Analysis method of cilium magnetic sensor

Abstract

The invention discloses an analysis method of a cilia magnetic sensor, and relates to the technical field of sensors, and the analysis method comprises the following steps: carrying out a tensile experiment on cilia with different magnetic powder contents in a selected scene to obtain Young modulus of the cilia with different magnetic powder contents in the selected scene; obtaining the optimal magnetic powder content of the cilia in the selected scene based on the Young modulus of the cilia with different magnetic powder contents in the selected scene; the optimal cilium height, the optimal cilium diameter and the optimal cilium spacing of the cilium in the selected scene are obtained through finite element simulation analysis; performing a normal pressure sensing experiment on the cilia magnetic sensor in the selected scene, performing a sliding sensing experiment on the cilia magnetic sensor in the selected scene, and performing a non-contact sensing experiment on the cilia magnetic sensor in the selected scene; according to the invention, the design and application scenarios of the cilium magnetic sensor can be optimized.

Description

Technical Field

[0001] The present invention relates to the field of sensor technology, and more particularly to an analysis method of a ciliary magnetic sensor. Background Art

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

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

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

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, a method for analyzing a ciliary magnetic sensor is provided, comprising the following steps:

[0007] S1: Perform a tensile test 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;

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

[0009] S2: Analyzing the three-axis magnetic field change data output by the magnetic sensor at different ciliary heights, different ciliary diameters, and different ciliary spacings through finite element simulation to obtain the optimal ciliary height, optimal ciliary diameter, and optimal ciliary spacing of the cilia in the selected scenario; wherein, during the finite element simulation, the ciliary magnetic powder content is set to the optimal magnetic powder content; the ciliary spacing is the distance between adjacent cilia;

[0010] S3: performing a normal pressure sensing experiment on the ciliary magnetic sensor in the selected scene to obtain three-axis magnetic field change data corresponding to different normal pressures; wherein, the ciliary magnetic powder content of the ciliary magnetic sensor in the selected scene 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] Conduct sliding sensing experiments on the ciliary magnetic sensor in selected scenarios to obtain triaxial magnetic field change data corresponding to tangential pressure in different directions and speeds;

[0012] A non-contact perception experiment was conducted on the ciliary magnetic sensor in the selected scene to obtain the perception sensitivity and perception range of the ciliary magnetic sensor to the target to be perceived in the selected scene.

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

[0014] Preferably, the ciliary height, ciliary diameter, and ciliary distance corresponding to the minimum three-axis magnetic field change data in S2 are respectively used as the optimal ciliary height, the optimal ciliary diameter, and the optimal ciliary distance.

[0015] Preferably, a normal pressure sensing experiment is performed on the ciliary magnetic sensor in a selected scene to obtain three-axis magnetic field change data corresponding to different normal pressures, specifically comprising the following steps:

[0016] S311: applying normal pressures of different magnitudes to the ciliary magnetic sensor in the selected scene to obtain a time series of three-axis magnetic field change data output by the magnetic sensor;

[0017] S312: Send the three-axis magnetic field change data time series obtained in S311 to the computing device via the Wifi module;

[0018] S313: The computing device denoises the time series of the three-axis magnetic field change data obtained in S311 to obtain a denoised time series of the three-axis magnetic field change data;

[0019] S314: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S313 to obtain time domain features;

[0020] The computing device performs fast Fourier transform on the time series of the denoised three-axis 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 the three-axis magnetic field change data corresponding to different normal pressures based on the time domain features obtained in S314, the frequency domain features obtained in S314, and the denoised three-axis magnetic field change data time series obtained in S313.

[0022] Preferably, the denoising in S313 is performed by combining 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 in S314 include total spectrum energy, main frequency, frequency band energy, spectrum centroid, spectrum entropy, and spectrum flatness.

[0025] Preferably, a sliding sensing experiment is performed on the ciliary magnetic sensor in a selected scene to obtain three-axis magnetic field change data corresponding to tangential pressures in different directions and speeds, specifically comprising the following steps:

[0026] S321: applying tangential pressure in different directions and speeds to the ciliary magnetic sensor in the selected scene to obtain a time series of three-axis magnetic field change data output by the magnetic sensor;

[0027] S322: Send the three-axis magnetic field change data time series obtained in S321 to the computing device via the Wifi module;

[0028] S323: The computing device denoises the time series of the three-axis magnetic field change data obtained in S321 to obtain a denoised time series of the three-axis magnetic field change data;

[0029] S324: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S323 to obtain time domain features;

[0030] The computing device performs fast Fourier transform on the time series of the denoised three-axis 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 the three-axis magnetic field change data corresponding to the tangential pressures in different directions and different speeds based on the time domain features obtained in S324, the frequency domain features obtained in S324, and the denoised three-axis magnetic field change data time series obtained in S323.

[0032] Preferably, the denoising in S323 is performed by combining 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 in S324 include the main components of the spectrum of the sliding behavior, the total spectrum energy, the main frequency, the frequency band energy, the spectrum centroid, the spectrum entropy and the spectrum flatness.

[0035] Preferably, a non-contact sensing experiment is performed on the ciliary magnetic sensor in a selected scene to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor to the target to be sensed in the selected scene, which specifically includes the following steps:

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

[0037] S332: Send the three-axis magnetic field change data time series obtained in S331 to the computing device via the Wifi module;

[0038] S333: The computing device denoises the time series of the three-axis magnetic field change data obtained in S331 to obtain a denoised time series of the three-axis magnetic field change data;

[0039] S334: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S333 to obtain time domain features;

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

[0041] S335: The computing device obtains the perception sensitivity and perception range of the ciliary magnetic sensor to the perceived target in the selected scenario based on the time domain features obtained in S334, the frequency domain features obtained in S334, and the denoised three-axis magnetic field change data time series obtained in S333.

[0042] Preferably, the denoising in S333 is performed by combining 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 in S334 include total spectrum energy, main frequency, frequency band energy, spectrum centroid, spectrum entropy and spectrum flatness.

[0045] In a second aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned analysis method of the ciliary magnetic sensor when executing the computer program.

[0046] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for analyzing ciliary magnetic sensors, which can achieve the following beneficial technical effects:

[0047] 1) By conducting tensile tests on cilia with different magnetic powder contents, the present invention 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 cilia and thus enhance the stability and reliability of the cilia magnetic sensor.

[0048] 2) The present invention uses finite element simulation to analyze the three-axis magnetic field change data output by the magnetic sensor at different ciliary heights, diameters and spacings, and selects the ciliary height, diameter and spacing corresponding to the minimum three-axis magnetic field change data as the optimal ciliary height, diameter and spacing, which can enable the ciliary magnetic sensor to achieve the best magnetic field response and improve the sensitivity of the ciliary magnetic sensor.

[0049] 3) The present invention conducts normal pressure sensing experiments on the ciliary magnetic sensor. Through a series of data processing steps, including denoising, time domain and frequency domain feature extraction, etc., the accuracy of the ciliary magnetic sensor's sensing of normal pressure can be improved.

[0050] 4) The present invention conducts sliding perception experiments on the ciliary magnetic sensor. Through a series of data processing steps, including denoising, time domain and frequency domain feature extraction, etc., the ciliary magnetic sensor can accurately sense changes in tangential pressure, providing reliable data support for subsequent applications.

[0051] 5) The present invention conducts non-contact sensing experiments on ciliary magnetic sensors. By continuously changing the distance between the target to be sensed and the ciliary magnetic sensor and performing a series of data processing steps, including denoising, time domain and frequency domain feature extraction, the perception sensitivity and perception range of the ciliary magnetic sensor to the target to be sensed can be accurately obtained, providing a scientific basis for the non-contact sensing application of ciliary magnetic sensors and helping to optimize the design and application scenarios of ciliary magnetic sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

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

[0054] Figure 2 This is a graph showing the tensile properties of cilia with different magnetic powder contents provided by the present invention;

[0055] Figure 3 This is a result diagram of the normal pressure perception experiment provided by the present invention when the normal pressure is 0.4N;

[0056] Figure 4 This is a result diagram of the normal pressure perception experiment provided by the present invention when the normal pressure is 0.7N;

[0057] Figure 5 A schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0059] First, as Figure 1 As shown, the embodiment of the present invention discloses a method for analyzing a ciliary magnetic sensor, comprising the following steps:

[0060] S1: Perform a tensile test 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;

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

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

[0063] Figure 2 The figure shows the Young's modulus of cilia with four different magnetic powder contents (50%, 60%, 70%, 80%) obtained by tensile tests under selected scenarios. Figure 2 It can be seen that when the tension is constant, as the magnetic powder content increases (from 50% to 80%), the Young's modulus of the cilia becomes higher and higher. When the magnetic powder content is 80%, the Young's modulus Et reaches 0.715153405, where Figure 2 The slope of the curve is Young's modulus.

[0064] S2: Analyzing the three-axis magnetic field change data output by the magnetic sensor at different ciliary heights, different ciliary diameters, and different ciliary spacings through finite element simulation to obtain the optimal ciliary height, optimal ciliary diameter, and optimal ciliary spacing of the cilia in the selected scenario; wherein, during the finite element simulation, the ciliary magnetic powder content is set to the optimal magnetic powder content; the ciliary spacing is the distance between adjacent cilia;

[0065] In one embodiment, the ciliary height, ciliary diameter, and ciliary distance corresponding to the minimum three-axis magnetic field change data in S2 are respectively used as the optimal ciliary height, the optimal ciliary diameter, and the optimal ciliary distance.

[0066] S3: performing a normal pressure sensing experiment on the ciliary magnetic sensor in the selected scene to obtain three-axis magnetic field change data corresponding to different normal pressures; wherein, the ciliary magnetic powder content of the ciliary magnetic sensor in the selected scene 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 performed on a ciliary magnetic sensor in a selected scenario to obtain triaxial magnetic field change data corresponding to different normal pressures, specifically comprising the following steps:

[0068] S311: applying normal pressures of different magnitudes to the ciliary magnetic sensor in the selected scene to obtain a time series of three-axis magnetic field change data output by the magnetic sensor;

[0069] S312: Send the three-axis magnetic field change data time series obtained in S311 to the computing device via the Wifi module;

[0070] Specifically, the three-axis magnetic field change data time series obtained by S311 is packaged into preset time windows 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 transmits the packaged data wirelessly to the computing device in a streaming manner.

[0071] S313: The computing device denoises the time series of the three-axis magnetic field change data obtained in S311 to obtain a denoised time series of the three-axis magnetic field change data;

[0072] S314: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S313 to obtain time domain features;

[0073] The computing device performs fast Fourier transform on the time series of the denoised three-axis 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 the three-axis magnetic field change data corresponding to different normal pressures based on the time domain features obtained in S314, the frequency domain features obtained in S314, and the denoised three-axis magnetic field change data time series obtained in S313.

[0075] In one embodiment, the denoising in S313 is performed by combining wavelet transform and bandpass filtering;

[0076] It can be understood that: using a combination of wavelet transform and bandpass filtering to denoise the data and limit the frequency to the range of 10 Hz to 200 Hz can retain 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 in S314 include total spectrum energy, main frequency, frequency band energy, spectrum centroid, spectrum entropy, and spectrum flatness.

[0079] like Figure 3 As shown in FIG, it shows the result of the normal pressure perception experiment when the normal pressure is 0.4N.

[0080] like Figure 4 As shown in FIG, it shows the result of the normal pressure perception experiment when the normal pressure is 0.7N.

[0081] It can be understood 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] Conduct sliding sensing experiments on the ciliary magnetic sensor in selected scenarios to obtain triaxial magnetic field change data corresponding to tangential pressure in different directions and speeds;

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

[0084] S321: applying tangential pressure in different directions and speeds to the ciliary magnetic sensor in the selected scene to obtain a time series of three-axis magnetic field change data output by the magnetic sensor;

[0085] S322: Send the three-axis magnetic field change data time series obtained in S321 to the computing device via the Wifi module;

[0086] Specifically, the three-axis magnetic field change data time series obtained by S321 is packaged into preset time windows 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 sends the packaged data wirelessly to the computing device through streaming.

[0087] S323: The computing device denoises the time series of the three-axis magnetic field change data obtained in S321 to obtain a denoised time series of the three-axis magnetic field change data;

[0088] S324: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S323 to obtain time domain features;

[0089] The computing device performs fast Fourier transform on the time series of the denoised three-axis 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 the three-axis magnetic field change data corresponding to the tangential pressures in different directions and different speeds based on the time domain features obtained in S324, the frequency domain features obtained in S324, and the denoised three-axis magnetic field change data time series obtained in S323.

[0091] In one embodiment, the denoising in S323 is performed by combining wavelet transform and bandpass filtering;

[0092] It can be understood that: using a combination of wavelet transform and bandpass filtering to denoise the data and limit the frequency to the range of 10 Hz to 200 Hz can retain 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 in S324 include the main components of the spectrum of the sliding behavior, the total spectrum energy, the main frequency, the frequency band energy, the spectrum centroid, the spectrum entropy and the spectrum flatness.

[0095] A non-contact perception experiment was conducted on the ciliary magnetic sensor in the selected scene to obtain the perception sensitivity and perception range of the ciliary magnetic sensor to the target to be perceived in the selected scene.

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

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

[0098] S332: Send the three-axis magnetic field change data time series obtained in S331 to the computing device via the Wifi module;

[0099] Specifically, the three-axis magnetic field change data time series obtained by S331 is packaged into preset time windows 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 sends the packaged data wirelessly to the computing device through streaming.

[0100] S333: The computing device denoises the time series of the three-axis magnetic field change data obtained in S331 to obtain a denoised time series of the three-axis magnetic field change data;

[0101] S334: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S333 to obtain time domain features;

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

[0103] S335: The computing device obtains the perception sensitivity and perception range of the ciliary magnetic sensor to the perceived target in the selected scenario based on the time domain features obtained in S334, the frequency domain features obtained in S334, and the denoised three-axis magnetic field change data time series obtained in S333.

[0104] In one embodiment, the denoising in S333 is performed by combining wavelet transform and bandpass filtering;

[0105] It can be understood that: using a combination of wavelet transform and bandpass filtering to denoise the data and limit the frequency to the range of 10 Hz to 200 Hz can retain 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 in S334 include total spectrum energy, main frequency, frequency band energy, spectrum centroid, spectrum entropy and spectrum flatness.

[0108] It can be understood that: the ciliary magnetic sensing system includes a ciliary magnetic sensor, a Wifi module and a computing device; the ciliary magnetic sensor includes a magnetic sensor and cilia; and the cilia are made of a composite material (coflex00-10 and neodymium iron boron magnetic powder).

[0109] In a second aspect, an embodiment of the present invention further provides 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, the communications interface 502, and the memory 503 communicate with each other via the communication bus 504. The processor 501 may call logic instructions in the memory 503 to execute the analysis method of the ciliary magnetic sensor.

[0110] Furthermore, the logic 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, or the portion that contributes to the prior art, or a portion 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 for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0112] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing a ciliary magnetic sensor, characterized in that: The following steps are involved: S1: Perform a tensile test 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; Based on the Young's modulus of cilia with different magnetic powder contents in the selected scene, the optimal magnetic powder content of cilia in the selected scene is obtained; S2: Analyzing the three-axis magnetic field change data output by the magnetic sensor at different ciliary heights, different ciliary diameters, and different ciliary spacings through finite element simulation to obtain the optimal ciliary height, optimal ciliary diameter, and optimal ciliary spacing of the cilia in the selected scenario; wherein, during the finite element simulation, the ciliary magnetic powder content is set to the optimal magnetic powder content; the ciliary spacing is the distance between adjacent cilia; S3: performing a normal pressure sensing experiment on the ciliary magnetic sensor in the selected scene to obtain three-axis magnetic field change data corresponding to different normal pressures; wherein, the ciliary magnetic powder content of the ciliary magnetic sensor in the selected scene 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; Conduct sliding sensing experiments on the ciliary magnetic sensor in selected scenarios to obtain triaxial magnetic field change data corresponding to tangential pressure in different directions and speeds; A non-contact perception experiment was conducted on the ciliary magnetic sensor in the selected scene to obtain the perception sensitivity and perception range of the ciliary magnetic sensor to the target to be perceived in the selected scene.

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

3. The analysis method of a ciliary magnetic sensor according to claim 1, characterized in that: The ciliary height, ciliary diameter, and ciliary spacing corresponding to the minimum three-axis magnetic field change data in S2 are respectively used as the optimal ciliary height, the optimal ciliary diameter, and the optimal ciliary spacing.

4. The analysis method of a ciliary magnetic sensor according to claim 1, characterized in that: Conduct a normal pressure sensing experiment on the ciliary magnetic sensor in a selected scenario to obtain the three-axis magnetic field change data corresponding to different normal pressures. The specific steps include: S311: applying normal pressures of different magnitudes to the ciliary magnetic sensor in the selected scene to obtain a time series of three-axis magnetic field change data output by the magnetic sensor; S312: Send the three-axis magnetic field change data time series obtained in S311 to the computing device via the Wifi module; S313: The computing device denoises the time series of the three-axis magnetic field change data obtained in S311 to obtain a denoised time series of the three-axis magnetic field change data; S314: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S313 to obtain time domain features; The computing device performs fast Fourier transform on the time series of the denoised three-axis magnetic field change data obtained by S313 and then performs frequency domain feature extraction to obtain frequency domain features; S315: The computing device obtains the three-axis magnetic field change data corresponding to different normal pressures based on the time domain features obtained in S314, the frequency domain features obtained in S314, and the denoised three-axis magnetic field change data time series obtained in S313.

5. The method for analyzing a ciliary magnetic sensor according to claim 4, characterized in that: Denoising 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 in S314 include total spectrum energy, main frequency, frequency band energy, spectrum centroid, spectrum entropy, and spectrum flatness.

6. The analysis method of a ciliary magnetic sensor according to claim 1, characterized in that: Conduct a sliding sensing experiment on the ciliary magnetic sensor in the selected scene to obtain the three-axis magnetic field change data corresponding to the tangential pressure in different directions and speeds. The specific steps include: S321: applying tangential pressure in different directions and speeds to the ciliary magnetic sensor in the selected scene to obtain a time series of three-axis magnetic field change data output by the magnetic sensor; S322: Send the three-axis magnetic field change data time series obtained in S321 to the computing device via the Wifi module; S323: The computing device denoises the time series of the three-axis magnetic field change data obtained in S321 to obtain a denoised time series of the three-axis magnetic field change data; S324: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S323 to obtain time domain features; The computing device performs fast Fourier transform on the time series of the denoised three-axis magnetic field change data obtained by S323 and then performs frequency domain feature extraction to obtain frequency domain features; S325: The computing device obtains the three-axis magnetic field change data corresponding to the tangential pressures in different directions and different speeds based on the time domain features obtained in S324, the frequency domain features obtained in S324, and the denoised three-axis magnetic field change data time series obtained in S323.

7. The analysis method of a ciliary magnetic sensor according to claim 6, characterized in that: Denoising 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 in S324 include the main components of the spectrum of the sliding behavior, the total spectrum energy, the main frequency, the frequency band energy, the spectrum centroid, the spectrum entropy and the spectrum flatness.

8. The analysis method of a ciliary magnetic sensor according to claim 1, characterized in that: Conduct a non-contact sensing experiment on the ciliary magnetic sensor in a selected scene to obtain the sensing sensitivity and sensing range of the ciliary magnetic sensor to the sensing target in the selected scene, specifically including the following steps: S331: Continuously changing the distance between the target to be sensed and the ciliary magnetic sensor in the selected scene to obtain a time series of three-axis magnetic field change data output by the magnetic sensor; S332: Send the three-axis magnetic field change data time series obtained in S331 to the computing device via the Wifi module; S333: The computing device denoises the time series of the three-axis magnetic field change data obtained in S331 to obtain a denoised time series of the three-axis magnetic field change data; S334: The computing device extracts time domain features from the denoised three-axis magnetic field change data time series obtained in S333 to obtain time domain features; The computing device performs fast Fourier transform on the time series of the denoised three-axis magnetic field change data obtained by S333 and then performs frequency domain feature extraction to obtain frequency domain features; S335: The computing device obtains the perception sensitivity and perception range of the ciliary magnetic sensor to the perceived target in the selected scenario based on the time domain features obtained in S334, the frequency domain features obtained in S334, and the denoised three-axis magnetic field change data time series obtained in S333.

9. The analysis method of a ciliary magnetic sensor according to claim 8, characterized in that: Denoising 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 in S334 include total spectrum energy, main frequency, frequency band energy, spectrum centroid, spectrum entropy and spectrum flatness.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the analysis method of the ciliary magnetic sensor according to any one of claims 1 to 9 is implemented.

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