A polar region communication channel quality assessment method

Abstract

This application discloses a method for assessing the channel quality of polar communication, relating to the field of polar communication quality assessment technology. It calculates a comprehensive quantization index T by analyzing historical communication quality data (such as signal-to-noise ratio and bit error rate) to assess the degree of ionospheric disturbance. This index is compared with a preset threshold to classify the disturbance into three levels: low, medium, and high. Subsequently, the channel quality assessment model processes the basic assessment value using corresponding correction strategies based on different disturbance levels. The parameters of the correction function are adjusted according to the severity of the disturbance to ensure that the assessment results accurately reflect the actual channel state. Finally, the system automatically generates and executes differentiated communication strategy adjustments based on the assessment results, thereby effectively improving the reliability and robustness of polar communication and reducing communication interruptions caused by environmental fluctuations.

Description

Technical Field

[0001] This application relates to the field of polar communication quality assessment technology, and in particular to a method for assessing the quality of polar communication channels. Background Technology

[0002] Due to the unique geographical location of polar regions, ionospheric disturbances are frequent and severe, significantly impacting communication channel quality. Traditional channel quality assessment methods have several shortcomings in this environment. Existing technologies typically employ fixed parameter thresholds or simple statistical models for evaluation, failing to fully consider the dynamic characteristics of ionospheric disturbances.

[0003] For example, assessment methods often rely on single parameters such as signal-to-noise ratio or bit error rate, lacking a comprehensive index to characterize the degree of disturbance. This results in low accuracy of assessment results, failing to accurately reflect the actual channel condition under disturbance. Furthermore, existing methods do not quantify and classify the degree of disturbance, and their assessment strategies are simplistic and cannot adapt to the specific needs of different disturbance levels. Assessments may be overly conservative for mild disturbances and overly optimistic for severe disturbances, leading to inaccurate communication strategy adjustments. They fail to provide differentiated information based on disturbance levels, such as lacking trend predictions or specific adjustment suggestions, thus limiting the adaptive capabilities of the communication system. Threshold settings are often based on empirical values ​​rather than environment-specific data. For example, the statistical quantiles of historical data are not used to define levels, resulting in unreasonable thresholds. This causes communication systems to face large assessment errors and response lags in polar environments, affecting communication reliability and efficiency, such as increased risk of improper transmission rate adjustments or communication outages. Summary of the Invention

[0004] This application provides a method for evaluating the channel quality of polar region communication to achieve both adaptability and accuracy in channel quality evaluation, comprising: Obtain historical communication quality data of polar communication channels within a preset time period, and calculate the characteristic index T representing the current degree of ionospheric disturbance based on the historical communication data; The value of the characteristic index T is compared with a first threshold Tth1 and a second threshold Tth2, wherein the first threshold Tth1 < the second threshold Tth2, to determine its perturbation level. When T∈[0, Tth1), it is determined to be the first level of disturbance; When T∈[Tth1, Tth2), it is determined to be the second level of disturbance; When T∈[Tth2, +∞), it is determined to be the third level of disturbance; The historical communication quality data and the characteristic index T are input into the channel quality assessment model; The channel quality assessment model selects an assessment strategy corresponding to the disturbance level based on the disturbance level, and outputs the channel quality assessment result.

[0005] Furthermore, the historical communication quality data includes at least the signal-to-noise ratio (SNR), bit error rate (BER), and signal strength index (RSSI), and the characteristic index T is determined by the following formula: ; Where α, β, and γ are weighting coefficients obtained by fitting historical data of polar communication environment, and α>β>γ>0.

[0006] Furthermore, the channel quality assessment model selects an assessment strategy corresponding to the disturbance level for processing based on the disturbance level, specifically including: The historical communication quality data is input into a preset unified evaluation function to calculate the basic channel quality evaluation value Q_base, where: If it is the first disturbance level, the first correction function is used to correct the basic evaluation value Q_base to obtain the final evaluation result Q_final; If it is the second disturbance level, the second correction function is used to correct the base evaluation value Q_base to obtain the final evaluation result Q_final; If it is the third disturbance level, then the third correction function is used to correct the base evaluation value Q_base to obtain the final evaluation result Q_final; Among them, the first correction function, the second correction function, and the third correction function are all different.

[0007] Furthermore, the first correction function, the second correction function, and the third correction function are all linear functions, wherein: The first correction function is: Q_final = K1 Q_base + C1; The second correction function is: Q_final = K2 Q_base + C2; The third correction function is: Q_final = K3 Q_base + C3; Where K1, K2, and K3 are weighting coefficients, and C1, C2, and C3 are compensation constants, with K1>K2>K3>0, C1 <C2<C3。

[0008] Furthermore, the method also includes the step of: when outputting the channel quality assessment result, generating an assessment report containing different information dimensions based on the current disturbance level. If it is the first level of disturbance, the evaluation report includes the final evaluation result Q_final and the channel quality level; If it is the second disturbance level, the assessment report will include the trend prediction information of the characteristic index T in addition to all the information of the first disturbance level; If the disturbance level is the third level, the assessment report will include a list of recommended communication protocol adjustments in addition to all information for the second level disturbance.

[0009] Furthermore, the preset unified evaluation function is an evaluation model based on a neural network, whose input is a time-series feature vector composed of the historical communication quality data, and whose output is the basic channel quality evaluation value Q_base.

[0010] Furthermore, based on the historical characteristic index T dataset of the polar communication channel over a long period, the statistical distribution of the historical characteristic index T dataset is calculated; the 70th percentile of the historical characteristic index T dataset is set as Tth1, and the 90th percentile is set as Tth2.

[0011] Furthermore, the method further includes: generating and executing a communication strategy adjustment instruction based on the final evaluation result Q_final output by the channel quality assessment model, wherein: When Q_final is higher than the first quality threshold Q_high, the channel quality is determined to be excellent, and the adjustment instruction is to maintain or increase the current transmission rate. When Q_final is between the first quality threshold Q_high and the second quality threshold Q_low, the channel quality is determined to be average. The adjustment instruction is to start forward error correction coding and adjust the transmission rate to R1 times the original rate, where 0.5≤R1<0.8. When Q_final is lower than the second quality threshold Q_low, the channel quality is determined to be poor, and the adjustment instruction is to switch to the backup communication frequency band.

[0012] The embodiments of this application have the following beneficial effects: By acquiring historical communication quality data within a preset time period, including parameters such as signal-to-noise ratio, bit error rate, and signal strength, a characteristic index T representing the current level of ionospheric disturbance is calculated. This index is comprehensively quantified based on weighted coefficients, which are determined by fitting historical data to ensure its applicability in different polar environments. The value of the characteristic index T is compared with a preset first threshold and a second threshold to divide the disturbance level into three levels, corresponding to different ionospheric activity intensities from low to high. By inputting historical communication quality data and the characteristic index into the channel quality assessment model, the model selects an appropriate assessment strategy for processing based on the disturbance level. Specifically, the model first uses a unified assessment function to calculate the basic channel quality assessment value, and then applies different linear correction functions according to the disturbance level to correct it, thereby outputting the final assessment result. The parameter settings of the correction function are adjusted based on the severity of the disturbance level, with the weighting coefficients and compensation constants showing a decreasing or increasing trend to ensure that the assessment result can accurately reflect the actual channel state. When outputting channel quality assessment results, differentiated assessments are generated based on the disturbance level. Low disturbance levels only include basic assessment information, while medium and high disturbance levels gradually add trend predictions and communication protocol adjustment suggestions. At the same time, based on the final assessment results, communication strategy adjustment instructions are automatically generated and executed, which effectively improves the reliability and real-time performance of polar communication channel quality assessment, supports the dynamic adjustment of communication systems, reduces communication interruptions or performance degradation caused by environmental fluctuations, and ultimately enhances the robustness and service quality of polar communication. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 An exemplary flowchart of a polar communication channel quality assessment method provided in an embodiment of this application is shown. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0016] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation steps as shown in the following embodiments or drawings, the method may include more or fewer operation steps based on conventional or non-inventive methods. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application.

[0017] This application provides a method for evaluating the quality of polar communication channels, which includes: acquiring historical communication quality data of polar communication channels within a preset time period, and calculating a characteristic index T representing the current degree of ionospheric disturbance based on the historical communication quality data.

[0018] Historical communication quality data includes at least signal-to-noise ratio (SNR), bit error rate (BER), and signal strength index (RSSI). The characteristic index T is determined using the following formula: ; Where α, β, and γ are weighting coefficients obtained by fitting historical data of polar communication environment, and α>β>γ>0.

[0019] Acquiring historical communication quality data of polar communication channels within a preset time period aims to establish a reliable benchmark for capturing the dynamic changes in ionospheric disturbances in polar environments. Since polar communication is susceptible to ionospheric instability, relying solely on instantaneous data is insufficient for accurately assessing channel quality. Therefore, collecting historical data provides more comprehensive contextual information, thereby enhancing the robustness of the assessment.

[0020] Historical communication quality data includes at least signal-to-noise ratio (SNR), bit error rate (BER), and signal strength index (RSSI). These parameters reflect the clarity, error rate, and energy level of signal transmission, respectively, and together constitute a multidimensional index for evaluating channel status.

[0021] The characteristic index T is calculated based on historical communication quality data. Its purpose is to quantify the current degree of ionospheric disturbance in order to solve the problem of inaccurate assessment caused by sudden environmental changes in polar communications.

[0022] The characteristic index T is obtained through the formula It is determined that the weighting coefficients α, β, and γ are obtained by fitting historical data of polar communication environment, and satisfy α>β>γ>0.

[0023] This weighting method ensures that different parameters contribute differently to the degree of disturbance. The inverse of SNR has the highest weight, highlighting the sensitivity of signal quality degradation to disturbances; BER is next, emphasizing the impact of error rate; while the inverse of RSSI has the lowest weight, but supplements the indirect indication of signal strength. By using data-driven fitting coefficients, the T value can adapt to specific polar conditions, improving the representativeness and practicality of the exponent.

[0024] The characteristic index T transforms complex ionospheric disturbances into quantifiable level indicators, thus providing accurate input for channel quality assessment models. This avoids the limitations of traditional methods that rely on single parameters or subjective judgment, achieving automation and standardization of the assessment process. By comparing the T value with a threshold, the system can quickly identify the disturbance level, triggering corresponding correction strategies and ultimately improving the adaptability and reliability of the communication system.

[0025] The value of the characteristic index T is compared with the first threshold Tth1 and the second threshold Tth2, where the first threshold Tth1 < the second threshold Tth2, to determine its perturbation level. When T∈[0, Tth1), it is determined to be the first level of disturbance.

[0026] When T∈[Tth1, Tth2), it is determined to be the second level of disturbance.

[0027] When T∈[Tth2, +∞), it is determined to be the third level of disturbance.

[0028] The ionospheric disturbance level is determined by comparing the value of the characteristic index T with the first threshold Tth1 and the second threshold Tth2, where Tth1 is less than Tth2. The purpose of this method is to quantify the continuous disturbance level into discrete level categories in order to solve the ambiguity problem in channel quality assessment caused by dynamic changes in the ionosphere in polar communications.

[0029] The feature index T, as an indicator of the degree of perturbation, may introduce complexity if used directly for evaluation. Therefore, it is simplified by using threshold comparison, which facilitates the efficient execution of the hierarchical strategy by the subsequent model.

[0030] Thresholds Tth1 and Tth2 are set based on the statistical distribution of historical characteristic index datasets. Tth1 is set to the 70th percentile and Tth2 to the 90th percentile, ensuring the objectivity and adaptability of the thresholds and reflecting the typical disturbance range of the polar communication environment.

[0031] The problem of inaccurate channel quality assessment in polar communication regions due to disturbances can be addressed by classifying disturbance levels to make the assessment process more targeted and operable. When T belongs to the first disturbance level, it indicates a low level of disturbance, and the assessment strategy can focus on basic processing. When T belongs to the second or third level, corresponding to medium and high disturbances respectively, the assessment strategy needs to gradually increase the correction strength, so that the channel quality assessment model can adaptively select the correction function according to the disturbance intensity. For example, a linear function can be used to differentiate the basic assessment value, thereby improving the accuracy and reliability of the assessment results.

[0032] By binding the disturbance level with the assessment strategy, the system can dynamically respond to changes in the ionosphere, optimize communication protocol adjustments, and ultimately enhance the stability and efficiency of polar communication systems. This feature directly serves the overall assessment model, ensuring the continuity and accuracy of channel quality assessment in a variable environment.

[0033] Historical communication quality data and the characteristic index T are input into the channel quality assessment model. Furthermore, based on the historical characteristic index T dataset of the polar communication channel over a long period, the statistical distribution of the historical characteristic index T dataset is calculated. The 70th quantile of the historical characteristic index T dataset is set as Tth1, and the 90th quantile is set as Tth2.

[0034] Historical communication quality data and the characteristic index T are input into the channel quality assessment model. This design aims to provide a comprehensive input basis for the assessment process, addressing the inaccuracy in channel quality assessment caused by dynamic changes in the ionosphere during polar communications. The characteristic index T, as a key indicator for quantifying the degree of disturbance, combined with historical communication quality data (such as signal-to-noise ratio, bit error rate, and signal strength), reflects the real-time state and historical trends of the channel. This allows the model to comprehensively consider multiple factors, avoiding assessment biases caused by relying on single instantaneous data.

[0035] Furthermore, based on the historical characteristic index T dataset of polar communication channels over a long period, the statistical distribution is calculated, and the 70th percentile is set as the first threshold Tth1 and the 90th percentile is set as the second threshold Tth2. This avoids the subjective arbitrariness of threshold setting and ensures that the threshold can truly reflect the distribution characteristics of historical disturbance levels, thereby making the classification match the actual fluctuation range of the polar communication environment.

[0036] By applying statistical quantiles, thresholds Tth1 and Tth2 can capture the dividing points of typical disturbance events. For example, Tth1 corresponds to most low-disturbance situations, while Tth2 corresponds to a few high-disturbance events, which provides a reliable basis for the selection of subsequent evaluation strategies.

[0037] To address the core issue of the vulnerability of polar communication channel quality assessment to environmental interference, this paper proposes a method that optimizes data input and threshold settings to enable the assessment model to adapt to changes in disturbance intensity. This reduces misjudgments of the level caused by improper threshold settings, and allows the model to dynamically adjust the correction function according to the disturbance level. Ultimately, this optimizes the adjustment decisions of the communication protocol and enhances the robustness and efficiency of the system in harsh environments.

[0038] The channel quality assessment model selects an assessment strategy corresponding to the disturbance level for processing based on the disturbance level, and outputs the channel quality assessment result. The channel quality assessment model selects an assessment strategy corresponding to the disturbance level for processing based on the disturbance level, specifically including: Historical communication quality data is input into a preset unified evaluation function to calculate the basic channel quality evaluation value Q_base. The preset unified evaluation function is an evaluation model based on a neural network. Its input is a time-series feature vector composed of historical communication quality data, and its output is the basic channel quality evaluation value Q_base.

[0039] If it is the first level of perturbation, the first correction function is used to correct the base evaluation value Q_base to obtain the final evaluation result Q_final.

[0040] If it is the second perturbation level, the second correction function is used to correct the base evaluation value Q_base to obtain the final evaluation result Q_final.

[0041] If the disturbance level is third, the base evaluation value Q_base is corrected using the third correction function to obtain the final evaluation result Q_final. The first, second, and third correction functions are all different.

[0042] The channel quality assessment model selects an appropriate assessment strategy based on the disturbance level. Its purpose is to achieve fine-tuning of channel quality assessment in response to changes in ionospheric disturbance levels in polar communications. By dividing the disturbance level into first, second, and third levels, the model can adaptively select a correction function based on the current disturbance intensity, thereby addressing the problem of inaccurate assessments caused by dynamic environmental changes.

[0043] Specifically, the model first inputs historical communication quality data into a pre-defined unified evaluation function. This function, based on a neural network model, processes the time-series feature vector composed of historical data and outputs a basic channel quality evaluation value Q_base. The role of the unified evaluation function is to provide an initial evaluation benchmark, ensure the consistency and basic reliability of the evaluation process, and lay the foundation for subsequent corrections.

[0044] Subsequently, different correction functions are applied according to the perturbation level: if it is the first perturbation level, the first correction function is used to correct Q_base; if it is the second perturbation level, the second correction function is used; if it is the third perturbation level, the third correction function is used. The correction functions are different from each other and are all linear functions.

[0045] The first correction function is Q_final = K1Q_base + C1, the second correction function is Q_final = K2Q_base + C2, and the third correction function is Q_final = K3 Q_base + C3, where the weight coefficients K1 > K2 > K3 > 0 and the compensation constants C1 < C2 < C3. This parameter setting makes the correction amplitude smaller at low perturbation levels to maintain the evaluation stability; at medium and high perturbation levels, the correction amplitude gradually increases to effectively compensate for the deviation caused by environmental interference.

[0046] Furthermore, the first correction function, the second correction function, and the third correction function are all linear functions, where: The first correction function is: Q_final = K1 Q_base + C1.

[0047] The second correction function is: Q_final = K2 Q_base + C2.

[0048] The third correction function is: Q_final = K3 Q_base + C3.

[0049] Among them, K1, K2, and K3 are weight coefficients, and C1, C2, and C3 are compensation constants, and K1 > K2 > K3 > 0, C1 < C2 < C3.

[0050] The first correction function, the second correction function, and the third correction function are all set as linear functions. The purpose of this setting is to differentially correct the basic channel quality evaluation value Q_base according to different ionospheric perturbation levels to solve the problem of inaccurate evaluation caused by environmental perturbation in polar region communication.

[0051] In the form of a linear function, the simplicity and efficiency of the calculation process are realized, which is convenient for rapid execution in a real-time evaluation system, and at the same time ensures the controllability and predictability of the correction operation. The application of the linear function enables the correction process to directly scale and offset based on the basic evaluation value, so as to adapt to the evaluation requirements under different perturbation intensities.

[0052] Specifically, the parameter relationship of the linear function is set as K1 > K2 > K3 > 0 and C1 < C2 < C3. This design targets the correlation between the perturbation level and the correction intensity in the polar region communication channel quality assessment. The decreasing relationship of the weight coefficients K1, K2, and K3 ensures that the correction amplitude is small at the low perturbation level (the first level) to maintain the stability of the evaluation result. At the medium and high perturbation levels (the second and third levels), the weight coefficients gradually decrease, but the increasing relationship of the compensation constants C1, C2, and C3 enhances the correction strength to effectively compensate for the signal quality degradation caused by high perturbations.

[0053] For example, at the third perturbation level, the combined effect of the smaller K3 and the larger C3 makes the corrected evaluation value Q_final better reflect the actual poor condition of the channel, avoiding an overly optimistic evaluation value.

[0054] Through hierarchical linear correction, the final evaluation result Q_final can dynamically respond to the ionospheric perturbation changes. This correction mechanism avoids the limitations of a single correction function, ensuring the reliability and consistency of the evaluation result under different environmental conditions, thus providing accurate input for subsequent communication strategy adjustments (such as transmission rate modification or frequency band switching), and ultimately enhancing the overall performance and robustness of the polar region communication system.

[0055] Furthermore, the method also includes the step of generating an evaluation report containing different information dimensions according to the current perturbation level when outputting the channel quality assessment result: If it is the first perturbation level, the evaluation report includes the final evaluation result Q_final and the channel quality level.

[0056] If it is the second perturbation level, on the basis of including all the information of the first perturbation level, the evaluation report adds the trend prediction information of the characteristic index T.

[0057] If it is the third perturbation level, on the basis of including all the information of the second perturbation level, the evaluation report adds a list of recommended communication protocol adjustment suggestions.

[0058] The method also includes generating an evaluation report containing different information dimensions according to the current perturbation level when outputting the channel quality assessment result. Its purpose is to provide differentiated information output for the differences in the ionospheric perturbation degree in polar region communication, so as to solve the problems of insufficient or excessive information that may exist in the practical application of the evaluation result.

[0059] Due to the dynamic and changeable polar region communication environment, simply outputting the numerical evaluation result may not meet the decision-making needs of the operators. Therefore, through the generation of hierarchical reports, the evaluation information becomes more hierarchical and practical, thus improving the efficiency of communication management.

[0060] Specifically, if it is the first level of disturbance, the assessment report includes the final assessment result Q_final and the channel quality level. Its purpose is to provide basic assessment information to facilitate the rapid confirmation of the channel status under low disturbance conditions and avoid unnecessary complex processing.

[0061] If it is the second level of disturbance, the report adds trend prediction information of the characteristic index T to all the information of the first level. This is to help predict changes and adjust communication strategies in advance in response to the risk that the channel may deteriorate further during moderate disturbances.

[0062] If the disturbance level is 3, the report will add a list of recommended communication protocol adjustments to all information from the 2nd level. The purpose is to provide actionable adjustment solutions, such as frequency band switching or rate modification, to deal with emergencies in high-disturbance environments and reduce decision-making delays.

[0063] By providing differentiated information output, the practical value of assessment results is enhanced, supporting operators in responding quickly to environmental changes and ultimately improving the reliability and operational efficiency of polar communication systems. This report generation mechanism works in conjunction with the overall assessment model to ensure that channel quality assessments not only remain at the numerical level but can also be transformed into effective inputs for actual communication management.

[0064] The method further includes: generating and executing communication strategy adjustment instructions based on the final evaluation result Q_final output by the channel quality assessment model, wherein: When Q_final is higher than the first quality threshold Q_high, the channel quality is determined to be excellent, and the adjustment instruction is to maintain or increase the current transmission rate.

[0065] When Q_final is between the first quality threshold Q_high and the second quality threshold Q_low, the channel quality is determined to be average. The adjustment instruction is to start forward error correction coding and adjust the transmission rate to R1 times the original rate, where 0.5≤R1<0.8.

[0066] When Q_final is lower than the second quality threshold Q_low, the channel quality is determined to be poor, and the adjustment instruction is to switch to the backup communication frequency band.

[0067] Based on the final evaluation result Q_final output by the channel quality assessment model, a communication strategy adjustment instruction is generated and executed. The purpose of this instruction is to achieve dynamic adaptive management of the polar communication system in order to solve the channel quality fluctuation problem caused by ionospheric disturbances.

[0068] Due to the complex and variable communication environment in polar regions, simply assessing channel quality without taking corresponding adjustment measures cannot guarantee the continuity and reliability of communication. Therefore, by comparing Q_final with a preset quality threshold and triggering different adjustment commands based on the comparison results, the system can respond to changes in channel state in real time, thereby optimizing communication performance.

[0069] Specifically, when Q_final is higher than the first quality threshold Q_high, the channel quality is determined to be excellent, and the adjustment instruction is to maintain or increase the current transmission rate. This addresses the need to maximize transmission efficiency when the channel conditions are good, and avoids the overhead caused by unnecessary strategy changes.

[0070] When Q_final is between Q_high and the second quality threshold Q_low, the channel quality is determined to be average. The adjustment instruction is to start forward error correction coding and adjust the transmission rate to R1 times the original rate, where 0.5≤R1<0.8. This compensates for potential errors through the error correction mechanism, while moderately reducing the rate to balance reliability and efficiency, and to cope with performance degradation under moderate disturbances.

[0071] When Q_final is lower than Q_low, the channel quality is judged to be poor, and the adjustment instruction is to switch to the backup communication frequency band. This is directly aimed at high disturbance scenarios. By switching the frequency band, communication interruption is avoided and basic connectivity is ensured. Through the hierarchical adjustment mechanism, communication resources are reasonably allocated, which not only ensures high-speed transmission under good channels, but also maintains basic services under adverse conditions, ultimately enhancing the overall stability and efficiency of polar region communication.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for evaluating the quality of polar communication channels, characterized in that, include: Obtain historical communication quality data of polar communication channels within a preset time period, and calculate the characteristic index T representing the current degree of ionospheric disturbance based on the historical communication data; The value of the characteristic index T is compared with a first threshold Tth1 and a second threshold Tth2, wherein the first threshold Tth1 < the second threshold Tth2, to determine its perturbation level. When T∈[0, Tth1), it is determined to be the first level of disturbance; When T∈[Tth1, Tth2), it is determined to be the second level of disturbance; When T∈[Tth2, +∞), it is determined to be the third level of disturbance; The historical communication quality data and the characteristic index T are input into the channel quality assessment model; The channel quality assessment model selects an assessment strategy corresponding to the disturbance level based on the disturbance level, and outputs the channel quality assessment result.

2. The polar communication channel quality assessment method according to claim 1, characterized in that, The historical communication quality data includes at least signal-to-noise ratio (SNR), bit error rate (BER), and signal strength index (RSSI). The characteristic index T is determined by the following formula: ; Where α, β, and γ are weighting coefficients obtained by fitting historical data of polar communication environment, and α>β>γ>0.

3. The polar communication channel quality assessment method according to claim 2, characterized in that, The channel quality assessment model selects an assessment strategy corresponding to the disturbance level for processing based on the disturbance level, specifically including: The historical communication quality data is input into a preset unified evaluation function to calculate the basic channel quality evaluation value Q_base, where: If it is the first disturbance level, the first correction function is used to correct the basic evaluation value Q_base to obtain the final evaluation result Q_final; If it is the second disturbance level, the second correction function is used to correct the base evaluation value Q_base to obtain the final evaluation result Q_final; If it is the third disturbance level, then the third correction function is used to correct the base evaluation value Q_base to obtain the final evaluation result Q_final; Among them, the first correction function, the second correction function, and the third correction function are all different.

4. The polar communication channel quality assessment method according to claim 3, characterized in that, The first, second, and third correction functions are all linear functions, wherein: The first correction function is: Q_final = K1 Q_base + C1; The second correction function is: Q_final = K2 Q_base + C2; The third correction function is: Q_final = K3 Q_base + C3; Where K1, K2, and K3 are weighting coefficients, and C1, C2, and C3 are compensation constants, with K1>K2>K3>0, C1 <C2<C3。 5. The polar communication channel quality assessment method according to claim 1, characterized in that, The method further includes the step of: when outputting the channel quality assessment result, generating an assessment report containing different information dimensions based on the current disturbance level. If it is the first level of disturbance, the evaluation report includes the final evaluation result Q_final and the channel quality level; If it is the second disturbance level, the assessment report will include the trend prediction information of the characteristic index T in addition to all the information of the first disturbance level; If the disturbance level is the third level, the assessment report will include a list of recommended communication protocol adjustments in addition to all information for the second level disturbance.

6. The polar communication channel quality assessment method according to claim 3, characterized in that, The preset unified evaluation function is a neural network-based evaluation model. Its input is a time-series feature vector composed of the historical communication quality data, and its output is the basic channel quality evaluation value Q_base.

7. The polar communication channel quality assessment method according to claim 1, characterized in that, Based on the historical characteristic index T dataset of the polar communication channel over a long period, the statistical distribution of the historical characteristic index T dataset is calculated; the 70th percentile of the historical characteristic index T dataset is set as Tth1, and the 90th percentile is set as Tth2.

8. The polar communication channel quality assessment method according to claim 3, characterized in that, The method further includes: generating and executing a communication strategy adjustment instruction based on the final evaluation result Q_final output by the channel quality assessment model, wherein: When Q_final is higher than the first quality threshold Q_high, the channel quality is determined to be excellent, and the adjustment instruction is to maintain or increase the current transmission rate. When Q_final is between the first quality threshold Q_high and the second quality threshold Q_low, the channel quality is determined to be average. The adjustment instruction is to start forward error correction coding and adjust the transmission rate to R1 times the original rate, where 0.5≤R1<0.

8. When Q_final is lower than the second quality threshold Q_low, the channel quality is determined to be poor, and the adjustment instruction is to switch to the backup communication frequency band.

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