Real-time electromagnetic shielding effectiveness monitoring system and method based on multi-source sensing

Abstract

The invention discloses a real-time electromagnetic shielding effectiveness monitoring system and method based on multi-source sensing, and relates to the technical field of electromagnetic compatibility. The system comprises a mixed signal emission source used for providing a reference or environment electromagnetic signal; the sensor arrays are arranged on the two sides of the shielding body and used for collecting signal data before and after the reference or environment electromagnetic signals penetrate through the shielding body; the data processing center is connected with the sensor array and is used for calculating the shielding effectiveness in real time according to the signal data, updating baseline data when the shielding effectiveness reaches the standard, and performing defect positioning and feedback control on a shielding structure when the shielding effectiveness does not reach the standard; and the visual platform is connected with the data processing center and is used for displaying all data. According to the invention, the real-time performance and accuracy of electromagnetic shielding effectiveness monitoring can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electromagnetic compatibility, in particular to a real-time electromagnetic shielding effectiveness monitoring system and method based on multi-source sensing. BACKGROUND

[0002] Electromagnetic shielding machine rooms need to maintain high shielding effectiveness (SE) for a long time, but the traditional monitoring method has obvious drawbacks. In terms of manual detection, periodic on-site tests are mainly conducted according to the national standard GB / T 12190-2021. The drawback of this method is that it cannot detect the performance degradation caused by deformation or material aging of the shielding body in real time. Existing monitoring equipment also has shortcomings, and most of them rely on a single cooperative signal source (such as a signal generator), and cannot use environmental electromagnetic signals (such as 5G and Wi-Fi signals) for all-weather monitoring. In terms of judgment, the traditional method only roughly judges the shielding effectiveness degradation by comparing the field strength, which is difficult to accurately locate the specific defect position, such as deformation of door gaps and waveguide openings, which brings great challenges to the stable operation and maintenance of electromagnetic shielding machine rooms. SUMMARY

[0003] The purpose of the present application is to provide a real-time electromagnetic shielding effectiveness monitoring system and method based on multi-source sensing, which aims to solve or improve at least one of the above technical problems.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] A real-time electromagnetic shielding effectiveness monitoring system based on multi-source sensing, comprising:

[0006] A mixed signal transmitting source for providing reference or environmental electromagnetic signals;

[0007] A sensor array arranged on both sides of the shielding body for collecting signal data before and after the reference or environmental electromagnetic signals penetrate the shielding body;

[0008] A data processing center connected to the sensor array for real-time calculation of shielding effectiveness according to the signal data, updating baseline data when the shielding effectiveness meets the standard, and positioning defects and feedback control of the shielding structure when the shielding effectiveness does not meet the standard;

[0009] A visualization platform connected to the data processing center for displaying all data.

[0010] Optionally, the mixed signal transmitting source includes a cooperative source and a non-cooperative source; wherein the cooperative source uses a controllable transmitter, and the non-cooperative source uses environmental electromagnetic signals.

[0011] Optionally, the sensor array includes indoor and outdoor electric field probes, magnetic field sensors, wideband spectrum analyzers, and directional couplers.

[0012] Optionally, the sensor array employs a reconfigurable MIMO antenna array.

[0013] The application also provides a real-time electromagnetic shielding effectiveness monitoring method based on multi-source sensing, applied to the system as described above, comprising:

[0014] Switching the application mode of the mixed signal transmission source according to the set time, and collecting signal data in the current mode;

[0015] Dynamically calculating the shielding effectiveness according to the signal data, and correcting the system error;

[0016] Inputting all shielding effectiveness data before the current time as historical data into the LSTM network for prediction to determine the attenuation trend in the future set time;

[0017] If the attenuation trend in the future set time does not exist substandard data, updating the baseline data, if the attenuation trend in the future set time exists substandard data, carrying out defect positioning and feedback control shielding structure, and optimizing the sensor layout, closing the redundant probe;

[0018] Visualizing all data.

[0019] Optionally, the dynamically calculating the shielding effectiveness according to the signal data, and correcting the system error, specifically comprises:

[0020] When switching to the non-cooperative source mode:

[0021] Collecting multi-angle incident signals and constructing a compressed sensing observation matrix; the incident signal is the signal data before penetrating the shielding body;

[0022] Based on the compressed sensing observation matrix, reconstructing the outdoor signal using the orthogonal matching pursuit algorithm, and calculating the shielding effectiveness; the outdoor signal is the signal data before penetrating the shielding body;

[0023] When switching to the cooperative source mode:

[0024] Correcting the system error using the reference signal.

[0025] Optionally, in the non-cooperative source mode, the dynamic calculation formula of the shielding effectiveness is:

[0026]

[0027] E ext (t) is the time-varying electric field intensity detected outdoors, Eint (t) is the electric field strength detected indoors over time, and a and b are the material temperature and deformation coefficients, respectively, and AT(t) is the temperature change, S deform is the change in shielding effectiveness caused by deformation.

[0028] Optionally, if the attenuation trend in the future set time has unqualified data, defect positioning and feedback control are performed, and the specific process includes:

[0029] If the attenuation trend in the future set time has unqualified data, the unmanned aerial vehicle carrying an infrared thermal imager is started in a non-cooperative source mode to scan the wall deformation and determine the deformation position and size; in a cooperative source mode, TDR pulse injection is triggered, and the time domain reflectometry principle is used to analyze the reflection peak delay of electromagnetic waves at the shielding gap, to locate the door gap or waveguide port defects, and output corresponding instructions to control the motor to adjust the shielding structure.

[0030] According to the specific embodiments of the present application, the following technical effects are disclosed:

[0031] The application discloses a real-time electromagnetic shielding effectiveness monitoring system and method based on multi-source sensing, which comprises a mixed signal transmitting source for providing a reference or environmental electromagnetic signal; a sensor array arranged on both sides of a shielding body for collecting signal data before and after the reference or environmental electromagnetic signal penetrating the shielding body; a data processing center connected with the sensor array for calculating the shielding effectiveness in real time according to the signal data, updating the baseline data when the shielding effectiveness meets the standard, and positioning defects and feedback controlling the shielding structure when the shielding effectiveness does not meet the standard; and a visual platform connected with the data processing center for displaying all data. The application can improve the real-time performance and accuracy of electromagnetic shielding effectiveness monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0033] Figure 1 It is a connection control relationship diagram between system modules in the embodiment.

[0034] Figure 2 It is a signal processing flowchart of the system in the embodiment.

[0035] Figure 3 It is a signal processing flowchart in embodiment 1. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0037] The present application aims to provide a multi-source sensing-based real-time electromagnetic shielding effectiveness monitoring system and method, aiming to solve or improve at least one of the above technical problems.

[0038] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0039] As shown in Figure 1 The present application provides a multi-source sensing-based real-time electromagnetic shielding effectiveness monitoring system, comprising:

[0040] A mixed signal transmitting source is used to provide a reference or environmental electromagnetic signal.

[0041] A sensor array is arranged on both sides of the shielding body and is used to collect signal data before and after the reference or environmental electromagnetic signal penetrates the shielding body.

[0042] A data processing center is connected with the sensor array and is used to calculate shielding effectiveness in real time according to the signal data, update baseline data when the shielding effectiveness meets the standard, and perform defect positioning and feedback control of the shielding structure when the shielding effectiveness does not meet the standard.

[0043] A visualization platform is connected with the data processing center and is used to display all data.

[0044] As a specific embodiment, the mixed signal transmitting source includes a cooperative source (controllable transmitter) and a non-cooperative source (environmental electromagnetic signal) for providing a reference or environmental electromagnetic signal to compare signals before and after penetrating the shielding body. The sensor array includes indoor and outdoor electric field probes, magnetic field sensors, wideband spectrum analyzers, and directional couplers for collecting electromagnetic field intensity, spectral characteristics, and signal attenuation data on both sides (indoor and outdoor) of the shielding body. The data processing center includes FPGA / GPU high-speed signal processing units, machine learning algorithm libraries, and dynamic baseline databases for real-time analysis of signal attenuation, spectral distortion, calculation of shielding effectiveness (SE), and prediction of attenuation trends. The visualization platform is used to provide a human-computer interaction interface and data report generation, including a 3D electromagnetic field distribution map, a historical effectiveness trend curve, and a multi-band SE heat map.

[0045] In addition, the system also includes a control and feedback module, which is composed of an adaptive calibration controller, a shielding door / window adjustment mechanism and an early warning system, for dynamically adjusting the shielding device or triggering a maintenance warning according to the monitoring results.

[0046] The connection control relationship between the system modules is shown in Figure 1 The whole signal processing procedure is shown in Figure 2 The sensor array collects signals and transmits data to the data processing center, which pre-processes the data and calculates key parameters, and then sends instructions to the control and feedback module, which may adjust the settings of the signal source or the sensor array, while all data are finally displayed on the visualization platform.

[0047] As a specific embodiment, the dynamic calculation formula of shielding effectiveness is:

[0048]

[0049] wherein, E ext (t) is the time-varying electric field strength detected outdoors, E int (t) is the time-varying electric field strength detected indoors, and α and β are the material temperature variation coefficient and the deformation coefficient, respectively, ΔT(t) is the temperature variation, S deform is the shielding effectiveness variation caused by deformation.

[0050] As a specific embodiment, the defect positioning algorithm adopts the time domain reflectometry principle, analyzes the reflection peak delay of electromagnetic waves at the shielding body gap, and locates the door gap or waveguide port defects, with a positioning accuracy of ±5 cm.

[0051] Based on the above technical solution, the following embodiments are provided as shown below.

[0052] Embodiment 1

[0053] The shielding effectiveness is monitored by using a cooperative source, and the hardware components of the system include a transmitting end, a receiving end, a data processing center, a control unit, a feedback module and a visualization platform.

[0054] Transmitting end: multi-band OFDM signal generator (covering 10 kHz-40 GHz), deployed outside the machine room.

[0055] Receiving end: indoor side uses near-field probe array (frequency response ±0.5 dB) and high-speed ADC acquisition card (sampling rate 1 GS / s), and the probe deployment position covers the places where shielding effectiveness may be degraded, including door edges, window edges, waveguide ports, etc.; outdoor side uses a directional coupler (coupling degree 20 dB) connected to the signal source.

[0056] Control unit: FPGA to realize real-time signal synchronization (time delay <1 ns).

[0057] Data processing center: process and analyze the signals collected by indoor and outdoor probes.

[0058] Feedback module: mainly includes door, window, and waveguide adjustment modules. According to the defect position obtained by analysis, adjustments are made to ensure that the overall shielding effectiveness meets the standards.

[0059] Visualization platform: mainly includes display systems such as computer screens or tablets, and visual display data reports, including 3D electromagnetic field distribution maps, historical effectiveness trend curves, and multi-band SE heat maps, etc.

[0060] The signal processing flow is shown in Figure 3 , which includes:

[0061] Step 1: Transmit known OFDM signals, and synchronously collect time-domain waveforms by indoor and outdoor probes.

[0062] Step 2: Perform FFT transformation on the received signals to extract the amplitude and phase of the subcarriers.

[0063] Step 3: Calculate the error vector magnitude (EVM) and signal attenuation:

[0064]

[0065] where S ideal is the original reference signal generated by the transmitting end, corresponding to the complex form of the OFDM subcarrier, containing amplitude and phase; S received is the signal collected by the indoor probe array and demodulated by FFT; | | is the modulus of the complex number.

[0066] Step 4: If EVM > 5% or the attenuation exceeds the threshold, trigger the TDR positioning module to inject nanosecond-level pulses into the shield, and determine the defect position by the reflection peak time delay.

[0067] Step 5: Control the motor to drive the shielding door / window adjustment mechanism to compensate for the gap deformation.

[0068] Implementation 2

[0069] Shielding effectiveness monitoring using non-cooperative sources, the hardware components of the system include the receiving end, control unit, feedback module.

[0070] Receiving end: outdoor side uses a wideband log-periodic antenna array (1 MHz-6 GHz), which can be deployed on the roof or at the same height on the ground, 1 meter away from the wall; indoor side uses a ring-shaped magnetic field sensor (sensitivity 1 μA / m), which is placed in positions that may cause shielding effectiveness degradation, including door edges, window edges, waveguide openings, etc.

[0071] Processing unit: GPU accelerates blind source separation algorithm and supports multi-channel parallel computing.

[0072] Feedback module: mainly includes a drone equipped with an infrared thermal imager.

[0073] The signal processing flow includes:

[0074] Step 1: An outdoor antenna array captures ambient signals (e.g., 5G, FM radio), and indoor sensors record leaked signals.

[0075] Step 2: Use independent component analysis (ICA) to separate the mixed signals and reconstruct the outdoor original signal S ext (f).

[0076] Step 3: Construct the channel transfer function matrix H:

[0077]

[0078] Among them, S ext (f) is the reconstructed outdoor original signal, S int (f) is the leakage signal recorded by the indoor sensor.

[0079] Step 4: Analyze the frequency response dips of H(f) and, combined with the direction of arrival (DOA) estimation, locate the shielding effectiveness weak areas.

[0080] Step 5: If the SE value of a certain frequency band is lower than the national standard requirement, start the drone equipped with an infrared thermal imager to scan the wall for deformation and determine the location and size of the deformation.

[0081] Example 3

[0082] A hybrid self-learning mode is adopted, and the system hardware consists of a dual-mode signal source, a sensor network and a learning module.

[0083] Dual-mode signal source: Enables the ambient signal receiving chain during the day (compatible with non-cooperative source mode), switches to cooperative source mode at night and transmits calibration signals.

[0084] Sensor networks: Reconfigurable MIMO antenna arrays supporting beamforming and sparse sampling.

[0085] Learning module: LSTM network is deployed in the cloud to receive historical SE data and temperature and humidity sensor input.

[0086] The signal processing flow includes:

[0087] Step 1: In the non-cooperative source mode, collect multi-angle incident signals and construct the compressed sensing observation matrix:

[0088] y=ΦΨ S+n

[0089] where Φ is the sparse sampling matrix, Ψ S is the Fourier basis, n is the noise, including environmental noise and hardware noise.

[0090] Step 2: Reconstruct the outdoor signal using the Orthogonal Matching Pursuit (OMP) algorithm, and calculate the daily average SE value.

[0091] Step 3: Start the cooperative source calibration at night to correct the system error monitored during the day.

[0092] Step 4: The LSTM network inputs the historical SE sequence to predict the decay trend in the next 3 months:

[0093] SE(t+Δt)=f LSTM (SE(t),T(t),Humidity(t))

[0094] where T(t) is the temperature time series data, Humidity(t) is the humidity time series data, and Δt is the prediction time span, here Δt = 3 months.

[0095] Step 5: Dynamically optimize the sensor layout through reinforcement learning, and turn off redundant probes to reduce power consumption.

[0096] In summary, the present application covers different application scenarios through multiple embodiments, and the innovation lies in:

[0097] 1. Multi-source fusion: cooperative sources and non-cooperative sources work together, taking into account the needs of active detection and covert monitoring.

[0098] 2. Intelligent processing: combining compressed sensing and machine learning to achieve efficient data utilization and trend prediction.

[0099] 3. Closed-loop control: from monitoring, positioning to mechanical adjustment, the feedback link is fully automated.

[0100] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be mutually referred to.

[0101] The principles and implementation modes of the present application are described in this paper using specific examples. The above description of the embodiments is only to help understand the core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In summary, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A real-time electromagnetic shielding effectiveness monitoring system based on multi-source sensing, characterized in that: include: A mixed signal transmitter is used to provide a reference or environmental electromagnetic signal; A sensor array is provided on both sides of the shielding body, and is used to collect signal data of the reference or ambient electromagnetic signal before and after it penetrates the shielding body; a data processing center connected to the sensor array, configured to calculate shielding effectiveness in real time based on the signal data, update baseline data when the shielding effectiveness meets the standard, and perform defect location and feedback control of the shielding structure when the shielding effectiveness does not meet the standard; The visualization platform is connected to the data processing center and is used to display all data.

2. The real-time electromagnetic shielding effectiveness monitoring system based on multi-source sensing according to claim 1 is characterized in that: The mixed signal transmission source includes a cooperative source and a non-cooperative source; wherein the cooperative source adopts a controllable transmitter, and the non-cooperative source adopts an environmental electromagnetic signal.

3. The real-time electromagnetic shielding effectiveness monitoring system based on multi-source sensing according to claim 1 is characterized in that: The sensor array includes indoor and outdoor electric field probes, a magnetic field sensor, a broadband spectrum analyzer, and a directional coupler.

4. The real-time electromagnetic shielding effectiveness monitoring system based on multi-source sensing according to claim 1 is characterized in that: The sensor array adopts a reconfigurable MIMO antenna array.

5. A real-time electromagnetic shielding effectiveness monitoring method based on multi-source sensing, applied to the system according to any one of claims 1 to 4, characterized in that: include: Switch the application mode of the mixed signal transmitter according to the set time and collect signal data in the current mode; dynamically calculating shielding effectiveness based on the signal data and correcting system errors; All shielding effectiveness data at and before the current moment are input into the LSTM network as historical data for prediction to determine the attenuation trend within a set time in the future; If there is no data that does not meet the standard in the attenuation trend within the future set time, the baseline data is updated; if there is data that does not meet the standard in the attenuation trend within the future set time, defect location and feedback control shielding structure are performed, and the sensor layout is optimized and redundant probes are turned off; Visualize all data.

6. The real-time electromagnetic shielding effectiveness monitoring method based on multi-source sensing according to claim 5 is characterized in that: The dynamically calculating shielding effectiveness according to the signal data and correcting the system error specifically includes: When switching to non-cooperative source mode: Collecting multi-angle incident signals and constructing a compressed sensing observation matrix; the incident signals are signal data before penetrating the shielding body; Based on the compressed sensing measurement matrix, an orthogonal matching pursuit algorithm is used to reconstruct the outdoor signal and calculate the shielding effectiveness; the outdoor signal is the signal data before penetrating the shielding body; When switching to cooperative source mode: The reference signal is used to correct the system error.

7. The real-time electromagnetic shielding effectiveness monitoring method based on multi-source sensing according to claim 6, characterized in that: In the non-cooperative source mode, the dynamic calculation formula of the shielding effectiveness is: Among them, E ext (t) is the electric field strength detected outdoors that varies with time, E int (t) is the electric field intensity detected indoors and changes with time, α and β are the temperature coefficient and deformation coefficient of the material respectively, ΔT(t) is the temperature change, S deform is the change in shielding effectiveness caused by deformation.

8. The real-time electromagnetic shielding effectiveness monitoring method based on multi-source sensing according to claim 6, characterized in that: If the attenuation trend within the future set time period does not meet the standard, defect location and feedback control are performed. The specific process includes: If the attenuation trend within the future set time does not meet the standard, the drone equipped with an infrared thermal imager is started in the non-cooperative source mode to scan the wall for deformation and determine the location and size of the deformation; in the cooperative source mode, TDR pulse injection is triggered, and the principle of time domain reflectometry is used to analyze the reflection peak delay of the electromagnetic wave at the gap of the shielding body, locate the door gap or waveguide mouth defect, output corresponding instructions, and control the motor to adjust the shielding structure.

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