method of GNSS and MEMS fusion as described in any one of claims 1 to 5.

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