Analytical methods for ciliary magnetic sensors

Abstract

This invention discloses an analysis method for a ciliary magnetic sensor, relating to the field of sensor technology, comprising the following steps: conducting tensile tests on cilia with different magnetic powder contents under selected scenarios to obtain the Young's modulus of cilia with different magnetic powder contents under selected scenarios; obtaining the optimal magnetic powder content of cilia under selected scenarios based on the Young's modulus of cilia with different magnetic powder contents under selected scenarios; obtaining the optimal ciliary height, optimal ciliary diameter, and optimal ciliary spacing of cilia under selected scenarios through finite element simulation analysis; conducting normal pressure sensing experiments, sliding sensing experiments, and non-contact sensing experiments on the ciliary magnetic sensor under selected scenarios; this invention helps to optimize the design and application scenarios of ciliary magnetic sensors.

Description

Technical Field

[0001] This invention relates to the field of sensor technology, and more specifically to an analytical method for a ciliary magnetic sensor. Background Technology

[0002] Currently, ciliary magnetic sensing systems show broad application prospects in many fields, such as robot tactile sensing, human motion monitoring in wearable devices, and object recognition and positioning in industrial automation. In these application scenarios, the performance of the ciliary magnetic sensor directly determines the reliability and accuracy of the entire system.

[0003] Therefore, how to provide an analytical method for a ciliary magnetic sensor that can determine the optimal parameters of the cilia and accurately analyze the performance of the ciliary magnetic sensor is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an analysis method for a ciliary magnetic sensor.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] Firstly, an analytical method for a ciliary magnetic sensor is provided, comprising the following steps:

[0007] S1: Tensile tests were conducted on cilia with different magnetic powder contents in a selected scenario to obtain the Young's modulus of cilia with different magnetic powder contents in the selected scenario.

[0008] Based on the Young's modulus of cilia with different magnetic powder contents in a selected scenario, the optimal magnetic powder content of cilia in a selected scenario is obtained.

[0009] S2: By analyzing the triaxial magnetic field change data output by the magnetic sensor under different cilia heights, different cilia diameters, and different cilia spacings through finite element simulation, the optimal cilia height, optimal cilia diameter, and optimal cilia spacing in a selected scenario are obtained; wherein, the cilia magnetic powder content is set to the optimal magnetic powder content during the finite element simulation; the cilia spacing is the distance between adjacent cilia;

[0010] S3: Conduct a normal pressure sensing experiment on the ciliary magnetic sensor in the selected scenario to obtain triaxial magnetic field change data corresponding to different normal pressures; wherein, the ciliary magnetic powder content of the ciliary magnetic sensor in the selected scenario adopts the optimal magnetic powder content, the ciliary height adopts the optimal ciliary height, the ciliary diameter adopts the optimal ciliary diameter, and the ciliary spacing adopts the optimal ciliary spacing.

[0011] A sliding sensing experiment was conducted on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to tangential pressure in different directions and at different speeds.

[0012] Non-contact sensing experiments were conducted on the ciliary magnetic sensor in a selected scenario to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor on the target to be sensed in the selected scenario.

[0013] Preferably, in S1, the content of the ciliary magnetic powder corresponding to the largest Young's modulus is selected as the optimal magnetic powder content.

[0014] Preferably, the ciliary height, ciliary diameter, and ciliary spacing corresponding to the smallest triaxial magnetic field change data in S2 are respectively taken as the optimal ciliary height, the optimal ciliary diameter, and the optimal ciliary spacing.

[0015] Preferably, a normal pressure sensing experiment is conducted on the ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to different normal pressures. This specifically includes the following steps:

[0016] S311: Apply normal pressure of different magnitudes to the ciliary magnetic sensor in the selected scene to obtain the time series of triaxial magnetic field change data output by the magnetic sensor.

[0017] S312: Send the time series of triaxial magnetic field variation data obtained in S311 to the computing device via the Wifi module;

[0018] S313: The computing device denoises the triaxial magnetic field change data time series obtained in S311 to obtain a denoised triaxial magnetic field change data time series.

[0019] S314: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field change data obtained in S313 to obtain time-domain features;

[0020] The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S313 and then performs frequency domain feature extraction to obtain frequency domain features.

[0021] S315: The computing device obtains triaxial magnetic field change data corresponding to different normal pressures based on the time domain characteristics obtained in S314, the frequency domain characteristics obtained in S314, and the time series of denoised triaxial magnetic field change data obtained in S313.

[0022] Preferably, the denoising in S313 uses a combination of wavelet transform and bandpass filtering;

[0023] The time-domain features obtained by S314 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis;

[0024] The frequency domain features obtained by S314 include total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

[0025] Preferably, a sliding sensing experiment is conducted on the ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to tangential pressure in different directions and at different speeds. Specifically, this includes the following steps:

[0026] S321: Apply tangential pressure in different directions and at different speeds to the ciliary magnetic sensor in the selected scene to obtain the time series of triaxial magnetic field change data output by the magnetic sensor.

[0027] S322: Send the time series of triaxial magnetic field variation data obtained in S321 to the computing device via the Wifi module;

[0028] S323: The computing device denoises the triaxial magnetic field change data time series obtained in S321 to obtain a denoised triaxial magnetic field change data time series.

[0029] S324: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field variation data obtained in S323 to obtain time-domain features;

[0030] The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S323, and then performs frequency domain feature extraction to obtain frequency domain features.

[0031] S325: The computing device obtains triaxial magnetic field change data corresponding to tangential pressure at different directions and speeds based on the time domain features obtained in S324, the frequency domain features obtained in S324, and the time series of denoised triaxial magnetic field change data obtained in S323.

[0032] Preferably, the denoising in S323 uses a combination of wavelet transform and bandpass filtering;

[0033] The time-domain features obtained by S324 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis;

[0034] The frequency domain features obtained by S324 include the principal components of the spectral behavior, total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

[0035] Preferably, a non-contact sensing experiment is conducted on the ciliary magnetic sensor in a selected scenario to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor on the target to be sensed in the selected scenario. Specifically, this includes the following steps:

[0036] S331: Continuously change the distance between the target to be sensed and the ciliary magnetic sensor in the selected scene to obtain the time series of three-axis magnetic field change data output by the magnetic sensor.

[0037] S332: Send the time series of triaxial magnetic field variation data obtained in S331 to the computing device via the Wifi module;

[0038] S333: The computing device denoises the triaxial magnetic field change data time series obtained in S331 to obtain a denoised triaxial magnetic field change data time series.

[0039] S334: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field change data obtained in S333 to obtain time-domain features;

[0040] The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S333, and then performs frequency domain feature extraction to obtain frequency domain features.

[0041] S335: Based on the time-domain features obtained in S334, the frequency-domain features obtained in S334, and the time series of denoised triaxial magnetic field change data obtained in S333, the computing device obtains the sensing sensitivity and sensing range of the ciliary magnetic sensor for the target to be sensed in the selected scene.

[0042] Preferably, the denoising in S333 uses a combination of wavelet transform and bandpass filtering;

[0043] The time-domain features obtained by S334 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis;

[0044] The frequency domain features obtained by S334 include total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

[0045] In a second aspect, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the analysis method of the cilia magnetic sensor described above.

[0046] As can be seen from the above technical solution, compared with the prior art, the present invention discloses an analysis method for a ciliary magnetic sensor, which can achieve the following beneficial technical effects:

[0047] 1) This invention conducts tensile tests on cilia with different magnetic powder contents, selects the magnetic powder content corresponding to the maximum Young's modulus as the optimal magnetic powder content, which can improve the mechanical properties of the cilia and thus enhance the stability and reliability of the cilia magnetic sensor.

[0048] 2) This invention analyzes the triaxial magnetic field change data output by the magnetic sensor under different cilia height, diameter and spacing through finite element simulation, and selects the cilia height, diameter and spacing corresponding to the minimum triaxial magnetic field change data as the optimal cilia height, diameter and spacing, which can enable the cilia magnetic sensor to achieve the best magnetic field response and improve the sensitivity of the cilia magnetic sensor.

[0049] 3) This invention conducts normal pressure sensing experiments on a ciliary magnetic sensor. Through a series of data processing steps, including noise reduction, time-domain and frequency-domain feature extraction, the sensing accuracy of the ciliary magnetic sensor for normal pressure can be improved.

[0050] 4) This invention conducts a sliding sensing experiment on a ciliary magnetic sensor. Through a series of data processing steps, including noise reduction, time-domain and frequency-domain feature extraction, the ciliary magnetic sensor can accurately sense changes in tangential pressure, providing reliable data support for subsequent applications.

[0051] 5) This invention conducts non-contact sensing experiments on a ciliary magnetic sensor. By continuously changing the distance between the target to be sensed and the ciliary magnetic sensor, and through a series of data processing steps, including noise reduction, time-domain and frequency-domain feature extraction, the sensing sensitivity and sensing range of the ciliary magnetic sensor can be accurately obtained. This provides a scientific basis for the non-contact sensing application of the ciliary magnetic sensor and helps to optimize the design and application scenarios of the ciliary magnetic sensor. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0053] Figure 1 A flowchart of an analysis method for a ciliary magnetic sensor provided by the present invention;

[0054] Figure 2 The tensile properties of cilia with different magnetic powder contents provided by this invention are shown in the figure.

[0055] Figure 3 The result of the normal pressure sensing experiment provided by this invention is shown in the figure.

[0056] Figure 4 The result of the normal pressure sensing experiment provided by this invention is shown in the figure.

[0057] Figure 5 This is a schematic diagram of an electronic device provided by the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Firstly, such as Figure 1 As shown in the figure, an embodiment of the present invention discloses an analysis method for a ciliary magnetic sensor, comprising the following steps:

[0060] S1: Tensile tests were conducted on cilia with different magnetic powder contents in a selected scenario to obtain the Young's modulus of cilia with different magnetic powder contents in the selected scenario.

[0061] Based on the Young's modulus of cilia with different magnetic powder contents in a selected scenario, the optimal magnetic powder content of cilia in a selected scenario is obtained.

[0062] In one embodiment, the content of the ciliary magnetic powder corresponding to the largest Young's modulus is selected as the optimal magnetic powder content in S1.

[0063] Figure 2 The diagram illustrates the Young's modulus obtained from tensile tests conducted on cilia with four different magnetic powder contents (50%, 60%, 70%, and 80%) in selected scenarios. Figure 2 It can be seen that when the tensile force is constant, the Young's modulus of the cilia increases with the increase of magnetic powder content (from 50% to 80%). The Young's modulus Et reaches 0.715153405 when the magnetic powder content is 80%. Figure 2 The slope of the curve is Young's modulus.

[0064] S2: By analyzing the triaxial magnetic field change data output by the magnetic sensor under different cilia heights, different cilia diameters, and different cilia spacings through finite element simulation, the optimal cilia height, optimal cilia diameter, and optimal cilia spacing in a selected scenario are obtained; wherein, the cilia magnetic powder content is set to the optimal magnetic powder content during the finite element simulation; the cilia spacing is the distance between adjacent cilia;

[0065] In one embodiment, the ciliary height, ciliary diameter, and ciliary spacing corresponding to the smallest triaxial magnetic field change data in S2 are respectively taken as the optimal ciliary height, the optimal ciliary diameter, and the optimal ciliary spacing.

[0066] S3: Conduct a normal pressure sensing experiment on the ciliary magnetic sensor in the selected scenario to obtain triaxial magnetic field change data corresponding to different normal pressures; wherein, the ciliary magnetic powder content of the ciliary magnetic sensor in the selected scenario adopts the optimal magnetic powder content, the ciliary height adopts the optimal ciliary height, the ciliary diameter adopts the optimal ciliary diameter, and the ciliary spacing adopts the optimal ciliary spacing.

[0067] In one embodiment, a normal pressure sensing experiment is conducted on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to different normal pressures. This specifically includes the following steps:

[0068] S311: Apply normal pressure of different magnitudes to the ciliary magnetic sensor in the selected scene to obtain the time series of triaxial magnetic field change data output by the magnetic sensor.

[0069] S312: Send the time series of triaxial magnetic field variation data obtained in S311 to the computing device via the Wifi module;

[0070] Specifically: The time series of triaxial magnetic field change data obtained by S311 is packaged into a preset time window and then sent to the computing device through the Arduino R4 Wi-Fi module; the Arduino R4 Wi-Fi module uses the TCP / IP protocol and streams the packaged data wirelessly to the computing device.

[0071] S313: The computing device denoises the triaxial magnetic field change data time series obtained in S311 to obtain a denoised triaxial magnetic field change data time series.

[0072] S314: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field change data obtained in S313 to obtain time-domain features;

[0073] The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S313 and then performs frequency domain feature extraction to obtain frequency domain features.

[0074] S315: The computing device obtains triaxial magnetic field change data corresponding to different normal pressures based on the time domain characteristics obtained in S314, the frequency domain characteristics obtained in S314, and the time series of denoised triaxial magnetic field change data obtained in S313.

[0075] In one embodiment, the denoising in S313 employs a combination of wavelet transform and bandpass filtering;

[0076] It is understandable that using a combination of wavelet transform and bandpass filtering to denoise the data and limit the frequency to the range of 10Hz to 200Hz can preserve the core information related to tactile behavior.

[0077] The time-domain features obtained by S314 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis;

[0078] The frequency domain features obtained by S314 include total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

[0079] like Figure 3 As shown, it displays the results of a normal pressure sensing experiment when the normal pressure is 0.4N.

[0080] like Figure 4 As shown, it displays the results of a normal pressure sensing experiment when the normal pressure is 0.7 N.

[0081] It is understandable that the three-axis magnetic field change data includes X-axis magnetic field change data, Y-axis magnetic field change data, and Z-axis magnetic field change data.

[0082] A sliding sensing experiment was conducted on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to tangential pressure in different directions and at different speeds.

[0083] In one embodiment, a sliding sensing experiment is conducted on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to tangential pressures at different directions and speeds. This specifically includes the following steps:

[0084] S321: Apply tangential pressure in different directions and at different speeds to the ciliary magnetic sensor in the selected scene to obtain the time series of triaxial magnetic field change data output by the magnetic sensor.

[0085] S322: Send the time series of triaxial magnetic field variation data obtained in S321 to the computing device via the Wifi module;

[0086] Specifically: The time series of triaxial magnetic field change data obtained by S321 is packaged into a preset time window and then sent to the computing device through the Arduino R4 Wi-Fi module; the Arduino R4 Wi-Fi module uses the TCP / IP protocol and streams the packaged data wirelessly to the computing device.

[0087] S323: The computing device denoises the triaxial magnetic field change data time series obtained in S321 to obtain a denoised triaxial magnetic field change data time series.

[0088] S324: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field variation data obtained in S323 to obtain time-domain features;

[0089] The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S323, and then performs frequency domain feature extraction to obtain frequency domain features.

[0090] S325: The computing device obtains triaxial magnetic field change data corresponding to tangential pressure at different directions and speeds based on the time domain features obtained in S324, the frequency domain features obtained in S324, and the time series of denoised triaxial magnetic field change data obtained in S323.

[0091] In one embodiment, the denoising in S323 employs a combination of wavelet transform and bandpass filtering;

[0092] It is understandable that using a combination of wavelet transform and bandpass filtering to denoise the data and limit the frequency to the range of 10Hz to 200Hz can preserve the core information related to tactile behavior.

[0093] The time-domain features obtained by S324 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis;

[0094] The frequency domain features obtained by S324 include the principal components of the spectral behavior, total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

[0095] Non-contact sensing experiments were conducted on the ciliary magnetic sensor in a selected scenario to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor on the target to be sensed in the selected scenario.

[0096] In one embodiment, a non-contact sensing experiment is conducted on a ciliary magnetic sensor in a selected scenario to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor on the target to be sensed in the selected scenario. This specifically includes the following steps:

[0097] S331: Continuously change the distance between the target to be sensed and the ciliary magnetic sensor in the selected scene to obtain the time series of three-axis magnetic field change data output by the magnetic sensor.

[0098] S332: Send the time series of triaxial magnetic field variation data obtained in S331 to the computing device via the Wifi module;

[0099] Specifically: The time series of triaxial magnetic field change data obtained by S331 is packaged into a preset time window and then sent to the computing device through the Arduino R4 Wi-Fi module; the Arduino R4 Wi-Fi module uses the TCP / IP protocol and streams the packaged data wirelessly to the computing device.

[0100] S333: The computing device denoises the triaxial magnetic field change data time series obtained in S331 to obtain a denoised triaxial magnetic field change data time series.

[0101] S334: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field change data obtained in S333 to obtain time-domain features;

[0102] The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S333, and then performs frequency domain feature extraction to obtain frequency domain features.

[0103] S335: Based on the time-domain features obtained in S334, the frequency-domain features obtained in S334, and the time series of denoised triaxial magnetic field change data obtained in S333, the computing device obtains the sensing sensitivity and sensing range of the ciliary magnetic sensor for the target to be sensed in the selected scene.

[0104] In one embodiment, the denoising in S333 employs a combination of wavelet transform and bandpass filtering;

[0105] It is understandable that using a combination of wavelet transform and bandpass filtering to denoise the data and limit the frequency to the range of 10Hz to 200Hz can preserve the core information related to tactile behavior.

[0106] The time-domain features obtained by S334 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis;

[0107] The frequency domain features obtained by S334 include total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

[0108] It is understandable that the cilia magnetic sensing system includes a cilia magnetic sensor, a Wi-Fi module, and a computing device; the cilia magnetic sensor includes a magnetic sensor and cilia; the cilia are made of composite materials (coflex00-10 and neodymium iron boron magnetic powder).

[0109] Secondly, embodiments of the present invention also provide an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 501, a communications interface 502, a memory 503, and a communication bus 504. The processor 501, communications interface 502, and memory 503 communicate with each other via the communication bus 504. The processor 501 can call logical instructions from the memory 503 to execute the analysis method of the ciliary magnetic sensor.

[0110] Furthermore, the logical instructions in the aforementioned memory 503 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0112] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An analytical method for a ciliary magnetic sensor, characterized in that, Includes the following steps: S1: Tensile tests were conducted on cilia with different magnetic powder contents in a selected scenario to obtain the Young's modulus of cilia with different magnetic powder contents in the selected scenario. Based on the Young's modulus of cilia with different magnetic powder contents in a selected scenario, the optimal magnetic powder content of cilia in a selected scenario is obtained. S2: By analyzing the triaxial magnetic field change data output by the magnetic sensor under different cilia heights, different cilia diameters, and different cilia spacings through finite element simulation, the optimal cilia height, optimal cilia diameter, and optimal cilia spacing in a selected scenario are obtained; wherein, the cilia magnetic powder content is set to the optimal magnetic powder content during the finite element simulation; the cilia spacing is the distance between adjacent cilia; S3: Conduct a normal pressure sensing experiment on the ciliary magnetic sensor in the selected scenario to obtain triaxial magnetic field change data corresponding to different normal pressures; wherein, the ciliary magnetic powder content of the ciliary magnetic sensor in the selected scenario adopts the optimal magnetic powder content, the ciliary height adopts the optimal ciliary height, the ciliary diameter adopts the optimal ciliary diameter, and the ciliary spacing adopts the optimal ciliary spacing. A sliding sensing experiment was conducted on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to tangential pressure in different directions and at different speeds. Non-contact sensing experiments were conducted on the ciliary magnetic sensor in a selected scenario to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor on the target to be sensed in the selected scenario.

2. The analytical method for a ciliary magnetic sensor according to claim 1, characterized in that, In S1, the content of the ciliary magnetic powder corresponding to the largest Young's modulus is selected as the optimal magnetic powder content.

3. The analytical method for a ciliary magnetic sensor according to claim 1, characterized in that, The ciliary height, ciliary diameter, and ciliary spacing corresponding to the smallest triaxial magnetic field change data in S2 are respectively taken as the optimal ciliary height, the optimal ciliary diameter, and the optimal ciliary spacing.

4. The analytical method for a ciliary magnetic sensor according to claim 1, characterized in that, A normal pressure sensing experiment was conducted on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to different normal pressures. The specific steps included: S311: Apply normal pressure of different magnitudes to the ciliary magnetic sensor in the selected scene to obtain the time series of triaxial magnetic field change data output by the magnetic sensor. S312: Send the time series of triaxial magnetic field variation data obtained in S311 to the computing device via the Wifi module; S313: The computing device denoises the triaxial magnetic field change data time series obtained in S311 to obtain a denoised triaxial magnetic field change data time series. S314: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field change data obtained in S313 to obtain time-domain features; The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S313 and then performs frequency domain feature extraction to obtain frequency domain features. S315: The computing device obtains triaxial magnetic field change data corresponding to different normal pressures based on the time domain characteristics obtained in S314, the frequency domain characteristics obtained in S314, and the time series of denoised triaxial magnetic field change data obtained in S313.

5. The analytical method for a ciliary magnetic sensor according to claim 4, characterized in that: The noise reduction in S313 uses a combination of wavelet transform and bandpass filtering. The time-domain features obtained by S314 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis; The frequency domain features obtained by S314 include total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

6. The analytical method for a ciliary magnetic sensor according to claim 1, characterized in that, A sliding sensing experiment was conducted on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to tangential pressure at different directions and speeds. The specific steps included: S321: Apply tangential pressure in different directions and at different speeds to the ciliary magnetic sensor in the selected scene to obtain the time series of triaxial magnetic field change data output by the magnetic sensor. S322: Send the time series of triaxial magnetic field variation data obtained in S321 to the computing device via the Wifi module; S323: The computing device denoises the triaxial magnetic field change data time series obtained in S321 to obtain a denoised triaxial magnetic field change data time series. S324: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field variation data obtained in S323 to obtain time-domain features; The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S323, and then performs frequency domain feature extraction to obtain frequency domain features. S325: The computing device obtains triaxial magnetic field change data corresponding to tangential pressure at different directions and speeds based on the time domain features obtained in S324, the frequency domain features obtained in S324, and the time series of denoised triaxial magnetic field change data obtained in S323.

7. The analytical method for a ciliary magnetic sensor according to claim 6, characterized in that: The noise reduction in S323 uses a combination of wavelet transform and bandpass filtering. The time-domain features obtained by S324 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis; The frequency domain features obtained by S324 include the principal components of the spectral behavior, total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

8. The analytical method for a ciliary magnetic sensor according to claim 1, characterized in that, A non-contact sensing experiment was conducted on the ciliary magnetic sensor in a selected scenario to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor on the target to be sensed in the selected scenario. The specific steps included: S331: Continuously change the distance between the target to be sensed and the ciliary magnetic sensor in the selected scene to obtain the time series of three-axis magnetic field change data output by the magnetic sensor. S332: Send the time series of triaxial magnetic field variation data obtained in S331 to the computing device via the Wifi module; S333: The computing device denoises the triaxial magnetic field change data time series obtained in S331 to obtain a denoised triaxial magnetic field change data time series. S334: The computing device performs time-domain feature extraction on the time series of denoised triaxial magnetic field variation data obtained in S333 to obtain time-domain features; The computing device performs a fast Fourier transform on the time series of denoised triaxial magnetic field change data obtained by S333 and then performs frequency domain feature extraction to obtain frequency domain features. S335: Based on the time-domain features obtained in S334, the frequency-domain features obtained in S334, and the time series of denoised triaxial magnetic field change data obtained in S333, the computing device obtains the sensing sensitivity and sensing range of the ciliary magnetic sensor for the target to be sensed in the selected scene.

9. The analytical method for a ciliary magnetic sensor according to claim 8, characterized in that: The noise reduction in S333 uses a combination of wavelet transform and bandpass filtering. The time-domain features obtained by S334 include signal mean, amplitude, standard deviation, root mean square, skewness, and kurtosis; The frequency domain features obtained by S334 include total spectral energy, dominant frequency, band energy, spectral centroid, spectral entropy, and spectral flatness.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the analysis method of the cilia magnetic sensor as described in any one of claims 1 to 9.

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