High-altitude strong electromagnetic environment monitoring method and system based on microsensor

Abstract

The invention discloses a high-altitude strong electromagnetic environment monitoring method and system based on a micro sensor. The method relates to the technical field of high-altitude strong electromagnetic environment monitoring, and comprises the following steps: obtaining electromagnetic signals from a plurality of positions in a high-altitude operation environment, and obtaining a first low-frequency-band electromagnetic data set in the high-altitude strong electromagnetic environment; extracting key features of the first low-frequency-band electromagnetic data set to obtain an effective low-frequency-band electromagnetic data set; performing multi-channel data fusion on the effective low-frequency-band electromagnetic data set, dynamically adjusting signal gain according to a signal-to-noise ratio by combining a blind signal enhancement technology to obtain a second low-frequency-band electromagnetic signal, and calculating a gain effect of the second low-frequency-band electromagnetic signal; and when the gain effect is within a preset threshold range, obtaining a low-frequency electromagnetic signal in a strong electromagnetic environment. The effect of improving the weak signal detection capability is achieved, and the problem that low-intensity or low-frequency electromagnetic signals cannot be accurately captured in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of high-altitude strong electromagnetic environment monitoring, and in particular to a high-altitude strong electromagnetic environment monitoring method and system based on a micro sensor. Background Art

[0002] With the development of modern communication technology, the electromagnetic environment at high altitudes has become increasingly complex. Various communication signals, navigation signals, etc. will have an impact on the normal operation of aircraft. Monitoring of strong electromagnetic environments at high altitudes is crucial to aviation safety. By monitoring and analyzing the electromagnetic environment at high altitudes, potential sources of interference can be discovered in a timely manner to ensure that the aircraft's communication and navigation systems are not affected, thereby ensuring flight safety.

[0003] Micro sensors use micro-electromechanical systems (MEMS) technology and have the characteristics of high sensitivity and low power consumption. Micro sensors play an important role in monitoring high-altitude strong electromagnetic environments. Through their high sensitivity, low power consumption, fast response and multi-parameter synchronous measurement, they can provide accurate and real-time environmental data, providing strong support for environmental protection and scientific research.

[0004] For example, the invention with the public number: CN105588923B provides an environmental monitoring method and an environmental monitoring device based on an environmental monitor. The environmental monitoring method includes a host awakening step, detecting the real-time clock signal sent by the real-time clock circuit, and waking up the host in a dormant state at a preset time point; a sensor awakening step, the host awakens at least one sensor, and the sensor collects air quality data after being awakened; a data processing step, the host receives the air quality data collected by the sensor and sends the air quality data to the cloud server; a dormant step, the sensor enters a dormant state after sending the collected air quality data to the host, and the host enters a dormant state after sending the air quality data to the cloud server. The environmental monitoring device provides an execution module for the method. The present invention reduces the power consumption of the environmental monitor and extends the standby time of the environmental monitor by reasonably dormant and awakening each circuit module of the environmental monitor.

[0005] For example, the invention with the public number: CN109211911B provides a water environment monitoring method. The water environment monitoring method includes the following steps: obtaining map information of a preset water body. The map information is transmitted to a fish-shaped monitoring device; a drone transports the fish-shaped monitoring device to the preset water body; the fish-shaped monitoring device dives into the preset water body and performs photographic monitoring based on the map information; the video information obtained by photographic monitoring is transmitted to the drone; the drone sends the video information to a control center; and the control center performs post-processing on the video information to obtain water environment information. The water environment monitoring method is highly efficient and can perform macroscopic real-time monitoring of the water environment.

[0006] The accuracy and sensitivity of many current high-altitude electromagnetic environment monitoring technologies are still affected by equipment performance, platform limitations, and the complexity of interference sources. For example, some detectors may not be able to accurately capture low-intensity or low-frequency electromagnetic signals in a strong electromagnetic environment. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a method and system for monitoring a high-altitude strong electromagnetic environment based on microsensors. The present invention achieves the effect of improving the detection capability of weak signals through a high-precision multi-point measurement method combined with high-resolution spectrum analysis, thereby solving the problem in the prior art that low-intensity or low-frequency electromagnetic signals cannot be accurately captured.

[0008] To achieve the above object, the present invention provides the following technical solutions: A method for monitoring a high-altitude strong electromagnetic environment based on a micro-sensor comprises the following steps: acquiring electromagnetic signals from a plurality of positions in a high-altitude working environment, performing independent component analysis on the electromagnetic signals, and obtaining a first low-frequency band electromagnetic data set in the high-altitude strong electromagnetic environment; extracting key features of the first low-frequency band electromagnetic data set in combination with a deep neural network, and obtaining an effective low-frequency band electromagnetic data set; performing multi-channel data fusion on the effective low-frequency band electromagnetic data set based on a multi-path signal reconstruction technique, and dynamically adjusting the signal gain according to the signal-to-noise ratio in combination with a blind signal enhancement technique, and obtaining a second low-frequency band electromagnetic signal, and calculating the gain effect of the second low-frequency band electromagnetic signal; when the gain effect is within a preset threshold range, obtaining a low-frequency electromagnetic signal in a strong electromagnetic environment.

[0009] In a preferred embodiment, the first low-frequency band electromagnetic data set in the high-altitude strong electromagnetic environment is obtained by the following method: calculate the density matrix of the source signal matrix, subtract the total signal mean for each electromagnetic signal, and construct the covariance matrix of the signal; calculate the maximum negative entropy of the covariance matrix of the constructed signal, perform signal blind source separation based on the maximum negative entropy of the covariance matrix, and convert the covariance matrix into a unit matrix; construct and update the separation matrix through an iterative algorithm, and when the change in the separation matrix is ​​lower than a preset threshold, obtain the first low-frequency band electromagnetic data set in the high-altitude strong electromagnetic environment.

[0010] In a preferred embodiment, the key features of the first low-frequency band electromagnetic data set include time domain features, frequency domain features, signal patterns and anomaly detection features, specifically: the time domain features include signal waveform features, signal peak and valley values ​​and periodicity; the frequency domain features include power spectrum density and frequency band energy; the signal pattern and anomaly detection features include waveform morphology and signal gradual change trend.

[0011] In a preferred embodiment, the key features of the first low-frequency band electromagnetic dataset are extracted by combining a deep neural network, specifically: the first low-frequency band electromagnetic dataset is input into the deep neural network, the time series data in the first low-frequency band electromagnetic dataset is divided into multiple time windows according to a preset window length, and the data in each time window is input into the deep neural network as a sample; after training the network, pre-selected features are extracted from each time window; the extracted features are learned, and each feature parameter is adjusted at the same time, and a feature vector is output to obtain a valid low-frequency band electromagnetic dataset.

[0012] In a preferred embodiment, the multi-channel data fusion of the effective low-frequency band electromagnetic data set is performed based on the multi-path signal reconstruction technology, specifically: the time offset between each signal is calculated based on the cross-correlation, and the calculated time delay is time-shifted; the multiple signal channels after the shift processing are weighted averaged based on the signal-to-noise ratio of the signal to obtain a fused total estimate.

[0013] In a preferred embodiment, the calculation of the gain effect of the second low-frequency band electromagnetic signal is specifically as follows: analyzing the frequency spectrum of the second low-frequency band electromagnetic signal to determine the frequency range of the signal; integrating the signal in the frequency range to obtain the power of the signal in each frequency band, and obtaining a power spectrum density line graph based on the power of the signal in each frequency band; and obtaining the gain effect of the second low-frequency band electromagnetic signal based on the power of the signal in each frequency band combined with the calculation formula of the gain effect.

[0014] In a preferred embodiment, the system of the high-altitude strong electromagnetic environment monitoring method based on microsensors is characterized in that it includes an electromagnetic signal acquisition and analysis module, a feature extraction module, a data fusion and signal gain module, and an electromagnetic signal output module: the electromagnetic signal acquisition and analysis module is used to acquire electromagnetic signals from several positions in a high-altitude working environment, perform independent component analysis on the electromagnetic signals, and obtain a first low-frequency band electromagnetic data set in the high-altitude strong electromagnetic environment; the feature extraction module is used to extract the key features of the first low-frequency band electromagnetic data set in combination with a deep neural network to obtain an effective low-frequency band electromagnetic data set; the data fusion and signal gain module is used to perform multi-channel data fusion on the effective low-frequency band electromagnetic data set based on multi-path signal reconstruction technology, and dynamically adjust the signal gain according to the signal-to-noise ratio in combination with blind signal enhancement technology to obtain a second low-frequency band electromagnetic signal, and calculate the gain effect of the second low-frequency band electromagnetic signal; the electromagnetic signal output module is used to obtain a low-frequency electromagnetic signal in a strong electromagnetic environment when the gain effect is within a preset threshold range.

[0015] The technical effects and advantages of the high-altitude strong electromagnetic environment monitoring method and system based on micro-sensors of the present invention are as follows: 1. Through the high-precision multi-point measurement method, the detection capability of weak signals is improved, which effectively solves the problem in the existing technology that low-intensity or low-frequency electromagnetic signals cannot be accurately captured.

[0016] 2. By acquiring electromagnetic signals from multiple locations, the distribution of the electromagnetic field in space can be more comprehensively reflected, thereby improving the accuracy and reliability of signal analysis, and further achieving better separation of target signals and noise.

[0017] 3. Through the use of independent component analysis (ICA) technology, independent signal sources can be extracted from mixed observation signals, thereby optimizing signal separation effects and enhancing signal quality.

[0018] 4. By combining path signal reconstruction technology with blind signal enhancement technology, the accurate characteristics of the signal can be restored, thereby avoiding the errors caused by signal multipath effects in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flowchart of a high-altitude strong electromagnetic environment monitoring method based on a microsensor provided in an embodiment of the present application; Figure 2 A schematic diagram of the system structure of a high-altitude strong electromagnetic environment monitoring method based on a micro sensor provided in an embodiment of the present application; DETAILED DESCRIPTION The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0020] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0021] Embodiment 1, Figure 1 The present invention provides a high-altitude strong electromagnetic environment monitoring method based on a micro sensor, comprising the following steps: S1 acquires electromagnetic signals from a plurality of positions in a high-altitude working environment, performs independent component analysis on the electromagnetic signals, and obtains a first low-frequency band electromagnetic data set in a high-altitude strong electromagnetic environment; S2 extracts key features of the first low-frequency band electromagnetic data set by combining a deep neural network to obtain an effective low-frequency band electromagnetic data set; S3 performs multi-channel data fusion on the effective low-frequency band electromagnetic data set based on multi-path signal reconstruction technology, and dynamically adjusts the signal gain according to the signal-to-noise ratio in combination with blind signal enhancement technology to obtain a second low-frequency band electromagnetic signal, and calculates the gain effect of the second low-frequency band electromagnetic signal; S4: When the gain effect is within the preset threshold range, a low-frequency electromagnetic signal in a strong electromagnetic environment is obtained.

[0022] In high-altitude working environments, especially strong electromagnetic environments, using multiple receiving points for signal measurement can help analyze the changes in electromagnetic signals in space. By comprehensively analyzing the signal data from these different receiving points, we can have a more comprehensive understanding of the distribution characteristics of electromagnetic waves in the environment, and then extract the spatial characteristics of the target signal.

[0023] The first low-frequency band electromagnetic data set includes acquisition timestamp information, acquisition node location information, signal sampling frequency, signal amplitude distribution, and phase difference.

[0024] By acquiring electromagnetic signals from multiple locations and performing independent component analysis (ICA), environmental noise or mixed signals are removed and pure signal components are extracted. The role of independent component analysis is to separate independent source signals from multiple received signals.

[0025] The electromagnetic signals are obtained from several positions in the high-altitude working environment by an electromagnetic field detector, and the electromagnetic field detector includes the following parts: Magnetometer: used to measure changes in magnetic field in space; Electric field sensor: used to measure the electric field strength; Wideband receiver: can capture a wider frequency range, suitable for measuring electromagnetic signals of different frequency bands; Antenna array: can receive electromagnetic signals and perform spatial filtering and directionality analysis.

[0026] It should be noted that when sampling from multiple sensors, the time synchronization between different sensors needs to be ensured. Time deviation will affect the effect of ICA, especially when accurate signal separation is required.

[0027] The electromagnetic signal is defined as follows:

[0028] Where: is the observed signal matrix, i.e., the electromagnetic signal; is the mixing matrix, is the source signal matrix.

[0029] It should be noted that each column in the observed signal matrix is ​​a signal received by the sensor, and each column in the source signal matrix is ​​an independent signal source.

[0030] The first low-frequency band electromagnetic data set in the high-altitude strong electromagnetic environment is obtained as follows: Calculate the density matrix of the source signal matrix, subtract the total signal mean from each electromagnetic signal, and construct the signal covariance matrix; Calculating the maximum negative entropy of the covariance matrix of the constructed signal, performing signal blind source separation based on the maximum negative entropy of the covariance matrix, and converting the covariance matrix into a unit matrix; An updated separation matrix is ​​constructed through an iterative algorithm, and when the change in the separation matrix is ​​lower than a preset threshold, the first low-frequency band electromagnetic data set in a high-altitude strong electromagnetic environment is obtained.

[0031] The first low-frequency band electromagnetic data set in the high-altitude strong electromagnetic environment includes low-frequency communication signals, strong electromagnetic interference signals, and radar navigation signals.

[0032] The calculation formula for maximizing the negative entropy of the covariance matrix of the signal constructed by the calculation is as follows:

[0033] Where: is the maximum negative entropy of the covariance matrix of the signal, is the probability density function of the source signal matrix, is the covariance matrix of the signal.

[0034] The change amount of the separation matrix is ​​obtained by calculating the difference between the current separation matrix and the separation matrix of the previous iteration. The specific calculation formula is as follows:

[0035] Where: is the separation matrix, is the F-norm of the matrix, is the rank of the separation matrix with i rows and j columns, is the separation matrix obtained after the kth iteration.

[0036] The key features of the first low-frequency band electromagnetic data set include time domain features, frequency domain features, signal patterns and anomaly detection features, specifically: The time domain characteristics include signal waveform characteristics and signal peak and valley values ​​and periodicity; The frequency domain characteristics include power spectrum density and frequency band energy; The signal pattern and anomaly detection features include waveform shape and signal gradual change trend.

[0037] The extracting of key features of the first low-frequency band electromagnetic data set by combining a deep neural network is specifically as follows: Inputting the first low-frequency band electromagnetic data set into a deep neural network, dividing the time series data in the first low-frequency band electromagnetic data set into a plurality of time windows according to a preset window length, and inputting the data in each time window into the deep neural network as a sample; After training the network, pre-selected features are extracted from each time window; The extracted features are learned, and the characteristic parameters are adjusted at the same time, and the characteristic vector is output to obtain an effective low-frequency band electromagnetic data set.

[0038] Multipath signal refers to the phenomenon that signals propagated through different paths (such as reflection, refraction, scattering, etc.) reach the receiver. When processing electromagnetic signals, especially in complex environments, signals usually experience multiple reflections or refractions, thereby generating signals of multiple paths. These signals propagate through different paths and may be delayed in time or frequency, resulting in signal distortion. The goal of multipath signal reconstruction technology is to restore the accurate characteristics of the original signal by processing and fusing signals from different paths.

[0039] The multi-channel data fusion of the effective low-frequency band electromagnetic data set based on the multi-path signal reconstruction technology is specifically: Calculate the time offset between each signal based on the cross-correlation, and perform time shift processing on the calculated time delay; The multiple signal channels after the shift processing are weighted averaged based on the signal-to-noise ratio to obtain the fusion total estimation value.

[0040] The time offset between the signals is calculated based on the cross-correlation, specifically:

[0041] Where: For signal and signal The cross-correlation function between For signal and signal The time delay between.

[0042] The weighted averaging of the multiple signal channels after the shift processing is performed based on the signal-to-noise ratio of the signal, specifically:

[0043] Where: is the weight of the i-th signal channel, Represents the signal-to-noise ratio of the i-th signal channel, and N is the total number of channels.

[0044] The total estimated value is calculated as follows:

[0045] Where: represents the total estimated value, is the weight of the i-th signal channel, N is the total number of channels, is the output of the ith signal channel.

[0046] Blind Signal Enhancement is a method of processing and optimizing based on the characteristics of the received signal itself without prior knowledge of the characteristics or model of the signal source. The blind signal enhancement method mainly uses the statistical characteristics of the signal to enhance the useful signal and suppress the noise.

[0047] The combined blind signal enhancement technology dynamically adjusts the signal gain according to the signal-to-noise ratio, which is achieved through the following formula:

[0048] Where: is the adjusted gain, is the current gain, is the target signal-to-noise ratio, is the current signal-to-noise ratio.

[0049] It should be noted that gain adjustment depends on the signal-to-noise ratio. If the SNR of the signal is low, the signal strength can be improved by increasing the gain. Gain adjustment also depends on the time change of the signal, that is, the signal may change over time. By monitoring the changes in the signal in real time, the gain can be adjusted dynamically.

[0050] The gain effect usually refers to the increase in signal strength after gain adjustment. The gain effect can be calculated by comparing the signal strength before and after processing, and its calculation method is usually completed through the definition of gain factor.

[0051] The step of calculating the gain effect of the second low-frequency band electromagnetic signal is specifically as follows: Analyzing the frequency spectrum of the second low-frequency band electromagnetic signal to determine the frequency range of the signal; Integrate the signal in the frequency range to obtain the power of the signal in each frequency band, and obtain a power spectrum density line graph based on the power of the signal in each frequency band; The gain effect of the second low-frequency band electromagnetic signal is obtained according to the power of the signal in each frequency band combined with the calculation formula of the gain effect.

[0052] The calculation formula for calculating the gain effect of the second low-frequency band electromagnetic signal is as follows:

[0053] Where: is the calculation formula for the gain effect, is the power of the signal input, is the power of the signal output.

[0054] It should be noted that the unit of gain effect is decibel.

[0055] It should be noted that the purpose of weighted averaging is to allow each signal channel to contribute different weights to the final estimate based on its reliability or importance. The weight of a signal is proportional to its signal-to-noise ratio (SNR). A signal with a higher SNR is more reliable and is given a greater weight.

[0056] Low-frequency signals in a strong electromagnetic environment have the following characteristics: Strong penetrating power: Low-frequency signals (e.g., 0.1 Hz to hundreds of Hz) have strong penetrating power and can penetrate solid materials such as buildings and the ground. Therefore, in a strong electromagnetic environment, low-frequency signals can propagate between obstacles better than high-frequency signals, which makes low-frequency signals have advantages in complex environments, especially in underground or remote detection, where the penetration effect of low-frequency signals is more obvious; Less interference from high-frequency noise: In a high electromagnetic noise environment, high-frequency signals are often seriously interfered by noise, resulting in a decrease in the quality of signal transmission. Low-frequency signals are relatively less susceptible to interference from high-frequency electromagnetic noise due to their lower frequency and longer wavelength. Therefore, in areas with strong electromagnetic environments, the reliability of low-frequency signals is relatively high, especially when conducting remote monitoring or long-term signal transmission, low-frequency signals are more stable; Smaller energy loss: Electromagnetic waves of low-frequency signals are usually less absorbed by the surrounding environment, thus reducing energy loss during transmission. High-frequency signals are more likely to interact with the medium due to their shorter wavelengths, resulting in greater energy attenuation. Strong anti-interference ability: In a complex electromagnetic environment, the frequency characteristics of low-frequency signals make them less sensitive to electromagnetic interference (such as from power lines, industrial equipment, etc.). Especially when performing signal processing, the anti-interference ability of low-frequency signals is relatively strong; Better transmission stability: The propagation characteristics of low-frequency signals make their signals decay slowly, especially for ground wave propagation or underground wave propagation. Low-frequency signals can often maintain a longer transmission distance and are not easily affected by environmental changes. In a strong electromagnetic environment, the propagation stability of low-frequency signals helps maintain the continuity of communication and measurement.

[0057] The system of the high-altitude strong electromagnetic environment monitoring method based on micro-sensors is characterized by comprising an electromagnetic signal acquisition and analysis module, a feature extraction module, a data fusion and signal gain module, and an electromagnetic signal output module: An electromagnetic signal acquisition and analysis module is used to acquire electromagnetic signals from several positions in a high-altitude working environment, perform independent component analysis on the electromagnetic signals, and obtain a first low-frequency band electromagnetic data set in a high-altitude strong electromagnetic environment; A feature extraction module, used to extract key features of the first low-frequency band electromagnetic data set in combination with a deep neural network to obtain an effective low-frequency band electromagnetic data set; A data fusion and signal gain module, used to perform multi-channel data fusion on the effective low-frequency band electromagnetic data set based on multi-path signal reconstruction technology, and dynamically adjust the signal gain according to the signal-to-noise ratio in combination with blind signal enhancement technology to obtain a second low-frequency band electromagnetic signal, and calculate the gain effect of the second low-frequency band electromagnetic signal; The electromagnetic signal output module is used to obtain a low-frequency electromagnetic signal in a strong electromagnetic environment when the gain effect is within a preset threshold range.

[0058] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0059] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0060] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0061] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0062] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0063] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-altitude strong electromagnetic environment monitoring method based on microsensors, characterized in that: The following steps are involved: Acquire electromagnetic signals from a plurality of positions in a high-altitude working environment, perform independent component analysis on the electromagnetic signals, and obtain a first low-frequency band electromagnetic data set in a high-altitude strong electromagnetic environment; Extracting key features of the first low-frequency band electromagnetic dataset using a deep neural network to obtain an effective low-frequency band electromagnetic dataset; Based on the multi-path signal reconstruction technology, multi-channel data fusion is performed on the effective low-frequency band electromagnetic data set, and the signal gain is dynamically adjusted according to the signal-to-noise ratio in combination with the blind signal enhancement technology to obtain a second low-frequency band electromagnetic signal, and the gain effect of the second low-frequency band electromagnetic signal is calculated; When the gain effect is within the preset threshold range, a low-frequency electromagnetic signal in a strong electromagnetic environment is obtained.

2. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 1 is characterized in that: The first low-frequency band electromagnetic data set in the high-altitude strong electromagnetic environment is obtained as follows: Calculate the density matrix of the source signal matrix, subtract the total signal mean from each electromagnetic signal, and construct the signal covariance matrix; Calculating the maximum negative entropy of the covariance matrix of the constructed signal, performing signal blind source separation based on the maximum negative entropy of the covariance matrix, and converting the covariance matrix into a unit matrix; An updated separation matrix is ​​constructed through an iterative algorithm. When the change in the separation matrix is ​​lower than a preset threshold, the first low-frequency band electromagnetic data set in a high-altitude strong electromagnetic environment is obtained.

3. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 2 is characterized in that: The key features of the first low-frequency band electromagnetic data set include time domain features, frequency domain features, signal patterns and anomaly detection features, specifically: The time domain characteristics include signal waveform characteristics and signal peak and valley values ​​and periodicity; The frequency domain characteristics include power spectrum density and frequency band energy; The signal pattern and anomaly detection features include waveform shape and signal gradual change trend.

4. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 3 is characterized in that: The extracting of key features of the first low-frequency band electromagnetic data set by combining a deep neural network is specifically as follows: Inputting the first low-frequency band electromagnetic data set into a deep neural network, dividing the time series data in the first low-frequency band electromagnetic data set into a plurality of time windows according to a preset window length, and inputting the data in each time window into the deep neural network as a sample; After training the network, pre-selected features are extracted from each time window; The extracted features are learned, and the characteristic parameters are adjusted at the same time, and the characteristic vector is output to obtain an effective low-frequency band electromagnetic data set.

5. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 4 is characterized in that: The multi-channel data fusion of the effective low-frequency band electromagnetic data set based on the multi-path signal reconstruction technology is specifically: Calculate the time offset between each signal based on the cross-correlation, and perform time shift processing on the calculated time delay; The multiple signal channels after the shift processing are weighted averaged based on the signal-to-noise ratio to obtain the fusion total estimation value.

6. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 5 is characterized in that: The step of calculating the gain effect of the second low-frequency band electromagnetic signal is specifically as follows: Analyzing the frequency spectrum of the second low-frequency band electromagnetic signal to determine the frequency range of the signal; Integrate the signal in the frequency range to obtain the power of the signal in each frequency band, and obtain a power spectrum density line graph based on the power of the signal in each frequency band; The gain effect of the second low-frequency band electromagnetic signal is obtained according to the power of the signal in each frequency band combined with the calculation formula of the gain effect.

7. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 6 is characterized in that: The change amount of the separation matrix is ​​obtained by calculating the difference between the current separation matrix and the separation matrix of the previous iteration. The specific calculation formula is as follows: Where: is the separation matrix, is the F-norm of the matrix, is the rank of the separation matrix with i rows and j columns, is the separation matrix obtained after the kth iteration.

8. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 7 is characterized in that: The calculation formula for calculating the gain effect of the second low-frequency band electromagnetic signal is as follows: Where: is the calculation formula for the gain effect, is the power of the signal input, is the power of the signal output.

9. The high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in claim 8 is characterized in that: The combined blind signal enhancement technology dynamically adjusts the signal gain according to the signal-to-noise ratio, which is achieved through the following formula: Where: is the adjusted gain, is the current gain, is the target signal-to-noise ratio, is the current signal-to-noise ratio.

10. A system using the high-altitude strong electromagnetic environment monitoring method based on microsensors as claimed in any one of claims 1 to 9, characterized in that: Including electromagnetic signal acquisition and analysis module, feature extraction module, data fusion and signal gain module, electromagnetic signal output module: An electromagnetic signal acquisition and analysis module is used to acquire electromagnetic signals from several positions in a high-altitude working environment, perform independent component analysis on the electromagnetic signals, and obtain a first low-frequency band electromagnetic data set in a high-altitude strong electromagnetic environment; A feature extraction module, used to extract key features of the first low-frequency band electromagnetic data set in combination with a deep neural network to obtain an effective low-frequency band electromagnetic data set; A data fusion and signal gain module, used to perform multi-channel data fusion on the effective low-frequency band electromagnetic data set based on multi-path signal reconstruction technology, and dynamically adjust the signal gain according to the signal-to-noise ratio in combination with blind signal enhancement technology to obtain a second low-frequency band electromagnetic signal, and calculate the gain effect of the second low-frequency band electromagnetic signal; The electromagnetic signal output module is used to obtain a low-frequency electromagnetic signal in a strong electromagnetic environment when the gain effect is within a preset threshold range.

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