A test method for satellite measurement, operation and control data interface

Abstract

The present invention relates to the technical field of satellite measurement, operation and control data interface testing, and discloses a method for testing a satellite measurement, operation and control data interface, including: step S1, initializing a test environment, setting initial test parameters, including channel bandwidth, transmit power, and signal-to-noise ratio threshold, loading a communication protocol for the satellite measurement, operation and control data interface, setting a test channel environment, and configuring a synchronization mechanism between a signal transmitter and a receiver; and step S2, channel measurement and modeling. After the test environment is initialized, received signals are collected, channel gain is calculated using the signal strength obtained by the signal receiver, a Kalman filter method is used to perform a smooth estimation of the channel gain, the instantaneous signal-to-noise ratio of the channel is calculated, and a channel model is determined based on the test channel environment type. By combining the Kalman filter with the smooth estimation of the channel gain, the channel state can be obtained in a complex channel environment, resulting in accurate test data and reliable bit error rate estimation.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite measurement, operation and control data interface testing, and in particular to a method for testing a satellite measurement, operation and control data interface. Background Art

[0002] Satellite measurement, operation, and control (TT&C) data interfaces are critical communications channels for telemetry, remote control, and data transmission between satellites and ground stations. Due to the increasing complexity of satellite communication systems, T&C data interfaces face challenges such as dynamic channel environments, noise interference, and power constraints. Traditional testing methods are unable to accurately evaluate data interface performance in these complex environments.

[0003] Existing testing methods mainly rely on traditional indicators such as fixed bandwidth, constant transmit power, and statistical bit error rate, which have the following problems:

[0004] Existing methods usually use a simple channel gain measurement method, which cannot effectively suppress the impact of instantaneous noise on channel gain estimation, resulting in large deviations in channel capacity calculation results.

[0005] Traditional bit error rate calculation methods do not fully consider channel state information, rely on simple statistical calculations, and cannot adapt to dynamic channel environments, resulting in large deviations in bit error rate assessment.

[0006] Existing testing methods usually use fixed power and bandwidth parameters for testing, which cannot dynamically adjust parameters according to real-time channel status and cannot optimize test accuracy and stability in complex channel environments.

[0007] In complex channel environments, noise and interference signals can cause outliers to appear in test data. However, existing methods lack an effective anomaly detection mechanism, which causes test results to be affected by abnormal data and reduces test reliability.

[0008] To address the above problems, the present invention proposes a new satellite measurement, operation and control data interface testing method based on channel modeling optimization, bit error rate optimization estimation, test parameter dynamic optimization and abnormal data detection.

[0009] This method uses Kalman filtering to optimize channel gain measurement, optimizes test parameters through optimal control theory and the Pontryag principle, and combines Bayesian reasoning for abnormal data detection to improve test accuracy, stability, and adaptability, ensuring that the test method can adapt to complex channel environments and achieve efficient and accurate measurement, operation, and control data interface testing. Summary of the Invention

[0010] In view of the deficiencies in the prior art, the present invention provides a method for testing a satellite measurement, operation and control data interface to solve the problems raised in the above background technology.

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for testing a satellite measurement, operation and control data interface, comprising:

[0012] Step S1: Initialize the test environment, set initial test parameters, including channel bandwidth, transmit power, and signal-to-noise ratio threshold, load the communication protocol of the satellite measurement, operation, and control data interface, set the test channel environment, and configure the synchronization mechanism between the signal transmitter and receiver.

[0013] Step S2, channel measurement and modeling: After the test environment is initialized, the received signal is collected, the channel gain is calculated using the signal strength obtained at the signal receiving end, the channel gain is smoothed and estimated using the Kalman filter method, and the instantaneous signal-to-noise ratio of the channel is calculated. The channel model is determined based on the test channel environment type, and based on the selected channel model, a mathematical relationship between the input signal, channel gain, and noise is established;

[0014] Step S3, channel capacity calculation: Based on the results of channel measurement and modeling, the Shannon channel capacity theorem is used to calculate the maximum transmission rate of the data interface under the current channel conditions. The channel bandwidth, signal power, and noise power density are substituted into the channel capacity calculation formula to obtain the theoretical optimal transmission capacity. The calculated maximum transmission rate is stored in the test database.

[0015] Step S4, bit error rate optimization estimation, based on the result of the channel capacity calculation, uses the minimum mean square error estimation method to perform bit error rate analysis on the receiving end signal, calculates the estimated error of the signal, and uses the bit error rate calculation formula to obtain a preliminary bit error rate value. Combined with the maximum a posteriori probability estimation method, the bit error rate is corrected using the prior probability distribution, and the final calculated bit error rate value is stored in the test database;

[0016] Step S5: Optimal control optimizes test parameters. Based on the result of the bit error rate optimization estimation, a test parameter optimization model with the goal of minimizing the bit error rate is constructed. The channel capacity calculation results and the bit error rate calculation results stored in the test database are retrieved, and a functional relationship between bandwidth, power, and bit error rate is constructed. The optimal test parameters are solved using the optimal control method. The bandwidth and transmit power of the test channel are adjusted using the gradient descent method to make the current test channel configuration close to the theoretical optimal transmission capacity. The optimal test parameters are used as the final test configuration of the test system.

[0017] Step S6, automated test execution and data analysis, based on the results of the optimal control optimization test parameters, configure the test signal according to the determined optimal bandwidth and optimal power, run the automated test system, record the data indicators during the test process, and perform big data analysis on the test data, use pattern recognition methods to filter out abnormal test data, generate a test data analysis report to compare the test data with the stored channel capacity calculation results, so as to evaluate the rationality of the test results and determine the test performance of the data interface.

[0018] Preferably, the calculation formula of the channel gain G(t) in step S2 is:

[0019]

[0020] Where G(t) is the channel gain, P r (t) is the received signal power, P s (t) is the transmitted signal power.

[0021] Preferably, the calculation formula of the channel capacity C in step S3 is:

[0022]

[0023] Where C is the channel capacity, B is the channel bandwidth, G(t) is the channel gain, and P s (t) is the transmitted signal power, and N(t) is the noise power density.

[0024] Preferably, the bit error rate P in step S4 e The calculation of is based on the minimum mean square error estimation, the formula is:

[0025]

[0026] in, is the optimal estimate of the received signal, H is the channel matrix, and H H is the conjugate transpose of the channel matrix, σ 2 is the noise variance, I is the identity matrix, and Y is the received signal;

[0027] Q(·) is the Q function, which is used to calculate the bit error rate.

[0028] Preferably, in step S4, the maximum a posteriori probability estimation is used to correct the bit error rate, and the formula is:

[0029]

[0030] Where P(X=x|Y=y) is the posterior probability, which represents the probability that the original transmitted signal is X=x when the received signal Y=y.

[0031] P(Y=y) is the probability density of the received signal, P e is the bit error rate.

[0032] Preferably, when the optimal control optimizes the test parameters in step S5, the following objective function is adopted:

[0033]

[0034] Among them, J is the optimization target, P e (B,P s ) is the bit error rate function, P opt is the theoretical optimal power, λ is the adjustment coefficient, P s is the transmitted signal power, T is the test time, B is the channel bandwidth, P e is the bit error rate.

[0035] Preferably, when the optimal control optimizes the test parameters, the Pontryagin maximum principle is adopted to construct the Hamiltonian function H(P s ,B,λ1,λ2), the expression is as follows:

[0036]

[0037] Among them, H(P s ,B,λ1,λ2) is the Hamiltonian function, P e (B,P s ) is the bit error rate function, B is the channel bandwidth, P e is the bit error rate, λ1 and λ2 are Lagrange multipliers,

[0038] is the rate of change of power with time, is the rate of change of bandwidth over time.

[0039] Preferably, in step S6, the automated test system uses an adaptive gradient descent method to update the power P s and bandwidth B, the iterative update rule is as follows:

[0040]

[0041] in, and B (k+1) is the power and bandwidth of the k+1th iteration, α and β are the adaptive learning rates, and is the partial derivative of the bit error rate with respect to power and bandwidth.

[0042] Preferably, in step S6, the automated testing system uses an anomaly detection algorithm based on Bayesian reasoning to calculate the anomaly probability P of the test data. adn :

[0043]

[0044] Among them, P adn is the abnormal probability of the test data, d is the bit error rate deviation value of the test data, μ d and is the mean and variance of the bit error rate deviation, d th is the threshold for bit error rate anomaly detection,

[0045] is the Gaussian probability density function of the bit error rate deviation value,

[0046] is the standardized coefficient of the Gaussian distribution;

[0047] If P adn When the set threshold is exceeded, the system triggers the anomaly detection mechanism, filters out abnormal test data, and adjusts the test process to improve test stability.

[0048] Preferably, in step S6, the test data analysis report includes bit error rate trend analysis, channel capacity utilization calculation and dynamic power allocation evaluation, wherein the channel capacity utilization U C The calculation formula is as follows:

[0049]

[0050] Among them, U C is the channel capacity utilization, R test is the data transmission rate measured during the actual test process, and C is the channel capacity.

[0051] The present invention provides a method for testing a satellite measurement, operation and control data interface. It has the following beneficial effects:

[0052] 1. The present invention combines Kalman filtering to perform smooth estimation of channel gain, thereby obtaining channel status in a complex channel environment, and obtaining accurate test data and reliable bit error rate estimation.

[0053] 2. The present invention dynamically optimizes test parameters by adopting optimal control theory and the Pontryag principle, so that the test system can adaptively adjust bandwidth and power according to real-time channel status, thereby enhancing test stability and minimizing bit error rate.

[0054] 3. The present invention calculates the abnormal probability of test data by combining the Bayesian inference algorithm, realizes automatic identification and elimination of abnormal data during the test process, and obtains the effect of reliable test results and accurate data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0056] To help those skilled in the art understand the present invention, the following will provide a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only partial embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0057] The present invention is described in detail below with reference to the accompanying drawings:

[0058] Example:

[0059] Please see the attached Figure 1 The embodiment of the present invention provides a method for testing a satellite measurement, operation and control data interface, comprising:

[0060] Step S1: Initialize the test environment, set initial test parameters, including channel bandwidth, transmit power, and signal-to-noise ratio threshold, load the communication protocol of the satellite measurement, operation, and control data interface, set the test channel environment, and configure the synchronization mechanism between the signal transmitter and receiver.

[0061] Test environment initialization ensures controllability during the test process, enabling the test system to operate within the specified bandwidth, transmit power, and signal-to-noise ratio (SNR) conditions, preventing external interference from affecting test data. By loading standard communication protocols, the test data format is ensured to conform to expectations, avoiding data anomalies caused by protocol incompatibility. Furthermore, a rational configuration of the channel environment enables the test system to adapt to diverse satellite communication scenarios, improving test versatility and reproducibility. Furthermore, a synchronization mechanism ensures time alignment between the transmitter and receiver, improving data acquisition accuracy and mitigating the impact of clock drift.

[0062] Step S2, channel measurement and modeling: After the test environment is initialized, the received signal is collected, the channel gain is calculated using the signal strength obtained at the signal receiving end, the channel gain is smoothed and estimated using the Kalman filter method, and the instantaneous signal-to-noise ratio of the channel is calculated. The channel model is determined based on the test channel environment type, and based on the selected channel model, a mathematical relationship between the input signal, channel gain, and noise is established;

[0063] By measuring the power of the received signal and calculating the channel gain and signal-to-noise ratio, we can accurately characterize the characteristics of the satellite communication channel, providing real and effective data for subsequent calculations of channel capacity. Using Kalman filtering for smoothed channel gain estimation effectively suppresses measurement errors caused by transient noise and improves the accuracy of channel modeling.

[0064] Step S3, channel capacity calculation: Based on the results of channel measurement and modeling, the Shannon channel capacity theorem is used to calculate the maximum transmission rate of the data interface under the current channel conditions. The channel bandwidth, signal power, and noise power density are substituted into the channel capacity calculation formula to obtain the theoretical optimal transmission capacity. The calculated maximum transmission rate is stored in the test database.

[0065] The maximum transmission rate of the data interface is calculated based on Shannon's channel capacity theorem, enabling the test system to assess the theoretically optimal transmission capacity under current channel conditions. By incorporating channel bandwidth, signal power, and noise power density into the calculation, the accuracy of test data is improved, avoiding errors caused by relying on empirical values ​​to set test parameters. Furthermore, the calculated channel capacity is stored in the test database, allowing subsequent bit error rate calculations and test parameter optimization to be dynamically adjusted based on the theoretical optimal values, improving the scientific and rationality of the overall test.

[0066] Step S4, bit error rate optimization estimation, based on the result of the channel capacity calculation, uses the minimum mean square error estimation method to perform bit error rate analysis on the receiving end signal, calculates the estimated error of the signal, and uses the bit error rate calculation formula to obtain a preliminary bit error rate value. Combined with the maximum a posteriori probability estimation method, the bit error rate is corrected using the prior probability distribution, and the final calculated bit error rate value is stored in the test database;

[0067] Utilizing the minimum mean square error (LMSE) estimation method to analyze the bit error rate (BER) of the received signal can reduce BER calculation errors caused by noise interference. Combined with the maximum a posteriori probability (MAP) estimation method, the BER is corrected using a priori probability distribution, ensuring that the BER calculation matches the actual channel state and improving BER estimation accuracy. Furthermore, the final calculated BER value is stored in the test database, providing data support for subsequent optimal control and optimization of test parameters. This allows for precise adjustment of test parameters and reduces the impact of BER calculation errors on test results.

[0068] Step S5: Optimal control optimizes test parameters. Based on the result of the bit error rate optimization estimation, a test parameter optimization model with the goal of minimizing the bit error rate is constructed. The channel capacity calculation results and the bit error rate calculation results stored in the test database are retrieved, and a functional relationship between bandwidth, power, and bit error rate is constructed. The optimal test parameters are solved using the optimal control method. The bandwidth and transmit power of the test channel are adjusted using the gradient descent method to make the current test channel configuration close to the theoretical optimal transmission capacity. The optimal test parameters are used as the final test configuration of the test system.

[0069] By building a test parameter optimization model to minimize bit error rates and employing the Pontryag principle to determine optimal test parameters, the test system can dynamically adjust bandwidth and transmit power, ensuring that the test configuration always approaches the theoretically optimal transmission capacity. Parameter optimization using gradient descent enables adaptive adjustment, avoiding the limitations of fixed-parameter testing methods and improving test stability. Furthermore, by adjusting the test channel bandwidth and power in real time, the bit error rate is minimized, enhancing test accuracy and reliability.

[0070] Step S6, automated test execution and data analysis, based on the results of the optimal control optimization test parameters, configure the test signal according to the determined optimal bandwidth and optimal power, run the automated test system, record the data indicators during the test process, and perform big data analysis on the test data, use pattern recognition methods to filter out abnormal test data, generate a test data analysis report to compare the test data with the stored channel capacity calculation results, so as to evaluate the rationality of the test results and determine the test performance of the data interface.

[0071] By optimizing test parameters based on optimal control, automated testing reduces manual intervention and improves testing efficiency. Data metrics during the test process are recorded and, combined with big data analysis methods, abnormal data is screened, enabling the test system to accurately assess the reliability of test data. Pattern recognition algorithms are used for data screening to ensure the stability and consistency of test data. Furthermore, test data analysis reports are generated, allowing testers to quickly evaluate the transmission performance of data interfaces, optimize test processes, and enhance the intelligence of testing.

[0072] The calculation formula of the channel gain G(t) in step S2 is:

[0073]

[0074] Where G(t) is the channel gain, P r (t) is the received signal power, P s (t) is the transmitted signal power.

[0075] The calculation formula of the channel capacity C in step S3 is:

[0076]

[0077] Where C is the channel capacity, B is the channel bandwidth, G(t) is the channel gain, and P s (t) is the transmitted signal power, and N(t) is the noise power density.

[0078] It provides an intuitive and quantifiable method to characterize the attenuation of signals caused by channel influence during transmission.

[0079] By measuring the received signal power P r (t) and the known transmit signal power P s (t), can dynamically evaluate channel characteristics without relying on additional prior information, and is applicable to various channel environments.

[0080] The formula is simple to calculate, easy to implement in engineering, and can be quickly used for testing and analysis of satellite measurement, operation and control data interfaces.

[0081] Combined with Kalman filter signal processing technology, the channel gain can be further smoothed and estimated, the measurement accuracy can be improved, and the impact of instantaneous noise can be reduced.

[0082] Based on the Shannon formula, a theoretically optimal transmission capacity evaluation method is provided for the test system.

[0083] By considering the channel bandwidth B, channel gain G, and transmission power P s (t) and noise power density N(t). This formula can accurately calculate the maximum transmission rate of the data interface under the current channel conditions.

[0084] This calculation method avoids the errors in traditional methods that rely on fixed bandwidth and fixed power to estimate transmission capacity, and improves the scientificity and reliability of test data.

[0085] Combined with the channel measurement results, this formula can be used to dynamically adjust the test parameters so that the bandwidth and power allocation of the test channel meet the theoretical optimal conditions, thereby improving test efficiency.

[0086] In step S4, the bit error rate P e The calculation of is based on the minimum mean square error estimation, the formula is:

[0087]

[0088] in, is the optimal estimate of the received signal, H is the channel matrix, and H H is the conjugate transpose of the channel matrix, σ 2 is the noise variance, I is the identity matrix, and Y is the received signal;

[0089] Q(·) is the Q function, which is used to calculate the bit error rate.

[0090] When calculating the optimal estimated value of a signal, the minimum mean square error estimation can minimize the mean square error, making the estimated result of the received signal close to the true value, thereby reducing the bit error rate calculation deviation.

[0091] Compared with the traditional bit error rate calculation method based on statistical average, this method combines the channel matrix H and its conjugate transpose H H , so that the bit error rate estimation can adapt to different channel conditions and improve the robustness of the test system.

[0092] By introducing the noise variance σ 2 Calculation shows that the minimum mean square error method can effectively suppress noise interference, improve the accuracy of signal detection, and make the bit error rate calculation consistent with actual communication conditions.

[0093] In a satellite channel environment with multipath fading and strong interference, this method can still stably calculate the bit error rate and is not restricted by a specific channel model. It is suitable for various satellite measurement, operation and control data interface test scenarios.

[0094] The minimum mean square error estimation uses matrix operations, which can ensure the numerical stability of the bit error rate calculation process and is not easily affected by extreme noise or specific channel conditions.

[0095] In step S4, the maximum a posteriori probability estimation is used to correct the bit error rate, and the formula is:

[0096]

[0097] Where P(X=x|Y=y) is the posterior probability, which represents the probability that the original transmitted signal is X=x when the received signal Y=y.

[0098] P(Y=y) is the probability density of the received signal, P e is the bit error rate.

[0099] Compared with traditional statistical methods, maximum a posteriori probability estimation can combine prior information to optimize the bit error rate calculation, making the calculation results close to the actual bit error rate.

[0100] In complex channel environments, such as Rayleigh fading and Rician fading, which are common in satellite measurement, operation and control data interfaces, the maximum a posteriori probability estimation can dynamically adjust the calculation method to ensure that the bit error rate estimation meets the actual communication conditions.

[0101] The maximum a posteriori probability estimation takes into account the probability distribution of the received signal, which can effectively reduce the impact of noise interference on the bit error rate calculation and improve the accuracy of bit error rate correction.

[0102] This method utilizes the probability density information of the received signal, enabling the test system to obtain reliable bit error rate estimation in a low signal-to-noise ratio environment, avoiding the problem of decreased bit error rate calculation accuracy in traditional methods under low signal-to-noise ratio conditions.

[0103] When the channel state changes, the maximum a posteriori probability estimation can adjust the bit error rate calculation in real time, so that the test system can maintain high bit error rate accuracy assessment under different channel conditions.

[0104] When optimizing the test parameters in step S5, the following objective function is used:

[0105]

[0106] Among them, J is the optimization target, P e (B,P s ) is the bit error rate function, P opt is the theoretical optimal power, λ is the adjustment coefficient, P s is the transmitted signal power, T is the test time, B is the channel bandwidth, P e is the bit error rate.

[0107] When optimizing the test parameters, the Pontryagin maximum principle is used to construct the Hamiltonian function H(P s ,B,λ1,λ2), the expression is as follows:

[0108]

[0109] Among them, H(P s ,B,λ1,λ2) is the Hamiltonian function, P e (B,P s ) is the bit error rate function, B is the channel bandwidth, P e is the bit error rate, λ1 and λ2 are Lagrange multipliers,

[0110] is the rate of change of power with time, is the rate of change of bandwidth over time.

[0111] The objective function clearly sets minimizing the bit error rate as the optimization goal. By introducing the theoretical optimal power, transmitted signal power, channel bandwidth and test time, an evaluation system that comprehensively considers the bit error rate and system resource constraints is formed.

[0112] Specifically, the objective function has the following benefits:

[0113] The objective function can be used to intuitively express the performance deviation of the test system under dynamic channel conditions, guiding the system to continuously approach the theoretical optimal state during the test process.

[0114] In the implementation, the deviation between the transmitted signal power and the theoretical optimal power is corrected by adjusting the coefficient, so that the system can adjust adaptively, thereby reducing the bit error rate.

[0115] This objective function takes into account the instantaneous bit error rate and comprehensively considers the system status during the test time, which helps to achieve long-term stable optimization.

[0116] Using the Pontryag principle to construct the Hamiltonian function brings the following significant advantages:

[0117] The Hamiltonian function closely combines the system state with the cost function, and through the introduction of Lagrange multipliers, the rate of change of the test parameters is effectively incorporated into the optimization analysis.

[0118] This method can process multiple sets of constraints simultaneously, ensuring that the optimal control solution can be obtained while satisfying the dynamic changes of the system.

[0119] Specifically, the Hamiltonian function can be used to derive the necessary optimality conditions, providing a theoretical basis for parameter adjustment of the actual system, so that the test configuration can move closer to the theoretical optimal state in real time.

[0120] This method takes into account the changes in the bit error rate function and the channel bandwidth, and effectively reduces the impact of environmental interference and system errors on the test results by constructing the relationship between the Lagrange multiplier and the state change rate.

[0121] In step S6, the automated test system uses the adaptive gradient descent method to update the power P s and bandwidth B, the iterative update rule is as follows:

[0122]

[0123] in, and B (k+1) is the power and bandwidth of the k+1th iteration, α and β are the adaptive learning rates, and is the partial derivative of the bit error rate with respect to power and bandwidth.

[0124] In general, the adaptive gradient descent method is used to iteratively update the power and bandwidth, which can make full use of the gradient information of the bit error rate with respect to each parameter and realize dynamic adjustment.

[0125] This method can quickly respond to changes in complex channel environments, reducing the bit error rate during testing while maintaining the stability of system parameters.

[0126] Specifically, in one implementation, the iterative update rule of the adaptive gradient descent method can be expressed as:

[0127]

[0128] in, and B (k+1) is the power and bandwidth of the k+1th iteration, α and β are the adaptive learning rates, and is the partial derivative of the bit error rate with respect to power and bandwidth.

[0129] In one implementation, utilizing this iteration rule has the following benefits:

[0130] Gradient information can clearly indicate the direction of parameter adjustment, allowing the system to continuously approach the optimal state.

[0131] The adaptive learning rate can automatically adjust according to the current bit error rate changes, avoiding the problems of oscillation and slow convergence caused by a fixed step size.

[0132] Specifically, this method quickly reduces the bit error rate locally and takes into account the robustness of parameters during the global search process, ensuring that the test system can obtain a better configuration under different channel conditions.

[0133] The adaptive gradient descent method enables the test system to capture channel state changes in real time, and dynamically adjust power and bandwidth based on bit error rate feedback to reduce system errors.

[0134] The numerical calculation of this method is highly stable and can maintain good convergence characteristics in a variety of test scenarios.

[0135] Specifically, through iterative updates, the test system can continuously optimize transmission performance, reduce bit error rates, improve the utilization efficiency of channel resources, and provide data support for subsequent data analysis and system optimization.

[0136] In step S6, the automated test system uses an anomaly detection algorithm based on Bayesian reasoning to calculate the anomaly probability P of the test data. adn :

[0137]

[0138] Among them, P adn is the abnormal probability of the test data, d is the bit error rate deviation value of the test data, μ d and is the mean and variance of the bit error rate deviation, d th is the threshold for bit error rate anomaly detection,

[0139] is the Gaussian probability density function of the bit error rate deviation value,

[0140] is the standardized coefficient of the Gaussian distribution;

[0141] If P adn When the set threshold is exceeded, the system triggers the anomaly detection mechanism, filters out abnormal test data, and adjusts the test process to improve test stability.

[0142] An anomaly detection algorithm based on Bayesian reasoning provides an efficient and accurate solution for filtering abnormal data in automated test systems. This method can promptly identify and filter abnormal data during actual testing, ensuring test data quality and providing data support for subsequent data analysis and system optimization. Furthermore, the algorithm can dynamically adjust the test process, improving the adaptability and stability of the test system, and providing technical support for the testing of satellite measurement, operation, and control data interfaces.

[0143] In step S6, the test data analysis report includes bit error rate trend analysis, channel capacity utilization calculation and dynamic power allocation evaluation, wherein the channel capacity utilization U C The calculation formula is as follows:

[0144]

[0145] Among them, U C is the channel capacity utilization, R test is the data transmission rate measured during the actual test process, and C is the channel capacity.

[0146] The channel capacity utilization calculation formula can intuitively quantify the resource utilization efficiency of the test system by comparing the actual transmission rate with the theoretical channel capacity.

[0147] This formula helps reveal system performance bottlenecks and provides clear target indicators for subsequent dynamic parameter optimization. Its simplicity ensures real-time and accurate test data analysis in a variable channel environment, providing data support for the optimized design of satellite measurement, operation and control data interface test systems.

[0148] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for testing a satellite measurement, operation and control data interface, characterized in that: include: Step S1: Initialize the test environment, set initial test parameters, including channel bandwidth, transmit power, and signal-to-noise ratio threshold, load the communication protocol of the satellite measurement, operation, and control data interface, set the test channel environment, and configure the synchronization mechanism between the signal transmitter and receiver. Step S2, channel measurement and modeling: After the test environment is initialized, the received signal is collected, the channel gain is calculated using the signal strength obtained at the signal receiving end, the channel gain is smoothed and estimated using the Kalman filter method, and the instantaneous signal-to-noise ratio of the channel is calculated. The channel model is determined based on the test channel environment type, and based on the selected channel model, a mathematical relationship between the input signal, channel gain, and noise is established; The calculation formula of the channel gain G(t) in step S2 is: Where G(t) is the channel gain, P r (t) is the received signal power, P s (t) is the transmitted signal power; Step S3, channel capacity calculation: Based on the results of channel measurement and modeling, the Shannon channel capacity theorem is used to calculate the maximum transmission rate of the data interface under the current channel conditions. The channel bandwidth, signal power, and noise power density are substituted into the channel capacity calculation formula to obtain the theoretical optimal transmission capacity. The calculated maximum transmission rate is stored in the test database. The calculation formula of the channel capacity C in step S3 is: Where C is the channel capacity, B is the channel bandwidth, G(t) is the channel gain, and P s (t) is the transmitted signal power, N(t) is the noise power density; Step S4, bit error rate optimization estimation, based on the result of the channel capacity calculation, uses the minimum mean square error estimation method to perform bit error rate analysis on the receiving end signal, calculates the estimated error of the signal, and uses the bit error rate calculation formula to obtain a preliminary bit error rate value. Combined with the maximum a posteriori probability estimation method, the bit error rate is corrected using the prior probability distribution, and the final calculated bit error rate value is stored in the test database; The bit error rate P in step S4 e The calculation of is based on the minimum mean square error estimation, the formula is: in, is the optimal estimate of the received signal, H is the channel matrix, and H H is the conjugate transpose of the channel matrix, σ 2 is the noise variance, I is the identity matrix, and Y is the received signal; Q(·) is the Q function, which is used to calculate the bit error rate; Step S5: Optimal control optimizes test parameters. Based on the result of the bit error rate optimization estimation, a test parameter optimization model with the goal of minimizing the bit error rate is constructed. The channel capacity calculation results and the bit error rate calculation results stored in the test database are retrieved, and a functional relationship between bandwidth, power, and bit error rate is constructed. The optimal test parameters are solved using the optimal control method. The bandwidth and transmit power of the test channel are adjusted using the gradient descent method to make the current test channel configuration close to the theoretical optimal transmission capacity. The optimal test parameters are used as the final test configuration of the test system. Step S6, automated test execution and data analysis, based on the results of the optimal control optimization test parameters, configure the test signal according to the determined optimal bandwidth and optimal power, run the automated test system, record the data indicators during the test process, and perform big data analysis on the test data, use pattern recognition methods to filter out abnormal test data, generate a test data analysis report to compare the test data with the stored channel capacity calculation results, so as to evaluate the rationality of the test results and determine the test performance of the data interface.

2. The method for testing a satellite measurement, operation and control data interface according to claim 1, wherein: In step S4, the maximum a posteriori probability estimation is used to correct the bit error rate, and the formula is: Where P(X=x|Y=y) is the posterior probability, which represents the probability that the original transmitted signal is X=x when the received signal Y=y. P(Y=y) is the probability density of the received signal, P e is the bit error rate.

3. The method for testing a satellite measurement, operation and control data interface according to claim 1, wherein: When the optimal control optimizes the test parameters in step S5, the following objective function is used: Among them, J is the optimization target, P e (B,P s ) is the bit error rate function, P opt is the theoretical optimal power, λ is the adjustment coefficient, P s is the transmitted signal power, T is the test time, B is the channel bandwidth, P e is the bit error rate.

4. The method for testing a satellite measurement, operation and control data interface according to claim 3, wherein: When the optimal control optimizes the test parameters, the Pontryagin maximum principle is used to construct the Hamiltonian function H(P s ,B,λ1,λ2), the expression is as follows: Among them, H(P s ,B,λ1,λ2) is the Hamiltonian function, P e (B,P s ) is the bit error rate function, B is the channel bandwidth, P e is the bit error rate, λ1 and λ2 are Lagrange multipliers, is the rate of change of power with time, is the rate of change of bandwidth over time.

5. The method for testing a satellite measurement, operation and control data interface according to claim 1, wherein: In step S6, the automated test system uses an adaptive gradient descent method to update the power P s and bandwidth B, the iterative update rule is as follows: in, and B (k+1) is the power and bandwidth of the k+1th iteration, α and β are the adaptive learning rates, and is the partial derivative of the bit error rate with respect to power and bandwidth.

6. The method for testing a satellite measurement, operation and control data interface according to claim 1, wherein: In step S6, the automated test system uses an anomaly detection algorithm based on Bayesian reasoning to calculate the anomaly probability P of the test data. adn : Among them, P adn is the abnormal probability of the test data, d is the bit error rate deviation value of the test data, μ d and is the mean and variance of the bit error rate deviation, d th is the threshold for bit error rate anomaly detection, is the Gaussian probability density function of the bit error rate deviation value, is the standardized coefficient of the Gaussian distribution; If P adn When the set threshold is exceeded, the system triggers the anomaly detection mechanism, filters out abnormal test data, and adjusts the test process to improve test stability.

7. The method for testing a satellite measurement, operation and control data interface according to claim 1, wherein: In step S6, the test data analysis report includes bit error rate trend analysis, channel capacity utilization calculation and dynamic power allocation evaluation, wherein the channel capacity utilization U C The calculation formula is as follows: Among them, U C is the channel capacity utilization, R test is the data transmission rate measured during the actual test process, and C is the channel capacity.

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