Deformation detection method and device based on distributed mems accelerometer and medium

Abstract

The invention discloses a deformation detection method and device based on a distributed mems accelerometer and a medium, and relates to the technical field of deformation detection. The method comprises the following steps: acquiring acceleration information; wherein the acceleration information is acquired by MEMS sensors distributed in natural environment soil; performing sliding filtering on the acceleration information; carrying out strapdown inertia calculation based on the acceleration information of the sliding filtering to obtain attitude, speed and position information of the sensor; analyzing the speed and position information to obtain regional soil transition information; performing Fourier transform on the acceleration information to obtain first frequency spectrum information of the acceleration information; performing wavelet transform on the acceleration information to obtain second frequency spectrum information under each part of the collected acceleration information; and performing analysis based on the first frequency spectrum information and the second frequency spectrum information to obtain the vibration condition of the regional soil. The problems of low satellite remote sensing accuracy and instrument remote sensing limitation are solved.

Description

Technical Field

[0001] The present invention relates to the field of deformation detection technology, and more specifically, to a deformation detection method, device and medium based on a distributed MEMS accelerometer. Background Art

[0002] Early geographical surveys were mainly conducted by the surveyors using simple instruments and relying on their senses, experience and knowledge to make direct observations. For phenomena that are difficult to observe directly, such as the time series of geographical phenomena, indirect inferences are used. For rocks, soils and plants that have special geographical significance or require detailed identification, analysis and testing, specimens or samples are taken for indoor research. These methods are post-processing and analysis, and have poor real-time performance. For natural disasters such as earthquakes, or natural changes, it is impossible to obtain the changes in soil, rocks and plants in the natural environment in a timely and accurate manner.

[0003] Now, with the development of aerospace remote sensing, instrument remote measurement, infrared photography and other technologies, people can obtain geographic information of outdoor natural environment in a timely manner, expand the scope of actual observation, improve the scope of actual observation, and speed up the scope of actual observation. However, in the existing technology, although aerospace remote sensing can obtain global geographic information, it is difficult to obtain more detailed information, and the existing instrument remote measurement not only has a considerable equipment size, but is also difficult to deploy in large quantities. The geographic information obtained is local and limited, and it is difficult to reflect the characteristics of the entire environment. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a deformation detection method, device and medium based on distributed MEMS accelerometers. Based on the deformation detection of distributed MEMS accelerometers, the characteristics of MEMS, such as small size and small impact on the environment, are utilized. The acceleration information of MEMS accelerometers distributed in various places is used to analyze the direction of soil change in the overall environment, and the soil movement conditions are analyzed when natural disasters such as earthquakes and mudslides occur, thereby solving the problems of low satellite remote sensing accuracy and instrument telemetry limitations.

[0005] In a first aspect, the present invention provides a deformation detection method based on a distributed MEMS accelerometer, the method comprising:

[0006] Acquiring acceleration information; wherein the acceleration information is collected by MEMS sensors distributed in the soil of a natural environment;

[0007] Performing sliding filtering on the acceleration information;

[0008] Based on the acceleration information of sliding filtering, strapdown inertial solution is performed to obtain the attitude, velocity and position information of the sensor;

[0009] Analyze the speed and position information to obtain the regional soil change information;

[0010] Perform Fourier transform on the acceleration information to obtain the first spectrum information of the acceleration information;

[0011] Perform wavelet transform on the acceleration information to obtain the second spectrum information under each part of the collected acceleration information;

[0012] Analyze based on the first spectrum information and the second spectrum information to obtain the vibration condition of the regional soil.

[0013] Further, perform sliding filtering on the acceleration information through the following formula:

[0014]

[0015] where, represents the filtered acceleration signal, N is the size of the sliding window, and a(t + i) is the value of the acceleration signal within the window.

[0016] Further, based on the acceleration information obtained by sliding filtering, perform strapdown inertial solution in combination with Equation (2) to obtain the attitude, speed, and position information of the sensor:

[0017]

[0018] where, the superscripts n and b respectively represent the navigation coordinate system and the vehicle coordinate system; V n and respectively represent the three-dimensional speed and its differential in the navigation coordinate system; P n represents the three-dimensional position in the navigation coordinate system; and respectively represent the attitude matrix and its differential for the transformation from the b system to the n system; Ω b represents the skew-symmetric matrix composed of the angular velocity output by the gyroscope; f b represents the specific force in the vehicle coordinate system; g n represents the earth gravity field vector.

[0019] Further, analyze the speed and position information to obtain the regional soil change information, including:

[0020] Calculate the speed v(t), and the calculation formula is:

[0021]

[0022] where, t is the time, and v(0) is the initial speed;

[0023] Calculate the position p(t), and the calculation formula is:

[0024]

[0025] Among them, p(0) is the initial position.

[0026] Furthermore, the acceleration information is Fourier-transformed through the following formula to obtain the first spectral information A(f) of the acceleration information:

[0027]

[0028] Among them, a(t) is the acceleration information, f is the frequency, j is the imaginary unit, t is the time, and e is the natural constant.

[0029] Furthermore, when performing wavelet transform on the acceleration information, the wavelet transform coefficients are calculated through the following formula:

[0030]

[0031] Among them, W a (s,τ) are the wavelet transform coefficients, ψ is the mother wavelet, s is the scale, τ is the translation parameter, and * represents the complex conjugate operation.

[0032] Furthermore, based on the first spectral information and the second spectral information for analysis, the vibration condition of the regional soil is obtained through the following formula:

[0033] I = ∑ i |A(f i )| 2 + ∑ j |W a (s j ,τ j )| 2 (7)

[0034] Among them, I is the soil vibration intensity, A(f i ) is the i-th frequency component after Fourier transform; W a (s j ,τ j ) are the coefficients of the j-th scale and position after wavelet transform; |A(f i )| 2 and |W a (s j ,τ j )| 2 respectively represent the energy intensities in the frequency domain and the time-frequency domain.

[0035] In a second aspect, the present invention provides a deformation detection device based on a distributed mems accelerometer, and the device includes:

[0036] A data acquisition unit, configured to acquire acceleration information; wherein, the acceleration information is collected by MEMS sensors distributed in the soil of the natural environment;

[0037] A data filtering unit, configured to perform sliding filtering on the acceleration information;

[0038] An inertial solution unit, configured to perform strapdown inertial solution based on the acceleration information after sliding filtering to obtain the attitude, velocity, and position information of the sensor;

[0039] A first analysis unit, configured to analyze the velocity and position information to obtain regional soil change information;

[0040] A first transformation unit, configured to perform Fourier transform on the acceleration information to obtain the first spectrum information of the acceleration information;

[0041] A second transformation unit, configured to perform wavelet transform on the acceleration information to obtain the second spectrum information under each part of the collected acceleration information;

[0042] A second analysis unit, configured to analyze based on the first spectrum information and the second spectrum information to obtain the vibration condition of the regional soil.

[0043] In a third aspect, the present invention provides a readable storage medium storing one or more programs, and the one or more programs can be executed by one or more processors to implement the method as described above.

[0044] The present invention has at least the following beneficial effects:

[0045] Based on distributed MEMS accelerometers, taking advantage of their small size, by being scattered in a certain area, it can not only obtain the detailed soil information of the area but also obtain the environmental information of the overall area. It has:

[0046] (1) The distributed design makes the influence of individual errors small under the condition of overall environment analysis;

[0047] (2) The MEMS accelerometers are small in size, and compared with other detection devices, they have little impact on the environment;

[0048] (3) The MEMS accelerometers have low cost. Description of the Drawings

[0049] Figure 1 Shows an implementation scenario diagram of a deformation detection method based on distributed MEMS accelerometers according to an embodiment of the present invention.

[0050] Figure 2The flowchart of a deformation detection method based on a distributed MEMS accelerometer according to an embodiment of the present invention is shown.

[0051] Figure 3 The structural diagram of a deformation detection device based on a distributed MEMS accelerometer according to an embodiment of the present invention is shown. Detailed implementation manners

[0052] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation manners. The embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, but this is not a limitation to the present invention. For the various steps described herein, if there is no necessity for a front-back relationship between them, the order in which they are described as examples herein should not be regarded as a limitation. Those skilled in the art should know that they can be adjusted in order as long as the logic between them is not destroyed and the entire process cannot be realized.

[0053] An embodiment of the present invention provides a deformation detection method based on a distributed MEMS accelerometer, aiming to utilize the characteristics of small volume of the MEMS accelerometer. Through a distributed design, environmental detection takes into account both details and the overall situation, and comprehensively analyzes the soil change situation in a certain area. As Figure 1 shown, it is a schematic diagram of the implementation scenario of the deformation detection method based on the distributed MEMS accelerometer. A plurality of MEMS sensors are distributed in the natural environment soil, and data receiving and sending devices are installed within a certain area range. The acceleration information collected by the MEMS sensors is received, and the received acceleration information is fed to the server background for processing. The local and overall soil movement conditions are analyzed based on the velocity information, the soil change information within the area is analyzed based on the position information, the soil vibration information within the area is analyzed based on the acceleration spectrum, and the soil change information within the area is comprehensively reflected based on the local and overall soil movement conditions, the soil change information within the area, and the soil vibration information within the area. Among them, the "area" refers to the area where a plurality of MEMS sensors are installed.

[0054] The flow of the deformation detection method based on the distributed MEMS accelerometer is as Figure 2 shown, and is implemented through the following steps S10 to S80.

[0055] S10: Obtain acceleration information; among them, the acceleration information is collected by MEMS sensors distributed in the natural environment soil.

[0056] S20: Perform sliding filtering on the acceleration information. Sliding filtering is usually processed in a weighted average manner. Assume that the original acceleration signal is a(t), and its filtered acceleration signal can be expressed by the following formula:

[0057]

[0058] Among them, N is the size of the sliding window, and a(t + i) is the value of the acceleration signal within the window.

[0059] S30: Based on the acceleration information obtained by sliding filtering, perform strapdown inertial solution to obtain the attitude, velocity, and position information of the sensor.

[0060] In some embodiments, based on the acceleration information obtained by sliding filtering, combined with Equation (2), perform strapdown inertial solution to obtain the attitude, velocity, and position information of the sensor:

[0061]

[0062] Among them, the superscripts n and b respectively represent the navigation coordinate system and the vehicle coordinate system; V n and respectively represent the three-dimensional velocity and its differential in the navigation coordinate system; P n represents the three-dimensional position in the navigation coordinate system; and respectively represent the attitude matrix for the transformation from the b system to the n system and its differential; Ω b represents the skew-symmetric matrix composed of the angular velocity output by the gyroscope; f b represents the specific force in the vehicle coordinate system; g n represents the vector of the Earth's gravity field.

[0063] S40: Analyze the velocity and position information to obtain the regional soil change information.

[0064] The velocity and position can be obtained by integrating the acceleration signal. According to the relationship between acceleration and velocity, assuming that the acceleration a(t) is known, the velocity v(t) can be obtained by integrating the acceleration:

[0065]

[0066] The position p(t) is also obtained by integrating the velocity v(t):

[0067]

[0068] Among them, v(0) and p(0) are the initial velocity and the initial position respectively.

[0069] S50: Perform Fourier transform on the acceleration information to obtain the first spectral information of the acceleration information.

[0070] The Fourier transform can transform the time-domain signal a(t) to the frequency domain to obtain the spectrum A(f) of the acceleration signal:

[0071]

[0072] Among them, f is the frequency, j is the imaginary unit. After Fourier transform, the vibration characteristics of the soil can be identified by analyzing the peaks in the spectrum.

[0073] S60: Perform wavelet transform on the acceleration information to obtain the second spectrum information under each part of the collected acceleration information.

[0074] Wavelet transform is applicable to non-stationary signals and can perform multi-scale analysis on acceleration signals. Assuming the acceleration signal is a(t), the wavelet transform can be carried out by the following formula:

[0075]

[0076] Among them, W a (s,τ) is the wavelet transform coefficient, ψ is the mother wavelet, s is the scale, τ is the translation parameter, and * represents the complex conjugate operation. Wavelet transform can decompose the signal into components in different frequency ranges, and local characteristics of vibration can be extracted on each frequency band.

[0077] S70: Analyze based on the first spectrum information and the second spectrum information to obtain the vibration condition of the regional soil.

[0078] Combining the results of Fourier transform and wavelet transform, the characteristics of soil vibration can be obtained through frequency domain analysis. Assuming the spectrum obtained by Fourier transform is A(f) (the first spectrum information), and the multi-scale coefficients obtained by wavelet transform are W a (s,τ) (the second spectrum information), the soil vibration intensity I can be obtained through the following comprehensive analysis formula:

[0079] I = ∑ i |A(f i )| 2 + ∑ j |W a (s j ,τ j ) 2 (7)

[0080] Among them, A(f i ) is the i-th frequency component after Fourier transform; W a (s j ,τ j ) is the coefficient of the j-th scale and position after wavelet transform; |A(f i )| 2 and |W a (s j ,τ j )| 2respectively represent the energy intensities in the frequency domain and the time-frequency domain. This formula synthesizes the information of the Fourier transform and the wavelet transform, and can simultaneously reflect the overall spectral characteristics and local time-frequency characteristics of soil vibrations, and finally obtain the vibration intensity I for evaluating the vibration conditions of the regional soil.

[0081] S80: Determine whether to end according to whether the MEMS accelerometer continues to collect data. If so, end the operation; if not, return to step S20.

[0082] An embodiment of the present invention also provides a deformation detection device based on a distributed mems accelerometer, such as Figure 3 shown, the device includes:

[0083] A data acquisition unit 301, configured to acquire acceleration information; wherein, the acceleration information is acquired through MEMS sensors distributed in the natural environment soil;

[0084] A data filtering unit 302, configured to perform sliding filtering on the acceleration information;

[0085] An inertial solution unit 303, configured to perform strapdown inertial solution based on the acceleration information after sliding filtering to obtain the attitude, velocity, and position information of the sensor;

[0086] A first analysis unit 304, configured to analyze the velocity and position information to obtain regional soil change information;

[0087] A first transformation unit 305, configured to perform Fourier transform on the acceleration information to obtain the first spectral information of the acceleration information;

[0088] A second transformation unit 306, configured to perform wavelet transform on the acceleration information to obtain the second spectral information under each part of the acquired acceleration information;

[0089] A second analysis unit 307, configured to analyze based on the first spectral information and the second spectral information to obtain the vibration conditions of the regional soil.

[0090] In some embodiments, the data filtering unit is further configured to perform sliding filtering on the acceleration information through the following formula:

[0091]

[0092] wherein, represents the filtered acceleration signal, N is the size of the sliding window, and a(t + i) is the value of the acceleration signal within the window.

[0093] In some embodiments, the inertial solution unit is further configured to perform strapdown inertial solution based on the acceleration information of sliding filtering, and combine with Equation (2) to obtain the attitude, velocity, and position information of the sensor:

[0094]

[0095] where the superscripts n and b represent the navigation coordinate system and the vehicle coordinate system respectively; V n and respectively represent the three-dimensional velocity and its differential in the navigation coordinate system; P n represents the three-dimensional position in the navigation coordinate system; and respectively represent the attitude matrix and its differential for the transformation from the b-system to the n-system; Ω b represents the skew-symmetric matrix composed of the angular velocity output by the gyroscope; f b represents the specific force in the vehicle coordinate system; g n represents the earth gravity field vector.

[0096] In some embodiments, the first analysis unit is further configured to:

[0097] Calculate the velocity v(t), and the calculation formula is:

[0098]

[0099] where t is the time, and v(0) is the initial velocity;

[0100] Calculate the position p(t), and the calculation formula is:

[0101]

[0102] where p(0) is the initial position.

[0103] In some embodiments, the first transformation unit is further configured to perform Fourier transform on the acceleration information through the following formula to obtain the first spectral information A(f) of the acceleration information:

[0104]

[0105] where a(t) is the acceleration information, f is the frequency, j is the imaginary unit, t is the time, and e is the natural constant.

[0106] In some embodiments, when the second transformation unit is further configured to perform wavelet transform on the acceleration information, the wavelet transform coefficient is calculated through the following formula:

[0107]

[0108] where Wa (s, τ) is the wavelet transform coefficient, ψ is the mother wavelet, s is the scale, τ is the translation parameter, and * represents the complex conjugate operation.

[0109] In some embodiments, the second analysis unit is further configured to perform analysis based on the first spectrum information and the second spectrum information, and obtain the vibration condition of the regional soil through the following formula:

[0110] I = ∑ i |A(f i )| 2 + ∑ j |W a (s j , τ j )| 2 (7)

[0111] wherein, I is the soil vibration intensity, A(f i ) is the i-th frequency component after Fourier transform; W a (s j , τ j ) is the coefficient of the j-th scale and position after wavelet transform; |A(f i )| 2 and |W a (s j , τ j )| 2 respectively represent the energy intensities in the frequency domain and the time-frequency domain.

[0112] It should be noted that the structures of the deformation detection devices based on the distributed MEMS accelerometers described in this embodiment belong to the same technical concept as the deformation detection method based on the distributed MEMS accelerometers described previously, and achieve the same beneficial effects through the same principle, which will not be elaborated here.

[0113] The embodiment of the present invention also provides a readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in any of the above embodiments.

[0114] In addition, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention with equivalent elements, modifications, omissions, combinations (e.g., solutions that cross various embodiments), adaptations, or changes. The elements in the claims will be broadly interpreted based on the language used in the claims and are not limited to the examples described in this specification or during the implementation of this application, and the examples will be interpreted as non-exclusive. Therefore, this specification and the examples are only intended to be considered as examples, and the true scope and spirit are indicated by the full scope of the following claims and their equivalents.

[0115] The foregoing description is intended to be illustrative and not restrictive. For example, the above examples (or one or more aspects thereof) may be used in combination with each other. For instance, other embodiments may be utilized by those of ordinary skill in the art upon reading the above description. Additionally, in the above detailed description, various features may be grouped together to simplify the present invention. This should not be construed as an intention that the features of an unclaimed invention are necessary for any claim. On the contrary, the subject matter of the present invention may be less than all of the features of a particular embodiment of the invention. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, where each claim stands on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with each other in various combinations or permutations. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to which those claims are entitled.

Claims

1. A deformation detection method based on a distributed MEMS accelerometer, characterized in that, The method includes: Obtaining acceleration information; wherein, the acceleration information is collected by MEMS sensors distributed in the soil of the natural environment; Performing sliding filtering on the acceleration information; Based on the acceleration information after sliding filtering, performing strapdown inertial calculation to obtain the attitude, velocity, and position information of the sensor; Analyzing the velocity and position information to obtain regional soil change information; Performing Fourier transform on the acceleration information to obtain the first spectral information of the acceleration information; Performing wavelet transform on the acceleration information to obtain the second spectral information under each part of the collected acceleration information; Analyzing based on the first spectral information and the second spectral information to obtain the vibration condition of the regional soil.

2. The deformation detection method based on a distributed MEMS accelerometer according to claim 1, characterized in that The acceleration information is sliding-filtered through the following formula: Among them, represents the filtered acceleration signal, N is the size of the sliding window, and a(t + i) is the value of the acceleration signal within the window.

3. The deformation detection method based on a distributed MEMS accelerometer according to claim 1, wherein Based on the acceleration information after sliding filtering, combining with Equation (2) to perform strapdown inertial calculation to obtain the attitude, velocity, and position information of the sensor: where superscripts n and b represent the navigation coordinate system and the vehicle body coordinate system respectively; V n and represent the three-dimensional velocity and its differential in the navigation coordinate system respectively; P n represents the three-dimensional position in the navigation coordinate system; and represent the attitude matrix for transformation from the b-frame to the n-frame and its differential respectively; Ω b represents the skew-symmetric matrix formed by the angular velocity output from the gyroscope; f b represents the specific force in the vehicle body coordinate system; g n represents the earth gravity field vector.

4. The deformation detection method based on a distributed MEMS accelerometer according to claim 1, characterized in that Analyzing the velocity and position information to obtain regional soil change information, including: Calculating the velocity v(t), and the calculation formula is: wherein, t is time, and v(0) is the initial velocity; Calculating the position p(t), and the calculation formula is: wherein, p(0) is the initial position.

5. The deformation detection method based on a distributed MEMS accelerometer according to claim 1, characterized in that, The acceleration information is Fourier-transformed through the following formula to obtain the first spectral information A(f) of the acceleration information: wherein, a(f) is the acceleration information, f is the frequency, j is the imaginary unit, t is the time, and e is the natural constant.

6. The deformation detection method based on a distributed MEMS accelerometer according to claim 1, characterized in that, When performing wavelet transform on the acceleration information, the wavelet transform coefficient is calculated through the following formula: Among them, W a (s, τ) is the wavelet transform coefficient, ψ is the mother wavelet, s is the scale, τ is the translation parameter, and * represents the complex conjugate operation.

7. The deformation detection method based on a distributed MEMS accelerometer according to claim 1, characterized in that, Analyzing based on the first spectral information and the second spectral information, and obtaining the vibration condition of the regional soil through the following formula: I = ∑ i |A(f i )| 2 + ∑ j |W a (s j , τ j )| 2 (7) where I is the soil vibration intensity, A(f i ) is the i-th frequency component after Fourier transform; W a (s j , τ j ) is the coefficient of the j-th scale and position after wavelet transform; |A(f i )| 2 and |W a (s j , τ j )| 2 represent the energy intensities in the frequency domain and the time-frequency domain, respectively.

8. A deformation detection device based on a distributed MEMS accelerometer, characterized in that, The device includes: A data acquisition unit configured to obtain acceleration information; wherein, the acceleration information is collected by MEMS sensors distributed in the soil of the natural environment; A data filtering unit configured to perform sliding filtering on the acceleration information; An inertial calculation unit configured to perform strapdown inertial calculation based on the acceleration information after sliding filtering to obtain the attitude, velocity, and position information of the sensor; A first analysis unit configured to analyze the velocity and position information to obtain regional soil change information; A first transformation unit configured to perform Fourier transform on the acceleration information to obtain the first spectral information of the acceleration information; A second transformation unit configured to perform wavelet transform on the acceleration information to obtain the second spectral information under each part of the collected acceleration information; A second analysis unit configured to analyze based on the first spectral information and the second spectral information to obtain the vibration condition of the regional soil.

9. A non-transitory computer-readable storage medium storing instructions, which when executed by a processor, execute the method according to any one of claims 1 to 7.

Images