High-speed data transmission test method and system in satellite remote sensing

Abstract

The invention provides a high-speed data transmission testing method and system in satellite remote sensing, and relates to the technical field of data transmission testing, and the method comprises the steps: carrying out the real-time monitoring through a monitoring interface, and obtaining a first receiving state signal of remote sensing data, received by a first ground receiving station, of a satellite data source; activating signal acquisition equipment to generate a first signal feature vector; traversing in a fault diagnosis rule base embedded in the receiving fault inference engine to obtain a first traversing result; obtaining a target fault, and obtaining a fault feature set; calling a fault diagnosis report generator to analyze and obtain a first fault diagnosis report of the first ground receiving station; and matching the first satellite-ground link and carrying out optimization adjustment. According to the invention, the problem of poor stability of high-speed data transmission in existing satellite remote sensing can be solved, the technical target of improving the accuracy and efficiency of fault diagnosis is achieved, and the effect of improving the operation reliability of the ground receiving station in a complex environment is achieved.

Description

Technical Field

[0001] This application relates to the technical field of data transmission testing, and particularly to a high-speed data transmission testing method and system in satellite remote sensing. Background Art

[0002] With the rapid development of remote sensing satellite technology, the types and amounts of satellite remote sensing data have shown explosive growth. From early multi-spectral remote sensing data to today's hyperspectral and high-resolution remote sensing data, the data volume has leaped from the GB level to the TB level, posing unprecedented challenges to the data transmission system. To meet the complex requirements of multi-type and high-rate remote sensing data transmission, data transmission technology must keep pace with the times. In the existing technology, ground receiving stations undertake the important tasks of receiving and transmitting massive satellite data. However, when receiving satellite remote sensing data, ground receiving stations face problems such as complex signal processing, fault diagnosis, and link optimization. Traditional fault diagnosis methods rely on manual experience and simple rule bases, and it is difficult to cope with the dynamic changes of complex systems. In the field of satellite remote sensing data transmission, high-speed signal transmission technology has become the core. However, the application of high-speed signal transmission in satellite remote sensing is not without challenges. Signals are affected by various factors during long-distance transmission, such as free space loss, atmospheric attenuation, rain fade, etc. These factors may cause signal attenuation and distortion. In addition, the relative motion between the satellite and the ground station will also cause Doppler frequency shift, further increasing the complexity of signal transmission.

[0003] In summary, the high-speed signal transmission technology in satellite remote sensing plays a key role in promoting the efficient transmission of remote sensing data, but at the same time faces many technical problems. Therefore, researching and developing a high-speed data transmission testing method applicable to satellite remote sensing is of great significance for evaluating and optimizing the performance of high-speed signal transmission systems and ensuring the reliable transmission of satellite remote sensing data. Summary of the Invention

[0004] The purpose of this application is to provide a high-speed data transmission testing method and system in satellite remote sensing to solve the technical problems in the existing technology, such as the low efficiency of fault diagnosis of ground receiving stations and the poor link stability due to the dynamic changes of the satellite and the ground network, which in turn affect the data transmission stability of the satellite-ground link.

[0005] In view of the above problems, this application provides a high-speed data transmission testing method and system in satellite remote sensing.

[0006] In a first aspect, the present application provides a method for testing high-speed data transmission in satellite remote sensing. The method for testing high-speed data transmission in satellite remote sensing is implemented through a system for testing high-speed data transmission in satellite remote sensing. Among them, the method for testing high-speed data transmission in satellite remote sensing includes: obtaining a first reception status signal of a first ground receiving station receiving remote sensing data from a satellite data source in real time through a monitoring interface, where the first ground receiving station is any one of the distributed ground receiving stations; activating a signal acquisition device to perform signal processing and analysis on the first reception status signal to obtain a first signal feature set and generate a first signal feature vector; traversing the first signal feature vector in a fault diagnosis rule library embedded in a reception fault inference machine to obtain a first traversal result; screening a target fault based on the first traversal result and obtaining a fault feature set of the target fault; calling a fault diagnosis report generator to analyze the fault feature set to obtain a first fault diagnosis report of the first ground receiving station; matching a first satellite-ground link between the first ground receiving station and the satellite data source and optimizing and adjusting the first satellite-ground link in combination with the first fault diagnosis report.

[0007] In a second aspect, the present application further provides a system for testing high-speed data transmission in satellite remote sensing, which is used to execute the method for testing high-speed data transmission in satellite remote sensing as described in the first aspect. Among them, the system for testing high-speed data transmission in satellite remote sensing includes: a status monitoring module, which is used to obtain a first reception status signal of a first ground receiving station receiving remote sensing data from a satellite data source in real time through a monitoring interface, where the first ground receiving station is any one of the distributed ground receiving stations; a signal processing module, which is used to activate a signal acquisition device to perform signal processing and analysis on the first reception status signal to obtain a first signal feature set and generate a first signal feature vector; a feature traversal module, which is used to traverse the first signal feature vector in a fault diagnosis rule library embedded in a reception fault inference machine to obtain a first traversal result; a fault analysis module, which is used to screen a target fault based on the first traversal result and obtain a fault feature set of the target fault; a fault diagnosis module, which is used to call a fault diagnosis report generator to analyze the fault feature set to obtain a first fault diagnosis report of the first ground receiving station; an optimization and adjustment module, which is used to match a first satellite-ground link between the first ground receiving station and the satellite data source and optimize and adjust the first satellite-ground link in combination with the first fault diagnosis report.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0009] The first reception status signal of the remote sensing data of the satellite data source received by the first ground receiving station is monitored in real time through the monitoring interface, where the first ground receiving station is any one of the distributed ground receiving stations; the activation signal acquisition device performs signal processing and analysis on the first reception status signal to obtain a first signal feature set and generate a first signal feature vector; the first signal feature vector is traversed in the fault diagnosis rule library embedded in the reception fault inference engine to obtain a first traversal result; based on the first traversal result, a target fault is screened out, and a fault feature set of the target fault is obtained; the fault diagnosis report generator is called to analyze the fault feature set to obtain the first fault diagnosis report of the first ground receiving station; the first satellite-ground link between the first ground receiving station and the satellite data source is matched, and the first satellite-ground link is optimized and adjusted in combination with the first fault diagnosis report. That is to say, through the monitoring, processing and analysis of the reception status signal of the ground receiving station, a signal feature vector is obtained, traversed in the fault diagnosis rule library, the target fault is screened out and a fault diagnosis report is generated, and then the satellite-ground link is matched and optimized and adjusted in combination with the fault diagnosis report to improve the reception efficiency and reliability. Through signal processing and analysis and the fault diagnosis rule library, the automatic detection and accurate positioning of the faults of the ground receiving station are realized. Further, in combination with the fault diagnosis result, the satellite-ground link is optimized and adjusted in real time to improve the data transmission efficiency. In addition, it supports the collaborative work of distributed ground receiving stations and improves the operation efficiency of the overall system.

[0010] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are given below. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understandable through the following description. Brief Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and for those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0012] Figure 1 It is a schematic flow chart of a high-speed data transmission test method in satellite remote sensing of the present application;

[0013] Figure 2This is a schematic structural diagram of a high-speed data transmission test system in satellite remote sensing for this application.

[0014] Explanation of reference numerals:

[0015] 11. Status monitoring module; 12. Signal processing module; 13. Feature traversal module; 14. Fault analysis module; 15. Fault diagnosis module; 16. Optimization and adjustment module. Specific implementation manner

[0016] This application provides a high-speed data transmission test method and system in satellite remote sensing, which solves the technical problems in the prior art that the fault diagnosis efficiency of the ground receiving station is low, and due to the dynamic changes of the satellite and the ground network, the link stability is poor, which in turn affects the data transmission stability of the space-ground link. Through signal processing analysis and a fault diagnosis rule base, automatic detection and accurate positioning of ground receiving station faults are realized. Further, in combination with the fault diagnosis results, the space-ground link is adjusted in real time to improve the data transmission efficiency. In addition, it supports the collaborative work of distributed ground receiving stations to improve the operation efficiency of the overall system.

[0017] Next, the technical solutions in this application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the example embodiments described here. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application. In addition, it should be noted that for the sake of description, only the parts related to this application are shown in the drawings rather than all.

[0018] Embodiment 1, please refer to the attached Figure 1 , this application provides a high-speed data transmission test method in satellite remote sensing. Among them, the method is applied to a high-speed data transmission test system in satellite remote sensing. The high-speed data transmission test method in satellite remote sensing specifically includes the following steps:

[0019] Step P10: Obtain the first reception status signal of the remote sensing data received by the first ground receiving station from the satellite data source through real-time monitoring of the monitoring interface, where the first ground receiving station is any one of the distributed ground receiving stations;

[0020] Further, the first ground receiving station is preset with a first signal acquisition strategy for acquiring the first reception status signal. The first signal acquisition strategy refers to a sampling frequency of 10 kHz, a resolution of 16 bits, a filtering parameter of 10 Hz, a triggering mode of level triggering, a communication interface of RS422 / RS485, a rated voltage of 220 V, an acquisition interval of 1 ms, a data storage capacity of not less than 7 days, an operating temperature range of -60 to +85 °C, and an operating humidity range of 5% to 90% RH.

[0021] Specifically, the reception status signal of the ground receiving station is obtained through the monitoring interface, and data acquisition is performed according to the preset signal acquisition strategy. Thus, the efficient monitoring of the reception status of the ground receiving station is realized, ensuring the accuracy and reliability of data acquisition and providing a solid foundation for subsequent fault diagnosis and data processing. Specifically, first, the ground receiving station monitors the remote sensing data of the received satellite data source in real time through the monitoring interface to obtain the first reception status signal. Among them, the first ground receiving station is any one of the distributed ground receiving stations, and such a design enables this solution to be flexibly applied to different receiving sites. Then, data acquisition is performed according to the preset first signal acquisition strategy, which details key parameters such as sampling frequency, resolution, filtering parameter, triggering mode, communication interface, rated voltage, acquisition interval, data storage capacity, and operating temperature and humidity ranges. Among them, the sampling frequency of 10 kHz and the resolution of 16 bits ensure the high precision of data acquisition; the filtering parameter of 10 Hz can effectively remove noise signals; the level triggering mode and the RS422 / RS485 communication interface ensure the stability and compatibility of signal acquisition. In addition, the acquisition interval is 1 ms and the data storage capacity is not less than 7 days, enabling this solution to collect data for a long time and at a high frequency, meeting the requirements of long-term monitoring. Moreover, the operating temperature range of -60 to +85 °C and the humidity range of 5% to 90% RH indicate that this solution can operate stably in extreme environments and adapt to various complex scenarios. Through these detailed acquisition strategy settings, this solution can efficiently and accurately complete the data acquisition task, providing high-quality data support for subsequent signal processing and analysis.

[0022] In summary, through the clear monitoring interface design and detailed signal acquisition strategy, the efficient monitoring of the reception status of the ground receiving station is realized. It can not only ensure the high precision and high reliability of data acquisition but also operate stably in complex environments, meeting the requirements of long-term and high-frequency data acquisition.

[0023] Step P20: Activate the signal acquisition device to perform signal processing and analysis on the first reception status signal to obtain a first signal feature set and generate a first signal feature vector;

[0024] Specifically, the first received status signal is processed and analyzed by the activation signal acquisition device to extract key signal features and generate a signal feature vector. This process can transform complex signal data into a representative feature set and feature vector, providing a basis for further fault diagnosis and data analysis. Specifically, first, the activation signal acquisition device is activated, which is used to perform preliminary processing on the first received status signal. Through the intervention of the signal acquisition device, necessary amplification, filtering, and digitization processing can be performed on the original signal, thus providing clear and usable data for subsequent analysis. Then, the preprocessed signal is deeply analyzed to extract the key features therein, forming the first signal feature set. This process requires the use of signal processing techniques such as Fourier transform and wavelet analysis to identify important features such as frequency, amplitude, and phase in the signal. Then, based on the first signal feature set, the first signal feature vector is generated. The feature vector is a mathematical representation that can integrate the multi-dimensional features of the signal into a compact vector form, facilitating subsequent calculations and analysis. In this way, complex signal data is transformed into a representative feature vector, providing an efficient data form for further fault diagnosis and data analysis. In addition, the generated feature vector can be used in subsequent machine learning or pattern recognition algorithms to achieve rapid classification and judgment of the signal state.

[0025] In summary, by activating the signal acquisition device and performing signal processing and analysis, an efficient transformation from the original signal to the feature vector is achieved. This process can not only extract the key information in the signal but also provide structured data support for subsequent fault diagnosis and data analysis.

[0026] Step P30: Traverse the first signal feature vector in the fault diagnosis rule library embedded in the received fault inference engine to obtain a first traversal result;

[0027] Specifically, the fault diagnosis rule base embedded in the receiving fault inference engine is used to traverse and analyze the first signal feature vector, thereby achieving efficient diagnosis of signal features. Specifically, first, the first signal feature vector is input into the receiving fault inference engine, which contains a carefully designed fault diagnosis rule base internally. This rule base stores various known fault patterns and their corresponding feature vector templates, which are obtained by analyzing and summarizing a large amount of historical fault data. Then, the inference engine starts to traverse the first signal feature vector, that is, the input feature vector is compared with each fault template in the rule base one by one. This process is achieved by calculating the similarity or difference degree between feature vectors, such as using methods like Euclidean distance and cosine similarity to quantify the matching degree between them. Next, according to the comparison results, the inference engine filters out the fault pattern that best matches the first signal feature vector and records the corresponding matching information to form the first traversal result. Obviously, this traversal process is automated and efficient, capable of processing a large amount of feature vector data in a short time, thereby quickly generating a preliminary diagnosis result. In addition, the embedded design of the fault diagnosis rule base makes the entire diagnosis process highly scalable and maintainable, and new fault patterns and diagnosis requirements can be adapted by continuously updating the rule base.

[0028] In summary, by traversing and analyzing the first signal feature vector in the receiving fault inference engine, rapid diagnosis of signal features and preliminary identification of fault patterns are achieved. The matching relationship between signal features and known fault patterns can be quickly identified, and then a preliminary diagnosis result is obtained, providing an important basis for subsequent fault location and handling, and significantly improving the intelligence level and reliability of the entire system.

[0029] Step P40: Screen out the target fault based on the first traversal result and obtain the fault feature set of the target fault;

[0030] Specifically, by analyzing the first traversal result, the target fault is screened out and its corresponding fault feature set is obtained. That is to say, from numerous possible fault modes, the specific fault type is accurately located, and the detailed feature information related to this fault is extracted, providing a clear basis for subsequent fault handling and repair. Specifically, first, the possible fault modes are screened based on the first traversal result. The first traversal result contains the matching information between the signal feature vector and the fault templates in the fault diagnosis rule library, which provides a basis for screening the target fault. According to the preset matching threshold or priority rule, the fault mode that best matches the current signal feature is screened out from the traversal result and determined as the target fault. This process needs to comprehensively consider factors such as the matching degree, the probability of fault occurrence, and the severity of the fault to ensure the accuracy and reliability of the screening result. Then, the fault feature set of the target fault is obtained. The fault feature set is a detailed description of the target fault, which contains all the key feature information related to this fault, such as the type, location, influence range, and possible causes of the fault, etc. By extracting the feature set corresponding to the target fault from the fault diagnosis rule library, comprehensive and specific data support is provided for subsequent fault analysis and processing.

[0031] In summary, through the screening based on the first traversal result and the acquisition of the fault feature set, the accurate positioning and detailed description of the target fault are realized, which not only improves the efficiency and accuracy of fault diagnosis, but also provides clear guidance for subsequent fault handling and system optimization, significantly enhancing the intelligent fault management ability of the entire system.

[0032] Step P50: Call the fault diagnosis report generator to analyze the fault feature set to obtain the first fault diagnosis report of the first ground receiving station;

[0033] Specifically, by calling the fault diagnosis report generator to analyze the fault feature set, the first fault diagnosis report for the first ground receiving station is generated. First, calling the fault diagnosis report generator is a key step to achieve this function. The fault diagnosis report generator is a specially designed module that can receive the fault feature set as input and deeply analyze these features based on the preset analysis rules and algorithms. During the analysis process, the generator will generate a detailed fault diagnosis report according to the information in the fault feature set, combining aspects such as fault mode, influence range, possible causes, and recommended repair measures. Among them, the key information in the fault feature set will be parsed one by one, such as fault type, severity, and relevant parameters, etc., and these information will be integrated into the report to provide clear guidance for subsequent maintenance work. Then, the generator will output the first fault diagnosis report according to the analysis result in a certain format and specification. The report not only contains the basic information of the fault, but also provides detailed descriptions of the fault, possible cause analysis, and recommended solutions, etc.

[0034] In summary, by invoking the fault diagnosis report generator to analyze the fault feature set, a first fault diagnosis report for the first ground receiving station is generated. By converting complex fault features into a report with clear guiding significance, it also provides a clear and accurate reference basis for the maintenance and optimization of the ground receiving station. Significantly improves the efficiency and reliability of fault handling, ensuring the stable operation of the ground receiving station.

[0035] Step P60: Match the first satellite-ground link between the first ground receiving station and the satellite data source, and optimize and adjust the first satellite-ground link in combination with the first fault diagnosis report.

[0036] Specifically, by matching the first satellite-ground link between the first ground receiving station and the satellite data source, and optimizing and adjusting the link in combination with the first fault diagnosis report. First, by matching the first satellite-ground link between the first ground receiving station and the satellite data source, a communication connection between the two is established. Then, the link is optimized and adjusted in combination with the first fault diagnosis report. Exemplarily, if the report indicates that there is a signal attenuation problem in the link, the system can solve this problem by adjusting the transmission power, optimizing the antenna direction, or selecting a more appropriate communication frequency band. In addition, the optimization and adjustment also need to consider the dynamic characteristics of the satellite. For example, the high-speed movement of the satellite may cause frequent switching of the link, so a multi-objective optimization algorithm needs to be introduced. By matching the satellite-ground link between the ground receiving station and the satellite data source, and optimizing and adjusting in combination with the fault diagnosis report, the efficient management and optimization of the satellite-ground link are realized.

[0037] Further, activate the signal acquisition device to perform signal processing and analysis on the first received status signal to obtain a first signal feature set, including:

[0038] Perform mean filtering on the first received status signal to obtain a first preprocessed signal;

[0039] Based on the multi-dimensional signal features of the first preprocessed signal, form the first signal feature set.

[0040] Further, after forming the first signal feature set based on the multi-dimensional signal features of the first preprocessed signal, it further includes:

[0041] Perform wavelet packet decomposition on the first received status signal to obtain a first decomposition signal set, where the first decomposition signal set includes a first signal block corresponding to a first frequency band;

[0042] Perform reconstruction processing on the first signal block to obtain a first energy signal corresponding to the first frequency band;

[0043] Add the normalized first energy signal to the first signal feature set.

[0044] Specifically, the processing process of the signal acquisition device for the first received status signal is further refined. Through mean filtering, wavelet packet decomposition, and the extraction and normalization of energy signals, a richer and more accurate first signal feature set is constructed.

[0045] First, activate the signal acquisition device to process and analyze the first received status signal. Performing mean filtering on the signal can effectively remove noise interference in the signal and smooth the signal waveform, thereby obtaining the first preprocessed signal. Then, based on the multi-dimensional signal features of the first preprocessed signal, a first signal feature set is formed. The multi-dimensional signal features may include information such as the amplitude, frequency, and phase of the signal, which can comprehensively reflect the characteristics of the signal. In addition, the wavelet packet decomposition technology is introduced to further process the first received status signal. Wavelet packet decomposition is a method that can perform multi-band analysis on signals and can decompose the signal into signal blocks in different frequency bands. By performing wavelet packet decomposition on the first received status signal, a first decomposition signal set is obtained, which includes the first signal block corresponding to the first frequency band. Then, the first signal block is reconstructed to obtain the first energy signal corresponding to the first frequency band. This process can extract the energy characteristics of the signal in a specific frequency band and further enrich the content of the signal feature set. Obviously, the energy signal is an important part of the signal features and can reflect the strength and stability of the signal. Finally, the normalized first energy signal is added to the first signal feature set. Normalization processing can standardize the energy characteristics of the signal, making it more comparable and universal in subsequent analysis. Through this series of processing steps, a signal feature set containing various features is constructed, which can more comprehensively reflect the characteristics of the signal.

[0046] In summary, through mean filtering, wavelet packet decomposition, and the extraction and normalization of energy signals, a richer and more accurate first signal feature set is constructed, providing high-quality data support for subsequent fault diagnosis and analysis.

[0047] Further, traverse the first signal feature vector in the fault diagnosis rule library embedded in the received fault inference engine to obtain the first traversal result, including:

[0048] Extract the first fault signal feature set corresponding to the first fault in the fault diagnosis rule library, and generate a first fault feature vector based on the first fault signal feature set;

[0049] Compare the first signal feature vector with the first fault feature vector to obtain the first correlation coefficient;

[0050] Form the first traversal result based on the first correlation coefficient.

[0051] Specifically, by extracting and analyzing the fault signal characteristics in the fault diagnosis rule base, a fault feature vector is generated and compared with the actually detected signal feature vector, thereby obtaining a correlation coefficient and forming a traversal result. First, extract the first fault signal feature set corresponding to the first fault from the fault diagnosis rule base. The process of extracting the first fault signal feature set is completed based on the mapping relationship between the fault type and the feature set, ensuring that the extracted feature set can accurately reflect the characteristics of the first fault. Then, generate the first fault feature vector based on the extracted first fault signal feature set. This process requires converting the multi-dimensional signal characteristics into a compact vector form for subsequent calculation and analysis. The generation of the feature vector usually involves the assignment of feature weights and normalization processing to ensure the comparability and consistency between different features. Then, compare the actually detected first signal feature vector with the generated first fault feature vector to obtain the first correlation coefficient. This comparison process is achieved by calculating the similarity between the two feature vectors. Commonly used methods include Euclidean distance, cosine similarity, or Pearson correlation coefficient, etc. The value of the correlation coefficient ranges from -1 to 1, where the value closer to 1 indicates a higher similarity between the two feature vectors, that is, a higher matching degree between the actual signal and the fault pattern. The traversal result is the summary of all correlation coefficients in the fault diagnosis process, which can provide comprehensive data support for subsequent fault screening and judgment. By analyzing the first traversal result, the system can determine the matching degree between the actual signal and the fault pattern, thereby quickly locating the fault type and providing a clear direction for further fault handling.

[0052] In summary, through a series of steps such as extracting the fault signal feature set, generating the fault feature vector, calculating the correlation coefficient, and forming the traversal result, the rapid identification and quantitative analysis of the fault pattern are realized.

[0053] Further, based on the first traversal result, filter to obtain the target fault and obtain the fault feature set of the target fault, including:

[0054] Using the first correlation coefficient as the screening constraint, obtain the fault corresponding to the maximum correlation coefficient and denote it as the target fault;

[0055] Obtain the fault feature set of the target fault, where the fault feature set at least includes the fault source, fault cause, fault frequency, and fault tree node number.

[0056] Specifically, using the first correlation coefficient as a screening constraint, the fault corresponding to the maximum correlation coefficient is determined from multiple possible faults as the target fault, and a detailed fault feature set of the target fault is further obtained. First, all possible faults in the fault diagnosis rule base are screened using the first correlation coefficient as the screening constraint. The correlation coefficient is an important indicator for measuring the similarity between the actual signal feature vector and the fault feature vector. By comparing these correlation coefficients, the fault mode that best matches the current signal features can be quickly identified. The fault corresponding to the maximum correlation coefficient is the target fault, indicating that this fault mode has the highest similarity to the actually detected signal features, thus providing a strong basis for accurate fault location. Then, the fault feature set of the target fault is obtained, which is a key step in the detailed description of the target fault. The fault feature set includes at least important information such as the fault source, fault cause, fault frequency, and fault tree node number. The fault source refers to the physical location or system component where the fault occurs; the fault cause details the specific factors that lead to the fault, such as hardware aging, software errors, or external interference; the fault frequency reflects the number of times the fault occurs within a certain period, helping to evaluate the severity and priority of the fault; the fault tree node number is the unique identifier of the fault in the fault tree analysis, facilitating the systematic management and analysis of the fault. By obtaining these detailed fault feature information, the solution can provide maintenance personnel with a comprehensive fault background, thus quickly formulating effective repair strategies.

[0057] In summary, by using the first correlation coefficient as a screening constraint, accurately locating the fault corresponding to the maximum correlation coefficient as the target fault, and obtaining its detailed fault feature set, it provides comprehensive and specific information support for subsequent fault handling and system optimization, significantly improving the intelligent maintenance level and reliability of the system.

[0058] Further, it includes:

[0059] Introduce a correlation coefficient analysis function, and analyze the first signal feature vector and the first fault feature vector according to the correlation coefficient analysis function to obtain the first correlation coefficient;

[0060] Among them, the expression of the correlation coefficient analysis function is as follows:

[0061]

[0062] κ(y t ,z i ) represents the correlation coefficient between the first signal feature vector y t and the diagnostic rule information z corresponding to the i-th fault tree node i , cov(y t ,z i ) represents the covariance value, D(yt ) and D(z i ) respectively represent the variance value of the first signal feature vector y t and the diagnostic rule information z corresponding to the i-th fault tree node i 's variance value. E(y t ) and E(z i ) respectively represent the expected value of the first signal feature vector y t and the expected value of the diagnostic rule information z corresponding to the i-th fault tree node i . α, β, and γ represent weight factors, which are respectively used to adjust the contributions of the expected value, the variance, and the square of the difference in the expected value.

[0063] Specifically, a correlation coefficient analysis function is introduced, and the first signal feature vector and the first fault feature vector are analyzed according to the correlation coefficient analysis function to obtain the first correlation coefficient; wherein, the expression of the correlation coefficient analysis function is as follows: κ(y t , z i ) represents the correlation coefficient between the first signal feature vector y t and the diagnostic rule information z corresponding to the i-th fault tree node i . cov(y t , z i ) represents the covariance value. D(y t ) and D(z i ) respectively represent the variance value of the first signal feature vector y t and the variance value of the diagnostic rule information z corresponding to the i-th fault tree node i . E(y t ) and E(z i ) respectively represent the expected value of the first signal feature vector y t and the expected value of the diagnostic rule information z corresponding to the i-th fault tree node i . α, β, and γ represent weight factors, which are respectively used to adjust the contributions of the expected value, the variance, and the square of the difference in the expected value.

[0064] Furthermore, after matching the first satellite-ground link between the first ground receiving station and the satellite data source and optimizing and adjusting the first satellite-ground link in combination with the first fault diagnosis report, it further includes:

[0065] Obtain a data transmission test plan, the data transmission test plan includes a first plan, wherein the first plan refers to a plan for performing data transmission tests on the first ground receiving station under the first environmental conditions;

[0066] Among them, the first environmental condition at least includes the first weather condition, the first electromagnetic interference intensity, and the first satellite orbital altitude.

[0067] Specifically, by obtaining the data transmission test plan, especially the first plan for the first ground receiving station under specific environmental conditions, it provides systematic guidance for the data transmission test of the ground receiving station. First, obtain the data transmission test plan, which is the basis for ensuring the performance verification of the ground receiving station. As a pre-designed plan, the test plan can provide clear processes and standards for the data transmission test. Among them, the first plan is specifically for the test of the first ground receiving station under the first environmental condition, indicating that the plan fully considers the impact of different environmental factors on the data transmission performance. The first environmental condition at least includes the first weather condition, the first electromagnetic interference intensity, and the first satellite orbital altitude, which are the key external conditions affecting the data transmission of the ground receiving station. For example, the weather condition may affect the signal propagation path and intensity, the electromagnetic interference intensity will affect the signal integrity and accuracy, and the satellite orbital altitude is directly related to the signal transmission distance and delay. Obviously, by clarifying these environmental conditions, the plan can provide targeted guidance for the test, ensuring the accuracy and reliability of the test results. Then, through the detailed description of the first plan, the specific requirements and conditions of the test are further clarified. The first plan not only stipulates the environmental conditions of the test but also implies the test objectives and methods. For example, when conducting the test under the first weather condition, it may be necessary to evaluate the impact of weather changes on signal reception and transmission; when conducting the test under a specific electromagnetic interference intensity, it aims to verify the anti-interference ability of the ground receiving station in a complex electromagnetic environment; and when conducting the test under a specific satellite orbital altitude, it is to evaluate the data transmission performance of the receiving station under different orbital parameters. In addition, the first plan may also include content such as the test time arrangement, the configuration of test equipment, and the specific requirements for data collection. The clarification of these details can ensure the standardization and normalization of the test process. By obtaining the data transmission test plan, especially the first plan for the first ground receiving station under specific environmental conditions, it provides comprehensive and systematic guidance for the data transmission test of the ground receiving station.

[0068] Further, after matching the first satellite-ground link between the first ground receiving station and the satellite data source and optimizing and adjusting the first satellite-ground link in combination with the first fault diagnosis report, it further includes:

[0069] Obtain the predetermined link performance index;

[0070] Based on the predetermined link performance index, conduct a performance test on the first satellite-ground link to obtain the first performance test result;

[0071] Optimize and adjust the first satellite-ground link in combination with the first performance test result and the first fault diagnosis report;

[0072] Among them, the predetermined link performance indicators at least include data transmission rate, bit error rate, and spectrum utilization rate.

[0073] Specifically, by performing performance testing and optimization adjustment on the first satellite-ground link, it is ensured that it meets the predetermined performance indicators during actual operation. By obtaining the predetermined link performance indicators and combining the performance test results with the fault diagnosis report for optimization adjustment, the solution can effectively improve the transmission efficiency and stability of the satellite-ground link, reduce the bit error rate, optimize the spectrum resource utilization rate, thereby enhancing the communication performance between the ground receiving station and the satellite, and providing guarantee for high-quality data transmission.

[0074] First, by obtaining the predetermined link performance indicators, it provides clear goals and reference standards for subsequent performance testing and optimization adjustment. The predetermined link performance indicators at least include data transmission rate, bit error rate, and spectrum utilization rate, and these indicators are key parameters for measuring the performance of the satellite-ground link. The data transmission rate reflects the efficiency of the link in transmitting data; the bit error rate is directly related to the accuracy and reliability of data transmission; the spectrum utilization rate reflects the transmission efficiency of the link under limited spectrum resources. By clarifying these indicators, it can provide quantitative goals for the performance testing and optimization adjustment of the satellite-ground link, ensuring that the testing and adjustment processes are targeted and operable. Then, based on the predetermined link performance indicators, perform performance testing on the first satellite-ground link to obtain the first performance test result. This process is to obtain the actual values of key parameters such as its data transmission rate, bit error rate, and spectrum utilization rate by actually testing the performance of the link during operation. Performance testing can be carried out under different environmental conditions to evaluate the performance of the link in various situations. The test results will directly reflect the current performance state of the link and provide data support for subsequent optimization adjustment. Finally, combine the first performance test result and the first fault diagnosis report to optimize and adjust the first satellite-ground link. By comprehensively analyzing the information in the performance test result and the fault diagnosis report, perform targeted optimization on the link. For example, if the performance test result shows a high bit error rate and the fault diagnosis report indicates that it is caused by signal interference, then the optimization adjustment can include increasing anti-interference measures or adjusting signal transmission parameters. In addition, the optimization adjustment can also optimize the data transmission rate and spectrum utilization rate according to the performance test result, such as improving the overall performance of the link by adjusting the modulation and demodulation method or optimizing the spectrum allocation. Through this comprehensive optimization adjustment, the performance of the satellite-ground link can be effectively improved to ensure that it meets the predetermined performance indicators during actual operation.

[0075] In summary, by obtaining the predetermined link performance indicators, performing performance testing, and combining the test results with the fault diagnosis report for optimization adjustment, the effective improvement of the performance of the first satellite-ground link is achieved.

[0076] In summary, the high-speed data transmission test method in satellite remote sensing provided by this application has the following technical effects:

[0077] The first reception status signal of the remote sensing data of the satellite data source received by the first ground receiving station is monitored and obtained in real time through the monitoring interface, where the first ground receiving station is any one of the distributed ground receiving stations; the signal acquisition device is activated to perform signal processing and analysis on the first reception status signal to obtain a first signal feature set and generate a first signal feature vector; the first signal feature vector is traversed in the fault diagnosis rule library embedded in the reception fault inference machine to obtain a first traversal result; based on the first traversal result, a target fault is screened out, and the fault feature set of the target fault is obtained; the fault diagnosis report generator is called to analyze the fault feature set to obtain the first fault diagnosis report of the first ground receiving station; the first satellite-ground link between the first ground receiving station and the satellite data source is matched, and the first satellite-ground link is optimized and adjusted in combination with the first fault diagnosis report. That is to say, by monitoring and processing the reception status signal of the ground receiving station, a signal feature vector is obtained, traversed in the fault diagnosis rule library, the target fault is screened out and a fault diagnosis report is generated, and then the satellite-ground link is matched and optimized and adjusted in combination with the fault diagnosis report to improve the reception efficiency and reliability. Through signal processing and analysis and the fault diagnosis rule library, automatic detection and accurate positioning of the faults of the ground receiving station are realized. Further, in combination with the fault diagnosis result, the satellite-ground link is optimized and adjusted in real time to improve the data transmission efficiency. In addition, it supports the collaborative work of distributed ground receiving stations and improves the operation efficiency of the overall system.

[0078] Embodiment 2, based on the same inventive concept as the high-speed data transmission test method in satellite remote sensing in the foregoing embodiment, this application also provides a high-speed data transmission test system in satellite remote sensing. Please refer to the appendix Figure 2 The high-speed data transmission test system in satellite remote sensing includes:

[0079] A status monitoring module 11, which is used to monitor and obtain the first reception status signal of the remote sensing data of the satellite data source received by the first ground receiving station in real time through the monitoring interface, where the first ground receiving station is any one of the distributed ground receiving stations;

[0080] A signal processing module 12, which is used to activate the signal acquisition device to perform signal processing and analysis on the first reception status signal to obtain a first signal feature set and generate a first signal feature vector;

[0081] A feature traversal module 13, which is used to traverse the first signal feature vector in the fault diagnosis rule library embedded in the reception fault inference machine to obtain a first traversal result;

[0082] A fault analysis module 14, which is used to screen out target faults based on the first traversal result and obtain a fault feature set of the target faults;

[0083] A fault diagnosis module 15, which is used to call a fault diagnosis report generator to analyze the fault feature set and obtain a first fault diagnosis report of the first ground receiving station;

[0084] An optimization and adjustment module 16, which is used to match a first satellite-ground link between the first ground receiving station and the satellite data source and optimize and adjust the first satellite-ground link in combination with the first fault diagnosis report.

[0085] Furthermore, the status monitoring module 11 in the high-speed data transmission test system in satellite remote sensing is further used for: The first ground receiving station is preset with a first signal acquisition strategy, and the first signal acquisition strategy is used to acquire the first reception status signal, where the first signal acquisition strategy refers to a sampling frequency of 10 kHz, a resolution of 16 bits, a filtering parameter of 10 Hz, a triggering mode of level triggering mode, a communication interface of RS422 / RS485, a rated voltage of 220 V, an acquisition interval of 1 ms, a data storage capacity of greater than or equal to 7 days, an operating temperature range of -60 to +85 °C, and an operating humidity range of 5% to 90% RH.

[0086] Furthermore, the signal processing module 12 in the high-speed data transmission test system in satellite remote sensing is further used for:

[0087] Perform mean filtering on the first reception status signal to obtain a first preprocessed signal;

[0088] Based on the multi-dimensional signal features of the first preprocessed signal, form the first signal feature set.

[0089] Furthermore, the high-speed data transmission test system in satellite remote sensing further includes a signal feature expansion module, which is used for:

[0090] Perform wavelet packet decomposition on the first reception status signal to obtain a first decomposition signal set, where the first decomposition signal set includes a first signal block corresponding to a first frequency band;

[0091] Perform reconstruction processing on the first signal block to obtain a first energy signal corresponding to the first frequency band;

[0092] Add the first energy signal after normalization processing to the first signal feature set.

[0093] Furthermore, the feature traversal module 13 in the high-speed data transmission test system in satellite remote sensing is further used for:

[0094] Extract the first fault signal feature set corresponding to the first fault in the fault diagnosis rule base, and generate a first fault feature vector based on the first fault signal feature set;

[0095] Compare the first signal feature vector with the first fault feature vector to obtain a first correlation coefficient;

[0096] Form the first traversal result based on the first correlation coefficient.

[0097] Furthermore, the fault analysis module 14 in the high-speed data transmission test system in satellite remote sensing is further configured to:

[0098] Using the first correlation coefficient as a screening constraint, obtain the fault corresponding to the maximum correlation coefficient, and denote it as the target fault;

[0099] Obtain the fault feature set of the target fault, where the fault feature set at least includes a fault source, a fault cause, a fault frequency, and a fault tree node number.

[0100] Furthermore, the feature traversal module 13 in the high-speed data transmission test system in satellite remote sensing is further configured to:

[0101] Introduce a correlation coefficient analysis function, and analyze the first signal feature vector and the first fault feature vector according to the correlation coefficient analysis function to obtain the first correlation coefficient;

[0102] Among them, the expression of the correlation coefficient analysis function is as follows:

[0103]

[0104] κ(y t ,z i ) represents the correlation coefficient between the first signal feature vector y t and the diagnostic rule information z corresponding to the i-th fault tree node i , cov(y t ,z i ) represents the covariance value, D(y t ) and D(z i ) respectively represent the variance value of the first signal feature vector y t , the variance value of the diagnostic rule information z corresponding to the i-th fault tree node i , E(y t ) and E(z i ) respectively represent the expected value of the first signal feature vector y t , the expected value of the diagnostic rule information z corresponding to the i-th fault tree node iThe expected value, where α, β, and γ represent weighting factors used to adjust the contributions of the expected value, variance, and the square of the difference in expected values, respectively.

[0105] Furthermore, the high-speed data transmission test system in satellite remote sensing further includes a transmission test module, which is used for:

[0106] Obtain a data transmission test plan, where the data transmission test plan includes a first plan. Here, the first plan refers to a plan for conducting data transmission tests on the first ground receiving station under the first environmental conditions.

[0107] Among them, the first environmental conditions at least include the first weather condition, the first electromagnetic interference intensity, and the first satellite orbital altitude.

[0108] Furthermore, the high-speed data transmission test system in satellite remote sensing further includes a link performance test module, which is used for:

[0109] Obtain predetermined link performance indicators;

[0110] Based on the predetermined link performance indicators, perform performance tests on the first space-ground link to obtain a first performance test result;

[0111] Combine the first performance test result and the first fault diagnosis report to optimize and adjust the first space-ground link;

[0112] Among them, the predetermined link performance indicators at least include data transmission rate, bit error rate, and spectrum utilization rate.

[0113] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The Figure 1 A high-speed data transmission test method and specific examples in satellite remote sensing in the first embodiment are equally applicable to the high-speed data transmission test system in satellite remote sensing in this embodiment. Through the detailed description of the high-speed data transmission test method in satellite remote sensing above, those skilled in the art can clearly understand the high-speed data transmission test system in satellite remote sensing in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be elaborated here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, refer to the description in the method section.

[0114] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A high-speed data transmission test method in satellite remote sensing, characterized in that: include: A first receiving state signal of the first ground receiving station receiving remote sensing data from a satellite data source is obtained by real-time monitoring through a monitoring interface, wherein the first ground receiving station is any one of the distributed ground receiving stations; Activate the signal acquisition device to perform signal processing and analysis on the first receiving state signal to obtain a first signal feature set and generate a first signal feature vector; Traversing the first signal feature vector in a fault diagnosis rule base embedded in a receiving fault inference engine to obtain a first traversal result; Filtering a target fault based on the first traversal result, and acquiring a fault feature set of the target fault; Invoking a fault diagnosis report generator to analyze the fault feature set to obtain a first fault diagnosis report of the first ground receiving station; A first satellite-to-ground link between the first ground receiving station and the satellite data source is matched, and the first satellite-to-ground link is optimized and adjusted in combination with the first fault diagnosis report.

2. According to the high-speed data transmission test method in satellite remote sensing of claim 1, it is characterized in that: The first ground receiving station is preset with a first signal acquisition strategy, which is used to acquire the first receiving status signal, wherein the first signal acquisition strategy refers to a sampling frequency of 10kHz, a resolution of 16 bits, a filtering parameter of 10Hz, a trigger mode of level triggering, a communication interface of RS422 / RS485, a rated voltage of 220V, an acquisition interval of 1ms, a data storage capacity of greater than or equal to 7 days, an operating temperature range of -60 to +85°C, and an operating humidity range of 5% to 90%RH.

3. The high-speed data transmission test method in satellite remote sensing according to claim 1, characterized in that: Activating the signal acquisition device to perform signal processing and analysis on the first receiving state signal to obtain a first signal feature set, including: Performing mean filtering on the first receiving state signal to obtain a first preprocessed signal; The first signal feature set is constructed based on the multi-dimensional signal features of the first preprocessed signal.

4. A high-speed data transmission test method in satellite remote sensing according to claim 3, characterized in that: After forming the first signal feature set based on the multi-dimensional signal features of the first preprocessed signal, the method further includes: Performing wavelet packet decomposition on the first receiving state signal to obtain a first decomposed signal set, wherein the first decomposed signal set includes a first signal block corresponding to a first frequency band; Reconstructing the first signal block to obtain a first energy signal corresponding to the first frequency band; The normalized first energy signal is added to the first signal feature set.

5. The high-speed data transmission test method in satellite remote sensing according to claim 1, characterized in that: Traversing the first signal feature vector in a fault diagnosis rule base embedded in a receiving fault inference engine to obtain a first traversal result includes: Extracting a first fault signal feature set corresponding to a first fault in the fault diagnosis rule base, and generating a first fault feature vector based on the first fault signal feature set; Comparing the first signal feature vector with the first fault feature vector to obtain a first correlation coefficient; The first traversal result is composed based on the first correlation coefficient.

6. A high-speed data transmission test method in satellite remote sensing according to claim 5, characterized in that: Screening a target fault based on the first traversal result and acquiring a fault feature set of the target fault includes: Taking the first correlation coefficient as a screening constraint, obtaining a fault corresponding to the maximum correlation coefficient and recording it as the target fault; The fault feature set of the target fault is obtained, wherein the fault feature set at least includes a fault source, a fault cause, a fault frequency, and a fault tree node number.

7. A high-speed data transmission test method in satellite remote sensing according to claim 5, characterized in that: include: Introducing a correlation coefficient analysis function, and analyzing the first signal feature vector and the first fault feature vector according to the correlation coefficient analysis function to obtain the first correlation coefficient; Wherein, the correlation coefficient analysis function is expressed as follows: κ(y t ,z i ) represents the first signal feature vector y t The diagnostic rule information z corresponding to the i-th fault tree node i The correlation coefficient, cov(y t ,z i ) represents the covariance value, D(y t ) and D(z i ) represent the first signal feature vector y t The variance value of the fault tree node i, the diagnostic rule information z i The variance value, E(y t ) and E(z i ) represent the first signal feature vector y t The expected value of the fault tree node z, the diagnostic rule information corresponding to the i-th fault tree node i , α, β, and γ represent weight factors, which are used to adjust the contribution of the expected value, the contribution of the variance, and the contribution of the square of the expected value difference, respectively.

8. The high-speed data transmission test method in satellite remote sensing according to claim 1, characterized in that: After matching the first satellite-to-ground link between the first ground receiving station and the satellite data source, and optimizing and adjusting the first satellite-to-ground link in combination with the first fault diagnosis report, the method further includes: Acquire a data transmission test plan, wherein the data transmission test plan includes a first plan, wherein the first plan refers to a plan for performing a data transmission test on the first ground receiving station under a first environmental condition; The first environmental condition includes at least a first weather condition, a first electromagnetic interference intensity, and a first satellite orbit altitude.

9. A high-speed data transmission test method in satellite remote sensing according to claim 1, characterized in that: After matching the first satellite-to-ground link between the first ground receiving station and the satellite data source, and optimizing and adjusting the first satellite-to-ground link in combination with the first fault diagnosis report, the method further includes: Obtaining predetermined link performance indicators; Performing a performance test on the first satellite-to-ground link based on the predetermined link performance indicator to obtain a first performance test result; optimizing and adjusting the first satellite-to-ground link in combination with the first performance test result and the first fault diagnosis report; The predetermined link performance indicators include at least data transmission rate, bit error rate and spectrum utilization rate.

10. A high-speed data transmission test system in satellite remote sensing, characterized in that: The system is used to perform a high-speed data transmission test method in satellite remote sensing as described in any one of claims 1 to 9, comprising: A status monitoring module, which is used to monitor in real time through a monitoring interface to obtain a first receiving status signal of a first ground receiving station receiving remote sensing data from a satellite data source, wherein the first ground receiving station is any one of the distributed ground receiving stations; A signal processing module, which is used to activate the signal acquisition device to perform signal processing and analysis on the first receiving state signal, obtain a first signal feature set, and generate a first signal feature vector; A feature traversal module, which is used to traverse the first signal feature vector in a fault diagnosis rule base embedded in a receiving fault inference engine to obtain a first traversal result; A fault analysis module, which is used to screen and obtain a target fault based on the first traversal result, and obtain a fault feature set of the target fault; a fault diagnosis module, configured to call a fault diagnosis report generator to analyze the fault feature set to obtain a first fault diagnosis report of the first ground receiving station; An optimization and adjustment module is used to match a first satellite-to-ground link between the first ground receiving station and the satellite data source, and optimize and adjust the first satellite-to-ground link in combination with the first fault diagnosis report.

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