CN111950410B - On-line electromagnetic environment monitoring device and method based on radio fingerprint recognition - Google Patents

Abstract

The present invention relates to an online electromagnetic environment monitoring device and method based on radio fingerprint recognition. After collecting electromagnetic waves in the target frequency band, they are converted into digital signals to obtain effective sample signals, and time-frequency domain transformation is completed. After spectrum analysis, characteristic parameters of the sample signals are obtained; comparison and judgment are performed based on the frequency database. If it is judged to be co-channel interference in the authorized frequency band, the radio fingerprint is extracted from the sample signal through a radio fingerprint recognition algorithm, and the result is further compared and analyzed with the radio fingerprint database module; the preliminary result of interference recognition is combined with the conditions of co-channel interference protection and adjacent frequency interference protection corresponding to the authorized frequency signal format to perform interference degree analysis and obtain monitoring results. The present invention can continuously and dynamically monitor the electromagnetic environment, does not rely on signal demodulation, can realize simultaneous monitoring of signals in multiple frequency bands and multiple formats, and can accurately identify co-channel interference through a radio fingerprint algorithm.

Description

On-line electromagnetic environment monitoring device and method based on radio fingerprint recognition

Technical Field

The present invention relates to the technical field of urban rail transit and high-speed railway monitoring, and in particular to an online electromagnetic environment monitoring device and method based on radio fingerprint recognition.

Background technique

At present, the domestic urban rail transit industry is developing rapidly, and the scale of the line network and the operating mileage are constantly expanding. The total operating mileage of domestic high-speed railways has reached 35,000 kilometers. The reliable operation and communication quality of wireless communication systems such as wireless dispatching and wireless train control in high-speed railways and rail transit systems are crucial factors for the efficient and stable operation of subways and high-speed railways and the safety of passengers. However, with the application of various wireless communication standards, the operating environment of the above systems is always facing the threat of external radio electromagnetic interference. The subway lines in various cities have experienced wireless interference to varying degrees, resulting in failures, suspension of operation or degraded operation. The high-speed rail wireless dispatching system is also often threatened by telecom operators and other illegal stations.

In order to avoid the above situation, electromagnetic environment tests along the line are usually carried out. However, the traditional electromagnetic environment test has many shortcomings. First, the traditional test is a discrete and transient test. It only organizes testers and simple equipment to conduct transient tests at a specific time and place. It cannot be continuously, comprehensively and dynamically monitored throughout the whole process. The test results are only responsible for the sampling values of this test. In the later operation stage, it is impossible to monitor and identify the interference generated by randomly generated external unauthorized base stations and terminals in real time; secondly, the data collection and interference analysis processes are separated, and the conclusions mostly rely on human analysis. The measurement accuracy is closely related to the level and rigor of the testers; thirdly, except for individual standard signals that can be tested by demodulating the signal analysis content, signals in other frequency bands need to use basic spectrum analyzers, which requires high business capabilities of testers. Fourth, it is difficult to identify the same-frequency interference signal by manual means, and it is necessary to cooperate with the railway base station switch operation to eliminate interference. The detection requires a large number of requests for cooperation, which is difficult to carry out. In addition, the traditional test cost is high, which is related to the number of data collection points, the number of standards, the frequency band range, and the number of tests; overall, the actual application effect is poor and it is difficult to meet the actual operation needs.

In recent years, the high-speed rail industry has seen the emergence of automated testing or online monitoring equipment for the GSM-R system, but it only monitors the radio electromagnetic environment of the single GSM-R signal. The online monitoring equipment generally uses two methods to identify and process co-channel interference: signal demodulation, which is achieved by comparing and analyzing various base stations or signaling identifiers at the back end; or by using fast Fourier transform-based technologies such as digital fluorescent spectrum display.

If co-channel interference identification is achieved through demodulation, the monitoring equipment must be equipped with RF and baseband equipment of the corresponding frequency band. For the urban rail transit field with more wireless standards, the software and hardware scale of the monitoring equipment for this implementation method will become huge and the cost will be very expensive. For the digital fluorescent spectrum display technology to achieve co-channel interference identification, it is essentially required that the interference signal and the useful signal do not completely overlap in the time domain, and there are also restrictions on the strength of the interference signal. Low-energy signals at the same time and frequency point cannot be identified. Both solutions are very limited in actual application and still have serious monitoring loopholes.

Summary of the invention

The purpose of the present invention is to provide an online electromagnetic environment monitoring device and method based on radio fingerprint recognition, so as to overcome the defects of the prior art and realize simultaneous monitoring of multi-band and multi-standard signals.

The technical solution adopted by the present invention is:

The online electromagnetic environment monitoring device based on radio fingerprint recognition is characterized by:

The device comprises:

RF receiving module: collects electromagnetic waves within the target frequency band;

Digital-to-analog conversion and sampling module: converts electromagnetic wave analog signals into digital signals, sets thresholds, and extracts valid sample signals within the target frequency band;

Time-frequency domain transformation digital processing module: completes the time-frequency domain transformation of the collected sample signal, and obtains the characteristic parameters of the sample signal after spectrum analysis;

Frequency database module: used to store authorized frequencies and frequency point information;

Radio fingerprint database module: used to store radio fingerprint information of authorized base stations and terminal devices;

Interference identification module: According to the authorized frequency, frequency point information and characteristic parameters of the sample signal, the module compares and judges with the frequency database. If it is determined to be co-frequency interference in the authorized frequency band, the radio fingerprint recognition algorithm is used to extract the radio fingerprint of the sample signal, and then the result is further compared and analyzed with the radio fingerprint database module.

Result analysis module: used to analyze the interference degree based on the preliminary results of interference identification, combined with the co-channel interference protection and adjacent-channel interference protection conditions corresponding to the authorized frequency signal system, and obtain the monitoring results.

The RF receiving module includes a receiving antenna, a filter, a low noise amplifier, a RF local oscillator, and a mixer. It collects electromagnetic waves within the target frequency band and receives, filters, amplifies, and down-converts RF signals.

The time-frequency domain transformation digital processing module completes the time-frequency domain transformation of the collected sample signal through a processing method based on fast Fourier transform, and performs spectrum analysis through energy domain and feature domain perception algorithms to obtain the characteristic parameters of the sample signal, including the center frequency, bandwidth, and amplitude.

The frequency database module has the function of dynamic update.

Thresholds are set in the radio fingerprint database module. New fingerprints outside the authorized database that appear with a certain frequency and regularity are automatically added to the whitelist, which has a dynamic update function.

The interference identification module makes comparison and judgment based on the frequency database:

When it is determined to be adjacent frequency interference of the authorized frequency point, further amplitude analysis is performed;

When it is determined to be co-channel interference in the authorized frequency band, the radio fingerprint is extracted from the sample signal through the radio fingerprint recognition algorithm, and then the result is further compared and analyzed with the radio fingerprint database module.

The online electromagnetic environment monitoring method based on radio fingerprint recognition is characterized by:

The method comprises the following steps:

S1. Set system parameters remotely online or locally, and configure the authorized frequency points, authorized base stations, radio fingerprints of terminal equipment, and sensitivity information and requirements for interference identification in the electromagnetic environment monitoring device;

S2. Set up one or more monitoring device front-end equipment near the target area to collect electromagnetic wave signals within the target frequency band on site;

S3, performing RF down-conversion, digital-to-analog conversion, and spectrum analysis on the collected valid sample signals; comparing with the authorized frequency database to obtain preliminary analysis results;

S4. Push the adjacent frequency signal obtained through preliminary analysis to the adjacent frequency interference analysis process;

S5. Push the co-frequency signal obtained through preliminary analysis to the co-frequency interference analysis process, start the radio fingerprint extraction and processing function of the interference signal, and compare it with the authorized device fingerprint library to determine whether it is an unauthorized base station or terminal;

S6. Report the adjacent-frequency and co-frequency interference analysis results to the background equipment of the monitoring device, quantify the interference degree, obtain the monitoring results, and attach time and location information to the event analysis results.

In S2, according to the set threshold, relying on the feature domain algorithm and software radio technology, the effective sample signal in the target frequency band is extracted from the electromagnetic wave signal to identify the interference of the effective sample signal; through the energy domain algorithm and the fast Fourier transform algorithm, the parameters of the center frequency, bandwidth, and signal amplitude are identified, and based on the information in the authorized database, it is preliminarily qualitatively distinguished whether it belongs to adjacent frequency interference or co-frequency interference signal.

The identification of co-channel interference is achieved based on the radio fingerprinting algorithm.

The quantitative adjacent-channel/co-channel interference analysis process is as follows: for signals initially identified as adjacent-channel/co-channel interference, their frequency and amplitude information are sent to the background, where they are quantitatively compared with the preset radio frequency index parameters of the engineering equipment in use and the standard protocol parameters of the corresponding wireless communication system to determine whether the intensity of the adjacent-channel/co-channel interference will cause damage to the engineering equipment in use.

The present invention has the following advantages:

The present invention is an online monitoring device for wireless electromagnetic environment, and a method for monitoring target interference identification and analysis. For the target frequency band, the device realizes electromagnetic environment monitoring, interference analysis, and alarm reporting in a 24-hour continuous and dynamic monitoring manner. The device is based on a software radio architecture and a unified algorithm platform, is not limited to specific wireless communication standards and air interface protocols, does not rely on signal demodulation, and can realize multi-band and multi-standard signal simultaneous monitoring through the selection and configuration of a few optional supporting components, and can accurately identify co-frequency interference through the radio fingerprint algorithm.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 is a schematic diagram of the monitoring device network.

Figure 2 is a schematic diagram of the logical structure of the monitoring device.

Figure 3 is a flowchart of online monitoring signal processing.

Detailed ways

The present invention is described in detail below in conjunction with specific implementation modes.

The present invention relates to an online electromagnetic environment monitoring device based on radio fingerprint recognition, the device comprising:

RF receiving module: collects electromagnetic waves within the target frequency band;

Digital-to-analog conversion and sampling module: converts electromagnetic wave analog signals into digital signals, sets thresholds, and extracts valid sample signals within the target frequency band;

Time-frequency domain transformation digital processing module: completes the time-frequency domain transformation of the collected sample signal, and obtains the characteristic parameters of the sample signal after spectrum analysis;

Frequency database module: used to store authorized frequencies and frequency point information;

Radio fingerprint database module: used to store radio fingerprint information of authorized base stations and terminal devices;

Interference identification module: According to the authorized frequency, frequency point information and characteristic parameters of the sample signal, the module compares and judges with the frequency database. If it is determined to be co-frequency interference in the authorized frequency band, the radio fingerprint recognition algorithm is used to extract the radio fingerprint of the sample signal, and then the result is further compared and analyzed with the radio fingerprint database module.

Result analysis module: used to analyze the interference degree based on the preliminary results of interference identification, combined with the co-channel interference protection and adjacent-channel interference protection conditions corresponding to the authorized frequency signal system, and obtain the monitoring results.

The RF receiving module includes a receiving antenna, filter, low noise amplifier, RF local oscillator, and mixer. It collects electromagnetic waves in the target frequency band, receives, filters, amplifies, and down-converts RF signals. The monitored frequency bands include 350MHz, 800MHz, 900MHz, 1.8GHz, 2.4GHz, and 5GHz; the monitored wireless signal formats include PDT, TETRA, GSM-R, LTE-M, and 802.11 series.

Digital-to-analog conversion and sampling module: converts analog signals into digital signals, sets thresholds, and extracts valid sample signals within the target frequency band. The analog-to-digital conversion is implemented using a high-speed ADC with a sampling rate of more than 11 bits and a sampling rate of more than 200Msps.

The time-frequency domain transformation digital processing module completes the time-frequency domain transformation of the collected sample signal through a processing method based on fast Fourier transform (FFT), performs spectrum analysis through energy domain and feature domain perception algorithms, and obtains the characteristic parameters of the sample signal, including the center frequency, bandwidth, and amplitude.

The frequency database module has the function of dynamic update.

The radio fingerprint database module stores the radio fingerprint (Radio Frequency Fingerprinting) information of authorized base stations and terminal devices, sets thresholds, and automatically assigns new fingerprints outside the authorized database that appear with a certain frequency and regularity to the whitelist. It has dynamic update and learning functions.

The interference identification module makes comparative judgments based on the frequency database: when it is judged to be adjacent-frequency interference of the authorized frequency point, further amplitude analysis is performed; when it is judged to be co-frequency interference of the authorized frequency band, the radio fingerprint is extracted from the sample signal through the radio fingerprint identification algorithm, and then the result is further compared and analyzed with the radio fingerprint database module.

The functional modules of the monitoring device of the present invention are logically divided. When implemented, multiple functional modules can be combined on the same physical module, or the functional module can be further divided into multiple physical or functional modules, but the functions achieved are consistent.

Preferably, the front-end acquisition equipment of the monitoring device is an outdoor device with a protection level of IP65 or above.

Preferably, the front-end acquisition equipment of the monitoring device is fixed or mobile.

Preferably, the monitoring device is online and can monitor continuously 24 hours a day, 7 days a week; however, the monitoring results can be uploaded in real time or semi-real time.

Preferably, the monitoring device is capable of dynamically learning and updating the database.

Preferably, the monitoring device can realize multi-point collaborative sensing through a distributed monitoring method, and improve the accuracy of spectrum sensing through a "training" method.

Preferably, the monitoring device is based on software radio and can monitor one or more standards and one or more frequency bands by changing a small number of radio frequency components without relying on digital demodulation.

Preferably, the monitoring device can be powered by remote power supply, battery or solar energy on-site. For solar power supply, the front-end equipment of the monitoring device is equipped with solar panels and batteries. For centralized power supply, the AC and DC power supply equipment on the machine room side is remotely powered through power supply cables. For battery power supply, the mobile device is powered by a built-in battery. Various power supply methods should be able to achieve a stable and reliable power supply effect.

Preferably, the data backhaul solution of the front-end equipment of the monitoring device can be implemented through optical fiber backhaul or Internet of Things (wired or wireless) backhaul.

Preferably, the radio fingerprint recognition algorithm of the monitoring device can select a fingerprint extraction technology based on transient signals or a fingerprint extraction technology based on steady-state signals, for example, using bispectrum, energy entropy, color moment, support vector machine, wavelet analysis, Hilbert-Huang transform and other algorithms.

The online electromagnetic environment monitoring method based on radio fingerprint recognition implemented on the basis of the above device includes the following steps:

S1. Set system parameters remotely online or locally, and configure the authorized frequency points, authorized base stations, radio fingerprints of terminal equipment, and sensitivity information and requirements for interference identification in the electromagnetic environment monitoring device;

S2. Set up one or more monitoring device front-end equipment near the target area to collect electromagnetic wave signals within the target frequency band on site;

S3, performing RF down-conversion, digital-to-analog conversion, and spectrum analysis on the collected valid sample signals; comparing with the authorized frequency database to obtain preliminary analysis results;

S4. Push the adjacent frequency signal obtained through preliminary analysis to the adjacent frequency interference analysis process;

S5. Push the co-frequency signal obtained through preliminary analysis to the co-frequency interference analysis process, start the radio fingerprint extraction and processing function of the interference signal, and compare it with the authorized device fingerprint library to determine whether it is an unauthorized base station or terminal;

S6. Report the adjacent-frequency and co-frequency interference analysis results to the background equipment of the monitoring device, quantify the interference degree, obtain the monitoring results, and attach time and location information to the event analysis results.

In the above method:

In S2, according to the set threshold, relying on the feature domain algorithm and software radio technology, the effective sample signal in the target frequency band is extracted from the electromagnetic wave signal to identify the interference of the effective sample signal. Through the energy domain algorithm and the fast Fourier transform algorithm, the center frequency, bandwidth, signal amplitude and other parameters are identified, and based on the information in the authorized database, it is preliminarily qualitatively distinguished whether it is an adjacent frequency interference or a same frequency interference signal.

The device has the function of identifying co-channel interference, and the identification of co-channel interference is achieved based on a radio fingerprint recognition algorithm.

Quantitative adjacent-channel/co-channel interference analysis process: For signals that are initially identified as adjacent-channel/co-channel interference, their frequency and amplitude information are sent to the background, where they are quantitatively compared with the preset RF index parameters of the engineering equipment in use and the standard protocol parameters of the corresponding wireless communication system to determine whether the intensity of the adjacent-channel/co-channel interference will cause damage to the engineering equipment in use.

Referring to the attached drawings, the present invention is further described in detail by taking the online electromagnetic environment monitoring implementation of the 800MHz frequency band of the subway TETRA system as an example. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

The device consists of the front-end equipment for online monitoring of the electromagnetic environment and the back-end equipment of the monitoring center. The specific equipment includes the following functional modules:

RF receiving module: used to collect radio wave signals, scan the target frequency band, receive, filter, amplify and down-convert RF signals. It is composed of 800MHz receiving antenna, filter, low noise amplifier, RF local oscillator, mixer, etc.

Digital-to-analog conversion and sampling module: The analog intermediate frequency signal is converted into a digital signal after being sampled by the ADC, and the threshold is set to extract the valid sample signal within the target frequency band. The analog-to-digital conversion is implemented using a high-speed ADC with more than 11 bits and a sampling rate of more than 200Msps.

Time-frequency domain transformation digital processing module: The collected sample signal is transformed in the time-frequency domain through a processing method based on the fast Fourier transform (FFT); spectrum analysis is performed through energy domain and feature domain perception methods to obtain characteristic parameters such as the center frequency, bandwidth, and amplitude of the sample signal.

Interference identification module: Based on the information of the authorized frequency and the characteristic parameters of the sample signal, the device frequency database is used for comparison and judgment. If it is determined to be adjacent frequency interference of the authorized frequency, the amplitude analysis is further performed. If it is determined to be co-frequency interference of the authorized frequency band, the radio fingerprint is extracted from the sample signal through the radio fingerprint identification algorithm, and then the result is further compared and analyzed with the radio fingerprint database module of the device.

Frequency database module: used to store authorized frequencies and frequency point information. It has dynamic update function.

Radio Frequency Fingerprint Database Module: used to store the radio frequency fingerprint information of authorized base stations and terminal devices. It has dynamic update and learning functions. Set thresholds, and automatically assign new fingerprints outside the authorized database that appear regularly and with a certain frequency to the whitelist.

Result analysis module: used to analyze the interference degree based on the preliminary results of interference identification, combined with the co-channel interference protection, adjacent-channel interference protection and other conditions corresponding to the authorized frequency signal system, and obtain the monitoring results.

An online electromagnetic environment monitoring method based on radio fingerprint recognition is implemented based on an online electromagnetic environment monitoring device based on radio fingerprint recognition. The method comprises the following steps:

S1. Set system parameters, limit the frequency sweep range to 806-821MHz and 851-866MHz, configure the frequency points authorized by the Radio Commission for this project, and the radio fingerprints of all base stations, vehicle-mounted stations, fixed stations, handheld stations and other terminal equipment in this project in the system;

S2. Deploy multiple monitoring device front-end equipment at a certain density near the subway line to collect electromagnetic wave signals within the frequency range of 806-821MHz and 851-866MHz on-site; 24-hour online collection is required.

S3. Perform RF down-conversion, digital-to-analog conversion, and FFT spectrum analysis on the collected valid sample signals. By comparing with the authorized frequency database, it can be preliminarily determined whether the collected signal is an adjacent frequency signal or a same frequency signal;

S4. Push the adjacent frequency signal obtained through preliminary analysis to the adjacent frequency interference analysis process;

S5. Push the co-frequency signal obtained through preliminary analysis to the co-frequency interference analysis process, start the radio fingerprint extraction and processing function of the interference signal, and compare it with the authorized device fingerprint library to determine whether it is an unauthorized base station or terminal;

S6. Report the preliminary analysis results of adjacent-frequency and co-frequency interference to the backend device through the IoT backhaul unit. For the TETRA system, the co-frequency interference protection ratio is 19dB, and the adjacent-frequency interference protection ratio is -45dB. The interference degree is quantified and the monitoring results are obtained. The time and location information are attached to the event analysis results.

The front-end equipment of the monitoring device is powered by on-site solar energy, has an IP67 protection level, and has high outdoor survivability.

The technical means disclosed in the scheme of the present invention are not limited to the technical means disclosed in the above specific implementation manner, but also include technical schemes composed of any combination of the above technical features.

The content of the present invention is not limited to the embodiments listed, and any equivalent changes made to the technical solution of the present invention by ordinary technicians in this field after reading the description of the present invention are covered by the claims of the present invention.

Claims (7)

1. On-line electromagnetic environment monitoring device based on radio fingerprint identification, its characterized in that:

the device comprises:

a radio frequency receiving module: collecting electromagnetic waves in a target frequency band;

digital-to-analog conversion and sampling module: converting the electromagnetic wave analog signal into a digital signal, setting a threshold value, and extracting an effective sample signal in a target frequency band;

the time-frequency domain transformation digital processing module: completing time-frequency domain transformation on the acquired sample signals, and obtaining characteristic parameters of the sample signals after spectrum analysis;

a frequency database module: the method is used for storing authorized frequency and frequency point information;

a radio fingerprint database module: for storing the radio fingerprint information of the authorized base station, terminal equipment;

interference identification module: comparing and judging according to the authorized frequency, the frequency point information and the characteristic parameters of the sample signal and the frequency database, and if the same frequency interference of the authorized frequency band is judged, extracting a radio fingerprint from the sample signal through a radio fingerprint identification algorithm, and further comparing and analyzing the sample signal with a radio fingerprint database module to obtain a result;

and a result analysis module: the method comprises the steps of carrying out interference degree analysis on a preliminary result of interference identification by combining with the conditions of same-frequency interference protection and adjacent-frequency interference protection corresponding to a signal system of an authorized frequency point to obtain a monitoring result;

the frequency database module has a dynamic updating function;

setting a threshold value in a radio fingerprint database module, and automatically attaching new fingerprints outside an authorized library, which occur regularly at a certain frequency, to a white list, thereby having a dynamic updating function;

the interference recognition module performs comparison and judgment according to the frequency database:

when the adjacent frequency interference of the authorized frequency point is judged, further carrying out amplitude analysis;

and when the same-frequency interference of the authorized frequency band is judged, the radio fingerprint is extracted from the sample signal through a radio fingerprint identification algorithm, and then the sample signal is further compared with a radio fingerprint database module for analysis to obtain a result.

2. The online electromagnetic environment monitoring device based on radio fingerprint recognition according to claim 1, wherein:

the radio frequency receiving module comprises a receiving antenna, a filter, a low-noise amplifier, a radio frequency local oscillator and a mixer, and is used for collecting electromagnetic waves in a target frequency band, receiving, filtering, amplifying and down-converting radio frequency signals.

3. The online electromagnetic environment monitoring device based on radio fingerprint recognition according to claim 2, wherein:

the time-frequency domain transformation digital processing module completes time-frequency domain transformation on the acquired sample signals through a processing mode based on fast Fourier transformation, and spectrum analysis is carried out through an energy domain and feature domain sensing algorithm to obtain feature parameters of the sample signals, wherein the feature parameters comprise a center frequency point, a frequency bandwidth and an amplitude.

4. A method of monitoring an online electromagnetic environment monitoring device based on radio fingerprinting according to any of claims 1-3, characterized in that:

the method comprises the following steps:

s1, remotely setting system parameters on line or locally, and configuring radio fingerprints of an authorized frequency point, an authorized base station and terminal equipment, sensitivity information for interference identification and requirements in an electromagnetic environment monitoring device;

s2, arranging one or more monitoring device front-end devices near the target area, and collecting electromagnetic wave signals within the range of the target frequency band on site;

s3, performing radio frequency down conversion, digital-to-analog conversion and spectrum analysis on the collected effective sample signals; comparing the result with an authorized frequency database to obtain a preliminary analysis result;

s4, pushing the preliminarily analyzed adjacent frequency signals to an adjacent frequency interference analysis flow;

s5, pushing the preliminarily analyzed same-frequency signals to a same-frequency interference analysis flow, starting a radio fingerprint extraction processing function of the interference signals, comparing the radio fingerprint extraction processing function with a fingerprint library of authorized equipment, and judging whether the radio fingerprint extraction processing function is an unauthorized base station or a terminal;

s6, reporting the adjacent frequency and same frequency interference analysis results to background equipment of the monitoring device, quantitatively judging the interference degree, obtaining the monitoring result, and attaching time and place information to the event analysis result.

5. The method for monitoring the online electromagnetic environment monitoring device based on radio fingerprint recognition according to claim 4, wherein:

s2, extracting effective sample signals in a target frequency band from electromagnetic wave signals according to a set threshold value and by means of a characteristic domain algorithm and a software radio technology, and performing interference identification on the effective sample signals; and identifying parameters of a center frequency point, a bandwidth and a signal amplitude of the signal through an energy domain algorithm and a fast Fourier transform algorithm, and primarily and qualitatively distinguishing the signal belonging to adjacent frequency interference or co-frequency interference signals according to information in an authorization database.

6. The method for monitoring the online electromagnetic environment monitoring device based on radio fingerprint recognition according to claim 5, wherein:

the identification of co-channel interference is achieved by a radio fingerprint based identification algorithm.

7. The method for monitoring the online electromagnetic environment monitoring device based on radio fingerprint recognition according to claim 6, wherein:

the quantized adjacent frequency/common frequency interference analysis flow is as follows: and (3) sending the frequency and amplitude information of the signal which is primarily characterized as adjacent frequency/same frequency interference to a background, and quantitatively comparing the background with the preset radio frequency index parameters of the on-site engineering equipment and the standard protocol parameters of the corresponding wireless communication system to judge whether the intensity of the adjacent frequency/same frequency interference can damage the on-site engineering equipment.