Method and system for detecting communication link disturbance based on signal quality assessment

Abstract

This invention discloses a communication link disturbance detection method and system based on signal quality assessment, specifically relating to the field of communication link status detection and testing technology. It addresses the problem that existing technologies struggle to assess link status in real-time and automatically, and effectively detect antenna misalignment caused by environmental disturbances. The method involves acquiring receiver signal quality parameters in real-time and processing them within at least two observation windows of different time lengths to obtain corresponding stability indices. It analyzes the changing trends and time-varying patterns of each stability index in the horizontal and vertical spatial dimensions to obtain disturbance characteristics in both spatial and temporal dimensions. This allows for the determination of whether alignment disturbances caused by environmental factors exist and their types. When disturbances are present, a corresponding spatial search strategy is determined based on the type, and this strategy is executed based on the stability indices to find an antenna pointing position that optimizes the overall stability. Finally, a control command is generated to drive the antenna to adjust to this position.

Description

Technical Field

[0001] This invention relates to the field of communication link status detection and testing technology, and more specifically, to a communication link disturbance detection method and system based on signal quality assessment. Background Technology

[0002] In directional communication technologies such as microwave and satellite, communication links typically rely on precise alignment between transmitting and receiving antennas to maintain high-quality signal transmission. However, in real-world deployment environments, especially in outdoor or high-altitude settings, the carriers or platforms on which antennas are mounted are susceptible to continuous or transient disturbances from environmental factors such as wind, mechanical vibration, and thermal expansion and contraction. These disturbances can cause slow shifts or momentary jitter in antenna pointing, leading to a deterioration in receiver signal quality, manifested as weakened received signal strength and increased bit error rate, ultimately affecting the stability and reliability of the communication link. Existing technologies typically rely on fixed mechanical structures, periodic manual inspections, or the inclusion of large signal margins in system design to address these issues. These methods struggle to detect and compensate for antenna alignment deviations caused by continuous or random environmental disturbances in real time and effectively.

[0003] The drawback of existing technologies is that they lack an automated mechanism that can assess the status of communication links in real time and effectively detect disturbances caused by environmental factors. This makes it impossible for communication systems to detect the deterioration trend of link quality in a timely manner when faced with environmental disturbances, and even more impossible to trigger effective compensation or adjustment measures. As a result, it is difficult to guarantee the long-term stability and availability of communication links, and it increases the operation and maintenance costs and complexity of the system. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a communication link disturbance detection method and system based on signal quality assessment to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A communication link disturbance detection method based on signal quality assessment includes the following steps:

[0007] S1: Real-time acquisition of signal quality parameters at the receiving end of the communication link;

[0008] S2: Process the signal quality parameters within at least two observation windows of different time lengths to obtain the stability index for each observation window.

[0009] S3: Analyze the changing trends of the stability indices of each observation window in the horizontal azimuth and vertical pitch dimensions to obtain the spatial dimension perturbation characteristics; and analyze the pattern of the stability indices of each observation window changing over time to obtain the temporal dimension perturbation characteristics.

[0010] S4: Based on the combination of spatial and temporal disturbance characteristics, determine whether there is alignment disturbance caused by environmental factors in the communication link, and determine the type of alignment disturbance;

[0011] S5: When alignment disturbance is detected, determine the corresponding spatial search strategy according to the type of alignment disturbance; execute the spatial search strategy within the preset spatial range based on the stability index of each observation window to find the antenna pointing position that optimizes the overall stability index.

[0012] S6: When an antenna pointing position that meets the preset optimization conditions is found through a spatial search strategy, a control command corresponding to the antenna pointing position is generated to drive the antenna to adjust.

[0013] Furthermore, the signal quality parameters of the receiving end of the communication link are acquired in real time, including:

[0014] Obtain the received signal strength indication parameter;

[0015] Obtain the signal-to-noise ratio parameter;

[0016] When the received signal strength indicator parameter is lower than the first threshold or the signal-to-noise ratio parameter is lower than the second threshold, the bit error rate parameter is obtained.

[0017] Furthermore, the signal quality parameters are processed within at least two observation windows of different time lengths to obtain stability indices for each observation window, including:

[0018] The received signal strength indication parameter, signal-to-noise ratio parameter, and bit error rate parameter are processed within the length of the first observation window to obtain the first stability index corresponding to the first observation window.

[0019] The received signal strength indication parameter, signal-to-noise ratio parameter, and bit error rate parameter are processed within the length of the second observation window to obtain the second stability index corresponding to the second observation window.

[0020] The lengths of the first and second observation windows are different.

[0021] Furthermore, the variation trends of stability indices for each observation window in the horizontal azimuth and vertical pitch dimensions were analyzed to obtain the spatial disturbance characteristics, including:

[0022] By analyzing the differences in the changing trends of the first stability index and the second stability index in the horizontal azimuth dimension, the perturbation characteristics in the horizontal azimuth dimension are obtained.

[0023] By analyzing the differences in the variation trends of the first stability index and the second stability index in the vertical pitch angle dimension, the perturbation characteristics in the vertical pitch angle dimension are obtained.

[0024] Furthermore, analyzing the patterns of stability indices changing over time in each observation window reveals the time-dimensional perturbation characteristics, including:

[0025] The first pattern of analyzing the change of the first stability index over time;

[0026] Analyze the second pattern of how the second stability index changes over time;

[0027] Based on the comparison between the first and second modes, the perturbation characteristics in the time dimension are obtained.

[0028] Furthermore, based on a combination of spatial and temporal disturbance characteristics, it is determined whether alignment disturbances caused by environmental factors exist in the communication link, and the type of alignment disturbance is identified, including:

[0029] The judgment is based on a combination of disturbance characteristics in the horizontal azimuth dimension, the vertical pitch dimension, and the time dimension.

[0030] When it is determined that there is an alignment disturbance caused by environmental factors, the type of alignment disturbance is determined by the relative degree of change of the disturbance characteristics in the horizontal azimuth dimension and the vertical pitch dimension, as well as the duration of change indicated by the disturbance characteristics in the time dimension.

[0031] Furthermore, when alignment perturbation is detected, the corresponding spatial search strategy is determined based on the type of alignment perturbation, including:

[0032] When the type of alignment disturbance is the first type, the corresponding first spatial search strategy is determined to be to search within a first preset range in the horizontal azimuth dimension;

[0033] When the type of alignment disturbance is the second type, the corresponding second space search strategy is determined to be to search within the second preset range in the vertical pitch angle dimension.

[0034] Furthermore, based on the stability indices of each observation window, a spatial search strategy is executed within a preset spatial range to find the antenna pointing position that optimizes the overall stability indices, including:

[0035] When executing the first space search strategy, based on the changes of the first stability index and the second stability index in the horizontal azimuth dimension, the antenna pointing position that optimizes the first stability index and the second stability index as a whole is sought.

[0036] When executing the second space search strategy, the antenna pointing position that optimizes the first and second stability indices as a whole is sought based on the changes of the first and second stability indices in the vertical pitch angle dimension.

[0037] Furthermore, when an antenna pointing position that meets preset optimization conditions is found through a spatial search strategy, a control command corresponding to the antenna pointing position is generated to drive the antenna to adjust, including:

[0038] Determine whether the first stability index and the second stability index corresponding to the found antenna pointing position both meet the preset optimization conditions;

[0039] When both the first stability index and the second stability index meet the preset optimization conditions, a control command containing the horizontal azimuth coordinates and vertical elevation coordinates of the antenna pointing position is generated.

[0040] The antenna is driven by control commands to adjust to the pointing position determined by the horizontal azimuth coordinates and the vertical elevation coordinates.

[0041] On the other hand, the present invention provides a communication link disturbance detection system based on signal quality assessment, comprising the following modules:

[0042] The signal acquisition module is used to acquire the signal quality parameters of the receiving end of the communication link in real time.

[0043] The index processing module is used to process the signal quality parameters within at least two observation windows of different time lengths to obtain the stability index corresponding to each observation window.

[0044] The feature analysis module is used to analyze the changing trends of the stability index of each observation window in the horizontal azimuth and vertical pitch dimensions to obtain the spatial dimension perturbation characteristics; and to analyze the pattern of the stability index of each observation window changing over time to obtain the temporal dimension perturbation characteristics.

[0045] The disturbance judgment module is used to determine whether there is alignment disturbance caused by environmental factors in the communication link based on a combination of disturbance characteristics in the spatial dimension and disturbance characteristics in the temporal dimension, and to determine the type of alignment disturbance.

[0046] The strategy execution module is used to determine the corresponding spatial search strategy according to the type of alignment disturbance when it is determined that alignment disturbance exists; and to execute the spatial search strategy within a preset spatial range based on the stability index of each observation window in order to find the antenna pointing position that optimizes the overall stability index.

[0047] The adjustment control module is used to generate control commands corresponding to the antenna pointing position to drive the antenna to adjust when an antenna pointing position that meets the preset optimization conditions is found through a spatial search strategy.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. By constructing a technical framework for multi-timescale stability separation assessment and spatiotemporal composite pattern recognition, the intelligence level and accuracy of communication link disturbance detection are significantly improved. First, the traditional single signal strength assessment is extended to perform stability index separation calculations on multiple signal quality parameters within at least two different observation windows, thereby simultaneously capturing the instantaneous fluctuation characteristics and long-term drift trends of the link. Then, by analyzing the differences in the changes of these multi-scale stability indices in the horizontal and vertical spatial dimensions, and combining their evolutionary pattern characteristics over time, the comprehensive extraction and quantitative description of the spatial distribution characteristics and time-varying laws of disturbances are achieved. This enables the system to effectively separate alignment disturbance components caused by environmental factors from complex signal changes and accurately determine their types. This changes the limitation of traditional methods that can only make coarse-grained judgments of the presence or absence of disturbances, and provides richer and more accurate diagnostic dimensions for the state monitoring of communication links.

[0050] 2. Intelligent coupling of detection and response strategies has been achieved. By mapping specific disturbance types to differentiated spatial search strategies, the system can efficiently find the antenna pointing position that optimizes the overall link stability within a preset spatial range, guided by multi-scale stability indicators. This ensures the targeted and efficient nature of subsequent adjustment actions and avoids the time delays and energy losses caused by blind global searches in traditional methods. It forms a complete closed loop from real-time perception, multi-dimensional evaluation, intelligent diagnosis to strategy-based execution. This not only significantly improves the autonomous maintenance capability of the communication system in dynamic disturbance environments, but also transforms the maintenance of link stability from a passive mode that relies on fixed margins or manual intervention to an active guarantee mode based on real-time online evaluation and predictive adjustment. Attached Figure Description

[0051] Figure 1 This is a flowchart of the communication link disturbance detection method based on signal quality assessment according to the present invention.

[0052] Figure 2 This is a schematic diagram of the communication link disturbance detection system based on signal quality assessment according to the present invention. Detailed Implementation

[0053] 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0054] Example 1: Figure 1 The present invention provides a communication link disturbance detection method based on signal quality assessment, which includes the following steps:

[0055] S1: Real-time acquisition of signal quality parameters at the receiving end of the communication link;

[0056] S2: Process the signal quality parameters within at least two observation windows of different time lengths to obtain the stability index for each observation window.

[0057] S3: Analyze the changing trends of the stability indices of each observation window in the horizontal azimuth and vertical pitch dimensions to obtain the spatial dimension perturbation characteristics; and analyze the pattern of the stability indices of each observation window changing over time to obtain the temporal dimension perturbation characteristics.

[0058] S4: Based on the combination of spatial and temporal disturbance characteristics, determine whether there is alignment disturbance caused by environmental factors in the communication link, and determine the type of alignment disturbance;

[0059] S5: When alignment disturbance is detected, determine the corresponding spatial search strategy according to the type of alignment disturbance; execute the spatial search strategy within the preset spatial range based on the stability index of each observation window to find the antenna pointing position that optimizes the overall stability index.

[0060] S6: When an antenna pointing position that meets the preset optimization conditions is found through a spatial search strategy, a control command corresponding to the antenna pointing position is generated to drive the antenna to adjust.

[0061] In step S1, the real-time acquisition of signal quality parameters at the communication link receiver is accomplished collaboratively by the detection circuit and digital signal processing unit within the communication receiver. The process of acquiring the received signal strength indication parameter is as follows: after receiving a carrier signal from the microwave or satellite band, the front-end RF unit of the communication receiver performs low-noise amplification and down-conversion processing on the signal, converting it into an intermediate frequency (IF) signal. The IF signal is then sent to the signal strength detection circuit, which extracts the signal amplitude information through envelope detection and converts it into a digital quantity via an analog-to-digital converter. This digital quantity, after calibration and linearization, is recorded as the received signal strength indication parameter at the current moment, expressed as an absolute value in decibels and milliwatts.

[0062] The process of obtaining the signal-to-noise ratio (SNR) parameter is as follows: After obtaining the intermediate frequency (IF) signal, the digital signal processing unit performs a fast Fourier transform on a signal sample of a predetermined time length to obtain the signal's spectral distribution. In the spectrum, based on the signal carrier center frequency and bandwidth specified in the communication protocol, the main lobe spectral region occupied by the signal is determined, and the power integral in this region is taken as the signal power. In a spectral gap region adjacent to the main lobe of the signal with no signal allocation, its power is measured as an estimate of the noise power. The ratio of the calculated signal power to the noise power estimate is converted into a decibel value to obtain the SNR parameter at the current moment.

[0063] The first and second thresholds are set based on the long-term stable average values ​​of a set of received signal strength indication parameters and signal-to-noise ratio (SNR) parameters obtained during the historical maintenance period of the communication link, assuming good antenna alignment and no environmental disturbances. The long-term stable average values ​​are obtained by sampling the received signal strength indication parameters and SNR parameters once per second for 24 consecutive hours and calculating the arithmetic mean of all sampled values. The first threshold is set as the average received signal strength indication value in the long-term stable average value minus a first empirical margin. The first empirical margin is determined by multiplying the signal fading reserve calculated in the link budget by a scaling factor, for example, 0.2. The second threshold is set as the average SNR value in the long-term stable average value minus a second empirical margin. The second empirical margin is the minimum SNR value required to maintain the minimum demodulation performance required by the communication protocol.

[0064] When real-time monitoring detects that the received signal strength indicator parameter is below the first threshold, or the signal-to-noise ratio parameter is below the second threshold, the bit error rate (BER) parameter is acquired. The process of acquiring the BER parameter involves the digital signal processing unit initiating real-time decoding and verification of the received data frames. For communication systems employing forward error correction coding, the BER parameter is obtained by calculating the ratio of the number of error bits that the decoder cannot automatically correct within one second to the total number of received bits. For communication systems employing cyclic redundancy check (CRC), the BER parameter is obtained by calculating the ratio of the number of data frames that fail verification within one second to the total number of received data frames. If the signal is completely lost, making decoding impossible, the BER parameter is recorded as a predefined maximum value, for example, 1.

[0065] In step S2, the signal quality parameters are processed within at least two observation windows of different time lengths to obtain the stability index for each observation window. The first observation window is set to a relatively short time length, for example, 1 second. The second observation window is set to a relatively long time length, for example, 10 seconds. The lengths of the first and second observation windows are different. Choosing observation windows of different time lengths is to simultaneously capture the rapid fluctuations of the signal quality parameters on short time scales and their slow drift trends on long time scales.

[0066] The received signal strength indication parameter, signal-to-noise ratio parameter, and bit error rate parameter are processed within the first observation window length to obtain the first stability index corresponding to the first observation window. This processing is executed sequentially. Received signal strength indication parameters of the time series are continuously acquired and cached from step S1. For the first observation window, all received signal strength indication parameter sample values ​​within a consecutive 1-second period before the current time are taken to form the first received signal strength indication parameter sequence. The standard deviation of the first received signal strength indication parameter sequence is calculated as the first received signal strength stability component. The standard deviation is calculated as follows: first, the arithmetic mean of all sample values ​​in the first received signal strength indication parameter sequence is calculated; then, each sample value is subtracted from the arithmetic mean to obtain the difference; all differences are squared and summed; then, the sum is divided by the total number of sample values ​​minus one; finally, the square root of the result is taken, and the resulting value is the first received signal strength stability component, which is a value in decibels and milliwatts.

[0067] The signal-to-noise ratio (SNR) parameters of the time series are continuously acquired and cached from step S1. For the first observation window, all SNR parameter sample values ​​within a consecutive second prior to the current time are taken to form the first SNR parameter sequence. The standard deviation of the first SNR parameter sequence is calculated as the first SNR stability component. The calculation method for the first SNR stability component is the same as that for the first received signal strength stability component, and its unit is decibels (dB).

[0068] The bit error rate (BER) parameters of the time series are continuously acquired and cached from step S1. For the first observation window, all BER parameter sample values ​​within a consecutive second prior to the current time are taken to form the first BER parameter sequence. The common logarithmic value of each sample value in the first BER parameter sequence is calculated to form the first logarithmic BER sequence. The standard deviation of the first logarithmic BER sequence is calculated as the first BER stability component.

[0069] The first stability index is obtained by fusing the first received signal strength stability component, the first signal-to-noise ratio (SNR) stability component, and the first bit error rate (BER) stability component. The fusion method calculates the weighted sum of squares of the three stability components. A first weight is assigned to the first received signal strength stability component, a second weight to the first SNR stability component, and a third weight to the first BER stability component. The first, second, and third weights are set based on the sensitivity of each parameter to the quality of the communication link. In microwave point-to-point communication scenarios, the SNR parameter is more sensitive to phase noise and interference, and its changes better reflect the instantaneous alignment deviation of the antenna; therefore, the second weight is set higher than the first weight. The BER parameter directly reflects the reliability of data transmission, and its stability is crucial; therefore, the third weight is set higher than the second weight. A specific weight allocation scheme is, for example, a first weight of 0.3, a second weight of 0.4, and a third weight of 0.5. The fusion calculation process involves multiplying the value of the first received signal strength stability component by a first weight to obtain a first product, multiplying the value of the first signal-to-noise ratio stability component by a second weight to obtain a second product, and multiplying the value of the first bit error rate stability component by a third weight to obtain a third product. Then, the first, second, and third products are each squared, the three squared results are added together, and finally, the square root of the sum is taken. The resulting value is the first stability index, which is a dimensionless comprehensive evaluation value.

[0070] The received signal strength indication parameter, signal-to-noise ratio parameter, and bit error rate parameter are processed within the second observation window length to obtain the second stability index corresponding to the second observation window. This processing is similar to the process of obtaining the first stability index, but the observation window length is different. All received signal strength indication parameter sample values ​​within a consecutive 10-second period prior to the current time are taken to form the second received signal strength indication parameter sequence. Due to the long second observation window length and the large number of original sampling points, downsampling processing is performed on the second received signal strength indication parameter sequence to reduce computational load and reflect trends. The downsampling method involves dividing 10 consecutive original sampling points into a group, calculating the arithmetic mean of each group, and using these arithmetic means to form a new sequence for calculation. The standard deviation of this new sequence is calculated as the second received signal strength stability component.

[0071] A second signal-to-noise ratio (SNR) parameter sequence is constructed by taking all SNR parameter samples from the previous 10 seconds and performing the same downsampling averaging process. The standard deviation of the processed sequence is then calculated as the second SNR stability component. Similarly, a second bit error rate (BER) parameter sequence is constructed by taking all bit error rate (BER) parameter samples from the previous 10 seconds and calculating the common logarithmic value of each sample to form a second logarithmic BER sequence. This second logarithmic BER sequence is then subjected to the same downsampling averaging process, and the standard deviation of the processed sequence is calculated as the second BER stability component.

[0072] The second received signal strength stability component, the second signal-to-noise ratio stability component, and the second bit error rate stability component are fused to obtain the second stability index. The weights used in the fusion are the same as the first, second, and third weights used in calculating the first stability index. The value of the second received signal strength stability component is multiplied by the first weight to obtain the fourth product, the value of the second signal-to-noise ratio stability component is multiplied by the second weight to obtain the fifth product, and the value of the second bit error rate stability component is multiplied by the third weight to obtain the sixth product. Then, the fourth, fifth, and sixth products are squared respectively, the three squared results are added together, and finally, the square root of the sum is taken. The result is the second stability index. In this way, the first stability index mainly reflects the rapid and drastic changes in signal parameters within a second-level time window, while the second stability index mainly reflects the magnitude of slow drift or trend changes in signal parameters within a ten-second-level time window. Together, they provide stability quantification information at different time scales for subsequent steps.

[0073] In step S3, the variation trends of the stability indices for each observation window in the horizontal azimuth and vertical elevation dimensions are analyzed to obtain the spatial disturbance characteristics. The difference between the variation trends of the first and second stability indices in the horizontal azimuth dimension is analyzed to obtain the disturbance characteristics in the horizontal azimuth dimension. This analysis process requires the use of real-time pointing angle data of the antenna in the horizontal azimuth. The horizontal azimuth angle data of the antenna is provided in real time by an angle encoder installed on the antenna rotation axis. The system continuously records the values ​​of the first and second stability indices corresponding to different horizontal azimuth angles. To analyze the variation trends, the system selects an angle analysis interval centered on the current antenna horizontal azimuth angle, for example, a range of ±5 degrees. Within this angle analysis interval, the numerical sequence of the first stability index is subjected to linear regression analysis with the corresponding horizontal azimuth angle sequence to calculate the first linear regression slope of the first stability index with respect to the horizontal azimuth angle. The numerical sequence of the second stability index is subjected to linear regression analysis with the corresponding horizontal azimuth angle sequence to calculate the second linear regression slope of the second stability index with respect to the horizontal azimuth angle. The perturbation characteristics in the horizontal azimuth dimension are obtained by calculating the absolute value of the difference between the slopes of the first and second linear regressions. This absolute value reflects the inconsistency in the stability change trends in the horizontal direction across different time scales.

[0074] The perturbation characteristics in the vertical elevation angle dimension are obtained by analyzing the differences in the changing trends of the first and second stability indices. This analysis requires real-time pointing angle data of the antenna in the vertical elevation angle. The antenna's vertical elevation angle data is provided in real time by an angle encoder installed on the antenna's elevation axis. The system continuously records the values ​​of the first and second stability indices at different vertical elevation angles. An angle analysis interval is selected centered on the current antenna vertical elevation angle, for example, ±3 degrees. Within this angle analysis interval, a linear regression analysis is performed on the numerical sequence of the first stability index and the corresponding vertical elevation angle sequence to calculate the third linear regression slope of the first stability index with respect to the vertical elevation angle. Similarly, a linear regression analysis is performed on the numerical sequence of the second stability index and the corresponding vertical elevation angle sequence to calculate the fourth linear regression slope of the second stability index with respect to the vertical elevation angle. The perturbation characteristics in the vertical elevation angle dimension are obtained by calculating the absolute value of the difference between the third and fourth linear regression slopes. The perturbation characteristics in the horizontal azimuth dimension and the perturbation characteristics in the vertical pitch dimension together constitute the perturbation characteristics in the spatial dimension.

[0075] The stability index patterns over time are analyzed across observation windows to obtain the perturbation characteristics in the time dimension. The first pattern of the first stability index's time-varying change is analyzed. The system continuously records a one-dimensional sequence of the first stability index's time-varying change. For the first stability index sequence within the most recent time window, such as the first stability index sequence data within the past 30 seconds, its autocorrelation function is calculated. The autocorrelation function is calculated by multiplying the first stability index sequence by its own time-shifted sequence point by point and summing the results. The periodicity or randomness of the first stability index's change is assessed by analyzing the time it takes for the autocorrelation function to first cross zero or decay to half its initial value. If the autocorrelation function exhibits significant periodic fluctuations, the first pattern is identified as a periodic perturbation pattern, and its dominant period is recorded, for example, 2 seconds. If the autocorrelation function decays rapidly and is not periodic, the first pattern is identified as a random perturbation pattern, and its average rate of change is recorded. The average rate of change is obtained by calculating the average of the absolute values ​​of the differences between adjacent sampling points in the first stability index sequence.

[0076] The system analyzes the second pattern of the second stability index over time. It continuously records a one-dimensional sequence of the second stability index over time. For the second stability index sequence within a more recent longer time window, such as the data from the past 300 seconds, trend decomposition is performed. Trend decomposition uses a moving average method with a window length of 60 seconds. The original second stability index sequence is subtracted from its moving average sequence to obtain the detrended volatility component. The amplitude of this volatility component is analyzed, and its standard deviation is calculated. Simultaneously, the monotonicity of the moving average sequence itself is analyzed, i.e., whether the moving average sequence has been continuously rising, continuously falling, or remaining stable over a recent period. The second pattern is described by both the direction of the trend component and the amplitude of the volatility component.

[0077] Based on the comparison between the first and second models, the perturbation characteristics in the time dimension are obtained. These characteristics are a comprehensive judgment, derived by comparing the key attributes of the first and second models. The dimensions compared include the matching of change rates and the presence of a dominant cycle. The matching of change rates is assessed by comparing the ratio of the average change rate of the first model to the normalized standard deviation of the fluctuation component of the second model. Normalization involves dividing the standard deviation of the fluctuation component of the second model by a time scale factor, which is the ratio of the second observation window length to the first observation window length, for example, a time scale factor of 10. If this ratio is higher than a matching threshold, for example, a matching threshold of 5, then the fast and slow time scale change rates are considered mismatched, and the perturbation characteristics in the time dimension are marked as sudden intermittent. If the ratio is lower than the matching threshold, then the change rates are considered matched. Under the premise of matching change rates, it is further checked whether the first model identifies a dominant cycle. If the first model has a dominant cycle and the trend component of the second model is stable, then the perturbation characteristics in the time dimension are marked as periodically persistent. If the first pattern has no dominant cycle and the trend component of the second pattern is monotonically increasing or decreasing, then the time-dimensional perturbation characteristic is labeled as asymptotic drift. If the first pattern has no dominant cycle and the trend component of the second pattern is stable, then the time-dimensional perturbation characteristic is labeled as stationary randomness. The matching threshold is set based on the statistical lower bound of the ratio of events explicitly classified as sudden intermittent perturbations in historical data analysis. The time-dimensional perturbation characteristic, together with the spatial-dimensional perturbation characteristic, provides input for the comprehensive judgment in step S4.

[0078] In step S4, based on a combination of spatial and temporal disturbance characteristics, it is determined whether alignment disturbances caused by environmental factors exist in the communication link, and the type of alignment disturbance is identified. This determination is based on a combination of disturbance characteristics in the horizontal azimuth, vertical pitch, and temporal dimensions. The determination process is implemented through a hierarchical decision logic. A spatial disturbance comprehensive threshold is set. This threshold measures the overall intensity of spatial disturbances. The spatial disturbance comprehensive threshold is set based on a set of horizontal azimuth and vertical pitch disturbance characteristic data obtained during the laboratory calibration phase of the communication system by simulating a light wind vibration scenario. 80% of the maximum value in this set of data is taken as the initial value of the spatial disturbance comprehensive threshold. After field deployment, data is collected during a quiet, undisturbed period of 24 consecutive hours in the actual environment with no wind and small temperature differences. The long-term average value of the spatial disturbance comprehensive value during this period is calculated, and this long-term average value is used to fine-tune the initial value of the spatial disturbance comprehensive threshold to obtain the final spatial disturbance comprehensive threshold used.

[0079] Calculate the sum of squares of the current disturbance characteristics in the horizontal azimuth dimension and the vertical pitch dimension. Take the square root of this sum to obtain the current integrated spatial disturbance value. Compare the current integrated spatial disturbance value with a spatial disturbance integration threshold. If the current integrated spatial disturbance value is less than the spatial disturbance integration threshold, it is determined that there is no alignment disturbance caused by environmental factors. If the current integrated spatial disturbance value is greater than or equal to the spatial disturbance integration threshold, proceed to the next level of judgment.

[0080] The next level of judgment introduces time-dimensional perturbation features. In step S3, these time-dimensional perturbation features are categorized into several possible types, such as sudden intermittency, periodic persistence, gradual drift, or stationary randomness. A time feature mapping table is established. A weight factor is assigned to each category of time-dimensional perturbation features. The weight factor reflects the confidence level of the association between that category of time feature and the actual environmental alignment perturbation. For example, gradual drift is usually strongly correlated with slowly changing environmental factors such as thermal expansion and contraction, and its weight factor is set to 1.0. Periodic persistence is usually strongly correlated with mechanical vibration, and its weight factor is set to 0.9. Sudden intermittency may be related to instantaneous strong winds or bird obstruction, and its weight factor is set to 0.7. Stationary randomness may be related to background electromagnetic noise, and its weight factor is set to 0.3. The weight factor is set based on the proportion of cases where physical alignment deviations were confirmed through manual verification under various time feature scenarios in historical operation and maintenance data. The current comprehensive spatial disturbance value is multiplied by a weighting factor corresponding to the disturbance characteristics in the current time dimension to obtain a weighted evaluation value. A final judgment threshold is set, and the value of the final judgment threshold is equal to the value of the comprehensive spatial disturbance threshold. If the weighted evaluation value is greater than or equal to the final judgment threshold, it is determined that there is an alignment disturbance caused by environmental factors. If the weighted evaluation value is less than the final judgment threshold, it is determined that there is no alignment disturbance caused by environmental factors.

[0081] When alignment disturbances caused by environmental factors are identified, the type of alignment disturbance is determined by the relative degree of change between the disturbance characteristics in the horizontal azimuth and vertical pitch dimensions, as well as the persistence of change indicated by the disturbance characteristics in the time dimension. The relative degree of change is quantified by calculating the ratio of the disturbance characteristics in the horizontal azimuth to those in the vertical pitch dimension. A balance threshold for the degree of change is set, for example, a threshold of 2. If the ratio of the disturbance characteristics in the horizontal azimuth to those in the vertical pitch dimension is greater than the balance threshold, the disturbance characteristics in the horizontal azimuth dimension are considered significantly greater than those in the vertical pitch dimension, and the relative degree of change is labeled as azimuth-dominant. If the ratio of the disturbance characteristics in the horizontal azimuth to those in the vertical pitch dimension is less than the reciprocal of the balance threshold, i.e., 0.5, the disturbance characteristics in the vertical pitch dimension are considered significantly greater than those in the horizontal azimuth dimension, and the relative degree of change is labeled as pitch-dominant. If the ratio of the disturbance characteristics in the horizontal azimuth dimension to the disturbance characteristics in the vertical elevation dimension is between 0.5 and 2, then the disturbance levels are considered to be comparable, and the relative degree of change is labeled as balanced. The threshold for the degree of change balance is set based on the measured ratio of the stiffness difference of the antenna mechanical structure in the horizontal azimuth and vertical elevation dimensions, as well as the proportional range of the two directional force components in the statistics of common wind load disturbance forces.

[0082] The duration of change indicated by the time-dimensional perturbation characteristics is directly adopted from the labeling results obtained in step S3, such as gradual drift, periodic persistence, or sudden intermittency. The relative degree of change label is combined with the duration of change label to jointly determine the type of alignment perturbation. When the relative degree of change is azimuth-dominant and the duration of change is gradual drift, the determined type is Type I. When the relative degree of change is pitch-dominant and the duration of change is gradual drift, the determined type is Type II. When the relative degree of change is even and the duration of change is periodic persistence, the determined type is Type III. When the relative degree of change is either azimuth-dominant or pitch-dominant and the duration of change is sudden intermittent, the determined type is Type IV. When the relative degree of change is even and the duration of change is sudden intermittent, the determined type is Type V. The determined type result is a discrete classification label.

[0083] In step S5, when an alignment disturbance is detected, a corresponding spatial search strategy is determined based on the type of the disturbance. When the type of alignment disturbance is the first type, the corresponding first spatial search strategy is determined to search within a first preset range in the horizontal azimuth dimension. The first preset range is set based on the slow thermal offset or settlement characteristics in the horizontal direction indicated by the first type of disturbance. The first preset range extends in both directions, centered on the current antenna horizontal azimuth angle. The width of the first preset range is adaptively adjusted according to the disturbance characteristic value in the current horizontal azimuth dimension. Specifically, a reference search angle is set, for example, 5 degrees. The disturbance characteristic value in the current horizontal azimuth dimension is multiplied by a scaling factor, for example, 2, to obtain an extension angle. The reference search angle is added to the extension angle to obtain the total width of the first preset range. Half of the total width is the limit offset angle for searching in each direction. The search step size of the first spatial search strategy is set to a small value, for example, 0.2 degrees.

[0084] When the alignment disturbance is of type two, the corresponding second spatial search strategy is determined to be a search within a second preset range in the vertical elevation angle dimension. The second preset range is set based on the slow thermal offset or snow load characteristics in the vertical direction indicated by the type two disturbance. The second preset range extends primarily in one direction, centered on the current antenna vertical elevation angle. The width of the second preset range is adaptively adjusted according to the disturbance characteristic value in the current vertical elevation angle dimension. The adjustment method is to calculate it using the same reference search angle and scaling factor as used in calculating the width of the first preset range. The search step size of the second spatial search strategy is set to a small value, for example, 0.2 degrees.

[0085] When the type of alignment perturbation is type three, the corresponding third-space search strategy is determined to be a spiral search within a finite third preset range in a two-dimensional plane formed by the horizontal azimuth and vertical pitch dimensions. The third preset range is a two-dimensional rectangular area. The horizontal width of the third preset range is obtained by multiplying the perturbation characteristic value in the current horizontal azimuth dimension by a scaling factor and adding the reference search angle. The vertical height of the third preset range is obtained by multiplying the perturbation characteristic value in the current vertical pitch dimension by a scaling factor and adding the reference search angle. The search step size of the third-space search strategy is set to a single value, for example, 0.5 degrees.

[0086] Based on the stability indices of each observation window, a spatial search strategy is executed within a preset spatial range to find an antenna pointing position that optimizes the overall stability indices. During the execution of the first spatial search strategy, the antenna pointing position that optimizes both the first and second stability indices is sought based on the changes in the first and second stability indices in the horizontal azimuth dimension. The execution process involves the antenna moving gradually from one endpoint to the other in the horizontal azimuth dimension, according to the search step size determined by the first spatial search strategy. At each tested horizontal azimuth angle, the antenna maintains stability at that angle for at least the length of a second observation window, for example, 10 seconds. During this period, the system continuously collects signal quality parameters and calculates the first and second stability indices at that position in real time according to the method in step S2. To evaluate the overall optimization level, a target improvement threshold is set for each of the first and second stability indices. The target improvement threshold is defined as 80% of the initial first stability index value and 80% of the initial second stability index value recorded at the start of the search. During the search process, the system seeks an antenna pointing position such that the newly calculated first stability index at that position is less than the target improvement threshold for the first stability index, and the newly calculated second stability index is less than the target improvement threshold for the second stability index. If a position that simultaneously satisfies both conditions is found, the search stops, and this position is designated as the optimal position. If no position that simultaneously satisfies both conditions is found after traversing the entire first preset range, the position that minimizes the weighted sum of the first and second stability indices is selected as the optimal position. In the weighted sum calculation, a weight is assigned to the first stability index, for example, a weight of 0.4, and a weight is assigned to the second stability index, for example, a weight of 0.6. The weighted sum equals the value of the first stability index multiplied by its weight, plus the value of the second stability index multiplied by its weight.

[0087] When executing the second spatial search strategy, the antenna pointing position that optimizes both the first and second stability indices is sought based on the changes in the first and second stability indices along the vertical elevation angle dimension. The process involves the antenna gradually moving within a second preset range along the vertical elevation angle dimension, according to the search step size determined by the second spatial search strategy. At each tested vertical elevation angle, the antenna maintains stability at that angle for at least the length of a second observation window. The system collects data and calculates the first and second stability indices at that position. The same target improvement threshold method and weighted sum method as the first spatial search strategy are used to determine the optimal position.

[0088] When executing the third-space search strategy, the antenna pointing position is sought based on the changes of the first and second stability indices in two-dimensional space, aiming to optimize both indices. The execution process employs a spiral path covering a pre-defined third range. The starting point of the spiral path is the current antenna pointing position. The antenna moves sequentially to each test point on the spiral path according to a pre-defined combination of horizontal and vertical step sizes. At each test point, the antenna is allowed sufficient time to stabilize, and the first and second stability indices are calculated. The criterion for determining the optimal position also uses the target improvement threshold method: finding a position where the first stability index is less than its target improvement threshold and the second stability index is less than its target improvement threshold. If no position simultaneously meeting these conditions is found, the position that minimizes the weighted sum of the first and second stability indices is selected as the optimal position.

[0089] In step S6, when an antenna pointing position that meets the preset optimization conditions is found through a spatial search strategy, a control command corresponding to the antenna pointing position is generated to drive the antenna to adjust. It is then determined whether both the first stability index and the second stability index corresponding to the found antenna pointing position meet the preset optimization conditions. The preset optimization conditions are specifically defined during the execution of step S5. The preset optimization conditions consist of two parts. The first part is the absolute threshold condition. A first absolute threshold is set for the first stability index. A second absolute threshold is set for the second stability index. The first and second absolute thresholds are set based on the numerical distribution of the first and second stability indices obtained through long-term statistical testing of the communication system under ideal alignment conditions in the laboratory. The upper limit of this numerical distribution range is multiplied by a safety factor to obtain the absolute threshold. For example, the safety factor can be set to 1.2.

[0090] The second part is the relative improvement condition, namely the target improvement threshold condition defined in step S5. At the start of the search, the current value of the first stability index is recorded as the initial first stability index. The current value of the second stability index is recorded as the initial second stability index. The target improvement threshold is defined as 80% of the initial first stability index value as the first target improvement threshold. The target improvement threshold is defined as 80% of the initial second stability index value as the second target improvement threshold. The judgment process is to compare the value of the first stability index corresponding to the found antenna pointing position with the first absolute threshold and the first target improvement threshold. The value of the second stability index corresponding to the found antenna pointing position is compared with the second absolute threshold and the second target improvement threshold. Only when the value of the first stability index corresponding to the found antenna pointing position is simultaneously less than the first absolute threshold and the first target improvement threshold, and the value of the second stability index corresponding to the found antenna pointing position is simultaneously less than the second absolute threshold and the second target improvement threshold, is it determined that both the first stability index and the second stability index meet the preset optimization conditions.

[0091] When both the first and second stability indices meet the preset optimization conditions, a control command is generated containing the horizontal azimuth and vertical elevation coordinates of the antenna's pointing position. The horizontal azimuth and vertical elevation coordinates of the antenna's pointing position are recorded by the controller executing the spatial search strategy when the optimal position is found. The horizontal azimuth coordinate is an angle value in degrees. The reference zero point of the horizontal azimuth coordinate is the mechanical mounting reference direction of the antenna. The positive direction of the horizontal azimuth coordinate is defined as clockwise when viewed from the top of the antenna. The vertical elevation coordinate is an angle value in degrees. The reference zero point of the vertical elevation coordinate is the horizontal plane. The positive direction of the vertical elevation coordinate is defined as the angle at which the antenna is tilted upwards.

[0092] A control command is a digitized instruction packet. It contains a command header, a horizontal azimuth coordinate data segment, a vertical elevation coordinate data segment, and a checksum. The command header is a predefined sequence of bytes. It identifies this as an antenna position adjustment command. The horizontal azimuth coordinate data segment is a sequence of bytes that converts the floating-point values ​​of the horizontal azimuth coordinates to fixed-point representation and encapsulates them. For example, the horizontal azimuth coordinate data segment can be represented using two bytes with a precision of 0.1 degrees. The vertical elevation coordinate data segment is encapsulated in the same way as the horizontal azimuth coordinate data segment. The checksum is calculated by summing all bytes in the command header, horizontal azimuth coordinate data segment, and vertical elevation coordinate data segment. The lower eight bits of the sum are used as the checksum. The process of generating the control command is executed by the controller's central processing unit (CPU). The CPU assembles the recorded horizontal azimuth and vertical elevation coordinates according to a predetermined data format. The CPU calculates the checksum. Finally, the CPU forms a complete control command byte stream.

[0093] The antenna is adjusted to its pointing position determined by the horizontal azimuth and vertical elevation coordinates based on control commands. The driving process involves two stages: command parsing and motor driving. The command parsing stage is completed by the antenna's servo driver. The servo driver receives the control command byte stream from the controller via a serial communication interface. The servo driver first checks the command header for correctness. Then, it parses the target's horizontal azimuth and vertical elevation coordinates from the horizontal and vertical elevation coordinate data segments, respectively. Finally, the servo driver recalculates the checksum and compares it with the checksum in the command. The servo driver ensures error-free command transmission.

[0094] The motor drive phase is executed based on the resolved coordinates. The servo driver internally includes a horizontal azimuth axis motor controller and a vertical pitch axis motor controller. For the horizontal azimuth axis, the horizontal azimuth axis motor controller reads the actual horizontal azimuth coordinates fed back by the current angle encoder. The horizontal azimuth axis motor controller calculates the difference between the target horizontal azimuth coordinates and the actual horizontal azimuth coordinates as the horizontal azimuth position error. The horizontal azimuth axis motor controller inputs the horizontal azimuth position error into a proportional-integral-derivative (PID) control algorithm. The PID control algorithm calculates the pulse width modulation (PWM) duty cycle signal to drive the horizontal azimuth axis motor. This PWM duty cycle signal is amplified and then drives the horizontal azimuth axis motor to rotate. The horizontal azimuth axis motor drives the antenna to move in the direction that reduces the horizontal azimuth position error.

[0095] For the vertical pitch axis, the vertical pitch axis motor controller executes the exact same control process. The vertical pitch axis motor controller uses the target vertical pitch coordinates and the actual vertical pitch coordinates. The movement of the two axes is independent but synchronous. During movement, the servo drive continuously monitors the position error. When the absolute value of the horizontal azimuth position error is less than a position error tolerance, and the absolute value of the vertical pitch position error is also less than that tolerance, the antenna is considered to be in position. For example, the position error tolerance can be set to 0.05 degrees. The servo drive then enters a position holding mode. The servo drive continuously fine-tunes to counteract holding disturbances such as wind resistance. The servo drive stabilizes the antenna at the antenna pointing position determined by the horizontal azimuth and vertical pitch coordinates.

[0096] Example 2: Figure 2 A schematic diagram of the communication link disturbance detection system based on signal quality assessment according to the present invention is provided. The communication link disturbance detection system based on signal quality assessment includes the following modules:

[0097] The signal acquisition module is used to acquire the signal quality parameters of the receiving end of the communication link in real time.

[0098] The index processing module is used to process the signal quality parameters within at least two observation windows of different time lengths to obtain the stability index corresponding to each observation window.

[0099] The feature analysis module is used to analyze the changing trends of the stability index of each observation window in the horizontal azimuth and vertical pitch dimensions to obtain the spatial dimension perturbation characteristics; and to analyze the pattern of the stability index of each observation window changing over time to obtain the temporal dimension perturbation characteristics.

[0100] The disturbance judgment module is used to determine whether there is alignment disturbance caused by environmental factors in the communication link based on a combination of disturbance characteristics in the spatial dimension and disturbance characteristics in the temporal dimension, and to determine the type of alignment disturbance.

[0101] The strategy execution module is used to determine the corresponding spatial search strategy according to the type of alignment disturbance when it is determined that alignment disturbance exists; and to execute the spatial search strategy within a preset spatial range based on the stability index of each observation window in order to find the antenna pointing position that optimizes the overall stability index.

[0102] The adjustment control module is used to generate control commands corresponding to the antenna pointing position to drive the antenna to adjust when an antenna pointing position that meets the preset optimization conditions is found through a spatial search strategy.

[0103] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0104] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0105] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0106] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0107] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0108] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0109] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A communication link disturbance detection method based on signal quality assessment, characterized in that, Includes the following steps: S1: Real-time acquisition of signal quality parameters at the receiving end of the communication link; S2: Process the signal quality parameters within at least two observation windows of different time lengths to obtain the stability index for each observation window. S3: Analyze the changing trends of the stability indices of each observation window in the horizontal azimuth and vertical pitch dimensions to obtain the spatial dimension perturbation characteristics; and analyze the pattern of the stability indices of each observation window changing over time to obtain the temporal dimension perturbation characteristics. S4: Based on the combination of spatial and temporal disturbance characteristics, determine whether there is alignment disturbance caused by environmental factors in the communication link, and determine the type of alignment disturbance; S5: When alignment disturbance is detected, determine the corresponding spatial search strategy according to the type of alignment disturbance; execute the spatial search strategy within the preset spatial range based on the stability index of each observation window to find the antenna pointing position that optimizes the overall stability index. S6: When an antenna pointing position that meets the preset optimization conditions is found through a spatial search strategy, a control command corresponding to the antenna pointing position is generated to drive the antenna to adjust.

2. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, Real-time acquisition of signal quality parameters at the receiving end of the communication link, including: Obtain the received signal strength indication parameter; Obtain the signal-to-noise ratio parameter; When the received signal strength indicator parameter is lower than the first threshold or the signal-to-noise ratio parameter is lower than the second threshold, the bit error rate parameter is obtained.

3. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, The signal quality parameters are processed within at least two observation windows of different time lengths to obtain stability indices for each observation window, including: The received signal strength indication parameter, signal-to-noise ratio parameter, and bit error rate parameter are processed within the length of the first observation window to obtain the first stability index corresponding to the first observation window. The received signal strength indication parameter, signal-to-noise ratio parameter, and bit error rate parameter are processed within the length of the second observation window to obtain the second stability index corresponding to the second observation window. The lengths of the first and second observation windows are different.

4. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, Analyzing the trends of stability indices for each observation window in the horizontal azimuth and vertical pitch dimensions yields spatial disturbance characteristics, including: By analyzing the differences in the changing trends of the first stability index and the second stability index in the horizontal azimuth dimension, the perturbation characteristics in the horizontal azimuth dimension are obtained. By analyzing the differences in the variation trends of the first stability index and the second stability index in the vertical pitch angle dimension, the perturbation characteristics in the vertical pitch angle dimension are obtained.

5. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, Analyzing the patterns of stability indices changing over time in each observation window reveals the following perturbation characteristics in the time dimension: The first pattern of analyzing the change of the first stability index over time; Analyze the second pattern of how the second stability index changes over time; Based on the comparison between the first and second modes, the perturbation characteristics in the time dimension are obtained.

6. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, Based on a combination of spatial and temporal disturbance characteristics, it is determined whether alignment disturbances caused by environmental factors exist in the communication link, and the type of alignment disturbance is identified, including: The judgment is based on a combination of disturbance characteristics in the horizontal azimuth dimension, the vertical pitch dimension, and the time dimension. When it is determined that there is an alignment disturbance caused by environmental factors, the type of alignment disturbance is determined by the relative degree of change of the disturbance characteristics in the horizontal azimuth dimension and the vertical pitch dimension, as well as the duration of change indicated by the disturbance characteristics in the time dimension.

7. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, When alignment perturbations are detected, the corresponding spatial search strategy is determined based on the type of alignment perturbation, including: When the type of alignment disturbance is the first type, the corresponding first spatial search strategy is determined to be to search within a first preset range in the horizontal azimuth dimension; When the type of alignment disturbance is the second type, the corresponding second space search strategy is determined to be to search within the second preset range in the vertical pitch angle dimension.

8. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, Based on the stability indices of each observation window, a spatial search strategy is executed within a preset spatial range to find the antenna pointing position that optimizes the overall stability indices, including: When executing the first space search strategy, based on the changes of the first stability index and the second stability index in the horizontal azimuth dimension, the antenna pointing position that optimizes the first stability index and the second stability index as a whole is sought. When executing the second space search strategy, the antenna pointing position that optimizes the first and second stability indices as a whole is sought based on the changes of the first and second stability indices in the vertical pitch angle dimension.

9. The communication link disturbance detection method based on signal quality assessment according to claim 1, characterized in that, When an antenna pointing position that meets preset optimization conditions is found through a spatial search strategy, a control command corresponding to the antenna pointing position is generated to drive the antenna to adjust, including: Determine whether the first stability index and the second stability index corresponding to the found antenna pointing position both meet the preset optimization conditions; When both the first stability index and the second stability index meet the preset optimization conditions, a control command containing the horizontal azimuth coordinates and vertical elevation coordinates of the antenna pointing position is generated. The antenna is driven by control commands to adjust to the pointing position determined by the horizontal azimuth coordinates and the vertical elevation coordinates.

10. A communication link disturbance detection system based on signal quality assessment, used to implement the communication link disturbance detection method based on signal quality assessment as described in any one of claims 1-9, characterized in that, Includes the following modules: The signal acquisition module is used to acquire the signal quality parameters of the receiving end of the communication link in real time. The index processing module is used to process the signal quality parameters within at least two observation windows of different time lengths to obtain the stability index corresponding to each observation window. The feature analysis module is used to analyze the changing trends of the stability index of each observation window in the horizontal azimuth and vertical pitch dimensions to obtain the spatial dimension perturbation characteristics; and to analyze the pattern of the stability index of each observation window changing over time to obtain the temporal dimension perturbation characteristics. The disturbance judgment module is used to determine whether there is alignment disturbance caused by environmental factors in the communication link based on a combination of disturbance characteristics in the spatial dimension and disturbance characteristics in the temporal dimension, and to determine the type of alignment disturbance. The strategy execution module is used to determine the corresponding spatial search strategy based on the type of alignment disturbance when it is determined that alignment disturbance exists. Based on the stability index of each observation window, a spatial search strategy is executed within a preset spatial range to find the antenna pointing position that optimizes the overall stability index. The adjustment control module is used to generate control commands corresponding to the antenna pointing position to drive the antenna to adjust when an antenna pointing position that meets the preset optimization conditions is found through a spatial search strategy.

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