Multi-band mobile phone signal detection and alarm method and system

Abstract

The invention provides a multi-band mobile phone signal detection and alarm method and system, and relates to the technical field of communication safety monitoring, and the method comprises the steps: collecting and preprocessing an electromagnetic signal of a target area, obtaining a signal frequency spectrum through fast Fourier transform, extracting mobile phone signal characteristic frequency band information, and carrying out the detection and alarm. The communication state is judged according to the uplink and downlink frequency band signal strength ratio, the position of a mobile phone signal source is calculated based on a signal strength difference positioning algorithm, the moving track is recorded, the use scene is judged according to the track data and the communication state time sequence relation, the threat level is evaluated, and alarm information is sent. According to the invention, real-time monitoring and accurate positioning of mobile phone use behaviors in the target area can be realized, and the safety management and control efficiency is improved.

Description

Technical Field

[0001] The present invention relates to communication security monitoring technology, and in particular to a multi-band mobile phone signal detection and alarm method and system. Background Art

[0002] Controlling mobile phone communications in specific locations is a crucial aspect of security. Existing mobile phone signal detection equipment typically uses a signal strength threshold method. This method cannot distinguish between uplink and downlink signal characteristics, making it easy to misidentify base station signals as mobile phone signals. Furthermore, effective identification is difficult when multiple mobile phone signal sources are present simultaneously.

[0003] Traditional mobile phone signal positioning methods primarily rely on single signal characteristics for calculation, failing to fully utilize the correlation characteristics of uplink and downlink during mobile phone communication, resulting in insufficient positioning accuracy. The reliability of positioning results is significantly reduced, especially in complex environments with large signal strength fluctuations or obstructions.

[0004] Existing systems for monitoring mobile phone signals lack the ability to dynamically analyze mobile phone usage behavior. They are unable to intelligently identify scenarios based on signal characteristics and location changes, making it difficult to differentiate between mobile phone usage behaviors with different threat levels. Therefore, a monitoring and early warning method that can accurately identify mobile phone signals, precisely locate them, and intelligently assess threat levels is urgently needed. Summary of the Invention

[0005] The embodiments of the present invention provide a multi-band mobile phone signal detection and alarm method and system, which can solve the problems in the prior art.

[0006] A first aspect of an embodiment of the present invention provides a multi-band mobile phone signal detection and alarm method, comprising: Collecting electromagnetic signals in a target area and performing preprocessing to obtain preprocessed electromagnetic signals; Performing a fast Fourier transform on the preprocessed electromagnetic signal to obtain a signal spectrum, extracting characteristic frequency band information of the mobile phone signal from the signal spectrum, wherein the characteristic frequency band information of the mobile phone signal includes an uplink frequency band signal strength value and a downlink frequency band signal strength value, and determining the communication status of the mobile phone based on the ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value; Obtain signal strength data within the target area. Based on the mobile phone's communication status and signal strength data, use the signal strength difference positioning algorithm to calculate the real-time location coordinates of the mobile phone signal source, and record the changes in the location coordinates over time to obtain movement trajectory data. Based on the temporal relationship between the mobile trajectory data and the mobile phone communication status, the current mobile phone usage scenario is judged, the threat level of the mobile phone usage scenario is evaluated according to the pre-set scenario threat level parameters, and an alarm message is sent to the monitoring terminal based on the threat level evaluation result.

[0007] In an optional embodiment, The electromagnetic signals in the target area are collected and preprocessed. The preprocessed electromagnetic signals include: Collecting full-band electromagnetic signals from mobile phone signals within the target area, detecting the signal strength of the electromagnetic signals in real time, and automatically adjusting the signal gain according to the signal strength to maintain the amplitude of the electromagnetic signals within a preset dynamic range, thereby obtaining a gain-adjusted signal; Performing multi-layer wavelet decomposition on the gain-adjusted signal to obtain wavelet coefficients of different scales, calculating the local variance of the wavelet coefficients, determining an adaptive threshold based on the local variance, performing soft threshold processing on the wavelet coefficients using the adaptive threshold, and obtaining a denoised signal through wavelet reconstruction; The denoised signal is divided into several time windows, the signal in each time window is Fourier transformed to obtain a spectrum, the spectrum is combined to obtain a time-spectrum, the mean square error of each time-frequency point in the time-spectrum is calculated, the noise power spectrum of each time-frequency point is estimated based on the mean square error, the corresponding Wiener filter coefficient is calculated according to the noise power spectrum, and the denoised signal is filtered using the Wiener filter coefficient to obtain a preprocessed electromagnetic signal.

[0008] In an optional embodiment, Performing fast Fourier transform on the pre-processed electromagnetic signal to obtain the signal spectrum, and extracting the characteristic frequency band information of the mobile phone signal from the signal spectrum includes: Performing wavelet decomposition on the preprocessed electromagnetic signal using an orthogonal wavelet basis function to obtain a plurality of frequency band coefficients, calculating an energy value of each frequency band based on the frequency band coefficients, comparing the energy value with a preset frequency band threshold to determine a target frequency band, performing phase compensation on the frequency band coefficients of the target frequency band, and performing Fourier transform on the compensated frequency band coefficients to obtain a target spectrum signal; Dividing the target spectrum signal into an uplink sub-band and a downlink sub-band, constructing a probability density function for each of the uplink sub-band and the downlink sub-band, and calculating an optimal segmentation point based on the probability density function to obtain an adaptive threshold as a signal peak detection threshold; Performing morphological filtering on the spectrum signals of the uplink sub-band and the downlink sub-band, performing peak search on the filtered spectrum signals using the signal peak detection threshold to obtain a signal peak position, calculating a signal bandwidth based on the signal peak position, and setting an integer multiple of the signal bandwidth as an energy concentration interval; The variational method is used to optimize the integration interval boundary within the energy concentration interval, and the energy density integration is performed on the spectrum within the optimized integration interval to obtain the uplink frequency band signal strength value and the downlink frequency band signal strength value.

[0009] In an optional embodiment, The communication status of a mobile phone can be determined based on the ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value. Performing time domain sliding processing on the uplink frequency band signal strength value and the downlink frequency band signal strength value, setting a decreasing weight coefficient according to the order of sampling time, multiplying the decreasing weight coefficient by the signal strength value at the corresponding moment, and accumulating the results to obtain a smoothed uplink signal strength value and a smoothed downlink signal strength value; Calculating a ratio of a smoothed uplink signal strength value to a smoothed downlink signal strength value, extracting a fluctuation amplitude and a change rate of the ratio, constructing a characteristic matrix based on the fluctuation amplitude and the change rate, and performing singular value decomposition on the characteristic matrix to obtain main characteristic components; A decision function is established based on the main eigenvector, and the maximum inter-class variance method is used to calculate the optimal segmentation point of the decision function. The optimal segmentation point is used as the discrimination threshold of the communication state. According to the discrimination threshold, the value range of the signal strength ratio is divided into the normal communication interval and the abnormal communication interval. The ratio of the smoothed uplink signal strength value to the smoothed downlink signal strength value is calculated in real time. When the current ratio falls into the normal communication interval, it is judged to be a normal communication state. When the current ratio falls into the abnormal communication interval, the link fault type is determined according to the relative position relationship between the current ratio and the normal communication interval, and the corresponding communication state judgment result is output.

[0010] In an optional embodiment, Obtain signal strength data within the target area. Based on the mobile phone's communication status and signal strength data, use the signal strength difference positioning algorithm to calculate the real-time location coordinates of the mobile phone signal source, and record the changes in the location coordinates over time to obtain movement trajectory data including: Set up multiple signal acquisition nodes in the target area to collect signal strength data in multiple directions; Calculating a ratio of a short-time standard deviation to a long-time standard deviation of the signal strength data according to a pre-acquired mobile phone communication state, and screening a valid signal detector according to the ratio; Calculating a signal strength change trend collected by an effective signal detector, determining a smoothing window length based on the change trend, determining a spatial weight based on the spatial distribution position of the signal detector, and performing weighted smoothing on the spatial weight and signal strength data to obtain a smoothed signal strength value; Select any two valid signal detectors to form a detector pair, calculate the difference in smoothed signal strength values ​​between the detector pairs, dynamically adjust the path loss index based on the mobile phone communication status, and calculate the distance difference between the detector pairs; Calculating the signal correlation and the stability of the signal strength difference of the detector pair to obtain a credibility score, constructing a hyperbolic equation based on the distance difference, using the credibility score as the initial weight of the hyperbolic equation, and solving the hyperbolic equation system in an iterative manner to obtain the real-time position of the signal source; The moving speed and direction are calculated according to the real-time position of the signal source within the continuous time window, and the abnormal position is constrained and corrected in combination with the mobile phone communication status to obtain a smooth moving trajectory.

[0011] In an optional embodiment, Calculating the signal correlation of the detector pair and the stability of the signal strength difference to obtain a credibility score, constructing a hyperbolic equation based on the distance difference, using the credibility score as the initial weight of the hyperbolic equation, and solving the hyperbolic equation system in an iterative manner to obtain the real-time position of the signal source includes: Obtaining a signal strength sequence of the detector pair, calculating a cross-correlation coefficient of the signal strength sequence and a standard deviation of a signal strength difference sequence, and performing an adaptive weighted combination of the cross-correlation coefficient and the inverse of the standard deviation to obtain an initial credibility score of the detector pair; Calculate the directional angle of the line connecting the detector pair, determine the spatial distribution weight based on the distribution of the directional angle, and multiply the spatial distribution weight by the initial credibility score to obtain a revised credibility score for the detector pair; weighting the distance differences between the detector pairs using the modified credibility scores, constructing a hyperbolic equation system based on the weighted distance differences, and solving the hyperbolic equation system using a constrained weighted least squares method to obtain an initial position estimate; Obtain the signal source position at the previous moment, determine the position constraint area at the current moment in combination with the maximum moving speed constraint, and perform an adaptive step-size iterative search within the position constraint area starting from the initial position estimate value to obtain the current iterative position; The theoretical distance difference of the detector pair is calculated based on the current iterative position, and the deviation between the theoretical distance difference and the measured distance difference is used as the positioning residual. The corrected credibility score is dynamically attenuated and updated according to the positioning residual to obtain the real-time credibility score. The real-time credibility score is used to update the weights of the hyperbolic equation group, and the iterative solution is continued until the position estimate converges to obtain the real-time position of the signal source.

[0012] In an optional embodiment, Based on the temporal relationship between the mobile trajectory data and the mobile phone communication status, the current mobile phone usage scenario is judged, and the threat level of the mobile phone usage scenario is evaluated according to the pre-set scenario threat level parameters. According to the threat level evaluation results, an alarm message is sent to the monitoring terminal, including: Acquire the mobile phone's movement trajectory data and communication status data, extract motion features and spatial distribution features from the movement trajectory data, extract communication behavior features and signal quality features from the communication status data, and generate time series feature data; Segmenting the time series feature data into time windows, calculating the correlation between features in each time window, constructing a feature correlation matrix, and matching the feature correlation matrix with a preset mobile phone usage scenario model to obtain the current mobile phone usage scenario; Comparing and analyzing the time series feature data with the preset normal scene features corresponding to the current mobile phone usage scene to generate a feature deviation sequence, performing time series clustering on the feature deviation sequence to obtain an abnormal event cluster, and generating abnormal event features based on the time span and number of occurrences of the abnormal event cluster; The threat score is calculated based on the characteristics of abnormal events and weighted by the preset threat level parameters corresponding to the current mobile phone usage scenario to obtain the real-time threat level. When the real-time threat level exceeds the warning threshold, the abnormal event characteristics that triggered the warning are used as a monitoring template. The number of matching abnormal events is counted during the observation period. When the number of matches exceeds the set threshold and the threat level continues to rise, an alarm message is sent to the monitoring terminal.

[0013] A second aspect of an embodiment of the present invention provides a multi-band mobile phone signal detection and alarm system, comprising: The first unit is used to collect electromagnetic signals in the target area and perform preprocessing to obtain preprocessed electromagnetic signals; The second unit is configured to perform a fast Fourier transform on the preprocessed electromagnetic signal to obtain a signal spectrum, extract characteristic frequency band information of the mobile phone signal from the signal spectrum, wherein the characteristic frequency band information of the mobile phone signal includes an uplink frequency band signal strength value and a downlink frequency band signal strength value, and determine the communication status of the mobile phone according to a ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value; The third unit is used to obtain signal strength data in the target area. Based on the mobile phone communication status and signal strength data, the signal strength difference positioning algorithm is used to calculate the real-time location coordinates of the mobile phone signal source, and the changes in the location coordinates over time are recorded to obtain movement trajectory data; The fourth unit is used to determine the current mobile phone usage scenario based on the temporal relationship between the movement trajectory data and the mobile phone communication status, evaluate the threat level of the mobile phone usage scenario according to the pre-set scenario threat level parameters, and send an alarm message to the monitoring terminal based on the threat level evaluation result.

[0014] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0015] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0016] In this embodiment, by collecting and analyzing electromagnetic signals within the target area, accurate detection and alarm of multi-band mobile phone signals are achieved, which can effectively identify different mobile phone communication formats and communication states, and improve the accuracy and comprehensiveness of mobile phone signal detection. The signal strength difference positioning algorithm is adopted, combined with the uplink and downlink frequency band signal strength ratio analysis, which can calculate the position of the mobile phone signal source in real time and record the movement trajectory, effectively solving the technical problem that traditional methods cannot accurately locate the mobile phone signal source, and significantly improving the positioning accuracy and real-time performance. Based on the temporal relationship between movement trajectory data and communication status, intelligent identification of mobile phone usage scenarios and threat level assessment are achieved, and graded alarm information is automatically sent according to the security risks of different scenarios, making the system more intelligent and practical, and meeting the differentiated needs under different security monitoring environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the process of a multi-band mobile phone signal detection and alarm method according to an embodiment of the present invention; Figure 2 Schematic diagram of signal strength ratio distribution and communication status judgment; Figure 3 Schematic diagram of the performance comparison and analysis of multi-dimensional credibility assessment and iterative optimization positioning technology. DETAILED DESCRIPTION

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0019] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0020] Figure 1 FIG. 1 is a flow chart of a multi-band mobile phone signal detection and alarm method according to an embodiment of the present invention, as shown in FIG. Figure 1 As shown, the method includes: Collecting electromagnetic signals in a target area and performing preprocessing to obtain preprocessed electromagnetic signals; Performing a fast Fourier transform on the preprocessed electromagnetic signal to obtain a signal spectrum, extracting characteristic frequency band information of the mobile phone signal from the signal spectrum, wherein the characteristic frequency band information of the mobile phone signal includes an uplink frequency band signal strength value and a downlink frequency band signal strength value, and determining the communication status of the mobile phone based on the ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value; Obtain signal strength data within the target area. Based on the mobile phone's communication status and signal strength data, use the signal strength difference positioning algorithm to calculate the real-time location coordinates of the mobile phone signal source, and record the changes in the location coordinates over time to obtain movement trajectory data. Based on the temporal relationship between the mobile trajectory data and the mobile phone communication status, the current mobile phone usage scenario is judged, the threat level of the mobile phone usage scenario is evaluated according to the pre-set scenario threat level parameters, and an alarm message is sent to the monitoring terminal based on the threat level evaluation result.

[0021] In an optional embodiment, collecting electromagnetic signals in a target area and preprocessing the electromagnetic signals to obtain the preprocessed electromagnetic signals includes: Collecting full-band electromagnetic signals from mobile phone signals within the target area, detecting the signal strength of the electromagnetic signals in real time, and automatically adjusting the signal gain according to the signal strength to maintain the amplitude of the electromagnetic signals within a preset dynamic range, thereby obtaining a gain-adjusted signal; Performing multi-layer wavelet decomposition on the gain-adjusted signal to obtain wavelet coefficients of different scales, calculating the local variance of the wavelet coefficients, determining an adaptive threshold based on the local variance, performing soft threshold processing on the wavelet coefficients using the adaptive threshold, and obtaining a denoised signal through wavelet reconstruction; The denoised signal is divided into several time windows, the signal in each time window is Fourier transformed to obtain a spectrum, the spectrum is combined to obtain a time-spectrum, the mean square error of each time-frequency point in the time-spectrum is calculated, the noise power spectrum of each time-frequency point is estimated based on the mean square error, the corresponding Wiener filter coefficient is calculated according to the noise power spectrum, and the denoised signal is filtered using the Wiener filter coefficient to obtain a preprocessed electromagnetic signal.

[0022] In one embodiment, electromagnetic signal acquisition equipment equipped with a broadband receiving antenna and a high-performance digital receiver is deployed within the target area. The equipment operates in a frequency range of 700MHz to 2700MHz, covering major mobile communication bands including GSM, CDMA, WCDMA, LTE, and 5GNR. The equipment scans the target area, capturing electromagnetic signals across the entire frequency range in real time. The sampling rate is set to 10MHz, with a quantization accuracy of 16 bits to ensure the integrity and accuracy of the collected data.

[0023] Real-time signal strength detection is implemented during the acquisition process, monitoring signal strength by calculating the RMS value. When the signal strength falls below -90dBm, the preamplifier gain is automatically increased in 5dB steps. When the signal strength exceeds -30dBm, the gain is automatically reduced to prevent signal saturation. This maintains the signal amplitude within the dynamic range of [-80dBm, -20dBm], resulting in a gain-adjusted signal.

[0024] The gain-adjusted signal is subjected to wavelet denoising. The signal is decomposed into five layers using the db4 wavelet basis to obtain wavelet coefficients of different scales. For each layer of wavelet coefficients, the sliding window size is set to 128 sampling points, and the window overlap ratio is 50%. The local variance of the wavelet coefficients is calculated within each window by dividing the sum of the squares of the wavelet coefficients within the window by the window length. Based on the calculated local variance, the threshold value is determined using an adaptive threshold estimation method based on the Bayesian criterion. Experimental data show that under the condition of a signal-to-noise ratio of approximately 5dB, the threshold values ​​of the first to fifth layers are approximately 0.15, 0.25, 0.42, 0.68, and 1.05, respectively.

[0025] The wavelet coefficients were soft-thresholded using the determined adaptive threshold. This soft-thresholding method sets the coefficient to zero when its absolute value is less than the threshold. When its absolute value is greater than or equal to the threshold, the threshold is subtracted from the coefficient's absolute value, retaining its original sign. After processing, the signal was reconstructed using an inverse wavelet transform to obtain a denoised signal. This processing improved the signal-to-noise ratio by approximately 8dB, significantly reducing background noise.

[0026] The denoised signal was divided into time windows for time-frequency analysis. The window length was set to 1024 samples with a 75% overlap. The signal within each time window was windowed using a Hanning window function to reduce spectral leakage. A 1024-point fast Fourier transform was performed on the signal within each windowed time window to obtain a spectrum. The spectra of all time windows were arranged in chronological order to form a time-spectrogram with a resolution of sampling rate / 1024, approximately 9.77 kHz.

[0027] The mean squared error (MSE) for each time-frequency point in the time-frequency spectrum is calculated to estimate the noise power spectrum. Specifically, a region of the time-frequency spectrum with low signal activity (typically high frequencies, such as those above 8 MHz) is selected as a pure noise region. The average value of the power spectrum within this region is calculated as the initial noise power estimate. Using a recursive averaging method, the power difference between each time-frequency point and the eight surrounding points is calculated. If the difference is less than a preset threshold (set to 3 dB), the point is considered a noise point and included in the noise statistics. Otherwise, the point is considered to contain signal components and is excluded from the statistics. After multiple iterations (typically five), a stable noise power spectrum estimate is obtained.

[0028] The Wiener filter coefficients are calculated based on the estimated noise power spectrum. For each time-frequency point at frequency f and time t, if the ratio of the signal power to the noise power at that point is greater than a preset threshold (set to 2.5), the point is retained; otherwise, the point is attenuated as a function of the signal-to-noise ratio. The attenuation coefficient is calculated by subtracting the noise power from the signal power at that point, then dividing it by the signal power. The resulting value is the Wiener filter coefficient for that time-frequency point.

[0029] The calculated Wiener filter coefficients are applied to the time-spectrum of the denoised signal to achieve frequency-domain filtering. The filtered time-spectrum is then inverse Fourier transformed to remove windowing effects and reconstruct the time-domain signal. Finally, the signals from each time window are overlap-added to obtain the complete preprocessed electromagnetic signal.

[0030] Based on the above technical solution, high-precision, low-distortion preprocessing of electromagnetic signals within the target area can be achieved. On the one hand, automatic gain control stabilizes the amplitude of the acquired signal within the dynamic range, effectively coping with changes in signal strength under different environments and improving signal quality. On the other hand, the combination of multi-layer wavelet decomposition and local variance adaptive thresholding accurately distinguishes signal from noise, achieving flexible denoising at multiple scales and preserving key signal features. Further combining time-frequency spectrum analysis with Wiener filtering techniques, through noise power spectrum estimation and fine-tuning of filter coefficients, residual noise can be further suppressed in the frequency domain, improving the signal-to-noise ratio and reducing spectral leakage. Ultimately, a cleaner, more stable preprocessed electromagnetic signal is obtained, suitable for subsequent feature extraction and location analysis.

[0031] In an optional embodiment, performing a fast Fourier transform on the preprocessed electromagnetic signal to obtain a signal spectrum, and extracting characteristic frequency band information of the mobile phone signal from the signal spectrum includes: Performing wavelet decomposition on the preprocessed electromagnetic signal using an orthogonal wavelet basis function to obtain a plurality of frequency band coefficients, calculating an energy value of each frequency band based on the frequency band coefficients, comparing the energy value with a preset frequency band threshold to determine a target frequency band, performing phase compensation on the frequency band coefficients of the target frequency band, and performing Fourier transform on the compensated frequency band coefficients to obtain a target spectrum signal; Dividing the target spectrum signal into an uplink sub-band and a downlink sub-band, constructing a probability density function for each of the uplink sub-band and the downlink sub-band, and calculating an optimal segmentation point based on the probability density function to obtain an adaptive threshold as a signal peak detection threshold; Performing morphological filtering on the spectrum signals of the uplink sub-band and the downlink sub-band, performing peak search on the filtered spectrum signals using the signal peak detection threshold to obtain a signal peak position, calculating a signal bandwidth based on the signal peak position, and setting an integer multiple of the signal bandwidth as an energy concentration interval; The variational method is used to optimize the integration interval boundary within the energy concentration interval, and the energy density integration is performed on the spectrum within the optimized integration interval to obtain the uplink frequency band signal strength value and the downlink frequency band signal strength value.

[0032] Exemplarily, the preprocessed electromagnetic signal is first subjected to wavelet decomposition using orthogonal wavelet basis functions. In this embodiment, the DB4 wavelet basis function is selected, and the electromagnetic signal is decomposed into five levels to obtain six frequency band coefficients, each corresponding to a different frequency range. For example, for a signal with a sampling rate of 100 MHz, after decomposition, six frequency bands can be obtained: [0-1.56 MHz], [1.56-3.13 MHz], [3.13-6.25 MHz], [6.25-12.5 MHz], [12.5-25 MHz], and [25-50 MHz]. The energy value is calculated for each frequency band coefficient by summing the squares of the coefficients. For example, the energy of the first frequency band is 10.5, the energy of the second frequency band is 15.2, the energy of the third frequency band is 120.6, the energy of the fourth frequency band is 325.8, the energy of the fifth frequency band is 95.3, and the energy of the sixth frequency band is 12.7. The energy value of each frequency band is compared with a preset frequency band threshold value, which is 50. Frequency bands with energy values ​​greater than the threshold value are determined as target frequency bands, which are the third, fourth, and fifth frequency bands in this example.

[0033] Phase compensation is performed on the target frequency band's coefficients, using a Hilbert transform to eliminate phase distortion. The third frequency band coefficients are multiplied by a compensation factor of 0.85; the fourth frequency band coefficients are multiplied by a compensation factor of 0.92; and the fifth frequency band coefficients are multiplied by a compensation factor of 0.88. A Fast Fourier Transform (FFT) is performed on the compensated frequency band coefficients to obtain the target spectrum signal. For example, after performing an FFT on the compensated third frequency band, a sequence of spectrum values ​​within the frequency range of [3.13-6.25 MHz] is obtained. Similarly, the sequence of spectrum values ​​for the other target frequency bands is obtained, and these sequences are combined to obtain the complete target spectrum signal.

[0034] The target spectrum signal is divided into an uplink sub-band and a downlink sub-band. In this embodiment, according to the communication standard, [890-915MHz] is divided into an uplink sub-band, and [935-960MHz] is divided into a downlink sub-band. Probability density functions are constructed for the uplink sub-band and the downlink sub-band respectively. During the construction process, the distribution of the spectrum amplitude is statistically analyzed to generate a histogram of the spectrum amplitude, with the amplitude as the horizontal axis and the normalized occurrence frequency as the vertical axis. For example, the spectrum amplitude distribution of the uplink sub-band is as follows: the [0-0.1] interval accounts for 0.72, the [0.1-0.2] interval accounts for 0.15, the [0.2-0.3] interval accounts for 0.08, and the [0.3 and above] interval accounts for 0.05. The optimal split point is calculated based on the probability density function, and the OTSU maximum inter-class variance method is used to iteratively calculate the inter-class variance. The amplitude when the variance is the largest is selected as the adaptive threshold. In this example, the adaptive threshold for the uplink sub-band is 0.18, and the adaptive threshold for the downlink sub-band is 0.23, which are used as signal peak detection thresholds.

[0035] Morphological filtering is performed on the spectrum signals of the uplink and downlink subbands. The filtering process uses a combination of opening and closing operations to remove noise and smooth the spectrum. In the opening operation, structuring elements of length 5 are used for erosion and dilation; in the closing operation, structuring elements of length 7 are used for dilation and erosion. A peak detection threshold is used to search for peaks in the filtered spectrum signals. This peak search uses a local maximum detection method to find points within a sliding window that are greater than the threshold and are local maxima. For example, the peaks detected in the uplink subband are at 897MHz, 905MHz, and 912MHz; and in the downlink subband, the peaks detected are at 942MHz, 950MHz, and 957MHz.

[0036] Calculate the signal bandwidth based on the signal peak position. For each detected peak, search for points on either side where the amplitude drops to half the peak amplitude. The frequency difference between these two points is the signal bandwidth corresponding to that peak. For example, the bandwidth of an 897MHz peak is 2.1MHz, the bandwidth of a 905MHz peak is 1.8MHz, and the bandwidth of a 912MHz peak is 2.3MHz. Take the average bandwidth of 2.07MHz and set its integer multiple, 2MHz, as the energy concentration interval.

[0037] A variational method is used to optimize the integration interval boundaries within the energy concentration range. For each peak, the initial integration interval is [peak -1MHz, peak +1MHz]. The variational method is used to iteratively optimize the boundaries to maximize the signal-to-noise ratio within the interval. For example, for the 897MHz peak, the optimized integration interval is [895.8MHz, 898.3MHz]. The energy density is integrated over the spectrum within the optimized integration interval using the trapezoidal integration method and linear interpolation between discrete frequency points. The signal strength values ​​for the uplink and downlink frequency bands are calculated. For example, the signal strength values ​​for the three uplink peaks are 0.76, 0.83, and 0.92, respectively, with a total strength value of 2.51. The signal strength values ​​for the three downlink peaks are 0.81, 0.95, and 0.89, respectively, with a total strength value of 2.65.

[0038] Based on the above technical solution, the characteristic information of mobile phone signals in different frequency bands can be accurately extracted, improving the accuracy and robustness of signal recognition. Through orthogonal wavelet decomposition and frequency band energy analysis, the initial identification of signal frequency bands and the positioning of target frequency bands can be effectively achieved. Combined with phase compensation and Fourier transform, the time-frequency focusing of the spectrum can be enhanced and spectral distortion can be reduced. By constructing a probability density function and calculating an adaptive peak detection threshold, dynamic segmentation and adaptive recognition of uplink and downlink signals can be achieved, avoiding missed detection or false detection caused by fixed thresholds. Morphological filtering combined with peak search can extract true signal feature points from complex backgrounds and accurately measure bandwidth and energy distribution areas. Finally, by optimizing the integration interval and performing energy density integration through the variational method, the resolution and stability of uplink and downlink signal strength estimation are effectively improved, providing reliable data support for communication status judgment and subsequent positioning analysis.

[0039] In an optional implementation, determining the mobile phone communication status according to the ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value includes: Performing time domain sliding processing on the uplink frequency band signal strength value and the downlink frequency band signal strength value, setting a decreasing weight coefficient according to the order of sampling time, multiplying the decreasing weight coefficient by the signal strength value at the corresponding moment, and accumulating the results to obtain a smoothed uplink signal strength value and a smoothed downlink signal strength value; Calculating a ratio of a smoothed uplink signal strength value to a smoothed downlink signal strength value, extracting a fluctuation amplitude and a change rate of the ratio, constructing a characteristic matrix based on the fluctuation amplitude and the change rate, and performing singular value decomposition on the characteristic matrix to obtain main characteristic components; A decision function is established based on the main eigenvector, and the maximum inter-class variance method is used to calculate the optimal segmentation point of the decision function. The optimal segmentation point is used as the discrimination threshold of the communication state. According to the discrimination threshold, the value range of the signal strength ratio is divided into the normal communication interval and the abnormal communication interval. The ratio of the smoothed uplink signal strength value to the smoothed downlink signal strength value is calculated in real time. When the current ratio falls into the normal communication interval, it is judged to be a normal communication state. When the current ratio falls into the abnormal communication interval, the link fault type is determined according to the relative position relationship between the current ratio and the normal communication interval, and the corresponding communication state judgment result is output.

[0040] In this embodiment, the mobile terminal's uplink and downlink signal strength values ​​are first collected. The uplink signal strength value indicates the strength of the signal sent from the mobile terminal to the base station, while the downlink signal strength value indicates the strength of the signal sent from the base station to the mobile terminal. These signal strength values ​​are acquired by the RF signal monitoring module integrated into the mobile terminal. The sampling period can be set to 100 milliseconds to ensure real-time and accurate data collection.

[0041] Figure 2 This is a schematic diagram of the signal strength ratio distribution and communication status judgment. In order to eliminate the influence of instantaneous signal fluctuations on the judgment results, the uplink frequency band signal strength value and the downlink frequency band signal strength value are subjected to time domain sliding processing. Specifically, a sliding window of length N is set, and N can be taken as 20, that is, the sampling data of the last 2 seconds is retained. The decreasing weight coefficient is set according to the order of sampling time. The latest sampling point is given the highest weight, and the weight gradually decreases over time. The decreasing weight coefficient can be calculated according to the attenuation rate of 0.95, that is, the weight of the i-th sampling point is 0.95^(Ni), where i represents the sequence number of the sampling point, from 1 to N. The decreasing weight coefficient is multiplied by the signal strength value at the corresponding moment and accumulated to obtain the smoothed uplink signal strength value and the smoothed downlink signal strength value.

[0042] Assume that the sequence of raw uplink signal strength values ​​collected is [-75, -76, -74, -73, -77, -76, -75, -74, -75, -76] dBm, and the sequence of raw downlink signal strength values ​​is [-65, -66, -64, -65, -67, -66, -65, -63, -64, -65] dBm. After applying the decreasing weight coefficients, the smoothed uplink signal strength value is calculated to be -74.8 dBm, and the smoothed downlink signal strength value is -64.7 dBm.

[0043] Calculate the ratio of the smoothed uplink signal strength value to the smoothed downlink signal strength value. In this example, the signal strength ratio is -74.8 / (-64.7) = 1.156. Since signal strength is expressed in negative decibel milliwatts (dBm), with larger values ​​indicating weaker signals, this ratio actually reflects the degree of attenuation of the uplink signal relative to the downlink signal.

[0044] The ratio's changes were monitored over a continuous time period, and its fluctuation amplitude and rate of change were extracted. The fluctuation amplitude was characterized by calculating the standard deviation of the signal strength ratio, while the rate of change was characterized by calculating the average of the absolute differences between the signal strength ratios of adjacent sampling points. The signal strength ratio was recorded every second for a total of 300 data points, with a 5-minute statistical period. The calculated fluctuation amplitude was 0.05, and the rate of change was 0.01 / second.

[0045] Construct a feature matrix based on the fluctuation amplitude and rate of change. The feature matrix contains fluctuation amplitude and rate of change data for multiple time windows. For example, divide 5 minutes of data into five 1-minute time windows, calculate the fluctuation amplitude and rate of change for each window, and form a 5x2 feature matrix. In this example, the feature matrix is ​​[[0.04, 0.008], [0.05, 0.01], [0.06, 0.012], [0.04, 0.009], [0.05, 0.011]].

[0046] Perform singular value decomposition (SVD) on the characteristic matrix to obtain the principal eigencomponents. SVD decomposes the characteristic matrix into the product of three matrices, where the singular values ​​in the diagonal matrices are arranged in descending order. The larger the singular value, the more important the corresponding eigencomponent. The eigenvector with the largest singular value is taken as the principal eigenvector. In this example, the principal eigenvector obtained by SVD is [0.7, 0.3], indicating that the weights of the fluctuation amplitude and the rate of change in determining the communication status are 0.7 and 0.3, respectively.

[0047] A decision function is established based on the principal eigenvector. The decision function is expressed as a weighted sum of the fluctuation amplitude and the rate of change: that is, the decision value = 0.7 × fluctuation amplitude + 0.3 × rate of change. The maximum inter-class variance method is used to calculate the optimal split point for the decision function. This method iterates through all possible split points and selects the point that maximizes the inter-class variance as the optimal split point. In this example, the optimal split point calculated is 0.046, which is used as the threshold for determining the communication status.

[0048] The signal strength ratio range is divided into a normal communication interval and an abnormal communication interval based on the discrimination threshold. The normal communication interval is defined as [1.0, 1.2], and the abnormal communication interval is divided into two parts: [0.8, 1.0) below the normal interval and (1.2, 1.4) above the normal interval. The ratio of the smoothed uplink signal strength value to the smoothed downlink signal strength value is calculated in real time. When the current ratio falls into the normal communication interval, it is determined to be a normal communication state. When the current ratio falls into the abnormal communication interval, the link fault type is determined based on the relative position relationship between the current ratio and the normal communication interval.

[0049] Specifically, when the ratio is less than 1.0, it indicates that the uplink signal is too strong relative to the downlink signal, and the uplink signal may be abnormally amplified, and it is determined to be an uplink abnormality; when the ratio is greater than 1.2, it indicates that the uplink signal is too weak relative to the downlink signal, and the downlink signal may be abnormally amplified or the uplink signal is suppressed, and it is determined to be a downlink abnormality.

[0050] To improve judgment accuracy, a continuous judgment mechanism is set up. The final communication status judgment result is output only when the judgment results of five consecutive samplings are consistent. This can effectively avoid misjudgments caused by instantaneous signal fluctuations.

[0051] Furthermore, to account for differences in signal strength ratios in different communication environments, this embodiment also provides an adaptive threshold adjustment mechanism. The system records the user's signal strength ratios under normal communication conditions at different locations and establishes a mapping relationship between location and signal strength ratio. When the user's location changes, the system automatically adjusts the normal communication interval based on the current location. Location information can be obtained via GPS or base station positioning, with an accuracy of 100 meters.

[0052] In practice, the system will issue an alarm when it detects abnormal communication conditions. This alarm can be displayed on the screen, with sound and vibration. Users can then take appropriate action based on the alarm, such as switching network modes, changing locations, or calling customer service.

[0053] In existing technologies, determining a mobile phone's communication status often relies on signal strength at a single point in time, typically using fixed thresholds or static ratio analysis methods. These methods struggle to accurately reflect dynamic changes in communication status and are sensitive to signal fluctuations and interference, resulting in unstable and inaccurate judgments. This application introduces a sliding weighting mechanism to smooth the uplink and downlink signal strength values ​​in the time domain. This reduces the interference of short-term mutations on the judgment results, extracts more representative smoothed signal features, and improves the temporal continuity and robustness of signal processing. Furthermore, a feature matrix is ​​constructed using the amplitude and rate of change of the ratios, and singular value decomposition is used to extract the main feature components, further eliminating redundant information and noise interference, resulting in more accurate feature identification of communication status. Furthermore, the maximum inter-class variance method is combined to construct a decision function and determine the optimal split point, adaptively generating a threshold for discriminating communication status. This overcomes the limitations of manually setting thresholds in traditional methods. These technical improvements not only enable dynamic identification of normal and abnormal communication states, but also further determine the type of link fault. This improves the accuracy and stability of the system's status judgment in complex environments, providing a more reliable basis for subsequent location and alarm processing.

[0054] In an optional embodiment, obtaining signal strength data within a target area, calculating the real-time location coordinates of the mobile phone signal source using a signal strength difference positioning algorithm based on the mobile phone communication status and signal strength data, and recording the changes in the location coordinates over time to obtain movement trajectory data includes: Set up multiple signal acquisition nodes in the target area to collect signal strength data in multiple directions; Calculating a ratio of a short-time standard deviation to a long-time standard deviation of the signal strength data according to a pre-acquired mobile phone communication state, and screening a valid signal detector according to the ratio; Calculating a signal strength change trend collected by an effective signal detector, determining a smoothing window length based on the change trend, determining a spatial weight based on the spatial distribution position of the signal detector, and performing weighted smoothing on the spatial weight and signal strength data to obtain a smoothed signal strength value; Select any two valid signal detectors to form a detector pair, calculate the difference in smoothed signal strength values ​​between the detector pairs, dynamically adjust the path loss index based on the mobile phone communication status, and calculate the distance difference between the detector pairs; Calculating the signal correlation and the stability of the signal strength difference of the detector pair to obtain a credibility score, constructing a hyperbolic equation based on the distance difference, using the credibility score as the initial weight of the hyperbolic equation, and solving the hyperbolic equation system in an iterative manner to obtain the real-time position of the signal source; The moving speed and direction are calculated according to the real-time position of the signal source within the continuous time window, and the abnormal position is constrained and corrected in combination with the mobile phone communication status to obtain a smooth moving trajectory.

[0055] For example, multiple signal acquisition nodes are first deployed within the target area. Each node is equipped with a directional antenna array that can simultaneously collect signal strength data from different directions. Signal acquisition nodes are typically located at the edges or high points of the target area to form a network covering the entire area. For example, in a 100m x 100m indoor scene, a signal acquisition node can be set up at each of the four corners and the center. Each node is equipped with four directional antennas, facing different directions. The signal acquisition nodes transmit the collected data in real time to a central processing server via wired or wireless means.

[0056] Analyze the mobile phone's communication status, including call status, data transmission status, and signal type. For the signal strength data collected by each signal acquisition node, calculate the standard deviation within a short-time window (e.g., 1 second) and the standard deviation within a long-time window (e.g., 10 seconds), and then calculate the ratio of the two. When the ratio is greater than a preset threshold (e.g., 0.8), the detector is effectively capturing signal changes and is marked as a valid signal detector. For example, for detector A, if the short-time standard deviation is 2.3dBm and the long-time standard deviation is 2.5dBm, the ratio is 0.92, which is greater than the threshold of 0.8 and is considered a valid detector.

[0057] For each valid signal detector, analyze the trend of signal strength changes. Calculate the first-order difference of signal strength at multiple consecutive time points to determine the severity of signal changes. If the signal changes gently (e.g., the absolute value of the difference is less than 1 dBm), use a longer smoothing window (e.g., 5 seconds); if the signal changes dramatically (e.g., the absolute value of the difference is greater than 3 dBm), use a shorter smoothing window (e.g., 1 second). Additionally, calculate spatial weights based on the spatial distribution of the signal detectors, assigning higher weights to detectors closer to the center of the target area. For example, a weight of 1.0 might be set for detectors at the center, while a weight of 0.7 might be set for detectors at the edge. A weighted average of the spatial weights and signal strength data is then calculated to produce a smoothed signal strength value. For example, if the raw signal strength of a detector at five consecutive time points is -65 dBm, -67 dBm, -63 dBm, -64 dBm, and -66 dBm, and the spatial weight is 0.9, the smoothed signal strength is -65.0 dBm.

[0058] Select any two valid signal detectors to form a detector pair, resulting in a total of n×(n-1) / 2 detector pairs. Calculate the smoothed signal strength difference between each detector pair. Dynamically adjust the path loss exponent based on the phone's communication status. For example, when the phone is in a call state, the path loss exponent can be set to 3.5; when transmitting data, to 3.2; and when in standby mode, to 3.0. Based on the signal strength difference and the path loss exponent, calculate the distance difference between the two detectors and the signal source. For example, if the smoothed signal strengths of detectors A and B are -65dBm and -70dBm, respectively, with a difference of 5dBm, and a path loss exponent of 3.2, the calculated distance difference is approximately 8.2 meters.

[0059] The signal correlation and stability of the signal strength difference are calculated for each detector pair, and the credibility score is obtained by combining them. The signal correlation is obtained by calculating the correlation coefficient of the signal strength changes of the two detectors, and the stability is obtained by calculating the standard deviation of the difference. For example, the signal correlation coefficient of detector pair AB is 0.85, and the standard deviation of the difference is 0.6dBm, so the credibility score can be calculated as 0.82. A hyperbolic equation is constructed based on the distance difference of each detector pair, and the credibility score is used as the initial weight of the equation. The weighted least squares method is used to iteratively solve the intersection of all hyperbolic equations to obtain the real-time position coordinates of the signal source. For example, in a certain calculation, the system selected 4 detector pairs, and after 5 iterative calculations, the signal source position coordinates were finally determined to be (45.3 meters, 67.8 meters).

[0060] The signal source location coordinates are recorded within a continuous time window (e.g., 5 seconds), and the distance and azimuth angle between two adjacent locations are calculated to determine the movement speed and direction. If the calculated speed exceeds a reasonable range (e.g., exceeding 2 meters per second in indoor scenarios) or the direction changes significantly (e.g., a change of more than 90 degrees between two consecutive directions), constraint corrections are performed based on the phone's communication status and historical trajectory. For example, if the phone is stationary and in a call, the abnormal location is aligned with a historically stable location; if the phone is in motion, smoothing is performed based on historical movement trends. The resulting movement trajectory data contains information such as timestamp, location coordinates, speed, and direction, which can be used for subsequent behavioral analysis or location-based services.

[0061] In this embodiment, by integrating multi-node signal acquisition and dynamic path loss modeling, high-precision positioning and trajectory tracking of mobile phone signal sources are achieved. Communication status information is integrated to dynamically adjust the path loss exponent, improving positioning adaptability and accuracy in complex electromagnetic environments. Effective detectors are screened based on the ratio of short-term and long-term standard deviations, enhancing the system's robustness to anomalous data and interference sources. Furthermore, spatial weighting and smoothing mechanisms are introduced to mitigate the impact of local fluctuations on positioning results and improve the continuity and spatial consistency of signal strength data. The positioning algorithm employs an iterative solution based on a system of hyperbolic equations, with confidence scores weighted by combining signal correlation and difference stability. This enhances precision control and error suppression during multi-node fusion. Finally, by analyzing velocity and direction within a continuous time window and dynamically constraining and correcting sudden trajectory changes based on communication status, the smoothness and practicality of the trajectory output are further improved. Overall, this solution achieves the goal of extracting reliable location data from unstable and multi-interference signals, providing accurate and continuous trajectory support for subsequent behavior recognition and alarm decision-making.

[0062] In an optional embodiment, calculating the signal correlation of the detector pair and the stability of the signal strength difference to obtain a credibility score, constructing a hyperbola equation based on the distance difference, using the credibility score as the initial weight of the hyperbola equation, and solving the hyperbola equation system in an iterative manner to obtain the real-time position of the signal source includes: Obtaining a signal strength sequence of the detector pair, calculating a cross-correlation coefficient of the signal strength sequence and a standard deviation of a signal strength difference sequence, and performing an adaptive weighted combination of the cross-correlation coefficient and the inverse of the standard deviation to obtain an initial credibility score of the detector pair; Calculate the directional angle of the line connecting the detector pair, determine the spatial distribution weight based on the distribution of the directional angle, and multiply the spatial distribution weight by the initial credibility score to obtain a revised credibility score for the detector pair; weighting the distance differences between the detector pairs using the modified credibility scores, constructing a hyperbolic equation system based on the weighted distance differences, and solving the hyperbolic equation system using a constrained weighted least squares method to obtain an initial position estimate; Obtain the signal source position at the previous moment, determine the position constraint area at the current moment in combination with the maximum moving speed constraint, and perform an adaptive step-size iterative search within the position constraint area starting from the initial position estimate value to obtain the current iterative position; The theoretical distance difference of the detector pair is calculated based on the current iterative position, and the deviation between the theoretical distance difference and the measured distance difference is used as the positioning residual. The corrected credibility score is dynamically attenuated and updated according to the positioning residual to obtain the real-time credibility score. The real-time credibility score is used to update the weights of the hyperbolic equation group, and the iterative solution is continued until the position estimate converges to obtain the real-time position of the signal source.

[0063] When implementing the present invention, multiple signal detectors are first deployed to form a detection network. Each detector can simultaneously detect mobile phone signals in multiple frequency bands. These detectors are distributed in different locations, and their coordinates are known and fixed. Each detector is equipped with a multi-band receiving antenna that can receive and record mobile phone signals of different standards, such as GSM, CDMA, WCDMA, LTE, and 5G. The detectors collect signal strength data according to a preset sampling period, which is set to 100 milliseconds to meet real-time requirements.

[0064] Detectors are paired in pairs to form detector pairs. For example, in a system consisting of four detectors, six detector pairs can be formed: (1, 2), (1, 3), (1, 4), (2, 3), (2, 4), and (3, 4). Each pair of detectors receives a signal from the same source and records the time and strength of the signal arrival. Since electromagnetic waves propagate at the speed of light, the difference in distance from the source to the two detectors can be calculated based on the time difference between the signal arrival at the two detectors. For each pair of detectors, a sequence of signal strengths within a time window is acquired. The time window is set to 5 seconds, during which each detector collects 50 signal strength samples. The cross-correlation coefficient of the two signal strength sequences is calculated. The cross-correlation coefficient reflects the consistency of the changing trends of the two signal sequences. The cross-correlation coefficient ranges from [-1 to 1]. The closer the value is to 1, the more consistent the changing trends of the two sequences are, and the higher the data quality. In practical applications, for good quality signals, the cross-correlation coefficient is typically greater than 0.8.

[0065] Calculate the standard deviation of the signal strength difference sequence for the detector pair. A signal strength difference sequence is the difference between the signal strengths measured by two detectors at the same moment. The standard deviation reflects the degree of fluctuation in the difference sequence; smaller standard deviations indicate more stable differences and higher positioning reliability. The initial credibility score for the detector pair is obtained by adaptively weighting the cross-correlation coefficient and the inverse of the standard deviation. The adaptive weighting is dynamically adjusted based on the actual signal environment. In environments with large signal fluctuations, the cross-correlation coefficient is weighted at 0.7, and the inverse standard deviation is weighted at 0.3. In environments with relatively stable signals, both weights are set to 0.5.

[0066] Assume that the signal strength sequences for detectors 1 and 2 are [-60, -62, -61, -63, -60] dBm and [-65, -67, -66, -68, -65] dBm, respectively. The calculated cross-correlation coefficient is 0.95, and the signal strength difference sequence is [5, 5, 5, 5, 5] dBm. The standard deviation is 0, and the inverse of the standard deviation is approximately a large value. For stability, it is limited to 10, so we take 10. In an environment with large signal fluctuations, the initial credibility score is 0.7 × 0.95 + 0.3 × 10 = 3.665.

[0067] Calculate the directional angle between the lines connecting detector pairs. The directional angle refers to the angle between the detector pairs and a reference direction (such as true north). To ensure uniform spatial coverage, the directional angle distribution of the detector pairs should be as even as possible. A spatial distribution weight is determined based on the distribution of the directional angles. The spatial distribution weight is calculated by dividing the 360-degree space into eight sectors, counting the number of detector pairs within each sector, and assigning higher weights to detector pairs in sectors with fewer detector pairs and lower weights to detector pairs in sectors with more detector pairs. This balances positioning accuracy across different directions. For example, if there is one detector pair in the 0-45 degree sector and three detector pairs in the 45-90 degree sector, the spatial distribution weight for the detector pair in the 0-45 degree sector is 1.5, and the spatial distribution weight for the detector pair in the 45-90 degree sector is 0.5. Multiplying the spatial distribution weight by the initial confidence score yields the revised confidence score for the detector pair. Assuming that the detector pair (1, 2) is located in the 0-45 degree sector and its spatial distribution weight is 1.5, the corrected confidence score is 3.665×1.5=5.4975.

[0068] The distance differences between detector pairs are weighted using a modified confidence score. The distance difference is the difference in distance from the signal source to the two detectors, calculated by multiplying the signal arrival time difference by the speed of light. A system of hyperbolic equations is constructed based on these weighted distance differences. In a two-dimensional plane, each detector pair defines a hyperbola, on which the signal source lies. Multiple detector pairs define multiple hyperbolas, with the signal source located near the intersection of these hyperbolas.

[0069] A constrained weighted least squares method is used to solve the hyperbolic equations to obtain an initial position estimate. During the solution process, due to measurement errors and noise, multiple hyperbolas may not intersect exactly at a single point. The goal of the weighted least squares method is to find a point that minimizes the sum of the weighted squared distances from that point to each hyperbola. The weights represent a modified confidence score, with detectors with higher confidence having a greater influence on the positioning calculation.

[0070] Obtain the signal source location at the previous moment and, combined with the maximum speed constraint, determine the current location constraint area. Assuming the mobile user's maximum speed is 10 meters per second, the previous location was (100, 200) meters, and the sampling interval is 0.1 seconds, the current location constraint area is a circular area centered at (100, 200) meters with a radius of 1 meter. Within the location constraint area, perform an iterative search with an adaptive step size starting from the initial position estimate to determine the current iterative position.

[0071] The specific method for adaptive step-size iterative search is: set an initial step size of 0.5 meters, move one step along the gradient direction, and calculate the objective function value (i.e., the weighted sum of squared residuals) at the new position. If the objective function value decreases, the move is accepted and the step size is increased (e.g., multiplied by 1.2). If the objective function value increases, the move is rejected, the step size is reduced (e.g., multiplied by 0.5), and a new direction is tried. Iterations terminate when the step size falls below a preset threshold (e.g., 0.01 meters) or the number of iterations reaches an upper limit (e.g., 50).

[0072] Calculate the theoretical distance difference between the detector pair based on the current iteration position. The theoretical distance difference is the difference between the distances from the signal source to the two detectors, assuming the signal source is at the current iteration position. The deviation between the theoretical distance difference and the measured distance difference is used as the positioning residual. For example, if the measured distance difference between the detector pair (1, 2) is 10 meters and the theoretical distance difference is 9.8 meters, the positioning residual is 0.2 meters.

[0073] The real-time credibility score is obtained by dynamically attenuating the corrected credibility score based on the positioning residual. Specifically, the residual threshold is set to 1 meter. When the positioning residual is less than the threshold, the credibility score remains unchanged. When the positioning residual is greater than the threshold, the credibility score decreases exponentially, with the attenuation coefficient proportional to the residual size. For example, if the positioning residual is 2 meters, exceeding the threshold by 1 meter, and the attenuation coefficient is set to 0.8, the real-time credibility score is the corrected credibility score multiplied by 0.8 to the power of 1, that is, 5.4975 × 0.8 = 4.398.

[0074] The real-time confidence score is used to update the weights of the hyperbolic equations. The iterative solution continues until the position estimate converges to the real-time location of the signal source. Convergence is achieved when the position change between two consecutive iterations is less than 0.05 meters or when the number of iterations reaches 20. The final position is the real-time location of the signal source.

[0075] Figure 3This figure shows a comparative analysis of the performance of multi-dimensional credibility assessment and iterative optimization positioning technologies. In terms of positioning accuracy, the present invention achieves high-precision positioning of 1.0 meters, a significant improvement over the 4.0 meters of traditional methods and the 3.0 meters of the basic credibility scoring method, resulting in a 75% increase in accuracy. In terms of convergence time, the present invention completes position calculation in just 2.0 seconds, saving 50% of the time required by traditional methods, demonstrating a significant improvement in algorithm efficiency.

[0076] In the anti-interference capability test, the proposed method achieved a score of 8.0, significantly higher than the 3.0 of the traditional method and the 5.0 of the basic credibility scoring method, demonstrating its excellent noise immunity in complex electromagnetic environments. System robustness testing showed that the proposed method scored 7.0, 5.0 points higher than the traditional method, demonstrating the algorithm's stability in various scenarios.

[0077] These performance improvements are mainly attributed to technical innovations such as multi-dimensional credibility assessment mechanism, spatial distribution weight adjustment, position constraint area limitation, and real-time positioning residual feedback, which effectively solve the problems of unstable accuracy and poor anti-interference ability faced by traditional positioning methods in complex environments.

[0078] In this embodiment, by introducing a multi-dimensional credibility assessment and iterative optimization mechanism, high-precision estimation of the signal source position in complex environments is achieved. Traditional positioning methods often use fixed weights or weighting methods that do not consider the dynamic characteristics of the signal. They are easily affected by signal noise, path obstruction, and uneven detector layout, resulting in unstable positioning accuracy. This solution first uses the cross-correlation coefficient and the standard deviation of the signal strength difference to construct an initial credibility score, which not only considers signal synchronization but also suppresses the uncertainty caused by sharp fluctuations, thereby improving the rationality of the score. The directional angle distribution between detector pairs is further introduced to construct spatial distribution weights, effectively alleviating the solution offset problem that is prone to occur in traditional hyperbolic positioning when the geometric distribution is uneven. Combining the weighted least squares method with the position constraint area limitation, the historical position and velocity boundaries of the signal source are used to control the search range, enhancing the continuity and physical rationality of the positioning results. A real-time positioning residual feedback mechanism is introduced during the iterative process to dynamically update the credibility score, making the weight more suitable for the current channel state, improving the convergence speed and solution accuracy. Overall, this solution significantly enhances the robustness and adaptability of the signal positioning system through multi-layer weighting and feedback optimization, and improves the real-time positioning accuracy in complex scenarios.

[0079] In an optional embodiment, judging the current mobile phone usage scenario based on the temporal relationship between the movement trajectory data and the mobile phone communication status, performing a threat level assessment on the mobile phone usage scenario based on pre-set scenario threat level parameters, and sending an alarm message to the monitoring terminal based on the threat level assessment result includes: Acquire the mobile phone's movement trajectory data and communication status data, extract motion features and spatial distribution features from the movement trajectory data, extract communication behavior features and signal quality features from the communication status data, and generate time series feature data; Segmenting the time series feature data into time windows, calculating the correlation between features in each time window, constructing a feature correlation matrix, and matching the feature correlation matrix with a preset mobile phone usage scenario model to obtain the current mobile phone usage scenario; Comparing and analyzing the time series feature data with the preset normal scene features corresponding to the current mobile phone usage scene to generate a feature deviation sequence, performing time series clustering on the feature deviation sequence to obtain an abnormal event cluster, and generating abnormal event features based on the time span and number of occurrences of the abnormal event cluster; The threat score is calculated based on the characteristics of abnormal events and weighted by the preset threat level parameters corresponding to the current mobile phone usage scenario to obtain the real-time threat level. When the real-time threat level exceeds the warning threshold, the abnormal event characteristics that triggered the warning are used as a monitoring template. The number of matching abnormal events is counted during the observation period. When the number of matches exceeds the set threshold and the threat level continues to rise, an alarm message is sent to the monitoring terminal.

[0080] For example, first, a mobile phone's movement trajectory data and communication status data are obtained. Movement trajectory data includes GPS location coordinates, accelerometer data, gyroscope data, and other data. Motion characteristics and spatial distribution characteristics can be extracted from this data. Motion characteristics include average speed, speed change rate, acceleration fluctuation, and frequency of direction changes. Spatial distribution characteristics include location concentration, dwell point density, activity radius, and deviation from a typical path. Communication status data includes call logs, SMS logs, network connection status, and signal strength. Communication behavior characteristics and signal quality characteristics can be extracted from this data. Communication behavior characteristics include call frequency, call duration distribution, SMS frequency, and data usage patterns. Signal quality characteristics include signal strength fluctuation, network switching frequency, and connection stability. For example, a user's movement trajectory data shows an average speed of 5.2 km / h, with locations concentrated in three areas. Communication status data shows eight calls per day on weekdays, with an average call duration of 3.5 minutes, and signal strength fluctuating between -85 dBm and -70 dBm.

[0081] Time series feature data is segmented into time windows, using time windows ranging from 5 to 60 minutes. The window size is dynamically adjusted based on different scenario characteristics. The correlation between features within each time window is calculated to construct a feature correlation matrix. This correlation calculation takes into account the consistency of eigenvalue trends, the time lag between eigenvalue changes, and the degree of mutual influence between eigenvalues. For example, by calculating the consistency of the changing trends of mobile speed and call frequency within the same time window, a correlation coefficient of 0.78 is obtained, indicating a strong positive correlation. The constructed feature correlation matrix contains the correlation coefficients between all extracted features, forming an n×n matrix (n is the number of features). The feature correlation matrix is ​​matched against pre-set mobile phone usage scenario models, including office, home, transportation, and social scenarios. Each scenario has a specific feature correlation pattern. The similarity between the current feature correlation matrix and each pre-set scenario model is calculated, and the scenario with the highest similarity is selected as the current mobile phone usage scenario. For example, if the similarity between a feature correlation matrix and the office scenario model is 0.85 and the similarity with the home scenario model is 0.62, the current scenario is determined to be office.

[0082] Compare and analyze the time series feature data with the preset normal scenario features corresponding to the current mobile phone usage scenario. The preset normal scenario features are the normal ranges and variation patterns of each feature in that scenario, derived from historical data statistics. By calculating the deviation between the current features and the normal features, a feature deviation sequence is generated. For example, in an office scenario, if the current location concentration is 0.35, while the normal range is 0.75-0.95, the location concentration deviation is -0.5. Time series clustering is performed on the feature deviation sequence, using a density clustering algorithm to group data points with similar time proximity and feature deviation patterns into one category, forming an abnormal event cluster. Abnormal event features are generated based on the time span and number of occurrences of the abnormal event cluster. For example, if the location concentration is detected to be more than 50% below the normal value for three consecutive hours, and during this period, call frequency increases by 200% and network switching frequency increases by 150%, an abnormal event cluster is formed with a time span of three hours and a frequency of one occurrence.

[0083] A threat score is calculated based on the characteristics of abnormal events, taking into account factors such as the severity of the anomaly, duration, and impact range. This score is weighted based on the preset threat level parameters corresponding to the current mobile phone usage scenario to produce a real-time threat level. These preset threat level parameters are set based on the security sensitivity of different scenarios, for example, 1.5 for office scenarios, 1.2 for home scenarios, and 1.8 for transportation scenarios. When the real-time threat level exceeds the warning threshold (for example, 75 points), the characteristics of the abnormal event that triggered the warning are used as a monitoring template. The number of matching abnormal events is counted over an observation period (for example, 24 hours). When the number of matches exceeds a set threshold (for example, three) and the threat level continues to rise, an alarm is sent to the monitoring terminal. The alarm information includes a description of the abnormal event characteristics, the threat level score, the duration of the anomaly, and the affected functional modules. For example, a user in an office scenario was detected to have abnormally dispersed locations, abnormal call patterns, and frequent network switching. The calculated threat score was 65 points. Combined with the office scenario threat level parameter of 1.5, the real-time threat level was 97.5 points, exceeding the warning threshold of 75 points. Four similar abnormal events were detected during the 24-hour observation period, and the threat level rose from 97.5 points to 105 points. The system sent an alarm message to the monitoring terminal.

[0084] The above technical solution integrates the temporal features of a mobile phone's movement trajectory and communication status to achieve intelligent identification of mobile phone usage scenarios and dynamic assessment of threat levels. Existing technologies often rely solely on static location or signal strength for anomaly detection, which fails to capture the complete evolution of user behavior and is prone to false positives or missed alerts. This solution extracts multidimensional features such as motion, space, communication, and signal quality, and constructs a feature correlation matrix. This allows accurate identification of the real-world mobile phone usage scenario from the overall temporal evolution. Combining scenario model matching with deviation analysis mechanisms, it effectively identifies anomalous events that deviate from normal behavior patterns and constructs event clusters through temporal clustering, enhancing sensitivity to continuous anomalous behavior. Furthermore, by introducing dynamic threat scoring and weighting of preset threat level parameters, threat assessment becomes more targeted and flexible, avoiding the errors associated with fixed rule-based judgments. Finally, combining warning thresholds with matching statistics within the observation period enables effective tracking and real-time alerting of persistent threats. Overall, this solution improves the ability to identify potential risks within complex behavior patterns, enhancing the system's scenario adaptability and security response efficiency.

[0085] A second aspect of an embodiment of the present invention provides a multi-band mobile phone signal detection and alarm system, the system comprising: The first unit is used to collect electromagnetic signals in the target area and perform preprocessing to obtain preprocessed electromagnetic signals; The second unit is configured to perform a fast Fourier transform on the preprocessed electromagnetic signal to obtain a signal spectrum, extract characteristic frequency band information of the mobile phone signal from the signal spectrum, wherein the characteristic frequency band information of the mobile phone signal includes an uplink frequency band signal strength value and a downlink frequency band signal strength value, and determine the communication status of the mobile phone according to a ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value; The third unit is used to obtain signal strength data in the target area. Based on the mobile phone communication status and signal strength data, the signal strength difference positioning algorithm is used to calculate the real-time location coordinates of the mobile phone signal source, and the changes in the location coordinates over time are recorded to obtain movement trajectory data; The fourth unit is used to determine the current mobile phone usage scenario based on the temporal relationship between the movement trajectory data and the mobile phone communication status, evaluate the threat level of the mobile phone usage scenario according to the pre-set scenario threat level parameters, and send an alarm message to the monitoring terminal based on the threat level evaluation result.

[0086] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0087] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0088] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-band mobile phone signal detection and alarm method, characterized in that: include: Collecting electromagnetic signals in a target area and performing preprocessing to obtain preprocessed electromagnetic signals; Performing a fast Fourier transform on the preprocessed electromagnetic signal to obtain a signal spectrum, extracting characteristic frequency band information of the mobile phone signal from the signal spectrum, wherein the characteristic frequency band information of the mobile phone signal includes an uplink frequency band signal strength value and a downlink frequency band signal strength value, and determining the communication status of the mobile phone based on the ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value; Obtain signal strength data within the target area. Based on the mobile phone's communication status and signal strength data, use the signal strength difference positioning algorithm to calculate the real-time location coordinates of the mobile phone signal source, and record the changes in the location coordinates over time to obtain movement trajectory data. Based on the temporal relationship between the mobile trajectory data and the mobile phone communication status, the current mobile phone usage scenario is judged, the threat level of the mobile phone usage scenario is evaluated according to the pre-set scenario threat level parameters, and an alarm message is sent to the monitoring terminal based on the threat level evaluation result.

2. The method according to claim 1, characterized in that The electromagnetic signals in the target area are collected and preprocessed. The preprocessed electromagnetic signals include: Collecting full-band electromagnetic signals from mobile phone signals within the target area, detecting the signal strength of the electromagnetic signals in real time, and automatically adjusting the signal gain according to the signal strength to maintain the amplitude of the electromagnetic signals within a preset dynamic range, thereby obtaining a gain-adjusted signal; Performing multi-layer wavelet decomposition on the gain-adjusted signal to obtain wavelet coefficients of different scales, calculating the local variance of the wavelet coefficients, determining an adaptive threshold based on the local variance, performing soft threshold processing on the wavelet coefficients using the adaptive threshold, and obtaining a denoised signal through wavelet reconstruction; The denoised signal is divided into several time windows, the signal in each time window is Fourier transformed to obtain a spectrum, the spectrum is combined to obtain a time-spectrum, the mean square error of each time-frequency point in the time-spectrum is calculated, the noise power spectrum of each time-frequency point is estimated based on the mean square error, the corresponding Wiener filter coefficient is calculated according to the noise power spectrum, and the denoised signal is filtered using the Wiener filter coefficient to obtain a preprocessed electromagnetic signal.

3. The method according to claim 1, characterized in that Performing fast Fourier transform on the pre-processed electromagnetic signal to obtain the signal spectrum, and extracting the characteristic frequency band information of the mobile phone signal from the signal spectrum includes: Performing wavelet decomposition on the preprocessed electromagnetic signal using an orthogonal wavelet basis function to obtain a plurality of frequency band coefficients, calculating an energy value of each frequency band based on the frequency band coefficients, comparing the energy value with a preset frequency band threshold to determine a target frequency band, performing phase compensation on the frequency band coefficients of the target frequency band, and performing Fourier transform on the compensated frequency band coefficients to obtain a target spectrum signal; Dividing the target spectrum signal into an uplink sub-band and a downlink sub-band, constructing a probability density function for each of the uplink sub-band and the downlink sub-band, and calculating an optimal segmentation point based on the probability density function to obtain an adaptive threshold as a signal peak detection threshold; Performing morphological filtering on the spectrum signals of the uplink sub-band and the downlink sub-band, performing peak search on the filtered spectrum signals using the signal peak detection threshold to obtain a signal peak position, calculating a signal bandwidth based on the signal peak position, and setting an integer multiple of the signal bandwidth as an energy concentration interval; The variational method is used to optimize the integration interval boundary within the energy concentration interval, and the energy density integration is performed on the spectrum within the optimized integration interval to obtain the uplink frequency band signal strength value and the downlink frequency band signal strength value.

4. The method according to claim 1, wherein The communication status of a mobile phone can be determined based on the ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value. Performing time domain sliding processing on the uplink frequency band signal strength value and the downlink frequency band signal strength value, setting a decreasing weight coefficient according to the order of sampling time, multiplying the decreasing weight coefficient by the signal strength value at the corresponding moment, and accumulating the results to obtain a smoothed uplink signal strength value and a smoothed downlink signal strength value; Calculating a ratio of a smoothed uplink signal strength value to a smoothed downlink signal strength value, extracting a fluctuation amplitude and a change rate of the ratio, constructing a characteristic matrix based on the fluctuation amplitude and the change rate, and performing singular value decomposition on the characteristic matrix to obtain main characteristic components; A decision function is established based on the main eigenvector, and the maximum inter-class variance method is used to calculate the optimal segmentation point of the decision function. The optimal segmentation point is used as the discrimination threshold of the communication state. According to the discrimination threshold, the value range of the signal strength ratio is divided into the normal communication interval and the abnormal communication interval. The ratio of the smoothed uplink signal strength value to the smoothed downlink signal strength value is calculated in real time. When the current ratio falls into the normal communication interval, it is judged to be a normal communication state. When the current ratio falls into the abnormal communication interval, the link fault type is determined according to the relative position relationship between the current ratio and the normal communication interval, and the corresponding communication state judgment result is output.

5. The method according to claim 1, wherein Obtain signal strength data within the target area. Based on the mobile phone's communication status and signal strength data, use the signal strength difference positioning algorithm to calculate the real-time location coordinates of the mobile phone signal source, and record the changes in the location coordinates over time to obtain movement trajectory data including: Set up multiple signal acquisition nodes in the target area to collect signal strength data in multiple directions; Calculating a ratio of a short-time standard deviation to a long-time standard deviation of the signal strength data according to a pre-acquired mobile phone communication state, and screening a valid signal detector according to the ratio; Calculating a signal strength change trend collected by an effective signal detector, determining a smoothing window length based on the change trend, determining a spatial weight based on the spatial distribution position of the signal detector, and performing weighted smoothing on the spatial weight and signal strength data to obtain a smoothed signal strength value; Select any two valid signal detectors to form a detector pair, calculate the difference in smoothed signal strength values ​​between the detector pairs, dynamically adjust the path loss index based on the mobile phone communication status, and calculate the distance difference between the detector pairs; Calculating the signal correlation and the stability of the signal strength difference of the detector pair to obtain a credibility score, constructing a hyperbolic equation based on the distance difference, using the credibility score as the initial weight of the hyperbolic equation, and solving the hyperbolic equation system in an iterative manner to obtain the real-time position of the signal source; The moving speed and direction are calculated according to the real-time position of the signal source within the continuous time window, and the abnormal position is constrained and corrected in combination with the mobile phone communication status to obtain a smooth moving trajectory.

6. The method according to claim 5, characterized in that Calculating the signal correlation of the detector pair and the stability of the signal strength difference to obtain a credibility score, constructing a hyperbolic equation based on the distance difference, using the credibility score as the initial weight of the hyperbolic equation, and solving the hyperbolic equation system in an iterative manner to obtain the real-time position of the signal source includes: Obtaining a signal strength sequence of the detector pair, calculating a cross-correlation coefficient of the signal strength sequence and a standard deviation of a signal strength difference sequence, and performing an adaptive weighted combination of the cross-correlation coefficient and the inverse of the standard deviation to obtain an initial credibility score of the detector pair; Calculate the directional angle of the line connecting the detector pair, determine the spatial distribution weight based on the distribution of the directional angle, and multiply the spatial distribution weight by the initial credibility score to obtain a revised credibility score for the detector pair; weighting the distance differences between the detector pairs using the modified credibility scores, constructing a hyperbolic equation system based on the weighted distance differences, and solving the hyperbolic equation system using a constrained weighted least squares method to obtain an initial position estimate; Obtain the signal source position at the previous moment, determine the position constraint area at the current moment in combination with the maximum moving speed constraint, and perform an adaptive step-size iterative search within the position constraint area starting from the initial position estimate value to obtain the current iterative position; The theoretical distance difference of the detector pair is calculated based on the current iterative position, and the deviation between the theoretical distance difference and the measured distance difference is used as the positioning residual. The corrected credibility score is dynamically attenuated and updated according to the positioning residual to obtain the real-time credibility score. The real-time credibility score is used to update the weights of the hyperbolic equation group, and the iterative solution is continued until the position estimate converges to obtain the real-time position of the signal source.

7. The method according to claim 1, characterized in that Based on the temporal relationship between the mobile trajectory data and the mobile phone communication status, the current mobile phone usage scenario is judged, and the threat level of the mobile phone usage scenario is evaluated according to the pre-set scenario threat level parameters. According to the threat level evaluation results, an alarm message is sent to the monitoring terminal, including: Acquire the mobile phone's movement trajectory data and communication status data, extract motion features and spatial distribution features from the movement trajectory data, extract communication behavior features and signal quality features from the communication status data, and generate time series feature data; Segmenting the time series feature data into time windows, calculating the correlation between features in each time window, constructing a feature correlation matrix, and matching the feature correlation matrix with a preset mobile phone usage scenario model to obtain the current mobile phone usage scenario; Comparing and analyzing the time series feature data with the preset normal scene features corresponding to the current mobile phone usage scene to generate a feature deviation sequence, performing time series clustering on the feature deviation sequence to obtain an abnormal event cluster, and generating abnormal event features based on the time span and number of occurrences of the abnormal event cluster; The threat score is calculated based on the characteristics of abnormal events and weighted by the preset threat level parameters corresponding to the current mobile phone usage scenario to obtain the real-time threat level. When the real-time threat level exceeds the warning threshold, the abnormal event characteristics that triggered the warning are used as a monitoring template. The number of matching abnormal events is counted during the observation period. When the number of matches exceeds the set threshold and the threat level continues to rise, an alarm message is sent to the monitoring terminal.

8. A multi-band mobile phone signal detection and alarm system, used to implement the method according to any one of claims 1 to 7, characterized in that: include: The first unit is used to collect electromagnetic signals in the target area and perform preprocessing to obtain preprocessed electromagnetic signals; The second unit is configured to perform a fast Fourier transform on the preprocessed electromagnetic signal to obtain a signal spectrum, extract characteristic frequency band information of the mobile phone signal from the signal spectrum, wherein the characteristic frequency band information of the mobile phone signal includes an uplink frequency band signal strength value and a downlink frequency band signal strength value, and determine the communication status of the mobile phone according to a ratio of the uplink frequency band signal strength value to the downlink frequency band signal strength value; The third unit is used to obtain signal strength data in the target area. Based on the mobile phone communication status and signal strength data, the signal strength difference positioning algorithm is used to calculate the real-time location coordinates of the mobile phone signal source, and the changes in the location coordinates over time are recorded to obtain movement trajectory data; The fourth unit is used to determine the current mobile phone usage scenario based on the temporal relationship between the movement trajectory data and the mobile phone communication status, evaluate the threat level of the mobile phone usage scenario according to the pre-set scenario threat level parameters, and send an alarm message to the monitoring terminal based on the threat level evaluation result.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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