Radio electromagnetic environment real-time monitoring system based on urban traffic

Abstract

The invention relates to the technical field of electromagnetic environment monitoring, in particular to a radio electromagnetic environment real-time monitoring system based on urban traffic, which comprises a signal acquisition module, a grid compression module, a signal processing module, a signal positioning module and a situation filling module. According to the method, the signal acquisition frequency is dynamically adjusted, the data redundancy problem in the high-speed moving scene is optimized, the local signal capturing capability is improved, the plane coordinate system is utilized to divide grids, normalization processing is combined, the data storage pressure is reduced, the feature integrity is guaranteed, the threshold line is dynamically generated by adopting the moving average algorithm, and the data storage efficiency is improved. The method improves the signal-noise separation precision, reduces the misjudgment rate, corrects a signal level value in combination with the vehicle position and the grid center distance, reduces the positioning error of a signal source, adjusts the fitting weight in combination with the geographic feature gradient and the building height parameter, reduces the data distortion and the electromagnetic situation map blind area, and improves the positioning accuracy. And high-precision and low-delay panoramic monitoring of the electromagnetic environment in a complex urban area environment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electromagnetic environment monitoring, and in particular 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 acquisition, 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 devices 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 devices 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 multiple frames of data within the same grid, segmenting the spectrum according to service frequency bands, dynamically calculating signal thresholds based on adjacent peak comparisons, screening frequency bands exceeding the threshold, correcting the spatial level values of the frequency bands by weighted fitting for signal positioning deviation, filling in 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 environments, 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 device 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 relatively high misjudgment rate. Signal positioning relies on a single propagation model, without combining the dynamic distance correction between the vehicle position and the grid center, resulting in generally large positioning deviations. The data filling process ignores the multi-path 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 deficiencies existing in the prior art, and a real-time monitoring system for radio electromagnetic environment based on urban traffic is proposed.

[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: The signal acquisition module obtains vehicle operation state information, extracts GPS coordinates and real-time draws the vehicle driving trajectory, adjusts the acquisition frequency by identifying the vehicle's motion state, and 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 normalizes and merges the data in 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 peak value of each sub-band and calculates the difference between adjacent peak values, obtains the threshold line by calculating the sliding average value of the signal peak level to denoise the signal, and obtains a threshold signal feature set; The signal positioning module calls the threshold signal feature set, calculates the distance attenuation factor according to the level value of the radio signal and the position code, and adjusts the level value of the narrowband high-power signal by using the distance between the vehicle position and the grid center, fits the two-dimensional intensity distribution surface and extracts the extreme points to generate a signal source coordinate set.

[0007] As a further solution of the present invention, the radio signal data set specifically includes 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.

[0008] As a further solution of the present invention, the signal acquisition module includes: The state monitoring sub-module obtains vehicle operation state information, extracts vehicle engine start-stop signals and GPS sensor data, and extracts 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 driving speed of the vehicle 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: ; Calculate to obtain the dynamic frequency adjustment coefficient, collect radio signals in real time, combine the real-time coordinate data and time stamps 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 identification at the current moment, and t - 1 is the sequence identification at the previous moment.

[0009] As a further solution of the present invention, the grid compression module includes: 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; 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; The signal normalization and merging sub-module, based on the coding assignment result, calls the radio signal data, maps the radio signals into the grids and normalizes and merges the data in the grids to generate a grid coding data set.

[0010] As a further solution of the present invention, 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-frequency bands according to the service frequency band to generate a sub-frequency band data set; The peak analysis sub-module, based on the sub-frequency band data set, extracts the peak level of each sub-frequency band signal, 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 value of the signal peak level, combines the dynamic matching weight parameters of adjacent peak differences, and uses the formula: ; Calculate 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-frequency band, is the arithmetic average value of the peak levels of all sub-frequency bands within the sliding window, The weight parameter matching the difference between the i-th adjacent peak values, P is the frequency band interference suppression coefficient, is the sliding average reference value 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.

[0011] As a further solution of the present invention, 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; 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; The intensity fitting sub-module is based on the level correction data set, calls the position coding and corrected level values, 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.

[0012] As a further solution of the present invention, the system further includes: The situation filling module uses 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 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, and generates an electromagnetic spectrum situation map; The electromagnetic spectrum situation map is specifically the fitting value of the uncovered grid, the building height correction parameter, and the heat map drawing record.

[0013] As a further solution of the present invention, 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 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, and 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 and combines the building height data to correct the initial fitting value and generate 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.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, by dynamically adjusting the signal acquisition frequency, the data redundancy problem in the high-speed moving scenario is optimized and the local signal capture ability is improved. By dividing the grid using the plane coordinate system and combining normalization processing, the data storage pressure is reduced and the feature integrity is guaranteed. The sliding average algorithm is used to dynamically generate the threshold line, which improves the signal-to-noise separation accuracy and reduces the misjudgment rate. By combining the vehicle position and the grid center distance 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 electromagnetic situation map blind areas are reduced, realizing high-precision and low-latency panoramic monitoring of the electromagnetic environment in complex urban areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the signal acquisition module of the present invention; Figure 3 is the flow chart of the grid compression module of the present invention; Figure 4 is the flow chart of the signal processing module of the present invention; Figure 5 is the flow chart of the signal positioning module of the present invention; Figure 6 is the flow chart of the situation filling module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer, 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.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is 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 therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0018] Please refer to Figure 1 , a real-time monitoring system for radio electromagnetic environment based on urban traffic includes: The signal acquisition module obtains vehicle operation state information, extracts GPS coordinates and real-time draws the vehicle driving trajectory. By identifying the motion state of the vehicle, 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 normalizes and merges the data in 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 peak value of each sub-band and calculates the difference between adjacent peak values. By calculating the sliding average of the signal peak level, it obtains the threshold line to denoise the signal and obtains the 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.

[0019] 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 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.

[0020] Please refer to Figure 2 , the signal acquisition module includes: The status monitoring sub-module obtains the vehicle running status information, extracts the vehicle engine start-stop signal and GPS sensor data, extracts the longitude and latitude coordinates in real time, and obtains the real-time coordinate dataset; The engine start-stop signal is obtained through the on-vehicle OBD interface. 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. It 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 time stamp, record a set of data every 100ms, and use the formula: ; 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: ; The calculation result indicates that the engine is in the start state. Combine the engine state value, longitude value, latitude value, and time stamp to generate the real-time coordinate dataset.

[0021] The trajectory analysis sub-module calls the real-time coordinate dataset, draws trajectory points according to the time stamp 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; 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: ; 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: ; The calculation results show that the vehicle is in a normal driving state, and the speed value is calculated every 100 ms to generate a speed identification value.

[0022] 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: ; Calculate to obtain the dynamic frequency adjustment coefficient, collect radio signals in real time, combine 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 identification at the current moment, and t-1 is the sequence identification at the previous moment; Using the speed identification value and the real-time coordinate data set, calculate the time difference between adjacent coordinate points through the timestamp sequence, and use the formula: ; Calculate to obtain the dynamic frequency adjustment coefficient, collect radio signals in real time, combine the real-time coordinate data with timestamps to generate a radio signal data set. In actual operation, the WGS84 coordinate system is converted to a plane coordinate system, the x coordinate range is between 0-1000 meters, the y coordinate range is between 0-1000 meters, and 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 set to 60 km / h, about 16.67 m / s, is 500 meters, is 450 meters, is 500 meters, is 450 meters, is 0.1 second, is 1.2, substitute the set values into the calculation: ; 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 with the time stamp, a radio signal data set is generated.

[0023] Please refer to Figure 3 , the grid compression module includes: 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. Obtain the radio signal data set, which contains information entries such as time stamp, longitude, latitude, and signal strength. 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, select the 6-degree zone for urban areas, and determine the longitude of the central meridian of this zone. Taking the selected central meridian and the equator as the reference, convert the longitude and latitude coordinates 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, add a constant, such as 500000 meters, to the abscissa value. Perform this conversion calculation for the longitude and latitude coordinates of each signal acquisition point recorded in the radio signal data set. Finally, convert the geographical coordinates of all signal data points into the corresponding plane coordinates to generate a plane coordinate data set.

[0024] 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. 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: ; ; 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, y is the abscissa of the plane coordinate, is the grid side length (unit: meters), represents the floor operation. Set x to 4418500 meters, y to 446540 meters, is 100 meters, and substitute the set values for calculation: ; ; The calculation results show that this point belongs to the grid at row 44185 and column 4465. The row and column numbers are combined to generate a grid code. A unique code is obtained by multiplying the row number by a base number (such as 100000) and then adding the column number, resulting in the grid code 4418504465. The corresponding grid codes are assigned to all plane coordinate points within the area.

[0025] Based on the coding assignment results, the signal normalization and merging sub-module calls the radio signal data, maps the radio signals into the grid, and normalizes and merges the data within the grid to generate a grid code data set; Based on the grid code data set, the signal strength data in the radio signal data set is called, and the radio signal strength value (-85 dBm) at each timestamp is mapped into its corresponding grid code (4418504465). All signal strength data collected within a set time window (1 minute) for the same grid is subjected to normalization and merging processing. First, the signal strength is normalized and mapped into the interval from 0 to 1 using the formula: ; 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 for this grid within the set time window, is the maximum signal strength value recorded for 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: ; The calculation results show that the value of the signal strength of -85 dBm after normalization is 0.5. All signal strength values for this grid within this time window are subjected to the same normalization calculation, and then these normalized values are processed according to a preset merging strategy. The representative normalized signal strength value obtained from this merged calculation is associated and stored with the corresponding grid code to form the final output grid code data set.

[0026] Please refer to Figure 4 , the signal processing module includes: The frequency band splitting sub-module calls the grid code data set and divides the monitoring data into multiple sub-bands according to the service frequency band to generate a sub-band data set;​ Call the grid-coded signal set, which contains the representative signal strength information of each grid within a specific time window. This information usually covers a relatively wide frequency spectrum range. For example, it records the overall signal strength index of a certain grid within one minute at 10:00 on April 8, 2025, in the frequency range from 80 MHz to 2.5 GHz. According to the pre-set 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, the set 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. Filter and classify the original broadband monitoring data recorded in the grid-coded signal set 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 pre-set service frequency band. If the value of a certain frequency point is within the frequency range of a certain service frequency band, extract the signal strength information of this frequency point and classify it into the data subset specifically created for this service frequency band. Perform this classification operation on the signal data of all frequency points recorded in the grid. Finally, form multiple independent data sets, each of which only contains the 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 sub-band data sets.

[0027] Based on the sub-band data sets, the peak analysis sub-module extracts the peak level of each sub-band signal, calculates the difference between adjacent peak levels, and generates a peak difference data set; Based on the sub-band data sets, such as the FM radio sub-data set (containing signal data in the range of 88 to 108 MHz), for the signals in each sub-data set, within the set analysis time window (such as 1 minute) or frequency scan range, extract the peak level of the signal, that is, find the maximum value of the signal strength within this frequency band. In the FM radio sub-data set of grid 4418504465, it is detected that the signal strength at 98.5 MHz is the highest, which is -72 dBm. Then the peak level of this sub-band is -72 dBm. Perform the peak extraction operation on all sub-bands (such as terrestrial digital TV, GSM900 downlink, etc.) to obtain a series of peak level data, and then calculate the difference between the peak levels of adjacent service frequency bands (sorted by frequency). For example, if the FM radio peak is -72 dBm and the adjacent terrestrial digital TV band peak is -85 dBm, using the formula: ; 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 (for terrestrial digital TV) to be -85 dBm, (for FM radio) to be -72 dBm, and substitute the set values for calculation: ; The calculation results show that the peak levels of 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 identifier, peak level, adjacent band identifier, peak difference, etc., to generate a peak difference data set.

[0028] The threshold generation sub-module calls the peak difference data set, calculates the moving average of the signal peak level, dynamically matches the weight parameter in combination with the adjacent peak difference, and uses the formula: ; Perform operations 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 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 band interference suppression coefficient, is the reference value of the moving 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; 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 band and the 2 adjacent bands before and after it, and calculate the arithmetic average of the peak levels of these 5 bands as the reference value , for example, if the peak levels of 5 adjacent bands are -72, -85, -80, -88, -75 dBm respectively, then is -80 dBm. Then, in combination with the adjacent peak differences recorded in the peak difference data set, dynamically match the weight parameter , and the weight The setting aims to reflect the contribution degree of the peak difference to the judgment of 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 frequency band interference suppression coefficient P. The coefficient is set according to the historical interference situation of the frequency band or the environmental complexity. For the frequency band with more known interferences, it is set to 0.7. If there are fewer interferences, it is set to 0.9. Here it is set to 0.8. Use the formula: ; Set the sliding window n = 3, which includes frequency bands A, B, and C. Their peak levels are -75 dBm, are -85 dBm, are -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 for calculation: ; ; ; ; 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 fluctuation of the local spectrum, distinguishing the real signal peak from the background noise fluctuation, improving the accuracy of signal detection. Perform this denoising operation on all sub-frequency bands, extract the key features of the denoised signal, and generate the threshold signal feature set.

[0029] Please refer to Figure 5 , 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 the free space path loss on the signal level, adjust the level value of the narrowband high-power signal, and generate the level correction data set; 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; 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. For example, for the effective signal frequency band A within the grid code 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 from the feature set ( ), and its grid code. Through the grid code, reverse lookup to obtain the central geographical coordinates of the grid. At the same time, 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, and determine whether the signal belongs to the "narrowband high-power" type. The narrowband signal can be defined as the signal bandwidth less than 200 kHz, and the high-power signal can be defined as the peak level greater than the preset power threshold. If the signal meets this condition, the level adjustment is required; Set , , , , substitute into the formula: ; The result shows 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 effective signals within all grids with their position codes (grid codes), and generating a level correction data set.

[0030] The intensity fitting sub-module, based on the level correction data set, calls the position code and the corrected level value to fit the two-dimensional signal intensity distribution surface and generate an intensity surface data set; Based on the level correction data set, the data set contains each grid code and the representative corrected level value of each effective frequency band within the grid. For example, the corrected level of grid 4418504465 corresponding to frequency band A is -55.06 dBm. Call the position codes (and their corresponding central 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 the method of spatial interpolation or function fitting, construct a continuous two-dimensional signal intensity distribution surface covering the monitored geographical area. Using bilinear interpolation, for any point within the area, its signal intensity It can be calculated from the coordinates of the four corner points of the rectangular grid where it is located (i.e., the centers of the adjacent four grids) and the corresponding corrected level values using the formula: ; Bilinear interpolation is performed to calculate the intensity. Among them, is the fitting signal intensity at point (unit: dBm), is the planar coordinate of the point to be interpolated, are the coordinates of the four grid centers surrounding point , are the corrected level values corresponding to these four grid centers respectively, is the area of this rectangular grid. Assuming the grid side length is 100m, the coordinates of the four corner points are m, and the corresponding corrected level values are dBm, dBm, dBm, dBm. Calculate the intensity at point m, , , , . Then , , , , and the area . Substitute the set values into the calculation: ; ; The calculation results show that the fitting signal intensity at point (4418580,446580) is approximately -57.31dBm. By performing interpolation calculations for 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 the two-dimensional plane, generating an intensity surface dataset.

[0031] The extreme value extraction sub-module calls the intensity surface dataset, identifies the signal source location by extracting the coordinates of the extreme points on the surface, and generates a signal source coordinate set; The call intensity surface data set represents the distribution of signal intensity in a planar region. To identify the potential locations of signal emission sources, local extreme points of the signal intensity are sought 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 at this point (i.e., the rates of change of the function in the x and y directions) are both equal to zero, and the second partial derivatives at this point (i.e., the curvatures of the function in the x and y directions) 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 data, local peaks can be found by comparing the intensity values of each grid point with those of its eight neighboring grid points. That is, if the intensity value of a point is greater than the intensity values of all its adjacent points, this point is identified as a local maximum point. The planar coordinates of all the found local maximum points are extracted to identify the signal source locations and generate a signal source coordinate set.

[0032] Please refer to Figure 6 , 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 values of the uncovered grids and generate an initial fitting data set; 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; 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 within the region. For example, the center coordinates of grid 4418504465 are (4418550, 446550) m, and the signal strength is -55 dBm. Extract the signal strength and geographical feature parameters of the regional grid. 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 grids (i.e., grids without direct measurement data), using the formula: ; Calculate the initial fitting value of the uncovered grid. Among them, is the initial fitting value (unit: dBm), is the signal strength of the kth grid (unit: dBm), is the geographical feature gradient of the kth grid (dimensionless, calculated from geographical feature parameters, such as building density gradient, terrain slope, etc.), is the abscissa of the center of the kth grid (unit: m), is the ordinate of the center of the kth grid (unit: m), is the average abscissa of the reference area (unit: m), is the average ordinate of the reference area (unit: m), m is the total number of grids participating in the calculation, 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, ). Assume that the reference area contains 5 grids, and their center 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 average coordinates of the reference area: , , Assume Calculate the initial fitting value of the uncovered grid (4418800, 446800), and substitute the set values for calculation: ; ; ; ; ; ; 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, taking into account the influence of the geographical environment on signal propagation. This calculation is performed for all uncovered grids to generate an initial fitting dataset.

[0033] Based on the initial fitting dataset, the parameter correction sub-module combines the building height data to adjust the fitting level value correction parameters and correct the initial fitting values, generating a corrected level parameter set. The parameter correction sub-module is based on the initial fitting dataset, which contains the initial fitting signal strength values of the uncovered grids. For example, the initial fitting value of the grid (4418800, 446800) is -2.516 dBm. Combining the building height data, which is obtained by lidar (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. Adjust the fitting level value correction parameters to correct the initial fitting values. The correction process takes into account the blocking and reflection effects of buildings on signals. High-rise buildings will cause signal attenuation and at the same time generate multipath effects. The correction parameters include building height coefficient, building density coefficient, building distribution uniformity coefficient, etc. For example, set the building height coefficient relationship with the average building height is , the building density coefficient relationship with the building coverage rate d is , the building distribution uniformity coefficient relationship with the height standard deviation is , then the corrected signal strength can be expressed as: ; where is the corrected signal strength (unit: dBm), is the initial fitting value (unit: dBm), , , are correction coefficients (dimensionless). Set the initial fitting value of the grid (4418800, 446800) , the average building height , the building coverage rate d = 0.6, the height standard deviation , calculate the correction coefficient: ; ; ; Substitute the set value for calculation: ; ; ; 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.

[0034] The thermal mapping sub-module calls the corrected level parameter set, maps the signal heat map of the target area by calculating the level strength gradient, and generates an electromagnetic spectrum situation map; 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 heat map of the target area. The gradient calculation uses the central difference method. For grid , its gradient components and can be expressed as: ; ; 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 certain 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 value for calculation: ; ; The calculation results show that the signal strength 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 strength. 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 strength values, the signal strength is visualized using color mapping, usually adopting a gradient color scale from red (high signal strength) to blue (low signal strength), to generate an electromagnetic spectrum situation map, intuitively showing the intensity distribution and change trend of electromagnetic signals in the region.

[0035] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence 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 comprises: The signal acquisition module obtains vehicle operation status information, extracts GPS coordinates and draws the vehicle's driving trajectory in real time. By identifying the vehicle's motion status, adjusting the acquisition frequency, and combining the location coordinates and timestamp, the radio signal data set is obtained; The grid compression module calls the radio signal data set, converts the latitude and longitude data into a plane coordinate system, divides the area into a plurality of grids and assigns grid codes, maps the radio signal to the grids and normalizes and merges the data in the grids to generate a grid code data set; The signal processing module calls the grid coded 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, obtains the threshold line to denoise the signal by calculating the sliding average of the signal peak level, and obtains the threshold signal feature set; The signal positioning module calls the threshold signal feature set, calculates the distance attenuation factor and adjusts the level value of the narrowband high-power signal according to the level value and position code of the radio signal and the distance between the vehicle position and the grid center, fits the two-dimensional intensity distribution surface and extracts the extreme points to generate a signal source coordinate set.

2. The real-time monitoring system for radio electromagnetic environment based on urban traffic according to claim 1 is characterized in that: The radio signal data set specifically includes signal frequency parameters, location coordinates, and timestamps; the grid coding data set includes plane coordinate system parameters, grid coding, and normalized merging parameters; the threshold signal feature set specifically refers to service frequency band division parameters, threshold line parameters, and a list of valid signal frequency bands; the signal source coordinate set includes level value parameters, a two-dimensional intensity distribution surface, and extreme point identification records.

3. The real-time monitoring system for radio electromagnetic environment based on urban traffic according to claim 1 is characterized in that: The signal acquisition module comprises: The status monitoring submodule obtains vehicle operation status information, extracts vehicle engine start / stop signals and GPS sensor data, extracts longitude and latitude coordinates in real time, and obtains real-time coordinate data sets; The trajectory analysis submodule 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 control submodule uses the speed identification value and real-time coordinate data to call the timestamp sequence to calculate the time difference between adjacent coordinate points using the formula: ； Calculate and obtain dynamic frequency adjustment coefficients, collect radio signals in real time, and generate radio signal data sets by combining real-time coordinate data and timestamps; in, 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 horizontal coordinate 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 of the current moment, and t-1 is the sequence identifier of the previous moment.

4. The real-time monitoring system for radio electromagnetic environment based on urban traffic according to claim 3 is characterized in that: The grid compression module comprises: The gridding conversion submodule obtains the radio signal data set and converts the longitude and latitude data into a plane coordinate system using Gaussian projection; The grid code allocation submodule calls the plane coordinate system, divides the coordinate system into a plurality of grids, allocates grid codes, and generates a code allocation result; The signal normalization and merging submodule calls the radio signal data based on the coding allocation result, maps the radio signal to the grid and normalizes and merges the data in the grid to generate a grid coding data set.

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

6. The real-time monitoring system for radio electromagnetic environment based on urban traffic according to claim 5 is characterized in that: The signal positioning module comprises: The level adjustment submodule calls the threshold signal feature set to extract the level value and position code of the radio signal, and uses the formula: ； Calculate the impact of free space path loss on signal level, adjust the level of narrowband high-power signals, and generate a level correction data set; 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 ratio of pi; The intensity fitting submodule calls the position code and the correction 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 submodule calls the intensity surface data set, extracts the coordinates of the surface extreme value points, identifies the signal source position, and generates a signal source coordinate set.

7. The real-time monitoring system for radio electromagnetic environment based on urban traffic according to claim 1 is characterized in that: The system further comprises: The situation filling module uses the signal source coordinate set and the grid code data set to call the regional grid signal data and regional geographical features, calculates the initial fitting value of the uncovered grid, adjusts the fitting level value correction parameter in combination with the building height, fits the equipotential line, calculates the level intensity gradient and maps it into a heat map, and generates an electromagnetic spectrum situation map; The electromagnetic spectrum situation map specifically includes uncovered grid fitting values, building height correction parameters, and thermal map drawing records.

8. The real-time monitoring system for radio electromagnetic environment based on urban traffic according to claim 7 is characterized in that: The situation filling module includes: The grid fitting submodule calls the signal source coordinate set and the grid coding data set to extract the regional grid signal strength and geographic feature parameters using the formula: ； Calculate the initial fitting values ​​of the uncovered grid and generate an initial fitting data set; in, is the initial fitting value, is the kth grid signal strength, is the k-th grid geographic feature gradient, is the horizontal coordinate of the kth grid center, is the ordinate of the kth grid center, is the mean value of the horizontal coordinate of the reference area, is the mean value of the vertical coordinate 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 submodule adjusts the correction parameters of the fitting level value based on the initial fitting data set and in combination with the building height data, corrects the initial fitting value, and generates a corrected level parameter set; The thermal mapping submodule calls the modified 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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