A real-time monitoring system for radio electromagnetic environment based on urban traffic

Abstract

The present invention relates to the technical field of electromagnetic environment monitoring, and specifically to a real-time monitoring system for radio electromagnetic environment based on urban traffic. The system includes: a signal acquisition module, a grid compression module, a signal processing module, a signal positioning module, and a situation filling module. In the present invention, by dynamically adjusting the signal acquisition frequency, the data redundancy problem in high-speed moving scenarios is optimized and the local signal capture ability is improved. By dividing the grid using a planar coordinate system and combining normalization processing, the data storage pressure is reduced and the feature integrity is ensured. The sliding average algorithm is used to dynamically generate a threshold line, which improves the signal-to-noise separation accuracy and reduces the misjudgment rate. By combining the distance between the vehicle position and the grid center to correct the signal level value, the positioning error of the signal source is reduced. By fusing the geographical feature gradient and the building height parameter to adjust the fitting weight, data distortion and blind spots in the electromagnetic situation map are reduced, and panoramic monitoring of the electromagnetic environment with high precision and low latency in complex urban environments is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic environment monitoring, and particularly to a real-time monitoring system for radio electromagnetic environment based on urban traffic. Background Art

[0002] The technical field of electromagnetic environment monitoring includes a technical system for real-time collection, analysis, and management of radio spectrum resource distribution, electromagnetic field intensity, signal characteristics, and interference sources. The core content of this field is to complete spectrum scanning, signal measurement, occupancy analysis, interference detection, and station verification of the target area through equipment such as fixed monitoring stations, mobile monitoring vehicles, and portable spectrum analyzers, covering multiple links such as data acquisition terminal deployment, multi-source heterogeneous data fusion, signal feature extraction and classification, and electromagnetic situation visualization. The current technical challenges mainly focus on problems such as limited coverage of fixed monitoring facilities, insufficient dynamic monitoring capabilities in high-density areas, low real-time processing efficiency of massive data, and insufficient signal-to-noise separation accuracy in complex electromagnetic environments.

[0003] Among them, a real-time monitoring system for radio electromagnetic environment based on urban traffic refers to a system that collects electromagnetic signals by carrying monitoring equipment on public transportation carriers and realizes signal extraction and positioning through grid data compression and dynamic threshold algorithms. Specifically, it includes deploying low-power monitoring receivers and integrated antennas on public transportation vehicles, automatically collecting full-band signals during vehicle operation and uploading them to the data center, dividing grids by converting longitude and latitude coordinates into Gaussian plane coordinates, normalizing and merging multi-frame data within the same grid, splitting the spectrum according to service frequency bands, dynamically calculating signal thresholds based on adjacent peak comparisons, screening frequency bands exceeding the threshold, and for signal positioning deviation, using a weighted fitting method to perform spatial correction on the frequency band level values, filling data in uncovered areas by using wavelet transform superimposed with the least squares method, and generating an electromagnetic spectrum situation map in combination with the free space propagation model.

[0004] In traditional real-time monitoring technologies for radio electromagnetic environment, fixed monitoring facilities rely on fixed power supply and network conditions, unable to cover special areas such as expressways and underground spaces, resulting in a relatively high proportion of monitoring blind spots. There is a lack of a normalization processing mechanism for multi-source heterogeneous data fusion. Differences in equipment frequency bands and sampling rates lead to a significant increase in redundant data volume, rising storage and transmission costs. Static threshold algorithms use fixed thresholds to screen signals, unable to adapt to the day-night signal intensity fluctuations, with a high misjudgment rate. Signal positioning relies on a single propagation model, without combining the dynamic distance correction between vehicle position and grid center, resulting in generally large positioning deviations. The data filling process ignores the multipath reflection effect of building groups, with obvious fitting errors in uncovered areas and significant deviations between the heat map and the actual electromagnetic distribution. Summary of the Invention

[0005] The object of the present invention is to solve the disadvantages existing in the prior art, and to propose a real-time monitoring system for radio electromagnetic environment based on urban traffic.

[0006] To achieve the above object, the present invention adopts the following technical solution: A real-time monitoring system for radio electromagnetic environment based on urban traffic includes:

[0007] The signal acquisition module obtains vehicle running state information, extracts GPS coordinates and real-time draws the vehicle driving trajectory. By identifying the motion state of the vehicle, adjusts the acquisition frequency, combines the position coordinates and time stamps to obtain a radio signal data set;

[0008] The grid compression module calls the radio signal data set, converts the longitude and latitude data into a plane coordinate system, divides the area into multiple grids and assigns grid codes, maps the radio signals into the grids and normalizes and merges the data in the grids to generate a grid code data set;

[0009] The signal processing module calls the grid code data set, splits the monitoring data according to the service frequency band, extracts the peak value of each sub-band and calculates the difference between adjacent peak values. By calculating the sliding average value of the signal peak level, obtains the threshold line to denoise the signal and obtains a threshold signal feature set;

[0010] The signal positioning module calls the threshold signal feature set, according to the level value and position code of the radio signal, uses the distance between the vehicle position and the grid center to calculate the distance attenuation factor and adjusts the level value of the narrowband high-power signal, fits the two-dimensional intensity distribution surface and extracts the extreme points to generate a signal source coordinate set.

[0011] As a further solution of the present invention, the radio signal data set specifically includes signal frequency parameters, position coordinates, time stamps. The grid code data set includes plane coordinate system parameters, grid codes, and normalized merge parameters. The threshold signal feature set specifically refers to service frequency band division parameters, threshold line parameters, and effective signal frequency band list. The signal source coordinate set includes level value parameters, two-dimensional intensity distribution surface, and extreme point identification record.

[0012] As a further solution of the present invention, the signal acquisition module includes:

[0013] The state monitoring sub-module obtains vehicle running state information, extracts vehicle engine start-stop signals and GPS sensor data, and real-time extracts longitude and latitude coordinates to obtain a real-time coordinate data set;

[0014] The trajectory analysis sub-module calls the real-time coordinate data set, according to the time stamp sequence, draws trajectory points, calculates the distance difference and time difference between adjacent coordinate points, real-time calculates the driving speed of the vehicle, identifies the motion state of the vehicle, and generates a speed identification value;

[0015] Based on the speed identification value, the signal regulation sub-module combines the real-time coordinate data, calls the timestamp sequence to calculate the time difference between adjacent coordinate points, and uses the formula:

[0016] ;

[0017] Performs operations to obtain the dynamic frequency adjustment coefficient, collects radio signals in real time, combines the real-time coordinate data and timestamps to generate a radio signal data set;

[0018] Among them, is the dynamic acquisition frequency adjustment coefficient, is the vehicle speed identification value at time t, is the abscissa of the plane coordinate system at time t, is the ordinate of the plane coordinate system at time t, is the abscissa of the plane coordinate system at time t - 1, is the ordinate of the plane coordinate system at time t - 1, is the time difference between adjacent coordinate points, is the frequency band coverage density coefficient, t is the sequence identification at the current moment, and t - 1 is the sequence identification at the previous moment.

[0019] As a further solution of the present invention, the grid compression module includes:

[0020] The grid conversion sub-module obtains the radio signal data set, and uses the Gaussian projection to convert the longitude and latitude data into a plane coordinate system;

[0021] The grid coding assignment sub-module calls the plane coordinate system, divides the coordinate system into multiple grids, and assigns grid codes to generate a coding assignment result;

[0022] The signal normalization and merging sub-module is based on the coding assignment result, calls the radio signal data, maps the radio signal into the grid and performs normalization and merging on the data in the grid to generate a grid coding data set.

[0023] As a further solution of the present invention, the signal processing module includes:

[0024] The frequency band splitting sub-module calls the grid coding data set, and divides the monitoring data into multiple sub-bands according to the service frequency band to generate a sub-band data set;

[0025] The peak analysis sub-module is based on the sub-band data set, extracts the peak level of each sub-band signal, calculates the difference between adjacent peak levels, and generates a peak difference data set;

[0026] The threshold generation sub-module calls the peak difference data set, calculates the sliding average of the signal peak level, dynamically matches the weight parameters in combination with the adjacent peak differences, and uses the formula:

[0027] ;

[0028] Performs operations to obtain the dynamic threshold line parameters, denoises the signal, extracts the list of effective signal frequency bands, and generates the threshold signal feature set;

[0029] Among them, is the threshold line parameter, is the peak level of the i-th sub-band, is the arithmetic average of the peak levels of all sub-bands within the sliding window, is the weight parameter matched by the i-th adjacent peak difference, P is the frequency band interference suppression coefficient, is the reference value of the sliding average of the signal peak level, i is the sub-band index number, and n is the total number of sub-bands within the current sliding window.

[0030] As a further solution of the present invention, the signal positioning module includes:

[0031] The level adjustment sub-module calls the threshold signal feature set, extracts the level value and position encoding of the radio signal, and according to the distance between the vehicle position and the grid center, uses the formula:

[0032] ;

[0033] Calculates the influence of free space path loss on the signal level, adjusts the level value of the narrowband high-power signal, and generates the level correction data set;

[0034] is the corrected signal power, d is the Euclidean distance between the vehicle position and the grid center, f is the signal center frequency, c is the propagation speed of electromagnetic waves in vacuum, is the reference value of the original signal level, is the pi;

[0035] The intensity fitting sub-module is based on the level correction data set, calls the position encoding and the corrected level value, fits the two-dimensional signal intensity distribution surface, and generates the intensity surface data set;

[0036] The extreme value extraction sub-module calls the intensity surface data set, identifies the signal source position by extracting the coordinates of the extreme points of the surface, and generates the signal source coordinate set.

[0037] As a further solution of the present invention, the system further includes:

[0038] The situation filling module utilizes the signal source coordinate set and the grid coding data set, calls the regional grid signal data and regional geographical features, calculates the initial fitting values of the uncovered grids, combines the building height to adjust the fitting level value correction parameter, fits the equipotential lines, calculates the level intensity gradient and maps it as a heat map, and generates an electromagnetic spectrum situation map;

[0039] The electromagnetic spectrum situation map specifically includes the fitting values of the uncovered grids, the building height correction parameters, and the heat map drawing records.

[0040] As a further solution of the present invention, the situation filling module includes:

[0041] The grid fitting sub-module calls the signal source coordinate set and the grid coding data set, extracts the regional grid signal intensity and geographical feature parameters, and uses the formula:

[0042] ;

[0043] Calculate the initial fitting values of the uncovered grids and generate an initial fitting data set;

[0044] Among them, is the initial fitting value, is the signal intensity of the k-th grid, is the geographical feature gradient of the k-th grid, is the abscissa of the center of the k-th grid, is the ordinate of the center of the k-th grid, is the average abscissa of the reference area, is the average ordinate of the reference area, m is the total number of grids, k is the index number of the grid, is the distance smoothing factor;

[0045] The parameter correction sub-module, based on the initial fitting data set and combined with the building height data, adjusts the fitting level value correction parameter, corrects the initial fitting value, and generates a corrected level parameter set;

[0046] The heat mapping sub-module calls the corrected level parameter set, calculates the level intensity gradient, maps the signal heat map of the target area, and generates an electromagnetic spectrum situation map.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are:

[0048] In the present invention, by dynamically adjusting the signal acquisition frequency, the data redundancy problem in high-speed moving scenarios is optimized and the local signal capture ability is enhanced. By dividing the grid using a planar coordinate system and combining normalization processing, the data storage pressure is reduced and the feature integrity is ensured. By using a moving average algorithm to dynamically generate a threshold line, the signal-to-noise separation accuracy is improved and the misjudgment rate is reduced. By combining the distance between the vehicle position and the grid center to correct the signal level value, the positioning error of the signal source is reduced. By fusing the geographical feature gradient and building height parameters to adjust the fitting weight, data distortion and blind spots in the electromagnetic situation map are reduced, realizing panoramic monitoring of the electromagnetic environment with high precision and low latency in complex urban environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is the system flow chart of the present invention;

[0050] Figure 2 is the flow chart of the signal acquisition module of the present invention;

[0051] Figure 3 is the flow chart of the grid compression module of the present invention;

[0052] Figure 4 is the flow chart of the signal processing module of the present invention;

[0053] Figure 5 is the flow chart of the signal positioning module of the present invention;

[0054] Figure 6 is the flow chart of the situation filling module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. 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.

[0056] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined.

[0057] Please refer to Figure 1 , a real-time radio electromagnetic environment monitoring system based on urban traffic includes:

[0058] The signal acquisition module obtains the vehicle operation status information, extracts the GPS coordinates and real-time draws the vehicle driving trajectory. By identifying the vehicle's motion state, it adjusts the acquisition frequency, combines the position coordinates and timestamps to obtain a radio signal data set;

[0059] The grid compression module calls the radio signal data set, converts the longitude and latitude data into a plane coordinate system, divides the area into multiple grids and assigns grid codes, maps the radio signals into the grids and normalizes and merges the data within the grids to generate a grid code data set;

[0060] The signal processing module calls the grid code data set, splits the monitoring data according to the service frequency band, extracts the peaks of each sub-band and calculates the difference between adjacent peaks. By calculating the sliding average of the signal peak level, it obtains the threshold line to denoise the signal and obtains a threshold signal feature set;

[0061] The signal positioning module calls the threshold signal feature set. According to the level value and position code of the radio signal, using the distance between the vehicle position and the grid center, it calculates the distance attenuation factor and adjusts the level value of the narrowband high-power signal, fits the two-dimensional intensity distribution surface and extracts the extreme points to generate a signal source coordinate set;

[0062] The situation filling module uses the signal source coordinate set and the grid code data set, calls the regional grid signal data and regional geographical features, calculates the initial fitting value of the uncovered grid, combines the building height to adjust the fitting level value correction parameter, fits the equipotential line, calculates the level intensity gradient and maps it as a heat map to generate an electromagnetic spectrum situation map.

[0063] The radio signal data set specifically refers to signal frequency parameters, position coordinates, and timestamps. The grid code data set includes plane coordinate system parameters, grid codes, and normalization and merging parameters. The threshold signal feature set specifically refers to service frequency band division parameters, threshold line parameters, and a list of effective signal frequency bands. The signal source coordinate set includes level value parameters, two-dimensional intensity distribution surfaces, and extreme point identification records. The electromagnetic spectrum situation map specifically includes the fitting value of the uncovered grid, building height correction parameters, and heat map drawing records.

[0064] Please refer to Figure 2 , the signal acquisition module includes:

[0065] The status monitoring sub-module obtains the vehicle operation status information, extracts the vehicle engine start-stop signal and GPS sensor data, and real-time extracts the longitude and latitude coordinates to obtain a real-time coordinate data set;

[0066] The engine start-stop signal is obtained through the in-vehicle OBD interface, and the signal level value fluctuates within the range of 0 - 12V. When the level value is greater than 8V, it is determined as the engine start state. The GPS sensor uses the U-blox M8N module, with a sampling frequency of 1Hz and an accuracy of 2.5 meters, and outputs the longitude and latitude data in the WGS84 coordinate system in real time. The longitude range is between 73° and 135°, and the latitude range is between 18° and 53°. Align the engine state and GPS data according to the timestamp, record a set of data every 100ms, and use the formula:

[0067] ;

[0068] Calculate the engine state value, where, is the engine state value, is the current level value, is the minimum level value, is the maximum level value. Set to 10V, to 0V, to 12V, and substitute the set values for calculation:

[0069] ;

[0070] The calculation result indicates that the engine is in the start state. Combine the engine state value, longitude value, latitude value, and timestamp to generate a real-time coordinate dataset.

[0071] The trajectory analysis sub-module calls the real-time coordinate dataset, draws trajectory points according to the timestamp sequence, calculates the distance difference and time difference between adjacent coordinate points, calculates the driving speed of the vehicle in real time, identifies the motion state of the vehicle, and generates a speed identification value;

[0072] Set that in actual operation, the time difference between adjacent coordinate points is 100ms. Calculate the distance between adjacent coordinate points through the Haversine formula. When the vehicle is in a stationary state, the distance between adjacent points is less than 0.1 meter. When the vehicle is in a driving state, the distance between adjacent points is between 0.1 - 50 meters. Use the formula:

[0073] ;

[0074] Calculate the speed value, where v is the speed value, d is the distance between adjacent coordinate points, is the time difference. Set d to 25 meters, to 0.1 second, and substitute the set values for calculation:

[0075] ;

[0076] The calculation results indicate that the vehicle is in a normal driving state, and the speed value is calculated every 100 ms to generate a speed identification value.

[0077] Based on the speed identification value, the signal regulation sub-module combines the real-time coordinate data, calls the timestamp sequence to calculate the time difference between adjacent coordinate points, and uses the formula:

[0078] ;

[0079] Through calculation, the dynamic frequency adjustment coefficient is obtained, radio signals are collected in real time, and combined with the real-time coordinate data and timestamps, a radio signal data set is generated;

[0080] Among them, is the dynamic acquisition frequency adjustment coefficient, is the vehicle speed identification value at time t, is the abscissa of the plane coordinate system at time t, is the ordinate of the plane coordinate system at time t, is the abscissa of the plane coordinate system at time t - 1, is the ordinate of the plane coordinate system at time t - 1, is the time difference between adjacent coordinate points, is the frequency band coverage density coefficient, t is the sequence identification at the current moment, and t - 1 is the sequence identification at the previous moment;

[0081] Using the speed identification value and the real-time coordinate data set, through the timestamp sequence, the time difference between adjacent coordinate points is calculated, and the formula is used:

[0082] ; [[ID=3�]]

[0083] Through calculation, the dynamic frequency adjustment coefficient is obtained, radio signals are collected in real time, and combined with the real-time coordinate data and timestamps, a radio signal data set is generated. In actual operation, the WGS84 coordinate system is converted into a plane coordinate system, the x coordinate range is between 0 - 1000 meters, the y coordinate range is between 0 - 1000 meters, the time difference between adjacent coordinate points is 100 ms, and the frequency band coverage density coefficient is set according to the urban area type, 1.2 for the commercial area, 0.͡8 for the residential area, 1.0 for the industrial area, and is set to 60 km / h, approximately 16.67 m / s, is 500 meters, is 450 meters, is 500 meters, is 450 meters, is 0.1 second, is 1.2, and the set values are substituted for calculation:

[0084] ;

[0085] The calculation results show that the dynamic frequency adjustment coefficient is 0.028. According to the dynamic frequency adjustment coefficient, the acquisition frequency of the radio signal is adjusted to enhance the local signal capture ability. The signal acquisition frequency range is between 2 - 10 Hz, and the acquired signal strength range is between -120 dBm and 0 dBm. Combining the real-time coordinate data and the time stamp, a radio signal data set is generated.

[0086] Please refer to Figure 3 , the grid compression module includes:

[0087] The grid conversion sub-module obtains the radio signal data set and uses the Gauss projection to convert the longitude and latitude data into a plane coordinate system;

[0088] Obtain the radio signal data set, which contains information entries such as time stamps, longitudes, latitudes, and signal strengths. Use the Gauss projection method to perform the conversion from geographic coordinates to plane coordinates. First, determine the Gauss projection zone used according to the geographical location of the signal acquisition point. For example, a 6-degree zone is selected for urban areas, and the longitude of the central meridian of this zone is determined. Taking the selected central meridian and the equator as the reference, the longitude and latitude coordinates are converted into the horizontal and vertical coordinate values in the Gauss plane rectangular coordinate system. This conversion process involves calculating the arc length distance of the coordinate point along the central meridian direction starting from the equator as the ordinate according to the parameters of the earth ellipsoid model, and calculating the perpendicular distance from the coordinate point to the central meridian as the abscissa. To avoid negative values, a constant, such as 500000 meters, is added to the abscissa value. This conversion calculation is performed for the longitude and latitude coordinates of each signal acquisition point recorded in the radio signal data set. Finally, the geographical coordinates of all signal data points are converted into the corresponding plane coordinates, generating a plane coordinate data set.

[0089] The grid coding assignment sub-module calls the plane coordinate system, divides the coordinate system into multiple grids, and assigns grid codes to generate the coding assignment result;

[0090] Call the plane coordinate system, divide the coordinate system into multiple grids, set the grid size to 100 meters by 100 meters, and calculate the row and column numbers of the grid to which the coordinate point (ordinate 4418500 meters, abscissa 446540 meters) belongs using the formula:

[0091] ;

[0092] ;

[0093] Calculate the grid row and column numbers, where is the grid row number, is the grid column number, x is the ordinate of the plane coordinate, and y is the abscissa of the plane coordinate. is the grid side length (unit: meter), represents the floor function operation. Set x to 4418500 meters and y to 446540 meters. is 100 meters. Substitute the set values for calculation:

[0094] ;

[0095] ;

[0096] The calculation result shows that this point belongs to the grid in row 44185 and column 4465. Combine the row and column numbers to generate the grid code. Obtain the unique code by multiplying the row number by the base number (such as 100000) and then adding the column number, getting the grid code 4418504465. Assign the corresponding grid code to all planar coordinate points in the area.

[0097] Based on the coding assignment result, the signal normalization and merging sub-module calls the radio signal data, maps the radio signals into the grid, and performs normalization and merging on the data within the grid to generate a grid code data set;

[0098] Based on the grid code data set, call the signal strength data in the radio signal data set, map the radio signal strength value (-85 dBm) at each timestamp into its corresponding grid code (4418504465), and perform normalization and merging processing on all signal strength data collected within the same grid within the set time window (1 minute). First, normalize the signal strength and map it to the interval from 0 to 1, using the formula:

[0099] ;

[0100] Calculate the normalized signal strength, where is the normalized signal strength value, P is the original signal strength value, is the minimum signal strength value recorded in this grid within the set time window, is the maximum signal strength value recorded in this grid within the set time window. Assume that the grid 4418504465 receives signal strengths of -85 dBm, -90 dBm, and -80 dBm within one minute, then is -90 dBm, is -80 dBm. For the signal strength P equal to -85 dBm, substitute the set values for calculation:

[0101] ;

[0102] The calculation results show that the normalized value of the signal strength of -85 dBm is 0.5. The same normalization calculation is performed on all signal strength values of this grid within this time window, and then these normalized values are processed according to a preset merging strategy. The representative normalized signal strength value obtained from this merging calculation is associated and stored with the corresponding grid code to form the final output grid code dataset.

[0103] Please refer to Figure 4 , the signal processing module includes:

[0104] The frequency band splitting sub-module calls the grid code dataset and divides the monitoring data into multiple sub-bands according to the service frequency band to generate a sub-band dataset;

[0105] Call the grid code signal set, which contains the representative signal strength information of each grid within a specific time window. This information usually covers a wide frequency spectrum range. For example, it records the overall signal strength index of a certain grid from 80 MHz to 2.5 GHz within one minute at 10:00 on April 8, 2025. According to the preset service frequency band division parameters, these parameters are determined based on the national radio frequency division regulations and the specific monitoring task requirements. For example, it is set that the frequency bands to be concerned about include FM radio (frequency range 88 to 108 MHz), terrestrial digital TV (frequency range 470 to 798 MHz), mobile communication GSM900 downlink (frequency range 935 to 960 MHz), LTE Band3 downlink (frequency range 1805 to 1880 MHz), etc. The original broadband monitoring data recorded in the grid code signal set is screened and classified according to the frequency value. The specific operation is to traverse each frequency point and its corresponding signal strength information in the monitoring data, and judge whether the value of this frequency point falls within the start and end frequency ranges of any preset service frequency band. If the value of a certain frequency point is within the frequency range of a certain service frequency band, the signal strength information of this frequency point is extracted and classified into a data subset specifically created for this service frequency band. This classification operation is performed on all frequency point signal data recorded in the grid. Finally, multiple independent data sets are formed, each of which only contains signal information of a specific service frequency band. For example, one data set contains FM radio signal data between 88 MHz and 108 MHz, and another data set contains GSM downlink signal data between 935 MHz and 960 MHz, generating a sub-band dataset.

[0106] The peak analysis sub-module extracts the peak level of each sub-band signal based on the sub-band dataset, calculates the difference between adjacent peak levels, and generates a peak difference dataset;

[0107] Based on the sub-band data set, such as the FM radio sub-data set (including signal data in the range of 88 to 108 MHz), for the signals in each sub-data set, within a set analysis time window (e.g., 1 minute) or frequency scan range, the signal peak level is extracted, that is, the maximum value of the signal intensity in this frequency band is found. In the FM radio sub-data set of grid 4418504465, the signal intensity at 98.5 MHz is the highest, which is -72 dBm. Then the peak level of this sub-band is -72 dBm. The peak extraction operation is performed on all sub-bands (such as terrestrial digital TV, GSM900 downlink, etc.) to obtain a series of peak level data. Then, the difference between the peak levels of adjacent service frequency bands (sorted by frequency) is calculated. For example, if the FM radio peak is -72 dBm and the peak of the adjacent terrestrial digital TV band is -85 dBm, using the formula:

[0108] ;

[0109] Calculate the difference between adjacent peak levels, where, is the peak level difference (unit: dB), is the peak level of the k-th sub-band (unit: dBm), is the peak level of the (k - 1)-th (adjacent in frequency) sub-band (unit: dBm). Set (terrestrial digital TV) to be -85 dBm, (FM radio) to be -72 dBm, and substitute the set values for calculation:

[0110] ;

[0111] The calculation results show that the peak levels of the FM radio and terrestrial digital TV bands differ by 13 dB. Repeat this calculation for all adjacent sub-band pairs, record the difference information of the peak levels between each band and its adjacent band, and form a data set containing fields such as band identification, peak level, adjacent band identification, peak difference, etc., to generate a peak difference data set.

[0112] The threshold generation sub-module calls the peak difference data set, calculates the moving average of the signal peak level, dynamically matches the weight parameters in combination with the adjacent peak differences, and uses the formula:

[0113] ;

[0114] Perform operations to obtain the dynamic threshold line parameters, denoise the signal, extract the list of effective signal frequency bands, and generate a threshold signal feature set;

[0115] where, is the threshold line parameter, is the peak level of the i-th sub-band, is the arithmetic mean of the peak levels of all sub - bands within the sliding window, is the weight parameter matched for the i - th adjacent peak difference, and P is the sub - band interference suppression coefficient, is the reference value of the sliding average of the signal peak level, i is the sub - band index number, and n is the total number of sub - bands within the current sliding window;

[0116] Call the peak difference data set and the peak level information in the sub - band data set. First, calculate the average value of the signal peak level within a sliding window ( ), and this sliding window can be defined as n adjacent sub - bands in frequency. For example, n is set to 5, select the current analysis sub - band and the 2 sub - bands before and after it, and calculate the arithmetic mean of the peak levels of these 5 sub - bands as the reference value , for example, if the peak levels of 5 adjacent sub - bands are - 72, - 85, - 80, - 88, - 75 dBm respectively, then is - 80 dBm. Then, combine the adjacent peak differences recorded in the peak difference data set and dynamically match the weight parameter , and the setting of the weight is designed to reflect the contribution degree of the peak difference to judging the signal validity. When the difference is small, it indicates background noise fluctuation and the weight should be small. When the difference is large, it indicates the edge of the real signal and the weight should be large. It is set that when the difference is less than 5 dB, is 0.2. When the difference is between 5 and 15 dB, is 0.6. When the difference is greater than 15 dB, is 1.0. The difference is 13 dB, and the weight is 0.6. Then introduce the sub - band interference suppression coefficient P, and the coefficient is set according to the historical interference situation or environmental complexity of the sub - band. For sub - bands with more known interferences, it is set to 0.7, and for those with fewer interferences, it is set to 0.9. Here it is set to 0.8. Use the formula:

[0117] ;

[0118] Set the sliding window n = 3, which includes sub - bands A, B, C, and their peak levels is - 75 dBm, is - 85 dBm, is - 80 dBm, =-80 dBm. =-80 dBm, is 0.6, is 0.6, is 0.6. Set the interference suppression coefficient P = 0.8. Substitute the set values into the calculation:

[0119] ;

[0120] ;

[0121] ;

[0122] ;

[0123] The calculation results show that the dynamic threshold line parameter is -79.32 dBm. By combining the sliding average value and the weighted peak deviation, the threshold can be adaptively adjusted according to the fluctuations of the local spectrum, distinguishing the true signal peak from the background noise fluctuations, improving the accuracy of signal detection. This denoising operation is performed on all sub-bands, the key features of the denoised signal are extracted, and a threshold signal feature set is generated.

[0124] Please refer to Figure 5 , the signal positioning module includes:

[0125] The level adjustment sub-module calls the threshold signal feature set, extracts the level value and position coding of the radio signal, and according to the distance between the vehicle position and the grid center, uses the formula:

[0126] ;

[0127] Calculate the influence of free space path loss on the signal level, adjust the level value of the narrowband high-power signal, and generate a level correction data set;

[0128] is the corrected signal power, d is the Euclidean distance between the vehicle position and the grid center, f is the signal center frequency, c is the propagation speed of electromagnetic waves in vacuum, is the original signal level reference value, is the pi;

[0129] The level adjustment sub-module calls the threshold signal feature set, which contains information such as the effective signal frequency band identification, peak level, peak frequency, and calculated dynamic threshold value after denoising processing. For example, for the effective signal frequency band A within the grid coding 4418504465, its characteristics are the center frequency of 98.5 MHz and the peak level of -75 dBm. First, extract the peak level value ( ) of each effective signal and its grid coding from the feature set, reverse look up the central geographical coordinates of the grid through the grid coding, simultaneously obtain the real-time GPS coordinates of the vehicle when the signal is recorded, and convert them into coordinates in the same plane coordinate system, calculate the Euclidean distance between the vehicle position and the grid center, determine whether the signal belongs to the "narrowband high-power" type. A narrowband signal can be defined as a signal bandwidth less than 200 kHz, and a high-power signal can be defined as a peak level greater than the preset power threshold. If the signal meets this condition, level adjustment is required;

[0130] Set , , , , substitute into the formula:

[0131] ;

[0132] The results show that the corrected signal power is , considering the distance, frequency and signal strength reference, quantifying the deviation effect generated during short-distance measurement, associating and storing the adjusted level values of all valid signals in all grids with their position encodings (grid encodings), and generating a level correction data set.

[0133] Based on the level correction data set, the intensity fitting sub-module calls the position encoding and the corrected level value to fit the two-dimensional signal intensity distribution surface and generate an intensity surface data set;

[0134] Based on the level correction data set, the data set contains each grid encoding and the corrected representative level value of each valid frequency band in the grid. For example, the corrected level of frequency band A corresponding to grid 4418504465 is -55.06 dBm. Call the position encodings (and their corresponding center coordinates) of multiple adjacent grids and the corrected level values of the same frequency band (or the concerned frequency band) in these grids. Using these discrete intensity data points, through spatial interpolation or function fitting methods, construct a continuous two-dimensional signal intensity distribution surface covering the monitored geographical area. Using bilinear interpolation, for any point in it, its signal intensity can be calculated from the coordinates of the four corner points (i.e., the centers of the adjacent four grids) of the rectangle grid where it is located and the corresponding corrected level values

[0135] ;

[0136] Perform bilinear interpolation to calculate the intensity. Among them, is the fitted signal intensity (unit: dBm) at point , is the plane coordinate of the point to be interpolated, is the four grid center coordinates surrounding point , are the corrected level values corresponding to the centers of these four grids respectively, is the area of this rectangle grid. Set the side length of the grid to 100 m, and the coordinates of the four corner points are m, the corresponding corrected level value dBm, dBm, dBm, dBm, the calculation point the intensity at , , , . Then , , , , area . Substitute the set value for calculation:

[0137] ;

[0138] ;

[0139] The calculation results show that the fitted signal intensity at the point (4418580, 446580) is approximately -57.31 dBm. By interpolating and calculating all positions within the monitoring area, a mathematical model or dataset that continuously describes the spatial distribution of signal intensity is obtained. The model reflects the change trend and distribution pattern of signal intensity on a two-dimensional plane, generating an intensity surface dataset.

[0140] The extreme value extraction sub-module calls the intensity surface dataset. By extracting the coordinates of the extreme points on the surface, the signal source location is identified, generating a signal source coordinate set;

[0141] Calling the intensity surface dataset represents the distribution of signal intensity in a planar region. To identify potential signal emission source locations, local extreme points of signal intensity are searched for on this intensity distribution surface. These points usually correspond to the central region of the signal source or the region with the strongest signal. Finding extreme points can be achieved by analyzing the first and second partial derivatives of the surface function. For a continuously differentiable surface function, a local maximum point must satisfy the following conditions: the first partial derivatives (i.e., the rates of change of the function in the x and y directions) at this point are both equal to zero, and the second partial derivatives (i.e., the curvatures of the function in the x and y directions) at this point satisfy specific conditions. Specifically, the determinant of the Hessian matrix (a matrix composed of second partial derivatives) of the function at this point must be greater than zero, and the second partial derivative of the function in the x direction must be less than zero. These conditions together ensure that this point is a local maximum point, rather than a minimum point or a saddle point. In actual operation, if the intensity surface exists in the form of discrete grid point data, local peaks can be found by comparing the intensity values of each grid point with its eight neighboring grid points around it. That is, if the intensity value of a certain point is greater than the intensity values of all its adjacent points, then this point is identified as a local maximum point. Extract the planar coordinates of all the found local maximum points to identify the signal source location, generating a signal source coordinate set.

[0142] Please refer to Figure 6, the situation filling module includes:

[0143] The grid fitting sub-module calls the signal source coordinate set and the grid coding data set, extracts the regional grid signal strength and geographical feature parameters, and uses the formula:

[0144] ;

[0145] Calculate the initial fitting value of the uncovered grid and generate the initial fitting data set;

[0146] Among them, is the initial fitting value, is the signal strength of the k-th grid, is the geographical feature gradient of the k-th grid, is the abscissa of the center of the k-th grid, is the ordinate of the center of the k-th grid, is the average abscissa of the reference area, is the average ordinate of the reference area, m is the total number of grids, k is the index number of the grid, is the distance smoothing factor;

[0147] The grid fitting sub-module calls the signal source coordinate set and the grid coding data set. This data set contains the coding, center coordinates, and signal strength information of each grid in the area. For example, the center coordinates of grid 4418504465 are (4418550, 446550) m, and the signal strength is -55 dBm. Extract the regional grid signal strength and geographical feature parameters. The geographical feature parameters include terrain elevation, building density, vegetation coverage, etc. These parameters are obtained through remote sensing data or geographic information systems. For example, the building density of a certain grid is 0.6, the vegetation coverage rate is 0.3, and the terrain elevation is 50 m. Use wavelet transform to superimpose the least squares method to fit the signal strength of the uncovered grid (that is, the grid without direct measurement data), and use the formula:

[0148] ;

[0149] Calculate the initial fitting value of the uncovered grid. Among them, is the initial fitting value (unit: dBm), is the signal strength of the k-th grid (unit: dBm), is the geographical feature gradient of the k-th grid (dimensionless, calculated from geographical feature parameters, such as building density gradient, terrain slope, etc.), is the abscissa of the center of the k-th grid (unit: m), is the ordinate of the center of the k-th grid (unit: m), is the average abscissa of the reference area (unit: m), is the mean ordinate of the reference area (unit: m), m is the total number of grids involved in the calculation, and k is the grid index number. is the distance smoothing factor (unit: m, usually taking 1% of the grid side length. For example, when the grid side length is 100 m, ). It is assumed that the reference area contains 5 grids, and their central coordinates are (4418550, 446550), (4418650, 446550), (4418550, 446650), (4418650, 446650), (4418750, 446750) m, and the corresponding signal strengths are -55, -60, -58, -62, -65 dBm respectively, and the geographical feature gradients are 0.8, 0.6, 0.7, 0.5, 0.4 respectively. Calculate the mean coordinates of the reference area: , , and set Calculate the initial fitting value of the uncovered grid (4418800, 446800), and substitute the set value for calculation:

[0150] ;

[0151] ;

[0152] ;

[0153] ;

[0154] ;

[0155] ;

[0156] The calculation results show that the initial fitting signal strength of the uncovered grid (4418800, 446800) is approximately -2.516 dBm. By combining the signal strength, geographical feature gradient, and spatial distance, the signal strength distribution of the uncovered area is accurately estimated, considering the influence of the geographical environment on signal propagation. This calculation is performed for all uncovered grids to generate an initial fitting data set.

[0157] Based on the initial fitting data set, the parameter correction sub-module combines the building height data to adjust the fitting level value correction parameter and correct the initial fitting value to generate a corrected level parameter set;

[0158] The parameter correction sub-module is based on the initial fitting data set, which contains the initial fitting signal strength values of the uncovered grids. For example, the initial fitting value of grid (4418800, 446800) is -2.516 dBm. Combining with the building height data, which is obtained by LiDAR or stereophotogrammetry. For example, the average building height of a certain grid is 30 m, the highest building height is 100 m, and the standard deviation of the building height distribution is 15 m. It adjusts the fitting level value correction parameters to correct the initial fitting value. The correction process takes into account the shielding and reflection effects of buildings on signals. High-rise buildings will cause signal attenuation and at the same time produce multipath effects. The correction parameters include building height coefficient, building density coefficient, building distribution uniformity coefficient, etc. For example, the building height coefficient has a relationship with the average building height as , the building density coefficient has a relationship with the building coverage rate d as , the building distribution uniformity coefficient has a relationship with the height standard deviation as , then the corrected signal strength can be expressed as:

[0159] ;

[0160] Among them, is the corrected signal strength (unit: dBm), is the initial fitting value (unit: dBm), , , are correction coefficients (dimensionless). Set the initial fitting value of grid (4418800, 446800) , the average building height , the building coverage rate d = 0.6, the height standard deviation , calculate the correction coefficients:

[0161] ;

[0162] ;

[0163] ;

[0164] Substitute the set values for calculation:

[0165] ;

[0166] ;

[0167] ;

[0168] The calculation results show that after considering the building influence, the corrected signal strength is about -4.216 dBm, which is about 1.7 dB lower than the initial fitting value. Perform this correction calculation on the initial fitting values of all uncovered grids, and associate and store the corrected signal strength values with the corresponding grid codes and coordinate information to generate a corrected level parameter set.

[0169] The thermal mapping sub-module calls the corrected level parameter set, maps the signal thermal map of the target area by calculating the level strength gradient, and generates an electromagnetic spectrum situation map;

[0170] The thermal mapping sub-module calls the corrected level parameter set. This data set contains the signal strength information of all grids in the area (including measured grids and grids after fitting and correction). For example, the signal strength of grid 4418504465 is -55 dBm, and the corrected signal strength of grid (4418800, 446800) is -4.216 dBm. By calculating the level strength gradient, map the signal thermal map of the target area. The gradient calculation uses the central difference method. For grid , its gradient components and in the x and y directions can be expressed as:

[0171] ;

[0172] ;

[0173] Among them, , are the gradient components (unit: dB / m), is the signal strength of grid (unit: dBm), , is the grid spacing (unit: m, usually ). Set the grid spacing to 100 m. The signal strength values (unit: dBm) of a 3×3 grid area are: -55 (1, 1), -58 (1, 2), -60 (1, 3), -53 (2, 1), -52 (2, 2), -57 (2, 3), -54 (3, 1), -56 (3, 2), -59 (3, 3). Calculate the gradient of the central grid (2, 2) and substitute the set values for calculation:

[0174] ;

[0175] ;

[0176] The calculation results show that the signal intensity at the central grid (2, 2) decreases at a rate of 0.02 dB / m in the x-direction and increases at a rate of 0.01 dB / m in the y-direction. The magnitude and direction of the gradient reflect the change trend of the signal intensity. Regions with larger gradients usually correspond to the signal source location or obstacles on the signal propagation path. Based on the calculated gradient information and combined with the corrected signal intensity values, the signal intensity is visualized using color mapping, usually adopting a gradient color scale from red (high signal intensity) to blue (low signal intensity), to generate an electromagnetic spectrum situation map, intuitively showing the intensity distribution and change trend of electromagnetic signals in the region.

[0177] The above is only the preferred embodiment of the present invention and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A real-time monitoring system for radio electromagnetic environment based on urban traffic, characterized in that, The system includes: The signal acquisition module obtains the vehicle operation status information, extracts the GPS coordinates and real-time draws the vehicle driving trajectory. By identifying the vehicle's motion state, it adjusts the acquisition frequency, combines the position coordinates and timestamps to obtain a radio signal data set. The grid compression module calls the radio signal data set, converts the longitude and latitude data into a plane coordinate system, divides the area into multiple grids and assigns grid codes, maps the radio signals into the grids and performs normalized merging on the data within the grids to generate a grid code data set. The signal processing module calls the grid code data set, splits the monitoring data according to the service frequency band, extracts the peaks of each sub-band and calculates the difference between adjacent peaks. By calculating the sliding average of the signal peak level, it obtains the threshold line to denoise the signal and obtains a threshold signal feature set. The signal positioning module calls the threshold signal feature set. According to the level value and position code of the radio signal, using the distance between the vehicle position and the grid center, it calculates the distance attenuation factor and adjusts the level value of the narrowband high-power signal, fits the two-dimensional intensity distribution surface and extracts the extreme points to generate a signal source coordinate set. The situation filling module uses the signal source coordinate set and the grid code data set, calls the regional grid signal data and regional geographical features, calculates the initial fitting value of the uncovered grid, combines the building height to adjust the fitting level value correction parameter, fits the equipotential line, calculates the level intensity gradient and maps it as a heat map to generate an electromagnetic spectrum situation map.

2. The real-time monitoring system for radio electromagnetic environment based on urban traffic according to claim 1, characterized in that, The radio signal data set specifically refers to signal frequency parameters, position coordinates, and timestamps. The grid code data set includes plane coordinate system parameters, grid codes, and normalized merging parameters. The threshold signal feature set specifically refers to service frequency band division parameters, threshold line parameters, and a list of effective signal frequency bands. The signal source coordinate set includes level value parameters, two-dimensional intensity distribution surface, and extreme point identification records. The electromagnetic spectrum situation map specifically includes the fitting value of the uncovered grid, building height correction parameters, and heat map drawing records.

3. The real-time monitoring system of radio electromagnetic environment based on urban traffic according to claim 1, characterized in that, The signal acquisition module includes: The status monitoring sub-module obtains the vehicle operation status information, extracts the vehicle engine start-stop signal and GPS sensor data, and extracts the longitude and latitude coordinates in real time to obtain a real-time coordinate data set. The trajectory analysis sub-module calls the real-time coordinate data set, draws trajectory points according to the timestamp sequence, calculates the distance difference and time difference between adjacent coordinate points, calculates the vehicle's driving speed in real time, identifies the vehicle's motion state, and generates a speed identification value. The signal regulation sub-module is based on the speed identification value, combines the real-time coordinate data, calls the timestamp sequence to calculate the time difference between adjacent coordinate points, and uses the formula: ； Performs operations to obtain a dynamic frequency adjustment coefficient, acquires radio signals in real time, and combines the real-time coordinate data and timestamps to generate a radio signal data set. Among them, is the dynamic acquisition frequency adjustment coefficient, is the vehicle speed identification value at time t, is the abscissa of the plane coordinate system at time t, is the ordinate of the plane coordinate system at time t, is the abscissa of the plane coordinate system at time t - 1, is the ordinate of the plane coordinate system at time t - 1, is the time difference between adjacent coordinate points, is the frequency band coverage density coefficient, t is the sequence identifier at the current moment, and t - 1 is the sequence identifier at the previous moment.

4. The real-time monitoring system of radio electromagnetic environment based on urban traffic according to claim 3, characterized in that, The grid compression module includes: The grid conversion sub-module obtains the radio signal data set and converts the longitude and latitude data into a plane coordinate system using the Gaussian projection. The grid code assignment sub-module calls the plane coordinate system, divides the coordinate system into multiple grids, and assigns grid codes to generate a code assignment result. Based on the encoding assignment result, the signal normalization and merging sub-module calls the radio signal data, maps the radio signal into the grid and performs normalization and merging on the data within the grid to generate a grid coding data set.

5. The real-time monitoring system of radio electromagnetic environment based on urban traffic according to claim 4, characterized in that, The signal processing module includes: The frequency band splitting sub-module calls the grid coding data set, and divides the monitoring data into multiple sub-bands according to the service frequency band to generate a sub-band data set; The peak analysis sub-module extracts the peak level of each sub-band signal based on the sub-band data set, calculates the difference between adjacent peak levels, and generates a peak difference data set; The threshold generation sub-module calls the peak difference data set, calculates the sliding average of the signal peak level, dynamically matches the weight parameter in combination with the adjacent peak difference, and uses the formula: ； Operate to obtain the dynamic threshold line parameter, denoise the signal, extract the list of effective signal frequency bands, and generate a threshold signal feature set; Among them, is the threshold line parameter, is the peak level of the i-th sub-band, is the arithmetic mean of the peak levels of all sub-bands within the sliding window, is the weight parameter for the matching of the i-th adjacent peak difference, and P is the band interference suppression coefficient, is the reference value of the sliding average of the signal peak level, i is the sub-band index number, and n is the total number of sub-bands within the current sliding window.

6. The real-time monitoring system of radio electromagnetic environment based on urban traffic according to claim 5, characterized in that The signal positioning module includes: The level adjustment sub-module calls the threshold signal feature set, extracts the level value and position coding of the radio signal, and according to the distance between the vehicle position and the grid center, uses the formula: ； Calculate the influence of free space path loss on the signal level, adjust the level value of the narrowband high-power signal, and generate a level correction data set; $P_{r}$ is the corrected signal power, $d$ is the Euclidean distance between the vehicle position and the grid center, $f$ is the signal center frequency, $c$ is the propagation speed of electromagnetic waves in vacuum, $P_{0}$ is the reference value of the original signal level, $\pi$ is the ratio of a circle's circumference to its diameter; The intensity fitting sub-module calls the position coding and the corrected level value based on the level correction data set, fits the two-dimensional signal intensity distribution surface, and generates an intensity surface data set; The extreme value extraction sub-module calls the intensity surface data set, identifies the signal source position by extracting the coordinates of the extreme points on the surface, and generates a signal source coordinate set.

7. The real-time monitoring system of radio electromagnetic environment based on urban traffic according to claim 1, characterized in that The situation filling module includes: The grid fitting sub-module calls the signal source coordinate set and the grid coding data set, extracts the regional grid signal intensity and geographical feature parameters, and uses the formula: ； Calculate the initial fitting value of the uncovered grid and generate an initial fitting data set; Among them, is the initial fitting value, is the k-th grid signal strength, is the k-th grid geographical feature gradient, is the abscissa of the center of the k-th grid, is the ordinate of the center of the k-th grid, is the average abscissa of the reference area, is the average ordinate of the reference area, m is the total number of grids, k is the index number of the grid, is the distance smoothing factor; The parameter correction sub-module adjusts the fitting level value correction parameter based on the initial fitting data set in combination with the building height data, corrects the initial fitting value, and generates a corrected level parameter set; The thermal mapping sub-module calls the corrected level parameter set, maps the signal thermal map of the target area by calculating the level intensity gradient, and generates an electromagnetic spectrum situation map.

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