Communication signal analysis method and device, equipment and storage medium

Abstract

The invention relates to the technical field of communication signal analysis, in particular to a communication signal analysis method and device, equipment and a storage medium. The method comprises the following steps: carrying out full-band signal continuous acquisition on a detection area, and extracting an original signal flow; performing potential electromagnetic interference detection and electromagnetic interference distribution marking on the original signal flow to obtain an interference source distribution diagram; dynamic interference suppression is carried out based on the interference source distribution map, real-time suppression parameter adjustment is carried out, and a calibration optimization signal flow is constructed; performing real-time intensity calculation on the calibration optimization signal flow, and constructing a regional signal intensity distribution model; and performing signal quality evaluation and real-time decision early warning based on the regional signal intensity distribution model. According to the method, the communication signal quality and stability of the area can be accurately and rapidly evaluated.

Description

Technical Field

[0001] This invention relates to the field of communication signal analysis technology, and in particular to a communication signal analysis method, apparatus, device, and storage medium. Background Technology

[0002] Traditional methods for detecting and analyzing regional communication signals typically rely on single-band acquisition or local sampling strategies, obtaining signal characteristics through base station measurements and spectrum scanning. While these methods can reflect local signal conditions to some extent, their limited sampling frequency and monitoring range make it difficult to comprehensively grasp the signal distribution and dynamic changes across the entire spectrum. Furthermore, existing methods often focus on post-event analysis and manual evaluation, lacking real-time and automation capabilities, making it difficult to meet the demands for signal stability and reliability monitoring in high-speed mobile communication and large-scale Internet of Things (IoT) environments. Over long-term operation, regional communication signals may be affected by various factors, including environmental factors (such as building obstruction and weather changes), human interference (such as radio interference and illegal signal transmission), and system fluctuations (such as base station load fluctuations and spectrum resource conflicts), leading to instantaneous signal fading, increased interference, or coverage blind spots. These problems not only affect network service quality but may also threaten the reliability of critical services. Therefore, developing a method capable of real-time, full-band perception of regional communication signal status, accurate identification of abnormal signal behavior, and comprehensive assessment of communication stability has become an important research direction for improving the reliability and service quality of regional communication networks. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention proposes a communication signal analysis method, apparatus, device, and storage medium to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, the present invention provides a communication signal analysis method, comprising the following steps: Step S1: Continuously acquire signals across the entire frequency band in the detection area and extract the original signal stream; Step S2: Detect potential electromagnetic interference and mark the distribution of electromagnetic interference on the original signal stream to obtain an interference source distribution map; Step S3: Perform dynamic interference suppression based on the interference source distribution map, adjust the suppression parameters in real time, and construct a calibration-optimized signal flow; Step S4: Perform real-time intensity calculation on the calibration and optimization signal stream to construct a regional signal intensity distribution model; Step S5: Conduct signal quality assessment and real-time decision-making and early warning based on the regional signal strength distribution model.

[0005] This specification provides a communication signal analysis apparatus for performing the communication signal analysis method described above, comprising: The signal acquisition module is used to continuously acquire signals across the entire frequency band of the detection area and extract the raw signal stream; The interference distribution detection module is used to detect potential electromagnetic interference and mark the distribution of electromagnetic interference in the original signal stream, thereby obtaining an interference source distribution map. The interference suppression module is used to perform dynamic interference suppression based on the interference source distribution map, adjust the suppression parameters in real time, and construct a calibrated and optimized signal flow. The real-time intensity calculation module is used to perform real-time intensity calculation on the calibration and optimization signal stream and construct a regional signal intensity distribution model. The signal assessment and decision-making module is used to assess signal quality and provide real-time decision-making and early warning based on a regional signal strength distribution model.

[0006] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the communication signal analysis method described in any of the preceding claims.

[0007] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the communication signal analysis method described in any of the preceding claims.

[0008] The specific benefits of this invention are as follows: Full-band acquisition ensures that communication signals and potential interference sources in different frequency bands are captured, avoiding the omission of critical signals. It provides unprocessed raw signal streams, offering reliable foundational data for subsequent analysis and ensuring that analysis results accurately reflect the on-site signal environment. It captures dynamic signal change characteristics, including short-term interference and burst signals, providing timing information for real-time assessment. It distinguishes between normal communication signals and potential interference signals, enabling early detection of electromagnetic environment problems that may affect communication quality. The interference source distribution map clearly shows the spatial distribution characteristics of interference within the area, facilitating targeted interference handling. Marking the location and intensity of interference sources provides a reference for subsequent interference suppression and resource scheduling. Dynamically suppressing electromagnetic interference significantly reduces the impact of noise on communication signals, making the signal more stable and cleaner. The suppression parameters can be dynamically adjusted to cope with changes in the location and intensity of interference sources, achieving continuous optimization. The calibrated signal stream more closely approximates the real communication signal state, improving the accuracy of signal strength modeling and quality assessment. Real-time calculation of signal strength allows for the acquisition of the true strength information of communication signals at various points within the area. It forms a spatial mapping of signal strength, revealing signal coverage blind spots, attenuation hotspots, and strong signal areas. Signal strength distribution models can be used for graphical representation, providing an intuitive basis for operation and maintenance decisions. They offer quantitative data for subsequent communication quality assessment, interference management, and resource optimization. Combining signal strength and interference suppression, a comprehensive evaluation of regional communication stability can be achieved. Abnormal signal changes or potential interference trends can be quickly identified, and timely warnings can be issued to reduce communication risks. Data support is provided for network adjustments, frequency planning, or interference mitigation, enabling dynamic optimization management. Through continuous monitoring, evaluation, and early warning, the stable operation of communication networks in complex electromagnetic environments is ensured. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the steps of a communication signal analysis method according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a detailed flowchart illustrating the implementation steps of step S2; Figure 4 This is a flowchart illustrating the detailed implementation steps of step S3. Detailed Implementation

[0010] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0011] This application provides a communication signal analysis method, apparatus, device, and storage medium. The execution entities of the communication signal analysis method, apparatus, device, and storage medium include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio / image management system, an information management system, and a cloud data management system.

[0012] Please see Figures 1 to 4 This invention provides a communication signal analysis method, comprising the following steps: Step S1: Continuously acquire signals across the entire frequency band in the detection area and extract the original signal stream; Step S2: Detect potential electromagnetic interference and mark the distribution of electromagnetic interference on the original signal stream to obtain an interference source distribution map; Step S3: Perform dynamic interference suppression based on the interference source distribution map, adjust the suppression parameters in real time, and construct a calibration-optimized signal flow; Step S4: Perform real-time intensity calculation on the calibration and optimization signal stream to construct a regional signal intensity distribution model; Step S5: Conduct signal quality assessment and real-time decision-making and early warning based on the regional signal strength distribution model.

[0013] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a communication signal analysis method according to the present invention. In this example, the steps of the communication signal analysis method include: Step S1: Continuously acquire signals across the entire frequency band in the detection area and extract the original signal stream; In this embodiment, a broadband signal receiving device or spectrum analyzer is used to cover the target frequency band, for example, from 0.1 MHz to 6 GHz, to ensure that all potential signal sources are monitored. The sampling rate is typically set to more than twice the original frequency to meet the Nyquist criterion; for example, a 12 GHz sampling rate can capture high-frequency transient signals. Automatic gain control (AGC) and low-noise amplifiers (LNAs) are used during the acquisition process to ensure that weak signals are not masked by noise and to avoid saturation of strong signals. The raw signal stream formed by continuous signal acquisition includes amplitude, phase, and timestamp information, and millions of sampling points can be acquired per second to ensure the integrity of communication signals in the coverage area. The acquired data is stored in channel and time order to provide high-precision basic data for subsequent interference detection, signal analysis, and stability assessment.

[0014] Step S2: Detect potential electromagnetic interference and mark the distribution of electromagnetic interference on the original signal stream to obtain an interference source distribution map; In this embodiment, abnormal power peaks are detected in the original signal stream. Signal events with amplitudes exceeding 3–5 times the background noise are identified through short-time RMS calculation or sliding window peak analysis. Subsequently, time difference of arrival (TDOA) and angle difference of arrival (AOA) are calculated using a multi-channel antenna array to deduce the spatial coordinates of the interference source, achieving three-dimensional localization. Spectrum analysis and modulation characteristic extraction are performed on each interference source, calculating the center frequency, bandwidth, and power density, while recording timestamps and repetition periods. Finally, the spatial location, power, and frequency characteristics of the interference sources are superimposed to generate an interference source distribution map, displaying the coverage area and frequency band distribution of different interference levels. For example, a high-power interference source with approximately 12 dBm has a coverage radius of approximately 50 meters, while a low-power source with approximately 0 dBm has a coverage radius of 5 meters, providing basic spatial and frequency information for regional signal stability assessment.

[0015] Step S3: Perform dynamic interference suppression based on the interference source distribution map, adjust the suppression parameters in real time, and construct a calibration-optimized signal flow; In this embodiment, the power density, occupied bandwidth, and spatial coverage information of the interference source are used as adaptive filtering weights input to the filter or frequency domain noise suppression module to dynamically adjust the gain and perform band-stop processing on signals in different frequency bands. For example, the gain of high-power interference bands is reduced by 5–10 dB, while the gain of low-power interference bands remains unchanged. The filter employs a real-time adaptive algorithm, refreshing parameters every 10–50 milliseconds to ensure stable output even when the signal is subjected to dynamic interference at different time periods. Simultaneously, power normalization and amplitude calibration are performed to unify the amplitude of signals in different frequency bands to the reference level, preserving the characteristics of the main communication signal. The resulting calibrated and optimized signal stream reduces the impact of interference while ensuring consistency in amplitude and dynamic range, providing high-precision data for subsequent signal strength calculations and regional model construction.

[0016] Step S4: Perform real-time intensity calculation on the calibration and optimization signal stream to construct a regional signal intensity distribution model; In this embodiment, the signal stream is divided by frequency band, and the Received Signal Strength Indication (RSSI) value for each frequency band is calculated. The dBm value is obtained using root mean square (RMS) or logarithmic power conversion. Then, the region is divided into a spatial grid, such as a two-dimensional 0.5×0.5 meter grid or a three-dimensional layer per meter height. The real-time signal strength of each grid point is mapped, and interpolation methods (such as Kriging interpolation) are used to fill in blank areas. Combined with directional antenna array measurements, signal trends at different azimuth and elevation angles are recorded, and multi-time-point fluctuations are analyzed to generate time-series data. The resulting regional signal strength distribution model not only reflects spatial coverage but also includes temporal fluctuations and directional information, which can be used for signal stability assessment, interference hotspot identification, and coverage optimization.

[0017] Step S5: Conduct signal quality assessment and real-time decision-making and early warning based on the regional signal strength distribution model.

[0018] In this embodiment, multi-time-point RSSI data is combined with signal fluctuation statistical analysis to calculate short-term stability index, periodic stability index, and long-term trend index. Based on the evaluation results, a signal quality fluctuation report is generated, marking high-fluctuation areas and potential interference points. Subsequently, based on the fluctuation amplitude and probability, a multi-level early warning strategy is designed; for example, short-term abnormal fluctuations trigger a level-one early warning, and a long-term downward trend triggers a level-two early warning. Real-time decision-making logic is automatically executed based on the early warning strategy, including early warning information dissemination, emergency response triggering (such as signal enhancement and frequency band switching), and recovery assessment processes, achieving all-weather intelligent monitoring. This process can dynamically sense signal changes and respond promptly to potential interference and fluctuation events, ensuring the stability and reliability of regional communication signal coverage.

[0019] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: The signal detection equipment continuously acquires signals across the entire frequency band in the detection area and extracts the original signal stream. The original signal stream is processed by Fast Fourier Transform to obtain power spectral density distribution data; The power spectral density distribution data is divided into frequency bands and analyzed at multiple scales to extract the center frequency, bandwidth and power parameters of each frequency band signal and fit them into a signal power characteristic set. Dynamic range compression is performed based on the signal power characteristic set, and sampling accuracy gain control is applied to obtain the accuracy gain signal stream.

[0020] In this embodiment, a broadband receiver or spectrum analyzer is used to fully cover the frequency band of interest (e.g., 0.1 MHz to 6 GHz), while ensuring that the sampling rate meets the Nyquist criterion, for example, setting the sampling rate to 12 GHz to capture high-frequency transient signal characteristics. During the acquisition process, the device employs automatic gain control (AGC) to ensure that the signal amplitude does not saturate within the detection range, while simultaneously improving the resolution of weak signals through a built-in low-noise amplifier. The raw signal stream formed by continuous acquisition includes amplitude, phase, and timestamp information, recording millions of sampling points to ensure coverage against potential frequency band interference, signal drift, and transient events in the coverage area. All acquired data is stored in channel number and time order, forming a complete raw signal stream, providing fundamental data for subsequent frequency domain analysis and multi-scale resolution, while ensuring information integrity and temporal continuity. The Fast Fourier Transform (FFT) algorithm is used to divide the continuous-time signal into multiple time windows (e.g., 1-millisecond windows with 50% overlap) for FFT calculation. Window selection typically uses Hanning windows or Blackman windows to reduce sidelobe leakage and spectral leakage. After the FFT calculation is completed, the amplitude is squared and normalized to obtain the power spectral density (PSD) distribution data, which can be expressed in dBm / Hz or µW / Hz. Taking a sampling rate of 12 GHz and an FFT point count of 2^16 as an example, 32,768 frequency resolution points can be obtained per time window, with a frequency resolution of approximately 183 kHz. The PSD data can reflect the energy distribution characteristics of regional communication signals across the entire frequency band, displaying the dominant frequency signal, subharmonics, noise floor, and transient interference peaks, providing a quantitative basis for subsequent frequency band division and multi-scale analysis.

[0021] The entire frequency band is divided into several preset frequency bands, such as a low-frequency band (0.1–30 MHz), a mid-frequency band (30–500 MHz), and a high-frequency band (500 MHz–6 GHz). Each band is further subdivided according to application characteristics. Then, wavelet transform or multi-resolution Fourier analysis is used to perform multi-scale analysis on each sub-band, extracting the center frequency and bandwidth (e.g., half-power bandwidth) of the main peak signal, while simultaneously calculating the total signal power, peak power, and root-mean-square power. For example, a main peak signal is found in the 500–600 MHz band, with a center frequency of 553.2 MHz, a half-power bandwidth of 4.5 MHz, and a total power of 12 dBm. By performing the same processing on each band, a complete set of signal power characteristics can be generated, including the frequency position, bandwidth, power intensity, and dynamic characteristics of each band. The signal power characteristic set is mapped to a logarithmic scale or a nonlinear compression function (e.g., μ-law or A-law compression) to moderately compress high-power peaks while preserving the discriminability of weak signals. Subsequently, gain control is performed based on the power range of each frequency band. The gain of the low-power band is increased to within the range that the sampler can resolve, while the high-power band is appropriately attenuated. For example, when the PSD peak range is from -90 dBm to +10 dBm, the gain of the low-power signal is increased by 30–40 dB, and the gain of the high-power signal is reduced by 10 dB to ensure that the signal across the entire frequency band can be accurately sampled within the dynamic range of the ADC. The resulting precision gain signal stream maintains the integrity of the signal across the entire frequency band while improving the observability of weak signals. It can be directly used for regional communication signal analysis, interference identification, and comprehensive stability assessment, ensuring the accuracy and reliability of subsequent analysis results.

[0022] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: The original signal stream is subjected to in-depth environmental noise identification, and the signal subspace is separated to extract the noise subspace; The noise frequency is calculated in the noise subspace, and noise similarity inference is performed to obtain the type of environmental noise. The original signal stream is subjected to abnormal power peak calculation and potential electromagnetic interference is detected and marked. Calculate the arrival time difference and arrival angle difference information of the potential electromagnetic interference source, perform spatial positioning, and obtain the location information of the interference source; Calculate the frequency characteristics, modulation characteristics, and time-domain envelope of the potential electromagnetic interference source to obtain the interference source characteristic parameters; Electromagnetic interference distribution is marked based on the location information of the interference source, and feature overlay mapping is performed based on the characteristics of the interference source to obtain the interference source distribution map.

[0023] In this embodiment, the original signal stream is input into a multi-channel signal processing module. Principal Component Analysis (PCA), Independent Component Analysis (ICA), or least squares subspace separation methods are used to decompose the signal into a signal subspace and a noise subspace. For example, for a signal stream with a sampling rate of 12 GHz and continuous sampling for 10 seconds, it can be divided into multiple time windows. The covariance matrix is ​​calculated for each time window, and eigenvalue decomposition is performed. The smaller eigenvalues ​​typically correspond to the noise subspace. Subsequently, by analyzing the energy proportion and spectral distribution of each subspace, it is possible to further identify which components are environmental noise, including thermal noise, power line interference, or electromagnetic scattering background. The extracted noise subspace contains both amplitude and phase information and retains temporal continuity. Fast Fourier Transform (FFT) or wavelet multi-scale analysis is performed on the noise subspace signal to extract the power spectral density and main peak values ​​of each frequency component. For example, in the 50 Hz to 500 MHz frequency band, PSD calculations can identify 60 Hz power line interference, radio frequency scattering background, and high-frequency equipment noise. Subsequently, based on the peak frequency, bandwidth, and modulation mode, template matching or pattern recognition algorithms are used to infer the noise type. For example, when the PSD displays periodic pulses with an amplitude of approximately -60 dBm, it can be identified as switching power supply interference; if broadband low-amplitude noise is present, it can be classified as environmental scattering background. Through this analysis, an environmental noise classification list can be generated, including noise type, main frequency, and intensity, providing a basis for signal stability assessment.

[0024] The extracted noise subspace components are subtracted from the original signal stream, and peak analysis is then performed on the remaining signal. Using a sliding window or short-time energy calculation method, anomalous power peaks with amplitudes exceeding 3–5 times the background noise can be identified. For example, if a signal peak reaches -10 dBm within a 1-millisecond window, while the background average is -60 dBm, it can be marked as a potential interference event. Subsequently, the location of each anomalous peak is recorded using timestamps, and the anomalous amplitude is filtered to eliminate random noise and retain possible electromagnetic interference sources, providing candidate objects for subsequent localization and feature analysis. Signals are acquired using array antennas, and three-dimensional localization is achieved through Time Difference of Arrival (TDOA) and Angle Difference of Arrival (AOA) analysis. For example, for a four-element array, the signal propagation speed is approximately 3 × 10^8 m / s. By calculating the time difference between the received interference signals by different antennas, the distance and direction from the interference source to the array can be accurately estimated. Combined with the AOA, the azimuth and elevation angles can be further determined, achieving three-dimensional spatial coordinate localization. The localization accuracy depends on the array spacing and signal sampling accuracy, typically reaching meter-level or sub-meter-level accuracy. The final result generates spatial coordinate information for each potential interference source, providing a basis for electromagnetic interference distribution analysis and feature overlay.

[0025] The signals from each interference source are extracted for spectrum analysis, modulation identification, and time-domain envelope calculation. Frequency characteristics, such as center frequency, bandwidth, and power distribution, can be obtained through FFT or multi-resolution wavelet analysis. Modulation characteristics can be extracted through instantaneous phase analysis, amplitude envelope analysis, or carrier period identification algorithms, such as detecting amplitude modulation (AM), frequency modulation (FM), or pulse modulation modes. The time-domain envelope is calculated using Hilbert transform or envelope detection to obtain signal peak value, duration, and repetition period information. For example, an interference source may have a center frequency of 915 MHz, a bandwidth of 10 MHz, use pulse modulation, have an envelope duration of 2 µs, and repeat 50 times per second. Combining these characteristics, a complete set of interference source parameters can be formed, providing data support for distribution mapping and interference impact assessment. The three-dimensional coordinates of each interference source are mapped onto a regional grid, and the interference intensity is represented by color or intensity coding, with features such as frequency, bandwidth, and modulation type superimposed. The overlay method can use weighted synthesis or overlaid heatmaps to mark high-intensity interference areas in red and low-intensity areas in blue, while also adding frequency labels and modulation symbols to the map to form an interactive three-dimensional distribution map. This not only visually displays the spatial and frequency domain distribution of potential interference sources, but also helps identify key interference hotspots and assess communication stability.

[0026] In this embodiment, reference Figure 4 The diagram below illustrates the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Based on the type of environmental noise, the precision gain signal stream is subjected to adaptive high-frequency filtering and denoising to obtain the filtered and denoised signal stream. Calculate the power density and occupied bandwidth of each interference source based on the interference source distribution map; Electromagnetic interference range is predicted based on the power density and occupied bandwidth to obtain the interference range of each interference source. Based on the interference range, the signal interference level of different frequency bands is quantified to obtain the multi-band interference level coefficient. Dynamic interference suppression is performed on the filtered and denoised signal stream based on the multi-band interference level coefficient, and the suppression parameters are adjusted in real time to obtain the interference-suppressed signal stream. Power normalization and amplitude calibration are performed on the interference suppression signal stream to construct a calibration-optimized signal stream.

[0027] In this embodiment, based on the preliminary noise subspace analysis and noise type determination results, a suitable adaptive filtering algorithm is selected, such as an FIR adaptive filter or an LMS (Least Mean Square) algorithm. For high-frequency sharp interference, such as switching power supply pulse noise or radar scattering noise, the filter dynamically adjusts the cutoff frequency and filtering order. For example, the cutoff frequency is set to within ±5 MHz of the center frequency of the interference signal, and the order is 128–256 to ensure the preservation of the effective part of the signal. During the adaptive filtering process, the filter coefficients are iteratively updated through real-time error signals, enabling rapid response to sudden changes in environmental noise or frequency band drift. After processing, the filtered and denoised signal stream retains the main frequency components and amplitude characteristics of the original signal, while significantly reducing high-frequency noise spikes and background interference, providing a stable signal basis for subsequent interference analysis and suppression. Power spectrum analysis is performed on the signal truncated from the interference source to calculate the power density (in dBm / Hz or µW / Hz) and its distribution in the frequency domain. For example, the signal energy within the frequency range is calculated using FFT or multi-resolution wavelet analysis, and the power density is obtained by dividing the total power by the frequency band width. The occupied bandwidth is calculated using the half-power bandwidth or 99% energy bandwidth method. For example, if an interference source has a total power of 12 dBm and a center frequency of around 915 MHz, and the half-power point frequency span is 10 MHz, then the occupied bandwidth is 10 MHz. By performing the same calculation on all interference sources, a complete interference source power characteristic matrix can be generated, providing accurate parameters for electromagnetic interference range prediction and multi-band interference quantification.

[0028] By combining free-space propagation loss models and environmental correction factors (such as building obstruction and wall reflection), spatial attenuation predictions are made for the radiated power of each interference source. For example, the Friis transmission formula or multipath model is used to calculate the power level of the interference signal at different distances and compare it with the sensitivity threshold of the communication signal to determine the effective interference range. For interference sources with high power density (>10 dBm / Hz), the interference range may reach a radius of 50 meters; for sources with low power density (<0 dBm / Hz), the interference range is only a few meters. The prediction results not only include the radius of the interference source but can also include directional information (if the antenna is directional), forming a spatial interference ellipsoid or cone, providing a basis for quantifying the interference level in different frequency bands. The filtered and denoised signal is divided into several frequency bands (e.g., low frequency 0.1–30 MHz, mid frequency 30–500 MHz, high frequency 500 MHz–6 GHz), and then the interference ratio of each frequency band is calculated by combining the coverage range and power density of the interference source. For example, in the 500–600 MHz band, if the power density of the interference source reaches 8 dBm / Hz and the coverage area includes the signal acquisition point, the interference level for this band can be set to 0.75; in the 0.1–30 MHz band, where there is no interference source coverage, the interference level is 0.05. The multi-band interference coefficients form a 0–1 normalized matrix, which can be used to guide dynamic signal suppression and filter parameter adjustment, allowing each band to be optimized according to the actual interference intensity.

[0029] The interference coefficient of each frequency band is used as a weight input to the adaptive filter or frequency domain noise suppression module to dynamically adjust the filter gain, cutoff frequency, and band-stop strength. For example, for a frequency band with an interference level of 0.75, the filter gain is reduced by 5–10 dB, while the original signal gain is maintained for a frequency band with an interference level of 0.05. During real-time suppression, the filter parameters are updated with a refresh step size of 10–50 milliseconds to ensure that the signal can maintain a stable output even when subjected to dynamic interference at different time periods. The interference-suppressed signal stream obtained after suppression reduces the impact of local high-power interference while preserving the characteristics of the main communication signal, and can be directly used for stability analysis and optimization. The root mean square power of the signal stream is calculated, and the amplitude of all frequency bands is linearly or nonlinearly normalized to a set reference level (such as 0 dBm or 80% of the full scale of the reference ADC). At the same time, the amplitude of each frequency band is calibrated to correct the amplitude deviation caused by filtering suppression or gain adjustment, so that the overall spectrum maintains a true distribution. For example, if the amplitude of the low-frequency band is too low after filtering, a 3 dB compensation can be added; if the high-frequency band has excessive attenuation due to suppression, a 5 dB correction can be added. The resulting calibrated and optimized signal stream not only has uniform signal amplitude and reasonable dynamic range, but also retains the full-band signal characteristics, providing high-precision data input for regional communication signal analysis and comprehensive stability assessment.

[0030] In this embodiment, step S4 includes the following steps: The received signal strength indication value is calculated in real time for the calibration and optimization signal stream to obtain the real-time signal strength of each frequency band; The calibration optimization signal stream is divided into spatial grids, and the signal intensity distribution at different locations is analyzed based on the real-time signal intensity to obtain spatial distribution data of signal intensity. The real-time signal strength is measured using a directional antenna array, and the strength trend at different azimuth and elevation angles is calculated to obtain the signal strength in multiple directions. Multi-time-point fluctuation analysis was performed on the real-time signal strength to obtain multi-time-point intensity fluctuation curves; Multidimensional intensity distribution modeling is performed based on multi-time point intensity fluctuation curves, spatial distribution data of signal intensity, and signal intensity in multiple directions to construct a regional signal intensity distribution model.

[0031] In this embodiment, the signal stream is divided into several frequency bands, such as low frequency (0.1–30 MHz), mid-frequency (30–500 MHz), and high frequency (500 MHz–6 GHz). The root mean square (RMS) power of the signal is then calculated within each band, and an RSSI value (in dBm) is generated through logarithmic conversion. The calculation process is based on a short time window, for example, calculated every 20 milliseconds, forming a real-time dynamic RSSI curve. For each frequency band, filter gain and calibration factors are also considered to ensure that the signal strength value reflects the true received power. For example, if the RMS power is 1.2 µW at a certain moment in the mid-frequency band, the RSSI obtained through normalization and dBm conversion is approximately 0.79 dBm. Real-time RSSI calculation can be used to capture transient signal changes and dynamic fluctuations, providing basic data for subsequent spatial distribution analysis and directional measurement. The target area is divided into two-dimensional or three-dimensional grid cells, for example, each grid cell has a side length of 0.5–1 meter and a height of 1 meter, forming a uniform sampling grid. Subsequently, the real-time RSSI value of each acquisition point is mapped to the corresponding grid location, and interpolation algorithms (such as Kriging interpolation or bilinear interpolation) are used to calculate the signal strength at the missing locations. By statistically analyzing the mean, maximum, and standard deviation within each grid cell, a spatial distribution data matrix can be obtained. For example, in a 50×50 meter area, the grid is divided into 50×50 cells, and the average RSSI of each cell is between -80 dBm and -40 dBm. Spatial grid analysis can clearly reveal areas of strong and weak signals and potential coverage dead zones.

[0032] A rotatable array antenna is used to acquire signals in preset step angles, such as 5° steps per azimuth from 0 to 360° and 2° steps per elevation from -30° to +30°. For each angle combination, the average power or RMS value of the signal stream in the corresponding frequency band is calculated to obtain the signal strength trend in each direction. This measurement identifies the main signal direction, sidelobes, and weak coverage areas. For example, in the high-frequency band, the main signal direction is located at 120° azimuth and 5° elevation, with an RMS power of approximately -35 dBm, while the sidelobe is located at 270° azimuth and -5° elevation, with the power dropping to -65 dBm. The directional measurement results form a signal strength data matrix in multiple directions, providing input for subsequent spatially and directionally coupled multidimensional strength distribution modeling. The RSSI values ​​of each grid cell or acquisition point are recorded in time series to form a time-series signal. Sliding window analysis and statistical calculation methods are used to calculate the signal mean, standard deviation, and transient peak value. For example, by sampling data 50 times per second and plotting the RSSI fluctuation curve within a 10-second time window, the intensity fluctuation characteristics at multiple time points can be obtained, reflecting the signal's stability and transient changes over time. Fluctuation analysis can also be combined with frequency domain spectral analysis to identify periodic interference or multipath fading effects. For instance, a signal with an average value of -50 dBm at a certain grid point in the mid-frequency band and a fluctuation range of ±5 dBm indicates that it is affected by multipath but remains generally stable. Multi-time point fluctuation curves are an important reference for constructing multidimensional signal models that combine dynamic and steady-state characteristics.

[0033] Using spatial grids, directional angles, and time series as three-dimensional coordinate axes, and the corresponding RSSI or power values ​​as the fourth dimension, a continuous multidimensional intensity distribution model is generated through interpolation, fitting, and multivariate statistical methods (such as three-dimensional Kriging interpolation or tensor decomposition). For example, measurement results of high-frequency signals within a 50×50 meter grid, 360° azimuth, and 10-second time window are fused, and a dynamic signal intensity field is obtained through weighted averaging. The model visually displays the coverage area of ​​the main signal, weak signal dead zones, and directional fluctuation trends. The resulting regional signal intensity distribution model not only provides spatial coverage information but also reflects temporal stability and directional characteristics, providing complete and multidimensional data support for regional communication signal analysis, interference assessment, and comprehensive stability evaluation.

[0034] In this embodiment, step S5 includes the following steps: Long-term signal variation trend analysis is performed on the regional signal intensity distribution model to generate long-term signal variation status; The signal change pattern is decomposed into multiple time-frequency components to extract the signal change pattern with different period lengths; Signal stability is assessed based on signal variation patterns of different period lengths to obtain multi-period signal stability assessment values; the periodic signal stability assessment values ​​include short-term stability index, long-term trend index, and periodic stability index. Design of multi-level signal early warning and real-time decision-making logic based on multi-cycle signal stability evaluation values.

[0035] In this embodiment, multidimensional signal strength data is organized according to a time series, with each spatial grid point recording continuously sampled RSSI values ​​to form a long-term signal sequence. To ensure the accuracy of trend analysis, the time span can be set to several hours to several days, and the sampling interval is set to 10 milliseconds to 1 second based on the frequency band characteristics. Subsequently, the mean, variance, and time-weighted average are calculated using statistical analysis methods to generate a signal change trend curve. For example, in the high-frequency band, the RSSI of a certain grid point has a mean of -50 dBm over 24 hours, with a fluctuation range of ±6 dBm. After smoothing by moving average filtering, a continuous trend curve is formed. The long-term signal change trend can reveal periodic changes, slow decay, or fluctuation patterns, providing basic data for multi-period stability assessment and helping to identify potential signal weakening areas and abnormal fluctuation events. Wavelet transform or short-time Fourier transform (STFT) is used to decompose the time series signal into components of different time scales, thereby identifying short-term, periodic, and long-term trends. For example, for a 24-hour signal variation sequence, selecting the mother wavelet as Daubechies 4 (db4) and decomposing it into 6 layers yields minute-level, hour-level, and all-day variation components. Each layer reflects the signal fluctuation characteristics of its corresponding period: short-term components reveal transient fluctuations and sudden events, periodic components capture regular fluctuations, and long-term components reflect the overall decay trend. Stability indices are calculated for short-term, periodic, and long-term components, including the short-term stability index, long-term trend index, and periodic stability index. The short-term stability index can be obtained by calculating the ratio of RSSI fluctuation amplitude to standard deviation. For example, if short-term fluctuations are within ±3dBm and the mean is -50 dBm, the short-term stability index is 0.94, indicating relatively small fluctuations. The periodic stability index is calculated by the energy proportion of the periodic component and the mean square deviation of the period, while the long-term trend index is obtained by fitting the rate of change of the long-term component's average value and the decay curve. For example, in the high-frequency band, the periodic stability index is 0.82, and the long-term trend index is 0.91.

[0036] Thresholds are set based on short-term, periodic, and long-term stability indicators. For example, a short-term stability index below 0.7 triggers a short-term warning, a periodic stability index below 0.75 triggers a periodic warning, and a long-term trend index below 0.8 triggers a long-term trend warning. Multi-level warning signals are automatically generated based on the triggering status of each indicator, and the affected area is determined by combining spatial distribution information. Simultaneously, real-time decision-making logic can be designed, such as increasing signal amplification or switching to backup frequency bands in areas of short-term abnormal fluctuations, implementing signal compensation strategies in areas of periodic downward trends, and adjusting network layout or adding relay nodes in areas of long-term downward trends. Through multi-period signal stability assessment and logical judgment, dynamic monitoring, early warning response, and real-time decision optimization of regional communication signals can be achieved, ensuring long-term guarantee of signal coverage and stability.

[0037] In this embodiment, the specific steps for designing the multi-level signal early warning and real-time decision-making logic based on multi-cycle signal stability evaluation values ​​are as follows: The signal quality fluctuation range is analyzed based on the multi-cycle signal stability assessment value, and the amplitude change probability is calculated to obtain a signal quality fluctuation report. Multi-level signal early warning is generated based on signal quality fluctuation reports, and multi-level signal early warning strategies are produced. The real-time signal decision-making logic is designed based on a multi-level signal early warning strategy to perform all-weather intelligent monitoring and early warning of regional signal status; the real-time signal decision-making logic design includes early warning information release, emergency response triggering, and recovery assessment processes.

[0038] In this embodiment, the short-term stability index, periodic stability index, and long-term trend index are combined with the RSSI time series of each spatial grid point to calculate the maximum fluctuation range, root mean square fluctuation amplitude, and probability distribution of signal amplitude. For example, for a grid point in the high-frequency band, the probability of a short-term fluctuation amplitude of ±3 dBm is approximately 70%, a periodic fluctuation amplitude of ±5 dBm is 50%, and a long-term trend amplitude of ±7 dBm is 30%. Through these statistical analyses, a signal fluctuation probability distribution map for each grid point can be generated. Furthermore, the entire area can be summarized to form a fluctuation intensity map within the coverage area, and the probability of amplitude change and fluctuation amplitude can be combined to quantify the signal quality fluctuation level, providing basic data for the design of multi-level early warning strategies. The signal quality fluctuation report can intuitively reflect the unstable areas, fluctuation trends, and potential risk points of the signal, providing data support for all-weather intelligent monitoring. Warning levels are set based on the probability of amplitude changes and the range of fluctuations. For example, a level 1 warning is triggered if the amplitude fluctuation exceeds ±5 dBm with a probability greater than 60%; a level 2 warning is triggered if the amplitude exceeds ±7 dBm with a probability greater than 50%; and a level 3 warning is triggered if the amplitude exceeds ±10 dBm with a probability greater than 30%. Subsequently, the warning levels of each grid point are combined with their spatial distribution to generate a multi-level warning map for the entire region, marking high-risk areas, potential interference areas, and signal attenuation areas. The warning strategy includes not only spatial positioning but also frequency band and time information, which can be used to guide communication scheduling, interference suppression, and resource allocation. For example, in areas with severe high-frequency signal attenuation, backup signal enhancement or frequency band switching can be triggered in advance. Through this multi-level strategy, graded responses to signal anomalies can be achieved, providing a basis for real-time decision-making.

[0039] The decision engine inputs warning level, spatial location, and time information, automatically triggering response actions according to preset rules. The decision logic comprises three core modules: warning information dissemination, emergency response triggering, and recovery assessment. The warning information dissemination module presents multi-level alarms in real-time through a visual interface, notifications, or a wireless signal management platform. The emergency response triggering module executes different measures based on the warning level, such as signal enhancement, frequency band switching, interference source location, or network rerouting. The recovery assessment process continuously monitors signal changes, evaluates the effectiveness of interventions, and automatically cancels the warning once the signal stabilizes. The entire system achieves closed-loop management from signal fluctuation detection and warning generation to decision execution, ensuring stable regional communication signal coverage and timely interference suppression. It also supports long-term trend analysis and dynamic optimization strategy updates, enabling 24 / 7 intelligent signal monitoring and warning capabilities.

[0040] In this embodiment, a communication signal analysis apparatus is provided for performing the communication signal analysis method described above, comprising: The signal acquisition module is used to continuously acquire signals across the entire frequency band of the detection area and extract the raw signal stream; The interference distribution detection module is used to detect potential electromagnetic interference and mark the distribution of electromagnetic interference in the original signal stream, thereby obtaining an interference source distribution map. The interference suppression module is used to perform dynamic interference suppression based on the interference source distribution map, adjust the suppression parameters in real time, and construct a calibrated and optimized signal flow. The real-time intensity calculation module is used to perform real-time intensity calculation on the calibration and optimization signal stream and construct a regional signal intensity distribution model. The signal assessment and decision-making module is used to assess signal quality and provide real-time decision-making and early warning based on a regional signal strength distribution model.

[0041] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the order data analysis method described in any of the above claims.

[0042] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the order data analysis method described in any of the preceding claims.

[0043] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0044] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A communication signal analysis method, characterized in that, Includes the following steps: Step S1: Continuously acquire signals across the entire frequency band in the detection area and extract the original signal stream; Step S2: Detect potential electromagnetic interference and mark the distribution of electromagnetic interference on the original signal stream to obtain an interference source distribution map; Step S3: Perform dynamic interference suppression based on the interference source distribution map, adjust the suppression parameters in real time, and construct a calibration-optimized signal flow; Step S4: Perform real-time intensity calculation on the calibration and optimization signal stream to construct a regional signal intensity distribution model; Step S5: Conduct signal quality assessment and real-time decision-making and early warning based on the regional signal strength distribution model.

2. The communication signal analysis method according to claim 1, characterized in that, The specific steps of step S1 are as follows: The signal detection equipment continuously acquires signals across the entire frequency band in the detection area and extracts the original signal stream. The original signal stream is processed by Fast Fourier Transform to obtain power spectral density distribution data; The power spectral density distribution data is divided into frequency bands and analyzed at multiple scales to extract the center frequency, bandwidth and power parameters of each frequency band signal and fit them into a signal power characteristic set. Dynamic range compression is performed based on the signal power characteristic set, and sampling accuracy gain control is applied to obtain the accuracy gain signal stream.

3. The communication signal analysis method according to claim 1, characterized in that, The specific steps of step S2 are as follows: The original signal stream is subjected to in-depth environmental noise identification, and the signal subspace is separated to extract the noise subspace; The noise frequency is calculated in the noise subspace, and noise similarity inference is performed to obtain the type of environmental noise. The original signal stream is subjected to abnormal power peak calculation and potential electromagnetic interference is detected and marked. Calculate the arrival time difference and arrival angle difference information of the potential electromagnetic interference source, perform spatial positioning, and obtain the location information of the interference source; Calculate the frequency characteristics, modulation characteristics, and time-domain envelope of the potential electromagnetic interference source to obtain the interference source characteristic parameters; Electromagnetic interference distribution is marked based on the location information of the interference source, and feature overlay mapping is performed based on the characteristics of the interference source to obtain the interference source distribution map.

4. The communication signal analysis method according to claim 1, characterized in that, Step S3 is as follows: Based on the type of environmental noise, the precision gain signal stream is subjected to adaptive high-frequency filtering and denoising to obtain the filtered and denoised signal stream. Calculate the power density and occupied bandwidth of each interference source based on the interference source distribution map; Electromagnetic interference range is predicted based on the power density and occupied bandwidth to obtain the interference range of each interference source. Based on the interference range, the signal interference level of different frequency bands is quantified to obtain the multi-band interference level coefficient. Dynamic interference suppression is performed on the filtered and denoised signal stream based on the multi-band interference level coefficient, and the suppression parameters are adjusted in real time to obtain the interference-suppressed signal stream. Power normalization and amplitude calibration are performed on the interference suppression signal stream to construct a calibration-optimized signal stream.

5. The communication signal analysis method according to claim 1, characterized in that, The specific steps of step S4 are as follows: The received signal strength indication value is calculated in real time for the calibration and optimization signal stream to obtain the real-time signal strength of each frequency band; The calibration optimization signal stream is divided into spatial grids, and the signal intensity distribution at different locations is analyzed based on the real-time signal intensity to obtain spatial distribution data of signal intensity. The real-time signal strength is measured using a directional antenna array, and the strength trend at different azimuth and elevation angles is calculated to obtain the signal strength in multiple directions. Multi-time-point fluctuation analysis was performed on the real-time signal strength to obtain multi-time-point intensity fluctuation curves; Multidimensional intensity distribution modeling is performed based on multi-time point intensity fluctuation curves, spatial distribution data of signal intensity, and signal intensity in multiple directions to construct a regional signal intensity distribution model.

6. The communication signal analysis method according to claim 1, characterized in that, The specific steps of step S5 are as follows: Long-term signal variation trend analysis is performed on the regional signal intensity distribution model to generate long-term signal variation status; The signal change pattern is decomposed into multiple time-frequency components to extract the signal change pattern with different period lengths; Signal stability is assessed based on signal variation patterns of different period lengths to obtain multi-period signal stability assessment values; the periodic signal stability assessment values ​​include short-term stability index, long-term trend index, and periodic stability index. Design of multi-level signal early warning and real-time decision-making logic based on multi-cycle signal stability evaluation values.

7. The communication signal analysis method according to claim 1, characterized in that, The specific steps for designing multi-level signal early warning and real-time decision-making logic based on multi-cycle signal stability evaluation values ​​are as follows: The signal quality fluctuation range is analyzed based on the multi-cycle signal stability assessment value, and the amplitude change probability is calculated to obtain a signal quality fluctuation report. Multi-level signal early warning is generated based on signal quality fluctuation reports, and multi-level signal early warning strategies are produced. The real-time signal decision-making logic is designed based on a multi-level signal early warning strategy to enable all-weather intelligent monitoring and early warning of regional signal status. The real-time decision-making logic design for the signal includes a process for issuing early warning information, triggering emergency response, and assessing recovery.

8. A communication signal analysis device, characterized in that, For performing the communication signal analysis method as described in claim 1, comprising: The signal acquisition module is used to continuously acquire signals across the entire frequency band of the detection area and extract the raw signal stream; The interference distribution detection module is used to detect potential electromagnetic interference and mark the distribution of electromagnetic interference in the original signal stream, thereby obtaining an interference source distribution map. The interference suppression module is used to perform dynamic interference suppression based on the interference source distribution map, adjust the suppression parameters in real time, and construct a calibrated and optimized signal flow. The real-time intensity calculation module is used to perform real-time intensity calculation on the calibration and optimization signal stream and construct a regional signal intensity distribution model. The signal assessment and decision-making module is used to assess signal quality and provide real-time decision-making and early warning based on a regional signal strength distribution model.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the communication signal analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the communication signal analysis method according to any one of claims 1 to 7.

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