A multi-parameter distribution network cable monitoring system

Abstract

The application discloses a multi-parameter distribution network cable monitoring system, and belongs to the field of distribution network cable monitoring; the system solves the problems of obvious deficiencies of the existing multi-parameter distribution network cable monitoring system in the aspects of sensing integration, collection synchronization and adaptability, edge real-time processing capability, transmission link reliability and overall system compatibility; the system comprises a sensing unit, which is used for synchronously collecting multi-channel resonator signals; an embedded main control unit, which is in communication connection with the sensing unit and is used for collecting voltage signals output by the sensing unit; a processing unit, which is in communication connection with the embedded main control unit and is used for performing modal analysis and fusion diagnosis on the collected signals; and a transmission unit, which is in communication connection with the processing unit and is used for realizing remote device access and data reception; the application is applied to distribution network cable monitoring.

Description

Technical Field

[0001] This invention relates to the field of distribution network cable monitoring technology, and specifically to a multi-parameter distribution network cable monitoring system. Background Technology

[0002] In existing power distribution cable monitoring technologies, the sensing component mostly uses traditional MEMS resonators or discrete single-parameter sensors. These sensors monitor individual parameters such as temperature, gas, or partial discharge through a single modal response. Some sensors require additional temperature compensation modules to compensate for the impact of ambient temperature on monitoring accuracy, resulting in complex system structures and low integration. The acquisition modules mostly use independent acquisition devices, lacking a unified clock synchronization mechanism between acquisition channels. This leads to significant timestamp deviations in the acquired signals, affecting the accuracy of subsequent signal analysis. Furthermore, the excitation current is often a fixed value, making it difficult to adapt to the characteristics of different sensor types, thus limiting system scalability. Signal processing largely relies on backend servers for data analysis, resulting in weak edge processing capabilities, large data transmission delays, and insufficient real-time performance, making it difficult to meet the real-time early warning requirements of cable monitoring. The transmission component mostly uses a single communication link, transmitting data through conventional antennas or IoT modules. This approach does not optimize radiation efficiency for the monitoring scenario, and the link reliability is not guaranteed. Interference or failure of the link can easily lead to data transmission interruptions. In summary, existing technologies have significant shortcomings in terms of sensor integration, data acquisition synchronization and adaptability, edge real-time processing capabilities, transmission link reliability, and overall system compatibility. The system deployment and maintenance costs are high, and it is difficult to adapt to the complex field monitoring environment of distribution network cables. Summary of the Invention

[0003] To address the significant shortcomings of existing multi-parameter distribution network cable monitoring systems in terms of sensor integration, data acquisition synchronization and adaptability, edge real-time processing capabilities, transmission link reliability, and overall system compatibility, this invention proposes a multi-parameter distribution network cable monitoring system.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a multi-parameter distribution network cable monitoring system, comprising:

[0005] The sensing unit includes a sensor array composed of multiple MEMS resonators for monitoring the multimodal parameters of the cable. Each MEMS resonator includes a BSE multimodal signal mapping module, which performs modal mapping of the multimodal parameters based on the time-domain signal excited by the blue sideband and outputs the BSE excitation signal to the signal mapping and acquisition module. The signal mapping and acquisition module converts the acquired vibration signals of each modal parameter into voltage signals for output.

[0006] Embedded main control unit: It includes a Raspberry Pi, on which a high-precision analog-to-digital conversion module, an adaptive constant current source module, and at least one acquisition board are communicatively connected. The adaptive constant current source module, the high-precision analog-to-digital conversion module, and the acquisition board are coupled to synchronously transmit the voltage signal output by the sensing unit to the Raspberry Pi;

[0007] Processing unit: It includes an edge computing platform built based on the Raspberry Pi operating system. The edge computing platform is embedded with a frequency domain decomposition module and a multi-parameter fusion diagnosis module, which are used to output the diagnostic data of each modal parameter;

[0008] Transmission unit: It includes a broadband antenna and a network communication module. A DC magnetic field structure is arranged around the broadband antenna, which is used to adjust the DC magnetic field direction and intensity of the DC magnetic field structure based on the magnetic field intensity adjustment model;

[0009] The local port of the Raspberry Pi is mapped to the public network for remote device access and data reception.

[0010] Further, the BSE multi-modal signal mapping module includes two symmetric cosine curve beams. The two symmetric cosine curve beams are called double cosine curve beams. The two symmetric cosine curve beams are coaxially spliced and extend along their own length directions; One end of the two cosine curve beams is fixedly connected to the fixed anchor area, and the other end is integrally formed with a tooth-shaped protrusion. The two tooth-shaped protrusions are symmetrically arranged;

[0011] The signal mapping and acquisition module includes two electrodes, which are respectively arranged on the opposite sides of the double cosine curve beam. The fixed anchor area and the two electrodes form a "C" - shaped structure, and electrical isolation between the two electrodes and the cosine curve beam is achieved through an insulating layer.

[0012] Further, a plurality of through - holes are opened at the joint of the two cosine curve beams, and the plurality of through - holes are arranged in an array.

[0013] Further, the structure of the tooth - shaped protrusion is an isosceles triangle structure.

[0014] Further, the broadband antenna and the network communication module are respectively electrically connected to the communication interface of the Raspberry Pi. The broadband antenna and the network communication module form a dual - link redundant backup, and the switching between the broadband antenna and the network communication module is realized through a link switching decision module.

[0015] Further, the frequency domain decomposition module is configured as:

[0016] Perform Hanning windowing on the acquired time - domain signal;

[0017] Calculate the power spectral density of the acquisition channels of each acquisition board based on the signal duration of the time - domain signal and the fast Fourier transform;

[0018] Singular value decomposition is performed on the multi-channel power spectral density matrix;

[0019] By picking out the singular peak values, the resonant frequency, damping ratio, and mode shape of each modal parameter are obtained.

[0020] Furthermore, the multi-parameter fusion diagnostic module is configured to: construct a fusion diagnostic model with temperature, gas concentration and partial discharge characteristics as inputs, and solve for the optimal weight combination by using a particle swarm optimization algorithm with the goal of minimizing diagnostic error.

[0021] Furthermore, the broadband antenna adopts a multi-layer structure, consisting of a 20°YX-LiNbO3 substrate, Cu interdigitated transducer electrodes, a silicon dioxide waveguide layer, a magnetostrictive layer, and a silicon dioxide protective layer, from bottom to top.

[0022] Furthermore, the fabrication method of the MEMS resonator includes the following steps:

[0023] Step S1, Substrate preparation: Select silicon-on-insulator wafer as the processing substrate. The wafer includes a substrate, an insulating layer and a single-crystal silicon device layer stacked sequentially.

[0024] Step S2, Patterning: A photoresist layer is spin-coated on the surface of the single-crystal silicon device layer away from the insulating layer; then, the photoresist layer is exposed and developed using a mask to simultaneously form a complete pattern on the photoresist layer for etching cosine curve beams, vias, toothed protrusions, and reserved electrode areas.

[0025] Step S3, Deep Etching: Using deep reactive ion etching, the exposed photoresist layer is etched vertically until it penetrates the single-crystal silicon device layer and stops at the insulating layer; a three-dimensional structure of cosine curve beam, through hole, and tooth-shaped protrusion is formed simultaneously through one etching process, and then the residual photoresist is removed.

[0026] Step S4, Insulating layer deposition: A silicon dioxide insulating film is deposited on the substrate and the surface of the cosine curve beam etched on the substrate. The silicon dioxide insulating film achieves electrical isolation between the substrate and the electrode, as well as electrical isolation between the cosine curve beam and the electrode.

[0027] Step S5, Electrode fabrication: Deposit a metal thin film in the reserved electrode area, and then use a secondary photolithography and etching process to form a driving electrode and a monitoring electrode on the metal thin film;

[0028] Step S6, Release and Encapsulation: Remove the insulating layer to release the cosine curve beam, allowing it to vibrate freely in a suspended state; finally, encapsulate it using wafer-level vacuum bonding technology to complete the fabrication of the MEMS resonator.

[0029] Furthermore, the method for manufacturing the transmission unit includes the following steps:

[0030] Step 1: Cleaning and pre-treatment of the substrate: Select a substrate of a set thickness, perform ultrasonic cleaning on the substrate to remove surface oil and particulate impurities, dry it with nitrogen gas after cleaning, and bake it to dehydrate.

[0031] Step 2: Fabrication of interdigital transducer electrodes: Photoresist is spin-coated onto the pretreated substrate surface, and the periodic pattern of the interdigital transducer electrodes is defined by exposure and development process using a photomask; subsequently, a metal electrode film is deposited on the periodic pattern of the interdigital transducer electrodes, and then a patterned interdigital transducer electrode is formed by a lift-off process. The width and spacing of the interdigital transducer electrodes are strictly matched with the center operating frequency of the broadband antenna.

[0032] Step 3: Fabrication of silicon dioxide waveguide layer: A silicon dioxide thin film is uniformly deposited on the surface of the interdigital transducer electrode and the substrate to form a surface acoustic waveguide structure;

[0033] Step 4: Fabrication of the magnetostrictive layer: Secondary photolithography patterning is performed on the surface of the silicon dioxide waveguide layer, and a nickel metal thin film is deposited in a vacuum environment using electron beam evaporation to form a magnetostrictive functional layer;

[0034] Step 5: Prepare a surface protective layer: Deposit a silicon dioxide passivation protective layer on the surface of the magnetostrictive layer;

[0035] Step 6, Structural Fine-tuning and Performance Calibration: The edges of the multilayer thin film are trimmed by dry etching to eliminate process burrs and obtain a broadband antenna; at the same time, a DC magnetic field structure is arranged around the broadband antenna, and the optimal matching of phonon-magneton resonant coupling is achieved by adjusting the magnetic field strength model.

[0036] Step 7, Device Integration and Packaging: The fabricated broadband antenna is fixed to the circuit board using a surface mount process. The interdigital transducer electrodes are electrically connected to the peripheral circuit using gold wire bonding. Finally, it is integrated and assembled with the network communication module to complete the overall packaging of the transmission unit.

[0037] The advantages of this invention over the prior art are as follows:

[0038] 1. The sensing unit of the present invention can realize synchronous monitoring and temperature self-compensation without the need for an additional compensation module: through the structural setting of two symmetrical cosine curve beams and the blue sideband excitation mechanism, the MEMS resonator generates multi-mode response. Different parameters are accurately characterized by the frequency or amplitude changes of the modes. At the same time, the reverse temperature drift characteristics of some modes are used to offset the temperature effect, which greatly improves the monitoring accuracy and simplifies the system structure.

[0039] 2. The embedded main control unit of the present invention has high synchronization, strong adaptability and scalability: The embedded main control unit is built on a data acquisition board compatible with edge computing platform. Each channel shares a unified clock source to ensure synchronous acquisition. At the same time, it has an adaptive excitation current adjustment function, which can automatically match the optimal excitation current according to the MEMS resonator type. Multiple data acquisition boards can be stacked to expand and meet the channel requirements of different monitoring scenarios.

[0040] 3. The processing unit of this invention has strong real-time signal processing and high diagnostic accuracy: The edge computing platform embeds a frequency domain decomposition module and a particle swarm optimization algorithm to realize real-time data processing on site, quickly extract the characteristic parameters of each mode, and improve the accuracy of fault identification by integrating the parameter fusion diagnostic module and combining the parameter information of each mode, avoiding the early warning delay caused by back-end transmission delay.

[0041] 4. The transmission unit of this invention has high transmission link reliability and excellent radiation efficiency: It integrates a broadband antenna and a network communication module to form a dual-link redundancy backup. The broadband antenna achieves switching between the broadband antenna and the network communication module through a magnetic field strength adjustment model and a link switching decision module, ensuring stable and uninterrupted data transmission and adapting to the complex communication environment of the distribution network.

[0042] 5. The invention has a high degree of system integration: the modules of the MEMS resonator are seamlessly compatible, the overall structure is compact, and there is no need for complex external devices, which reduces the size and power consumption of the system; at the same time, the sensing unit, embedded main control unit and processing unit work together to achieve synchronous acquisition and adaptability, which improves the overall compatibility of the system. Attached Figure Description

[0043] The present invention will be further described below with reference to the accompanying drawings:

[0044] Figure 1 This is a schematic diagram of the system of the present invention;

[0045] Figure 2 This is a schematic diagram of the structure of a MEMS resonator;

[0046] Figure 3 This is a schematic diagram illustrating the collaboration between the sensing unit and the embedded main control unit of the present invention.

[0047] Figure 4 This is a schematic diagram of the multi-channel synchronous acquisition architecture of the present invention;

[0048] Figure 5 This is a schematic diagram of the structure of the broadband antenna of the present invention;

[0049] Figure 6 This is an experimental curve of the multimode frequency response of the MEMS resonator of this invention;

[0050] In the diagram: 1 is a MEMS resonator, 2 is a HAT board, 3 is a Raspberry Pi, 4 is a network communication module, 5 is a broadband antenna, 6 is a cosine curve beam, 7 is a fixed anchor area, 8 is a toothed protrusion, 9 is a through hole, 10 is a driving electrode, 11 is a monitoring electrode, 12 is a signal mapping and acquisition module, 13 is a clock source, 14 is a GPIO interface, 15 is an edge computing platform, 16 is an adaptive constant current source module, 17 is a digital-to-analog converter, and 18 is an embedded main control unit. Detailed Implementation

[0051] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate relative orientations or positional relationships and are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0052] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0053] like Figures 1 to 6 As shown, the present invention provides a multi-parameter distribution network cable monitoring system, comprising:

[0054] Sensing unit: It includes a sensor array composed of multiple MEMS resonators 1 for monitoring multi-modal parameters of the cable; the MEMS resonator 1 is based on a single-crystal silicon material and is processed by an integrated process without splicing gaps. The MEMS resonator 1 includes a BSE (blue sideband excitation) multi-modal signal mapping module. The BSE multi-modal signal mapping module realizes the modal mapping of multi-modal parameters such as temperature, gas, and partial discharge signals based on the time-domain signal of blue sideband excitation, and outputs a BSE excitation signal to the signal mapping and acquisition module 12. The signal mapping and acquisition module 12 converts the vibration signals of each modal parameter collected into voltage signals and outputs them.

[0055] The BSE multi-modal signal mapping module includes a resonant beam. The resonant beam includes two symmetric cosine curve beam bodies 6 (two symmetric cosine curve beam bodies 6 are also called double cosine curve beam bodies). The cosine curve beam body 6 is a beam-shaped component with one side edge in the shape of a cosine curve. The two symmetric cosine curve beam bodies 6 are coaxially spliced and extend along their own length direction (X-axis). The transverse symmetry axis of the MEMS resonator 1 is the joint of the two cosine curve beam bodies 6. One end of the two cosine curve beam bodies 6 is fixedly connected to the fixed anchor area 7, and the other end is integrally formed with a toothed protrusion 8. The two toothed protrusions 8 are symmetrically arranged. Further, the structure of the toothed protrusion 8 is an isosceles triangle structure. A plurality of through holes 9 are also provided at the joint of the two cosine curve beam bodies 6. The plurality of through holes 9 are arranged in an array, and the through holes 9 are circular in structure.

[0056] In the MEMS resonator 1 of the present invention, the double cosine curve beam body structure has a higher overall line-plane ratio and stronger resistance to process deviations compared with the traditional basic double cosine bending beam.

[0057] The signal mapping and acquisition module 12 includes two electrodes. The two electrodes are respectively arranged on opposite sides of the double cosine curve beam body. The fixed anchor area 7 and the two electrodes form a "匚" type structure. Electrical isolation between the two electrodes and the cosine curve beam body 6 is achieved through an insulating layer; the two electrodes are respectively a driving electrode 10 and a monitoring electrode 11.

[0058] The manufacturing method of the MEMS resonator 1 includes the following steps:

[0059] Step S1, Substrate preparation: Select a silicon-on-insulator wafer as the processing substrate. The wafer includes a substrate, an insulating layer, and a single-crystal silicon device layer stacked in sequence; the insulating layer covers the upper surface of the substrate, and the single-crystal silicon device layer is formed on the upper surface of the insulating layer. That is, the substrate is the bottom layer, the insulating layer is the intermediate layer, and the single-crystal silicon device layer is the top layer.

[0060] Step S2, Patterning: A photoresist layer is spin-coated on the surface of the single-crystal silicon device layer away from the insulating layer; then, the photoresist layer is exposed and developed using a mask to simultaneously form a complete pattern on the photoresist layer for etching the double cosine curve beam, through hole 9, toothed protrusion 8, and reserved electrode area.

[0061] Step S3, Deep Etching: Using a deep reactive ion etching process, the exposed photoresist layer is etched vertically until it penetrates the single-crystal silicon device layer and stops at the intermediate insulating layer; a three-dimensional structure of double cosine curve beam, through hole 9, and tooth-shaped protrusion 8 is formed simultaneously through one etching process, and then the residual photoresist is removed.

[0062] In one embodiment, a double cosine curve beam structure with a thickness of 25 μm is formed by vertical etching of the exposed photoresist layer using a deep reactive ion etching (DRIE) process.

[0063] Step S4, Insulating layer deposition: A silicon dioxide insulating film is deposited on the substrate and the surface of the bicosine curve beam etched on the substrate. The silicon dioxide insulating film achieves electrical isolation between the substrate and the electrode, as well as electrical isolation between the bicosine curve beam and the electrode.

[0064] Step S5, Electrode fabrication: In the reserved electrode area, a metal thin film is deposited using magnetron sputtering. Subsequently, a secondary photolithography and etching process is used to form a driving electrode 10 and a monitoring electrode 11 on the metal thin film. The driving electrode 10 and the monitoring electrode 11 are symmetrically distributed about the transverse axis of symmetry of the double cosine curve beam. Furthermore, they are arranged along the length direction of the double cosine curve beam, and the edges of the driving electrode 10 and the monitoring electrode 11 are completely aligned with the contour of the double cosine curve beam without any relative offset.

[0065] In one embodiment, aluminum electrodes are deposited on the upper and lower surfaces of the double cosine curve beam to form capacitor plates using a sputtering and photolithography process.

[0066] Step S6, Release and Encapsulation: A wet etching process is used to remove the intermediate insulating layer beneath the bicosine curve beam to release it, placing it in a suspended state where it can vibrate freely. Finally, it is encapsulated using wafer-level vacuum bonding (vacuum degree 10). -5 Pa), to complete the fabrication of MEMS resonator 1.

[0067] The MEMS resonator 1 of this invention increases the line-to-surface ratio S / A of the bicosine curve beam geometry by increasing the outer perimeter S and decreasing the effective area A. This design enhances the sensitivity of the equivalent mass to process deviation δ, thereby making the frequency insensitive to process deviation (df / dδ=0). The expression for the resonant frequency generated by the MEMS resonator 1 of this invention is:

[0068] ;

[0069] In the formula:

[0070] : The resonant frequency (Hz) of the nth mode;

[0071] nth-order modal coefficients (determined by boundary conditions, first-order modes of the double cosine structure) =1.032, third-order mode =6.415);

[0072] L: Effective length of the cosine curve beam (μm);

[0073] E: Young's modulus (Pa) of the material (monocrystalline silicon) of the cosine curve beam 6;

[0074] I: Moment of inertia (m) of the cosine curve beam 6 4 ), calculated from beam width w and thickness h ;

[0075] ρ: Density of monocrystalline silicon (kg / m³) 3 );

[0076] A: Effective cross-sectional area of ​​cosine curve beam 6 (m²) 2 ), ( (This represents the total area of ​​array vias 9).

[0077] The BSE multimodal signal mapping module generates BSE excitation signals by mapping multimodal parameters such as temperature, gas, and partial discharge signals. The process involves synthesizing the BSE excitation signal using the superposition of two fundamental mode frequencies. The model for generating the excitation signal is as follows:

[0078] ;

[0079] In the formula:

[0080] : Time-domain signal (V) excited by blue sideband;

[0081] The peak amplitude (V) of the basic excitation signal, with a value of 5Vpp;

[0082] First-order bending mode frequency (Hz), corresponding to partial discharge signal monitoring;

[0083] Third-order bending mode frequency (Hz), corresponding to gas concentration monitoring;

[0084] : Time variable (s).

[0085] MEMS resonator 1, with a double cosine structure, is excited by the blue sideband excitation. and Two sets of combined modes exhibit opposite temperature drift trends due to structural geometry and modal coupling characteristics. One set of modes drifts positively with increasing temperature, while the other drifts negatively. By summing the frequencies of the two sets of combined modes with opposite drift trends, the frequency shift caused by temperature changes can be completely canceled out, achieving temperature self-stabilization output without an external compensation module, thus realizing temperature self-compensation. Models for achieving temperature self-compensation include a combined mode temperature drift cancellation model and a precise temperature parameter characterization model.

[0086] The expression for the combined modal temperature drift cancellation model is:

[0087] ;

[0088] In the formula: This is the stable output frequency after temperature self-compensation; The first-order bending mode frequency;

[0089] These are the third-order bending mode frequencies;

[0090] Through this summation operation, the temperature drift of the two combined modes cancels each other out, thus... It does not change with ambient temperature.

[0091] The expression for the precise characterization model of the temperature parameter is:

[0092] ;

[0093] In the formula:

[0094] The frequency difference used to characterize ambient temperature can be used to independently extract temperature parameters while ensuring the self-compensation and stability of the output frequency, thus combining the dual functions of multi-parameter monitoring and frequency self-stabilization.

[0095] By combining the temperature self-compensation model with the excitation mechanism, there is no need to add temperature sensors, compensation circuits or compensation materials. Temperature drift self-cancellation can be achieved solely by relying on the modal characteristics of the MEMS resonator 1 itself, while retaining the accurate detection capability of temperature parameters. This completely solves the technical pain points of traditional MEMS sensors, such as large temperature drift and the need for additional compensation.

[0096] In one embodiment, the signal mapping and acquisition module 12 converts the vibration signals of each modal parameter acquired by the electrodes of the MEMS resonator 1 into voltage signal outputs. Specifically, it applies the BSE excitation signal to the capacitor plates of the MEMS resonator 1 through dedicated electrodes to excite nonlinear modal coupling and form , Combination modes. Among them, the frequency shift of the first-order mode ( Characterizing the partial discharge signal, the amplitude variation of the third mode ( The gas concentration is characterized by the frequency difference of the combined modes. Temperature is indirectly characterized. Finally, the vibration signals of each modal parameter acquired through the electrodes of MEMS resonator 1 are converted into voltage signals for output.

[0097] Embedded main control unit 18: Communicatively connected to the sensing unit, used to acquire the voltage signal output by the sensing unit. Includes a Raspberry Pi 3, on which a high-precision analog-to-digital converter module, an adaptive constant current source module 16, and at least one acquisition board are communicatively connected. The adaptive constant current source module 16, the high-precision analog-to-digital converter module, and the acquisition board are coupled to synchronously transmit the voltage signal output by the sensing unit to the Raspberry Pi 3. In this embodiment, the acquisition board is a HAT board 2.

[0098] Specifically, HAT board 2 is seamlessly connected to Raspberry Pi 3 via 40-pin GPIO interface 14. HAT board 2 is equipped with EEPROM memory for storing configuration parameters. Raspberry Pi 3 reads the pre-stored parameters in EEPROM memory through GPIO interface 14 to realize automatic recognition of HAT board 2 and automatic configuration of GPIO interface 14.

[0099] In one embodiment, multiple HAT boards 2, model MCC 172, are communicatively connected to the Raspberry Pi 3. These HAT boards 2 are cascaded via GPIO interfaces 14. The 40 GPIO interfaces 14 of the bottom HAT board 2 are electrically connected to the 40-pin interface of the Raspberry Pi 3. Each pair of HAT boards 2 is electrically connected to each other via dedicated power and signal pins, achieving power sharing and signal synchronization. Based on the pre-installed open-source MCC DAQ HAT Library on the Raspberry Pi 3 operating system, a multi-HAT board 2 collaborative control program is written. Different HAT boards 2 are distinguished by their board addresses, enabling unified scheduling and data acquisition of all acquisition channels, thereby achieving multi-language commands and collaborative operation between multiple HAT boards 2.

[0100] The Raspberry Pi 3 operating system comes pre-installed with a multi-channel synchronous acquisition model for synchronous calibration of multi-channel signals. The expression for the multi-channel synchronous acquisition model is:

[0101] ;

[0102] In the formula:

[0103] : Power spectral density matrix of multi-channel signal (N×N, where N is the number of channels);

[0104] : Angular frequency (rad / s) at the i-th frequency point. ( (frequency value);

[0105] A unitary matrix (N×N) whose column vectors are the orthogonal mode vectors of the signal;

[0106] : A diagonal matrix (N×N), where the diagonal elements are singular values, corresponding to the signal strength of each modal parameter;

[0107] : The conjugate transpose of .

[0108] HAT board 2 includes two analog input channels, each equipped with an independent 24-bit Σ-Δ analog-to-digital converter (ADC) (AD7768-1). All ADCs are connected to the same clock source 13, meaning all ADCs share the same clock source 13 (1024kHz), and are connected to the I²C bus via the GPIO interface 14 of Raspberry Pi 3.

[0109] The EEPROM memory pre-stores parameters such as channel gain (1 / 2 / 4 / 8 times) and sampling rate (1kS / s~51.2kS / s). After the Raspberry Pi 3 is powered on, it automatically reads the parameters pre-stored in the EEPROM memory through the I²C protocol and completes the configuration, ensuring that the sampling timestamp deviation of multiple channels is less than 1μs.

[0110] The high-precision analog-to-digital converter module, including digital-to-analog converter 17 (LTC2606), communicates with Raspberry Pi 3 via I²C bus to receive digital commands sent by Raspberry Pi 3 and convert them into analog control voltage output.

[0111] The adaptive constant current source module 16 has a built-in programmable constant current source chip. In this embodiment, the programmable constant current source chip is an LT3092 chip. The input pin of the programmable constant current source chip is connected to the PWM pin of the Raspberry Pi 3 for communication, and is used to adjust the excitation current output by the programmable constant current source chip.

[0112] The Raspberry Pi 3 operating system also includes a pre-installed acquisition program module. This module contains parameter libraries for various types of MEMS resonators 1, including at least the capacitance value C and maximum output voltage value V for each type of MEMS resonator 1. Specifically, each type of MEMS resonator 1 refers to different operating states under different monitoring conditions and modes, such as partial discharge monitoring, gas concentration monitoring, and temperature monitoring. Since the equivalent capacitance, maximum output voltage, and other parameters of the MEMS resonator 1 differ under different operating conditions, the required optimal excitation current varies. Therefore, the acquisition program module pre-installs parameter libraries for the MEMS resonator 1 under different operating conditions and automatically calls the corresponding parameters to achieve precise matching of the excitation current.

[0113] The adaptive constant current source module 16 is configured to retrieve the capacitance value C and maximum output voltage value V of the MEMS resonator 1 connected to the current Raspberry Pi 3 from the parameter library, obtain the optimal excitation current according to the matching model of the excitation current and the wideband of the MEMS resonator 1, and obtain the required control voltage according to the optimal excitation current.

[0114] Receives analog control voltage from digital-to-analog converter 17 based on the required control voltage output;

[0115] The analog control voltage is input to the programmable constant current source chip, so that the excitation current output by the programmable constant current source chip is consistent with the calculated optimal excitation current, thus completing the adaptive matching.

[0116] The matching model between the excitation current and the broadband of MEMS resonator 1 is as follows:

[0117] ;

[0118] Explanation of formula parameters:

[0119] Maximum operating bandwidth (Hz) of IEPE type MEMS resonator 1;

[0120] The excitation current (mA) can be adjusted within the range of 2mA to 20mA.

[0121] : Total capacitance (pF) of MEMS resonator 1 and connecting cable;

[0122] : Maximum peak output voltage (V) of MEMS resonator 1.

[0123] Processing Unit: Communicates with the embedded main control unit and is used for modal analysis and fusion diagnosis of the acquired signals. This includes an edge computing platform 15 (e.g., a Python runtime unit) built on a Raspberry Pi 3 operating system (such as Linux). The edge computing platform 15 embeds a frequency domain decomposition (FDD) module and a multi-parameter fusion diagnosis module to output diagnostic data for each mode. The frequency domain decomposition module extracts the resonant frequency, damping ratio, and modal shape of each modal parameter; the multi-parameter fusion diagnosis module optimizes the multi-modal parameter fusion weights.

[0124] The frequency domain decomposition module is configured as follows:

[0125] The acquired time-domain signal x(t) is processed by Hanning windowing to suppress spectral leakage;

[0126] The power spectral density of each acquisition channel on each acquisition board is calculated based on the signal duration and fast Fourier transform of the time-domain signal. The model for obtaining the power spectral density is as follows:

[0127] ;

[0128] In the formula, T is the signal duration, and FFT is the Fast Fourier Transform;

[0129] The singular value decomposition (SVD) is performed on the multi-channel power spectral density matrix. The model for singular value decomposition is as follows:

[0130] ;

[0131] In the formula,

[0132] : Multi-channel power spectral density matrix (n is the number of channels, m is the number of frequency points);

[0133] : Left unitary matrix, column vectors represent the spatial modes of the signal;

[0134] : A diagonal matrix, with diagonal elements being singular values ​​(arranged in descending order);

[0135] The conjugate transpose of a right unitary matrix, with column vectors representing the time modes of the signal;

[0136] By picking out the singular peak values, the resonant frequency, damping ratio, and mode shape of each modal parameter can be obtained. For example, the characteristic frequency of a partial discharge signal is extracted from the first-order mode peak value.

[0137] The multi-parameter fusion diagnostic module is configured to: use temperature Gas concentration and partial discharge eigenvalues A fusion diagnostic model is constructed for the input, and the optimal weight combination is solved by the particle swarm optimization algorithm with the goal of minimizing the diagnostic error.

[0138] Specifically, the expression for the fusion diagnostic model is:

[0139] .

[0140] The fitness function corresponding to the objective of minimizing diagnostic error is:

[0141] ;

[0142] In the formula, This represents the actual fault condition.

[0143] The multi-parameter fusion diagnostic module includes a PSO optimization model, which incorporates a particle swarm optimization algorithm to optimize the fusion weights of multi-modal parameters, enabling collaborative diagnosis of multi-modal parameters. The expression for the PSO optimization model is:

[0144] ;

[0145] ;

[0146] In the formula:

[0147] , The i-th particle's th... Generation, First The speed of generation;

[0148] , The i-th particle's th... Generation, First The position of the generation (corresponding fusion weight);

[0149] Inertia weight (adjustable from 0.4 to 0.9) balances global and local search.

[0150] , Learning factor (usually 2), representing individual cognition and group cognition respectively;

[0151] , : A random number within the interval [0, 1];

[0152] : No. The historical optimal position of each particle;

[0153] The global optimal position of the entire particle swarm.

[0154] Transmission unit: Communicatively connected to the processing unit, used for remote device access and data reception. It includes a broadband antenna 5 and a network communication module 4. Miniature electromagnets are arranged around the broadband antenna 5 to adjust the DC magnetic field (e.g., 0~5000e) of the miniature electromagnets based on a magnetic field strength adjustment model, and to calibrate the magnetic field direction to the hard axis direction of the Ni layer of the broadband antenna 5. In this embodiment, the broadband antenna 5 is a magnetoelectric antenna.

[0155] The broadband antenna 5 and the network communication module 4 are electrically connected to the communication interface of the Raspberry Pi 3, forming a dual-link redundancy backup. The switching between the broadband antenna 5 and the network communication module 4 is realized through the link switching decision module.

[0156] The Raspberry Pi 3's local ports are mapped to the public network for remote device access and data reception.

[0157] The broadband antenna 5 adopts a multi-layer structure, which consists of a 20°YX-LiNbO3 substrate (thickness: 500μm), a Cu interdigitated transducer electrode (IDT, thickness: 50nm), a silicon dioxide waveguide layer (thickness: 250nm), a magnetostrictive layer (thickness: 50nm), and a silicon dioxide protective layer (thickness: 7nm) from bottom to top.

[0158] The model for optimizing the electrode width and spacing of Cu interdigitated transducers is as follows:

[0159] ;

[0160] In the formula:

[0161] λ: Electrode period (μm) of Cu interdigitated transducer;

[0162] c sThe SH-SAW propagation velocity (m / s) in the 20°YX-LiNbO3 substrate is fixed at 3480 m / s;

[0163] f0: The center operating frequency (Hz) of the broadband antenna 5, set to 2.4GHz according to the requirements of power distribution network monitoring.

[0164] The expression for the magnetic field strength adjustment model is:

[0165] ;

[0166] In the formula:

[0167] : Antenna radiation efficiency after coupling;

[0168] : The basic radiation efficiency when uncoupled;

[0169] Phonon-magneton coupling coefficient is positively correlated with magnetic field strength;

[0170] Phonon damping coefficient;

[0171] : Magnet damping coefficient.

[0172] The process of adjusting the DC magnetic field of a miniature electromagnet is as follows:

[0173] The receiving frequency of the Cu interdigitated transducer electrode The driving signal is used to excite the SH-SAW to propagate in the substrate, and the magnetic field strength is adjusted to match the phonon frequency with the magneton frequency. When this occurs, resonant coupling takes place, increasing radiation efficiency by more than 70%.

[0174] The expression for the link switching decision module is:

[0175] ;

[0176] In the formula,

[0177] Link quality assessment value;

[0178] Received signal strength indication (dBm): The normal range for broadband antenna 5-link is -40dBm to -80dBm, and the normal range for 4G link is -50dBm to -90dBm.

[0179] Bit error rate, normal threshold set to ;

[0180] , : Weighting coefficients (all set to 1) represent the importance of signal strength and bit error rate.

[0181] In one embodiment, the communication module uses a 4G communication module (such as the SIM7600G-H). The 4G communication module is electrically connected to the communication interface of the Raspberry Pi 3 via an M.2 interface; the wideband antenna 5 is electrically connected to the communication interface of the Raspberry Pi 3 via an SPI interface. The Raspberry Pi 3 collects the RSSI and BER of the two links in real time and calculates the link quality assessment value. , ( When the threshold is set (to a preset value), the system will automatically switch to another link.

[0182] The 4G communication module is equipped with an Alibaba Cloud reverse proxy client, which is used to achieve intranet penetration, mapping the local port of Raspberry Pi 3 to the public network, supporting remote device access and data reception.

[0183] The broadband antenna 5 employs a multilayer thin-film composite fabrication process, using 20°YX-LiNbO3 piezoelectric single crystal as the substrate. The entire process is completed in an ultra-clean vacuum environment, with each layer sequentially deposited and patterned. The fabrication method for the transmission unit includes the following steps:

[0184] Step 1: Cleaning and pre-treatment of the substrate: A 20° YX-LiNbO3 single crystal substrate with a set thickness is selected as the substrate for the broadband antenna. The substrate is ultrasonically cleaned in sequence with acetone, anhydrous ethanol and deionized water to remove surface oil and particulate impurities. After cleaning, nitrogen gas is used to dry the substrate and high temperature baking is used to dehydrate it, thereby improving the adhesion between the substrate surface and the thin film.

[0185] Step 2: Fabrication of Cu interdigitated transducer electrodes: Photoresist is spin-coated onto the pretreated substrate surface, and the periodic pattern of the interdigitated transducer electrodes is defined by photolithography mask exposure and development process; subsequently, a metal electrode film is deposited on the interdigitated transducer electrodes by magnetron sputtering process, and then a patterned interdigitated transducer electrode is formed by lift-off process. The electrode width and spacing are strictly matched with the antenna center operating frequency to ensure stable excitation of surface acoustic waves.

[0186] Step 3: Fabrication of silicon dioxide waveguide layer: A silicon dioxide thin film is uniformly deposited on the surface of Cu interdigitated transducer electrode and substrate using plasma-enhanced chemical vapor deposition (PECVD) to form a surface acoustic waveguide structure. The film thickness is uniform and controllable, which is used to confine acoustic wave energy and reduce transmission loss.

[0187] Step 4: Fabrication of the magnetostrictive layer: Secondary photolithography patterning is performed on the surface of the silicon dioxide waveguide layer. A nickel metal thin film is deposited in a high vacuum environment using electron beam evaporation to form a magnetostrictive functional layer. The vacuum level and deposition rate are strictly controlled during the deposition process to ensure that the nickel metal thin film is dense and free of pores, and has excellent magnetostrictive response characteristics.

[0188] Step 5: Prepare the surface protective layer: Deposit an ultrathin silicon dioxide passivation protective layer on the surface of the magnetostrictive layer to isolate external moisture, dust and electromagnetic interference, protect the internal multilayer structure from corrosion and mechanical damage, and at the same time ensure that the electromagnetic radiation performance of the broadband antenna 5 is not affected.

[0189] Step 6, Structural Fine-tuning and Performance Calibration: The edges of the multilayer thin film are trimmed by dry etching to eliminate process burrs and obtain the broadband antenna 5; at the same time, a miniature adjustable DC magnetic field structure is arranged around the broadband antenna 5, and the direction and intensity of the DC magnetic field are calibrated by the magnetic field strength adjustment model to achieve optimal matching of phonon-magneton resonant coupling.

[0190] Step 7, Device Integration and Packaging: The fabricated broadband antenna 5 is fixed to the circuit board using a patch process. The interdigital transducer electrodes are electrically connected to the peripheral circuit through gold wire bonding. Finally, it is integrated and assembled with the network communication module 4 (such as a 4G communication module) to complete the overall packaging of the transmission unit and form an integrated communication module with dual-link redundant transmission function.

[0191] The edge computing platform 15 also has a visual GUI interface for communication, which supports parameter configuration and data export, making the system deployment and maintenance process simple and efficient, and reducing human and material costs.

[0192] Experimental results of the multimodal frequency response of MEMS resonator 1 on a double cosine curve beam under blue sideband excitation, as follows: Figure 6 As shown, the experiment was conducted under a 15 Pa vacuum environment and a 20 V DC bias. The excitation signal was a composite signal of two fundamental mode frequencies, which was obtained by acquiring the vibration signal through a lock-in amplifier and then processing it. The curves cover the 20 to 300 kHz frequency band and exhibit six clear resonance peaks, corresponding to the sixth in-plane bending modes of MEMS resonator 1. The frequencies of these modes are distributed between 30 kHz and 290 kHz, clustering within 300 kHz, demonstrating the design advantage of the "high-density modes" of the double cosine curve beam structure of this invention, and providing a foundation for multi-parameter synchronous monitoring.

[0193] Significant differences exist in the amplitude and stability of the modal parameters. The first mode (corresponding to mode 1 in the figure) has the largest amplitude and is most sensitive to external disturbances, making it suitable as a high-sensitivity sensing channel. The sixth mode (corresponding to mode 6 in the figure) has the smallest amplitude but the best stability, making it suitable for auxiliary frequency reference calibration. Besides the fundamental mode peak, the curves also imply signals from multiple combined modes, verifying the nonlinear modal coupling effect induced by the blue-sideband excitation scheme. This characteristic is crucial for realizing the mapping of temperature, gas, and partial discharge multimodal parameters—different physical parameters are accurately characterized by changing the frequency or amplitude of specific modes. Some modes exhibit opposite temperature drift trends, providing experimental support for achieving temperature self-compensation through modal frequency summation, thus solving the accuracy problem of temperature-affected traditional MEMS resonators in cable monitoring. This curve not only clearly demonstrates the multimodal characteristics of the MEMS resonator 1 of the double cosine curve beam of the present invention and the effectiveness of the blue sideband excitation, providing direct experimental evidence for the sensing mechanism of the multi-parameter distribution network cable monitoring device, but also provides key basis for subsequent extraction of modal features through frequency domain decomposition algorithm and construction of multi-parameter fusion diagnostic model by combining optimization algorithm. At the same time, it complements the advantages of low-cost and highly portable Internet of Things devices, promoting the development of cable health monitoring technology towards integration and high precision.

[0194] Regarding the specific structure of this invention, it should be noted that the connection relationships between the various component modules used in this invention are definite and achievable. Except as specifically described in the embodiments, their specific connection relationships can bring about corresponding technical effects and solve the technical problems proposed by this invention without relying on the execution of corresponding software programs. The models of the components, modules, and specific components appearing in this invention, the connection methods between them, and the conventional usage methods and expected technical effects brought about by the above technical features, unless specifically described, are all publicly disclosed content in patents, journal articles, technical manuals, technical dictionaries, and textbooks that can be obtained by those skilled in the art before the application date, or belong to conventional technology, common knowledge, and other existing technologies in this field. There is no need to elaborate, which makes the technical solution provided in this case clear, complete, and achievable, and can reproduce or obtain corresponding physical products based on this technical means.

[0195] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-parameter distribution network cable monitoring system, characterized in that, Comprising: Sensing unit: including a sensor array composed of multiple MEMS resonators (1) for monitoring multimodal parameters of the cable; the MEMS resonator (1) includes a BSE multimodal signal mapping module, which realizes modal mapping of multimodal parameters based on the time-domain signal excited by the blue sideband and outputs a BSE excitation signal to the signal mapping and acquisition module (12); the signal mapping and acquisition module (12) converts the vibration signals of each modal parameter collected into voltage signals and outputs them. Embedded main control unit (18): including a Raspberry Pi (3), on which a high-precision analog-to-digital conversion module, an adaptive constant current source module (16) and at least one acquisition board are communicatively connected. The adaptive constant current source module (16), the high-precision analog-to-digital conversion module and the acquisition board are coupled to synchronously transmit the voltage signals output by the sensing unit to the Raspberry Pi (3). Processing unit: including an edge computing platform (15) built based on the operating system of the Raspberry Pi (3), and the edge computing platform (15) is embedded with a frequency-domain decomposition module and a multi-parameter fusion diagnosis module for outputting diagnostic data of each modal parameter. Transmission unit: including a broadband antenna (5) and a network communication module (4), and a DC magnetic field structure is arranged around the broadband antenna (5) for adjusting the DC magnetic field direction and intensity of the DC magnetic field structure based on a magnetic field intensity adjustment model. The local port of the Raspberry Pi (3) is mapped to the public network for remote device access and data reception.

2. The multi-parameter distribution network cable monitoring system according to claim 1, characterized in that, The BSE multimodal signal mapping module includes two symmetric cosine curve beam bodies (6), and the two symmetric cosine curve beam bodies (6) are called double cosine curve beam bodies. The two symmetric cosine curve beam bodies (6) are coaxially spliced and extend along their own length directions; one ends of the two cosine curve beam bodies (6) are fixedly connected to the fixed anchor area (7), and the other ends are integrally formed with tooth-shaped protrusions (8), and the two tooth-shaped protrusions (8) are symmetrically arranged. The signal mapping and acquisition module (12) includes two electrodes, which are respectively arranged on the opposite sides of the double cosine curve beam body. The fixed anchor area (7) and the two electrodes form a "匚" shaped structure, and electrical isolation between the two electrodes and the cosine curve beam body (6) is achieved through an insulating layer.

3. The multi-parameter distribution network cable monitoring system according to claim 2, characterized in that, A plurality of through holes (9) are also opened at the joint of the two cosine curve beam bodies (6), and the plurality of through holes (9) are arranged in an array.

4. The multi-parameter distribution network cable monitoring system according to claim 2, characterized in that, The structure of the tooth-shaped protrusion (8) is an isosceles triangle structure.

5. A multi-parameter distribution network cable monitoring system according to claim 1, characterized in that, The broadband antenna (5) and the network communication module (4) are respectively electrically connected to the communication interfaces of the Raspberry Pi (3). The broadband antenna (5) and the network communication module (4) form a dual-link redundant backup, and through a link switching decision module, switching between the broadband antenna (5) and the network communication module (4) is realized.

6. The multi-parameter distribution network cable monitoring system according to claim 1, characterized in that, The frequency-domain decomposition module is configured to: Perform Hanning windowing on the collected time-domain signal; Calculate the power spectral density of the acquisition channels of each acquisition board based on the signal duration of the time-domain signal and the fast Fourier transform; Perform singular value decomposition on the multi-channel power spectral density matrix; By picking the singular value peaks, obtain the resonant frequency, damping ratio and modal shape of each modal parameter.

7. The multi-parameter distribution network cable monitoring system according to claim 1, characterized in that, The multi-parameter fusion diagnostic module is configured to: construct a fusion diagnostic model with temperature, gas concentration and partial discharge characteristics as input, and solve for the optimal weight combination by minimizing the diagnostic error through particle swarm optimization algorithm.

8. A multi-parameter distribution network cable monitoring system according to claim 1, characterized in that, The broadband antenna (5) adopts a multi-layer structure, consisting of a 20°YX-LiNbO3 substrate, Cu interdigitated transducer electrode, silicon dioxide waveguide layer, magnetostrictive layer and silicon dioxide protective layer from bottom to top.

9. A multi-parameter distribution network cable monitoring system according to any one of claims 1-8, characterized in that, The fabrication method of the MEMS resonator (1) includes the following steps: Step S1, Substrate preparation: Select silicon-on-insulator wafer as the processing substrate. The wafer includes a substrate, an insulating layer and a single-crystal silicon device layer stacked sequentially. Step S2, pattern photolithography: spin-coat a photoresist layer on the surface of the single crystal silicon device layer away from the insulating layer; then, expose and develop the photoresist layer through a mask to simultaneously form a complete pattern on the photoresist layer for etching the cosine curve beam (6), through hole (9), toothed protrusion (8), and reserved electrode area. Step S3, Deep Etching: The photoresist layer after exposure is etched vertically using a deep reactive ion etching process until it penetrates the single-crystal silicon device layer and stops at the insulating layer; the three-dimensional structure of cosine curve beam (6), through hole (9), and toothed protrusion (8) is formed simultaneously through one etching process, and then the residual photoresist is removed. Step S4, Insulation layer deposition: A silicon dioxide insulating film is deposited on the substrate and the surface of the cosine curve beam (6) etched on the substrate. The silicon dioxide insulating film achieves electrical isolation between the substrate and the electrode, as well as electrical isolation between the cosine curve beam (6) and the electrode. Step S5, Electrode fabrication: Deposit a metal thin film in the reserved electrode area, and then use a secondary photolithography and etching process to form a driving electrode (10) and a monitoring electrode (11) on the metal thin film. Step S6, Release and Encapsulation: Remove the insulating layer to release the cosine curve beam (6) so that the cosine curve beam (6) is in a suspended state that can vibrate freely; finally, encapsulate it through wafer-level vacuum bonding process to complete the fabrication of MEMS resonator (1).

10. A multi-parameter distribution network cable monitoring system according to any one of claims 1-8, characterized in that, The method for manufacturing the transmission unit includes the following steps: Step 1: Cleaning and pre-treatment of the substrate: Select a substrate of a set thickness, perform ultrasonic cleaning on the substrate to remove surface oil and particulate impurities, dry it with nitrogen gas after cleaning, and bake it to dehydrate. Step 2: Fabrication of interdigital transducer electrodes: Photoresist is spin-coated onto the pretreated substrate surface, and the periodic pattern of the interdigital transducer electrodes is defined by photolithography mask exposure and development process; then, a metal electrode film is deposited on the periodic pattern of the interdigital transducer electrodes, and then a patterned interdigital transducer electrode is formed by a stripping process. The width and spacing of the interdigital transducer electrodes are strictly matched with the center operating frequency of the broadband antenna (5). Step 3: Fabrication of silicon dioxide waveguide layer: A silicon dioxide thin film is uniformly deposited on the surface of the interdigital transducer electrode and the substrate to form a surface acoustic waveguide structure; Step 4: Fabrication of the magnetostrictive layer: Secondary photolithography patterning is performed on the surface of the silicon dioxide waveguide layer, and a nickel metal thin film is deposited in a vacuum environment using electron beam evaporation to form a magnetostrictive functional layer; Step 5: Prepare a surface protective layer: Deposit a silicon dioxide passivation protective layer on the surface of the magnetostrictive layer; Step 6, Structural fine-tuning and performance calibration: The edges of the multilayer thin film are trimmed by dry etching to eliminate process burrs and obtain a broadband antenna (5); at the same time, a DC magnetic field structure is arranged around the broadband antenna (5), and the optimal matching of phonon-magneton resonant coupling is achieved by adjusting the magnetic field strength model. Step 7, Device Integration and Packaging: The fabricated broadband antenna (5) is fixed to the circuit board using a patch process. The interdigitated transducer electrodes are electrically connected to the peripheral circuits through gold wire bonding. Finally, it is integrated and assembled with the network communication module (4) to complete the overall packaging of the transmission unit.

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