A wireless signal intelligent detection method and system suitable for multi-band mode

Abstract

The application relates to the technical field of intelligent detection, and particularly discloses a wireless signal intelligent detection method and system suitable for multi-frequency band modes, which comprises the following steps: step S1: setting a detection buffer interval, equally dividing selected time points, acquiring wireless signals and communication quality indexes of terminal equipment in real time, and determining optimal communication quality indexes; step S2: generating a communication quality curve according to the communication quality indexes, extracting a plurality of characteristic indexes of the wireless signals, and acquiring characteristic curves of the indexes changing with time; and step S3: comparing the communication quality curve with the signal characteristic curves, establishing an adaptive rule model of signal characteristics and communication quality, predicting signal characteristics when the optimal communication quality is based on the model, and combining with equipment information and correcting in real time; the method optimizes signal detection performance of the terminal equipment under the multi-frequency band modes by dynamically analyzing the relationship between the signal characteristics and the communication quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, and specifically to an intelligent detection method and system for radio signals applicable to multi-band systems. Background Technology

[0002] In today's communication technology field, the detection and analysis of radio signals in the environment is a crucial task. Current technologies typically rely on specialized equipment and advanced algorithms to perform sophisticated processing of various radio signals. Specifically, this involves detecting the complex array of radio signals in the environment and extracting key information from multiple dimensions, including signal strength, frequency band, and unique signal characteristics. Then, using specialized algorithms and equipment, these signals are accurately classified, clearly displaying detailed information such as the carrier of the signal, the type of mobile phone used, and the precise distance.

[0003] However, in real-world applications, mobile phones don't remain static. To ensure real-time communication quality, they dynamically adjust characteristics such as radio signal transmission power based on the surrounding environment. While this adaptive adjustment mechanism helps ensure smooth communication, it causes the characteristics of the wireless signal to constantly change. For example, when a user moves from an area with weak signal to an area with strong signal, the phone may reduce its transmission power, complicating the identification process that was originally based on stable signal characteristics. Due to the continuous changes in signal characteristics, the identification results also fluctuate, posing significant challenges to accurate identification and analysis, and affecting the stability and reliability of related technologies in practical applications. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for intelligent detection of radio signals applicable to multi-band systems, and to solve the following technical problems.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for intelligent detection of radio signals applicable to multi-band systems includes the following steps:

[0007] Step S1: Set up a detection buffer zone, select several time points equally on the detection buffer zone, acquire the radio signal of the terminal device within the detection buffer zone, and acquire the communication quality index of the terminal device at each time point in real time; and determine the optimal communication quality index of the terminal device.

[0008] Step S2: Generate the communication quality curve of the terminal device based on the real-time acquired communication quality index; and extract several signal feature indicators of the radio signal, obtain the signal features of the signal feature indicators at each time point, and obtain the signal feature change curve of each signal feature indicator.

[0009] Step S3: For any signal characteristic index, compare the communication quality curve with the signal characteristic change curve of the signal characteristic index to obtain an adaptive rule model between the signal characteristic index and the communication quality index.

[0010] Based on the adaptive rule model, the signal characteristics of each signal characteristic index of the terminal device are predicted when the communication quality index is optimal, and the device information of the terminal device is obtained and the device information is corrected in real time.

[0011] As a further aspect of the present invention: the process of setting the detection buffer includes:

[0012] A preset time period length T is defined, and the time at which the detection of the radio signal of the terminal device begins is denoted as start time t0. Based on the time period length and the start time, the detection buffer interval [t0, ...] is obtained.

[0013] t0+T).

[0014] As a further aspect of the present invention: the process of obtaining the communication quality index of the terminal device includes:

[0015] A signal strength range of [-110, 0] dBm is defined, where the signal strength range represents the power strength range of the base station signal received by the terminal device. By obtaining the power strength of the base station signal received by the terminal device and the signal-to-noise ratio of the base station signal, the communication quality index of the terminal device is obtained. K1 and K2 are weighting coefficients, with K1 > 0 and K2 > 0. RSSI represents the power intensity of the base station signal received by the terminal device, and SNR represents the signal-to-noise ratio of the base station signal.

[0016] As a further aspect of the present invention: the process of determining the optimal communication quality index includes:

[0017] The maximum signal-to-noise ratio (SNR) of the base station signal is set, and the maximum SNR is set within the range of [20, 40] dB, to obtain the optimal communication quality index. SNR max This represents the maximum signal-to-noise ratio.

[0018] As a further aspect of the present invention: the extraction process of various signal feature indicators in the radio signal includes:

[0019] The radio signal is amplified and filtered to suppress out-of-band interference, and the sampling rate of the radio signal is determined based on the Quisther theorem. The analog signal is then converted into a digital signal according to the sampling rate. The synchronization sequence of the radio signal is identified through a detection algorithm to obtain the time position of the synchronization sequence, and the frame boundary and symbol period are determined. The digital signal is then subjected to a Fast Fourier Transform to obtain a frequency domain signal. Based on the frequency domain signal, several signal characteristic indicators of the radio signal are obtained, including modulation method, bandwidth, spectral characteristics, and frame structure.

[0020] As a further aspect of the present invention: the process of acquiring the signal features includes:

[0021] For any given time point, obtain the value of the signal characteristic index at that time point. If the value of the signal characteristic index is a numerical value, then directly record the numerical value as the signal characteristic of the signal characteristic index. If the value of the signal characteristic index is not a numerical value, then encode the value into binary to obtain a binary value, and record the binary value as the signal characteristic of the signal characteristic index.

[0022] As a further aspect of the present invention: the process of comparing the communication quality curve with the signal characteristic change curve includes:

[0023] Obtain the monotonically increasing interval and monotonically decreasing interval on the communication quality curve, both of which are denoted as signal change intervals; and obtain the monotonically increasing interval and monotonically decreasing interval on the signal characteristic change curve, both of which are denoted as characteristic change intervals.

[0024] Obtain the x-coordinates corresponding to the start and end points of each signal change interval to obtain the set of signal change endpoints {(P1)}. start P1 end ), (P2 start P2 end ), ..., (Pn start , Pn end )}, where Pn start Pn represents the x-coordinate corresponding to the starting point of the nth signal variation interval. end This represents the x-coordinate corresponding to the end point of the nth signal change interval, where n is the total number of signal change intervals;

[0025] Obtain the x-coordinates corresponding to the start and end points of each feature change interval to obtain the set of feature change endpoints {(P1´ start P1´ end ), (P2´ start P2´ end ), ..., (Pm´ start Pm´ end )}, where Pm´ startPm' represents the x-coordinate corresponding to the starting point of the m-th feature variation interval. end This represents the x-coordinate corresponding to the end point of the m-th feature change interval, where m is the total number of feature change intervals;

[0026] Set a delay threshold time; if the communication quality curve and the signal characteristic change curve satisfy... If the signal characteristic change curve is positive, the signal characteristic index corresponding to the curve is designated as a non-fixed feature; otherwise, the signal characteristic index corresponding to the curve is designated as a fixed feature; where Pi start Pi' represents the x-coordinate corresponding to the starting point of the i-th signal variation interval. start Let x represent the x-coordinate corresponding to the starting point of the i-th feature change interval, i∈[1,n] and i is a positive integer.

[0027] As a further aspect of the present invention: the process of obtaining the adaptive rule model includes:

[0028] Each signal variation interval is extracted, and the feature variation interval of each non-fixed feature is also extracted. For any communication quality index in the signal variation interval, the signal feature corresponding to the communication quality index is obtained in the feature variation interval with the same number. The communication quality index and its corresponding signal feature are recorded as a set of sample data, thus obtaining several sets of sample data, which are recorded as a sample dataset. An initial model is established based on a neural network. The sample dataset is input into the initial model, and the initial model is trained to obtain an adaptive rule model that represents the change of the signal feature with the change of the communication quality index.

[0029] A smart radio signal detection system applicable to multi-band standards, comprising:

[0030] Signal acquisition module: includes a detection center and terminal equipment; sets a detection buffer zone in the detection center, selects several time points equally divided in the detection buffer zone, acquires the radio signals of the terminal equipment in the detection buffer zone, and acquires the communication quality index of the terminal equipment at each time point in real time; and determines the optimal communication quality index of the terminal equipment.

[0031] Preprocessing module: Sets up a detection buffer zone, selects several time points equally divided within the detection buffer zone, acquires the radio signals of the terminal devices within the detection buffer zone, and acquires the communication quality index of the terminal devices at each time point in real time; and determines the optimal communication quality index of the terminal devices.

[0032] Feature matching module: For any signal feature index, compare the communication quality curve with the signal feature change curve of the signal feature index to obtain an adaptive rule model between the signal feature index and the communication quality index;

[0033] Based on the adaptive rule model, the signal characteristics of each signal characteristic index of the terminal device are predicted when the communication quality index is optimal, and the device information of the terminal device is obtained and the device information is corrected in real time.

[0034] The beneficial effects of this invention are:

[0035] This invention significantly improves the signal detection performance and stability of terminal devices in multi-band environments by dynamically predicting signal characteristics under optimal communication conditions through real-time analysis of the relationship between signal characteristics and communication quality. It establishes an adaptive rule model of signal characteristics and communication quality, which can automatically correct device information and adapt to dynamic changes in signal characteristics (such as transmission power adjustment), solving the problem of inaccurate identification caused by signal fluctuations in traditional methods. Furthermore, this invention combines neural networks and decision tree models to extract key indicators such as device distance, operator, and type from the signal characteristics under optimal communication quality, improving the accuracy and reliability of device identification. Through signal amplification, filtering, and Fourier transform techniques, it extracts features such as modulation mode and bandwidth, effectively suppressing out-of-band interference and enhancing signal resolution capabilities in complex electromagnetic environments. It is applicable to various terminal devices such as mobile phones, tablets, and smartwatches, supporting real-time monitoring and correction, and meeting the intelligent detection needs of multi-band standards (such as 5G and Wi-Fi). This invention solves the problem of unstable identification caused by dynamic signal changes, improving the accuracy, adaptability, and applicability of radio signal detection. Attached Figure Description

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] Figure 1 This is a schematic diagram illustrating the steps of an intelligent radio signal detection method applicable to multi-band systems according to the present invention;

[0038] Figure 2 This is a schematic diagram of an intelligent radio signal detection system applicable to multi-band systems according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 As shown, this invention is an intelligent detection method for radio signals applicable to multi-band systems, comprising the following steps:

[0041] Step S1: Set up a detection buffer zone, select several time points equally on the detection buffer zone, acquire the radio signal of the terminal device within the detection buffer zone, and acquire the communication quality index of the terminal device at each time point in real time; and determine the optimal communication quality index of the terminal device.

[0042] The acquisition of the radio signals is achieved through the multi-band receiving antenna (covering 800MHz to 6GHz) and radio frequency front-end of the detection center, which captures downlink or uplink communication signals between the terminal device and the base station in real time, including control signaling, data frames, etc., and converts the analog signals into digital IQ data (sampling rate ≥ 2 times the signal bandwidth, in accordance with Quine's theorem) through an analog-to-digital converter (ADC) and stores them in the local cache;

[0043] In a preferred embodiment of the present invention, the process of setting the probe buffer includes:

[0044] A preset time period length T is defined, and the time at which the detection of the radio signal of the terminal device begins is denoted as start time t0. Based on the time period length and the start time, the detection buffer interval [t0, ...] is obtained.

[0045] t0+T];

[0046] Specifically, the value of the preset time period length is determined according to the actual situation for different scenarios. For example, in indoor static environments (such as offices and homes), the terminal signal changes relatively smoothly, and T can be set to 30 seconds to 1 minute, which is sufficient to cover the stable fluctuation cycle of the signal. In outdoor mobile environments (such as streets and vehicles), the terminal signal changes frequently due to movement, and T needs to be extended to 2 to 5 minutes to ensure that at least 2 to 3 frequency band switching or power adjustment events are captured.

[0047] Within the buffer, select N time points (N≥10, to ensure data density), with an interval of T / N between adjacent time points; for example, if T=2 minutes (120 seconds) and N=12, then select a time point every 10 seconds, numbered from 1 to 12, to facilitate subsequent curve coordinate mapping.

[0048] In a preferred embodiment of the present invention, the terminal device includes a mobile phone, a tablet, a laptop computer, and a smartwatch;

[0049] In a preferred embodiment of the present invention, the process of obtaining the communication quality index of the terminal device includes:

[0050] A signal strength range of [-110, 0] dBm is defined, where the signal strength range represents the power strength range of the base station signal received by the terminal device. By obtaining the power strength of the base station signal received by the terminal device and the signal-to-noise ratio of the base station signal, the communication quality index of the terminal device is obtained. K1 and K2 are weighting coefficients, and K1 > 0 and K2 > 0. RSSI represents the power intensity of the base station signal received by the terminal device, and SNR represents the signal-to-noise ratio of the base station signal.

[0051] The weighting coefficients reflect the contribution ratio of signal strength to signal-to-noise ratio;

[0052] In a preferred embodiment of the present invention, the process of determining the optimal communication quality index includes:

[0053] The maximum signal-to-noise ratio (SNR) of the base station signal is set, and the maximum SNR is set within the range of [20, 40] dB, to obtain the optimal communication quality index. SNR max Indicates the maximum signal-to-noise ratio;

[0054] Specifically, the power intensity of the base station signal is sampled in real time by the power detection module of the radio frequency front end. The sampling is performed 10 times per second, and the average value is taken after removing the maximum and minimum values ​​to obtain the RSSI value at that time point. The effective range of RSSI is set to [-110, 0] dBm.

[0055] The signal-to-noise ratio (SNR) of the base station signal is separated into signal power and noise power through frequency domain analysis: noise power is measured during the silent period of the signal frame (such as the GP guard interval of LTE), and total power is measured during the data transmission period. The difference between the two is the SNR.

[0056] Furthermore, the weighting coefficients are set according to the scenario. When in a signal-dominant scenario (such as a suburb), K1=0.6 and K2=0.4, where signal strength has a greater impact on communication quality. When in an interference-dominant scenario (such as a densely populated urban area), K1=0.4 and K2=0.6, where anti-interference capability is more critical. In addition, K1+K2=1 to ensure that the index can be quantified and compared.

[0057] Step S2: Generate the communication quality curve of the terminal device based on the real-time acquired communication quality index; and extract several signal feature indicators of the radio signal, obtain the signal features of the signal feature indicators at each time point, and obtain the signal feature change curve of each signal feature indicator.

[0058] In a preferred embodiment of the present invention, the process of generating the communication quality curve includes:

[0059] Each time point is numbered, and a coordinate system is established with the number as the horizontal axis and the communication quality index as the vertical axis. On the coordinate system, each numbered time point and its corresponding communication quality index are converted into coordinate points at corresponding positions, and each coordinate point is fitted to obtain a communication quality curve.

[0060] Specifically, the communication quality index at each time point is mapped to coordinate points (P, Q) in the coordinate system. P ), P is the Pth number, Q P The communication quality index at the Pth time point is fitted to the coordinate points using cubic spline interpolation.

[0061] For adjacent points (P, Q) P (P+1, Q) and (P+1, Q) P+1 The curve is smoothed by interpolation function to avoid sudden rises and falls due to single-point noise. After fitting, outliers with residuals exceeding the threshold (such as twice the average residual) are removed to ensure that the curve can truly reflect the overall trend of communication quality (such as rising segments indicating signal improvement and falling segments indicating signal deterioration).

[0062] Calculate the average error between the fitted curve and the original coordinate points. If the error exceeds 5%, increase the number of time points (e.g., from N=30 to N=60) and refit until the error meets the requirements.

[0063] In a preferred embodiment of the present invention, the extraction process of each signal feature index in the radio signal includes:

[0064] The radio signal is amplified and filtered to suppress out-of-band interference, and the sampling rate of the radio signal is determined based on the Quisther theorem. The analog signal is then converted into a digital signal according to the sampling rate. The synchronization sequence of the radio signal is identified through a detection algorithm to obtain the time position of the synchronization sequence, and the frame boundary and symbol period are determined. The digital signal is then subjected to a Fast Fourier Transform to obtain a frequency domain signal. Based on the frequency domain signal, several signal characteristic indicators of the radio signal are obtained, including modulation scheme, bandwidth, spectral characteristics, and frame structure.

[0065] Specifically, the synchronization sequence detection uses a sliding correlation algorithm to calculate the correlation between the received signal and a preset standard synchronization sequence template (such as the LTE primary synchronization signal PSS sequence and the GSM training sequence). When the correlation value exceeds a threshold (such as 0.8), the time position of the synchronization sequence is recorded (accurate to the symbol level). The frame boundary and symbol period are determined based on the synchronization sequence position, and the frame structure is divided according to the protocol specification.

[0066] In a preferred embodiment of the present invention, the process of acquiring the signal features includes:

[0067] For any given time point, obtain the value of the signal characteristic index at that time point. If the value of the signal characteristic index is a numerical value, then directly record the numerical value as the signal characteristic of the signal characteristic index. If the value of the signal characteristic index is not a numerical value, then encode the value into binary to obtain a binary value, and record the binary value as the signal characteristic of the signal characteristic index.

[0068] In a preferred embodiment of the present invention, the process of obtaining the signal characteristic change curve of the signal characteristic index includes:

[0069] A new coordinate system is established with the time point number as the horizontal axis and the signal feature as the vertical axis. The time points with each number and their corresponding signal features are converted into coordinate points at corresponding positions in the new coordinate system. The coordinate points are connected by a smooth curve, and the curve is recorded as the signal feature change curve.

[0070] Step S3: For any signal characteristic index, compare the communication quality curve with the signal characteristic change curve of the signal characteristic index to obtain an adaptive rule model between the signal characteristic index and the communication quality index.

[0071] Based on the adaptive rule model, the signal characteristics of each signal characteristic index of the terminal device are predicted when the communication quality index is optimal, and the device information of the terminal device is obtained and the device information is corrected in real time.

[0072] In a preferred embodiment of the present invention, the process of comparing the communication quality curve with the signal characteristic change curve includes:

[0073] Obtain the monotonically increasing interval and monotonically decreasing interval on the communication quality curve, both of which are denoted as signal change intervals; and obtain the monotonically increasing interval and monotonically decreasing interval on the signal characteristic change curve, both of which are denoted as characteristic change intervals.

[0074] Obtain the x-coordinates corresponding to the start and end points of each signal change interval to obtain the set of signal change endpoints {(P1)}. start P1 end ), (P2 start P2 end ), ..., (Pn start , Pn end )}, where Pn start Pn represents the x-coordinate corresponding to the starting point of the nth signal variation interval. end This represents the x-coordinate corresponding to the end point of the nth signal change interval, where n is the total number of signal change intervals;

[0075] Obtain the x-coordinates corresponding to the start and end points of each feature change interval to obtain the set of feature change endpoints {(P1´start P1´ end ), (P2´ start P2´ end ), ..., (Pm´ start Pm´ end )}, where Pm´ start Pm' represents the x-coordinate corresponding to the starting point of the m-th feature variation interval. end This represents the x-coordinate corresponding to the end point of the m-th feature change interval, where m is the total number of feature change intervals;

[0076] Set a delay threshold time; if the communication quality curve and the signal characteristic change curve satisfy... If the signal characteristic change curve is positive, the signal characteristic index corresponding to the curve is designated as a non-fixed feature; otherwise, the signal characteristic index corresponding to the curve is designated as a fixed feature; where Pi start Pi' represents the x-coordinate corresponding to the starting point of the i-th signal variation interval. start Let x represent the x-coordinate of the starting point of the i-th feature change interval, i∈[1,n] and i is a positive integer;

[0077] Specifically, segmented analysis is performed on the communication quality curve and the curves showing changes in various signal characteristics to identify monotonically increasing and monotonically decreasing intervals, i.e., the stages in which the signal characteristic values ​​continuously rise or fall over time.

[0078] Traversing the communication quality curve, when the communication quality index shows a trend of "later value > previous value" (increasing) or "later value < previous value" (decreasing) for three or more consecutive time points, mark this segment as the signal change interval; for example, if the communication quality index increases from -30 to -20 (increasing) from time point 5 to 8, then the interval is (5, 8), with the starting x-coordinate P1. start =5, endpoint x-coordinate P1 end =8;

[0079] And perform the same operation on each signal characteristic change curve to identify the monotonic intervals of the characteristic values;

[0080] The delay threshold is determined based on signal propagation delay and device response speed. When in an indoor scene, there is less signal reflection and the device responds quickly, so time is set to an interval of 1 to 2 time points. When in an outdoor mobile scene, the multipath effect is obvious and the device switching delay is high, so time is set to an interval of 3 to 5 time points.

[0081] In a preferred embodiment of the present invention, the process of obtaining the adaptive rule model includes:

[0082] Each signal variation interval is extracted, and the feature variation interval of each non-fixed feature is also extracted. For any communication quality index in the signal variation interval, the signal feature corresponding to the communication quality index is obtained in the feature variation interval with the same number. The communication quality index and its corresponding signal feature are recorded as a set of sample data, thus obtaining several sets of sample data, which are recorded as a sample dataset. An initial model is established based on a neural network. The sample dataset is input into the initial model, and the initial model is trained to obtain an adaptive rule model that represents the change of the signal feature with the change of the communication quality index.

[0083] Specifically, a backpropagation (BP) neural network (a 3-layer structure: one neuron in the input layer (communication quality index), 10 to 20 neurons in the hidden layer, and one neuron in the output layer (feature value)) was selected as the initial model. The input sample dataset was used, and the mean squared error (MSE) was used as the loss function. Gradient descent was employed for iterative training. The learning rate was set to 0.01 to 0.05, and the number of iterations was 500 to 1000 until the loss function converged, i.e., MSE < 0.01. After training, the model can output the mapping relationship between the communication quality index and the non-fixed feature values.

[0084] In a preferred embodiment of the present invention, the process of obtaining the device information of the terminal device includes:

[0085] Based on the adaptive rule model, the signal features of each non-fixed feature are obtained when the optimal communication quality index is reached, and are denoted as the predicted signal features; and a sample library is established, which includes the signal features corresponding to each device index, including device distance, device operator and device type.

[0086] An initial classification model is established based on a decision tree. The initial classification model is trained based on the sample library to obtain a classification model. The signal features of each predicted signal feature and each fixed feature are sequentially input into the classification model to obtain several device indicators. The device information of the terminal device is obtained from all the device indicators.

[0087] Specifically, the sample library contains the mapping relationship between the features and indicators of known devices. For example, the associated feature of device distance is the signal strength attenuation rate, and the associated features of device operators are the fixed features PLMN code and frequency band license range.

[0088] Using the sample library as the training set, a decision tree is constructed: the root node represents the core feature, such as PLMN code to distinguish operators, and the child nodes represent detailed features, such as frequency band range to further confirm operators; during training, the tree depth is optimized through cross-validation to avoid overfitting, and the final model accuracy should be ≥90%;

[0089] In a preferred embodiment of the present invention, the process of real-time correction of the device information includes:

[0090] After the detection buffer period ends, the communication quality index of the terminal device is monitored in real time. When the communication quality index is consistent with the optimal communication quality index, the signal characteristics of each signal characteristic index at this time are obtained and recorded as the optimal signal characteristics. According to the classification model, the device index corresponding to each optimal signal characteristic is obtained and recorded as the optimal device index. The device index in the device information is corrected according to each optimal device index.

[0091] If any equipment indicator is inconsistent with the optimal equipment indicator, then the equipment indicator in the equipment information shall be replaced with the optimal equipment indicator.

[0092] Specifically, after the detection buffer period ends, the device enters real-time monitoring mode. When the communication quality index is consistent with the optimal value, the actual value of the non-fixed feature at this time is recorded, which is the optimal signal feature; while the fixed feature does not need to be collected repeatedly; the optimal signal feature is input into the classification model to obtain the optimal device index. If the optimal index is consistent with the original device information, the original information is kept. If there is a discrepancy, the corresponding index in the original device information is replaced with the optimal index to complete the correction.

[0093] A smart radio signal detection system applicable to multi-band standards, comprising:

[0094] Signal acquisition module: includes a detection center and terminal equipment; sets a detection buffer zone in the detection center, selects several time points equally divided in the detection buffer zone, acquires the radio signals of the terminal equipment in the detection buffer zone, and acquires the communication quality index of the terminal equipment at each time point in real time; and determines the optimal communication quality index of the terminal equipment.

[0095] Preprocessing module: Sets up a detection buffer zone, selects several time points equally divided within the detection buffer zone, acquires the radio signals of the terminal devices within the detection buffer zone, and acquires the communication quality index of the terminal devices at each time point in real time; and determines the optimal communication quality index of the terminal devices.

[0096] Feature matching module: For any signal feature index, compare the communication quality curve with the signal feature change curve of the signal feature index to obtain an adaptive rule model between the signal feature index and the communication quality index;

[0097] Based on the adaptive rule model, the signal characteristics of each signal characteristic index of the terminal device are predicted when the communication quality index is optimal, and the device information of the terminal device is obtained and the device information is corrected in real time.

[0098] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the present invention should still fall within the scope of the invention.

Claims

1. A method for intelligent detection of radio signals applicable to multi-band systems, characterized in that, Includes the following steps: Step S1: Set up a detection buffer zone, select several time points equally on the detection buffer zone, acquire the radio signal of the terminal device within the detection buffer zone, and acquire the communication quality index of the terminal device at each time point in real time; and determine the optimal communication quality index of the terminal device. Step S2: Generate the communication quality curve of the terminal device based on the real-time acquired communication quality index; and extract several signal feature indicators of the radio signal, obtain the signal features of the signal feature indicators at each time point, and obtain the signal feature change curve of each signal feature indicator. Step S3: For any signal characteristic index, compare the communication quality curve with the signal characteristic change curve of the signal characteristic index to obtain an adaptive rule model between the signal characteristic index and the communication quality index. Based on the adaptive rule model, the signal characteristics of each signal characteristic index of the terminal device are predicted when the communication quality index is optimal, and the device information of the terminal device is obtained and the device information is corrected in real time. In step S3, the process of comparing the communication quality curve with the signal characteristic change curve includes: Obtain the monotonically increasing interval and monotonically decreasing interval on the communication quality curve, both of which are denoted as signal change intervals; and obtain the monotonically increasing interval and monotonically decreasing interval on the signal characteristic change curve, both of which are denoted as characteristic change intervals. Obtain the x-coordinates corresponding to the start and end points of each signal change interval to obtain the set of signal change endpoints {(P1)}. start P1 end ), (P2 start P2 end ), ..., (Pn start Pn end )}, where Pn start Pn represents the x-coordinate corresponding to the starting point of the nth signal variation interval. end This represents the x-coordinate corresponding to the end point of the nth signal change interval, where n is the total number of signal change intervals; Obtain the x-coordinates corresponding to the start and end points of each feature change interval to obtain the set of feature change endpoints {(P1´ start P1´ end ), (P2´ start P2´ end ), ..., (Pm´ start Pm´ end )}, where Pm´ start Pm' represents the x-coordinate corresponding to the starting point of the m-th feature variation interval. end This represents the x-coordinate corresponding to the end point of the m-th feature change interval, where m is the total number of feature change intervals; Set a delay threshold time; if the communication quality curve and the signal characteristic change curve satisfy... If the signal characteristic change curve is positive, the signal characteristic index corresponding to the curve is designated as a non-fixed feature; otherwise, the signal characteristic index corresponding to the curve is designated as a fixed feature; where Pi start Pi' represents the x-coordinate corresponding to the starting point of the i-th signal variation interval. start Pi represents the x-coordinate corresponding to the starting point of the i-th feature change interval, where i ∈ [1, n] and i is a positive integer. end Pi' represents the x-coordinate corresponding to the end point of the i-th signal change interval. end The x-coordinate represents the endpoint of the i-th feature change interval; The process of obtaining the adaptive rule model includes: Each signal variation interval is extracted, and the feature variation interval of each non-fixed feature is also extracted. For any communication quality index in the signal variation interval, the signal feature corresponding to the communication quality index is obtained in the feature variation interval with the same number. The communication quality index and its corresponding signal feature are recorded as a set of sample data, thus obtaining several sets of sample data, which are recorded as a sample dataset. An initial model is established based on a neural network. The sample dataset is input into the initial model, and the initial model is trained to obtain an adaptive rule model that represents the change of the signal feature with the change of the communication quality index.

2. The intelligent detection method for radio signals applicable to multi-band systems according to claim 1, characterized in that, In step S1, the process of setting the probe buffer includes: A preset time period length T is defined, and the time at which the detection of the radio signal of the terminal device begins is denoted as start time t0. Based on the time period length and the start time, the detection buffer interval [t0, ...] is obtained. t0+T).

3. The intelligent detection method for radio signals applicable to multi-band systems according to claim 1, characterized in that, In step S1, the process of obtaining the communication quality index of the terminal device includes: A signal strength range of [-110, 0] dBm is defined, where the signal strength range represents the power strength range of the base station signal received by the terminal device. By obtaining the power strength of the base station signal received by the terminal device and the signal-to-noise ratio of the base station signal, the communication quality index of the terminal device is obtained. K1 and K2 are weighting coefficients, with K1 > 0 and K2 > 0. RSSI represents the power intensity of the base station signal received by the terminal device, and SNR represents the signal-to-noise ratio of the base station signal.

4. The intelligent detection method for radio signals applicable to multi-band systems according to claim 3, characterized in that, In step S1, the process of determining the optimal communication quality index includes: The maximum signal-to-noise ratio (SNR) of the base station signal is set, and the maximum SNR is set within the range of [20, 40] dB, to obtain the optimal communication quality index. SNR max This represents the maximum signal-to-noise ratio.

5. The intelligent detection method for radio signals applicable to multi-band systems according to claim 1, characterized in that, In step S2, the extraction process of each signal feature index in the radio signal includes: The radio signal is amplified and filtered to suppress out-of-band interference, and the sampling rate of the radio signal is determined based on the Quisther theorem. The analog signal is then converted into a digital signal according to the sampling rate. The synchronization sequence of the radio signal is identified through a detection algorithm to obtain the time position of the synchronization sequence, and the frame boundary and symbol period are determined. The digital signal is then subjected to a Fast Fourier Transform to obtain a frequency domain signal. Based on the frequency domain signal, several signal characteristic indicators of the radio signal are obtained, including modulation method, bandwidth, spectral characteristics, and frame structure.

6. The intelligent detection method for radio signals applicable to multi-band systems according to claim 1, characterized in that, In step S2, the process of acquiring the signal features includes: For any given time point, obtain the value of the signal characteristic index at that time point. If the value of the signal characteristic index is a numerical value, then directly record the numerical value as the signal characteristic of the signal characteristic index. If the value of the signal characteristic index is not a numerical value, then encode the value into binary to obtain a binary value, and record the binary value as the signal characteristic of the signal characteristic index.

7. A smart radio signal detection system suitable for multi-band systems, characterized in that, include: Signal acquisition module: includes detection center and terminal equipment; A detection buffer zone is set up in the detection center. Several time points are selected equally on the detection buffer zone to acquire the radio signals of the terminal devices within the detection buffer zone. The communication quality index of the terminal devices at each time point is acquired in real time. The optimal communication quality index of the terminal devices is then determined. Preprocessing module: Sets up a detection buffer zone, selects several time points equally divided within the detection buffer zone, acquires the radio signals of the terminal devices within the detection buffer zone, and acquires the communication quality index of the terminal devices at each time point in real time; and determines the optimal communication quality index of the terminal devices. Feature matching module: For any signal feature index, compare the communication quality curve with the signal feature change curve of the signal feature index to obtain an adaptive rule model between the signal feature index and the communication quality index; Based on the adaptive rule model, the signal characteristics of each signal characteristic index of the terminal device are predicted when the communication quality index is optimal, and the device information of the terminal device is obtained and the device information is corrected in real time. The process of comparing the communication quality curve with the signal characteristic change curve includes: Obtain the monotonically increasing interval and monotonically decreasing interval on the communication quality curve, both of which are denoted as signal change intervals; and obtain the monotonically increasing interval and monotonically decreasing interval on the signal characteristic change curve, both of which are denoted as characteristic change intervals. Obtain the x-coordinates corresponding to the start and end points of each signal change interval to obtain the set of signal change endpoints {(P1)}. start P1 end ), (P2 start P2 end ), ..., (Pn start Pn end )}, where Pn start Pn represents the x-coordinate corresponding to the starting point of the nth signal variation interval. end This represents the x-coordinate corresponding to the end point of the nth signal change interval, where n is the total number of signal change intervals; Obtain the x-coordinates corresponding to the start and end points of each feature change interval to obtain the set of feature change endpoints {(P1´ start P1´ end ), (P2´ start P2´ end ), ..., (Pm´ start Pm´ end )}, where Pm´ start Pm' represents the x-coordinate corresponding to the starting point of the m-th feature variation interval. end This represents the x-coordinate corresponding to the end point of the m-th feature change interval, where m is the total number of feature change intervals; Set a delay threshold time; if the communication quality curve and the signal characteristic change curve satisfy... If the signal characteristic change curve is positive, the signal characteristic index corresponding to the curve is designated as a non-fixed feature; otherwise, the signal characteristic index corresponding to the curve is designated as a fixed feature; where Pi start Pi' represents the x-coordinate corresponding to the starting point of the i-th signal variation interval. start Pi represents the x-coordinate corresponding to the starting point of the i-th feature change interval, where i ∈ [1, n] and i is a positive integer. end Pi' represents the x-coordinate corresponding to the end point of the i-th signal change interval. end The x-coordinate represents the endpoint of the i-th feature change interval; The process of obtaining the adaptive rule model includes: Each signal variation interval is extracted, and the feature variation interval of each non-fixed feature is also extracted. For any communication quality index in the signal variation interval, the signal feature corresponding to the communication quality index is obtained in the feature variation interval with the same number. The communication quality index and its corresponding signal feature are recorded as a set of sample data, thus obtaining several sets of sample data, which are recorded as a sample dataset. An initial model is established based on a neural network. The sample dataset is input into the initial model, and the initial model is trained to obtain an adaptive rule model that represents the change of the signal feature with the change of the communication quality index.

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