High-performance satellite communication signal transceiver

Abstract

The invention discloses a high-performance satellite communication signal transceiver, which relates to the field of communication transceiving and comprises a management module used for managing and controlling each functional module and registering child nodes with uploading and receiving permissions; the data receiving module is used for receiving signal data sent by a plurality of child nodes through a satellite and preprocessing the received signal data to obtain analysis data; the interference identification module is used for analyzing the quality of the received signal in real time in a signal receiving process, identifying an interference source type and an intensity parameter through an automatically planned threshold value and an algorithm, and generating an interference set; according to the method, an interference source is detected and positioned in real time, the type and strength of the interference source are analyzed, and adaptive adjustment is carried out on threshold values planned by the type and strength of the interference source and algorithm recognition, so that overlarge errors of overall data caused by local node faults or interference are avoided in the long-link communication process.

Description

Technical Field

[0001] The present invention relates to the technical field of communication transceiver, and in particular to a high-performance satellite communication signal transceiver. Background Art

[0002] With the acceleration of urbanization and the increase in traffic volume, traffic management has become more complex and difficult. Real-time and accurate traffic monitoring can help improve traffic efficiency, reduce congestion and accident rates. Traditional traffic monitoring technologies, such as ground cameras and sensors, have insufficient coverage in some areas, and the real-time and accuracy of data collection are limited. Therefore, new technologies are needed to provide wider coverage and higher reliability. Satellites can cover a wide geographical area and provide more stable communication guarantees. Data transmission via satellite can achieve real-time monitoring and feedback, and event prediction. Therefore, its application in the field of traffic monitoring shows great potential and advantages.

[0003] However, the existing satellite communication signal transceiver equipment, during the long-link multi-node communication process, has interference from various types and intensities of interference sources. As a result, during the communication process of this link and during the traffic monitoring process, the data error of any transmission node can easily lead to deviations in the final prediction results, making it impossible to effectively predict possible events in the future, resulting in the system's slow response in emergencies. There is a lack of reasonable error data compensation behavior, which is not conducive to improving the accuracy of event prediction, and there is a lack of verification mechanism for predicted events and correction mechanism for identified data. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the above-mentioned shortcomings of the prior art, the present invention provides a high-performance satellite communication signal transceiver device, which can effectively solve the problems of the prior art.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0008] The present invention discloses a high-performance satellite communication signal transceiver device, comprising:

[0009] The management module is used to control each functional module and register sub-nodes with upload and receiving permissions;

[0010] A data receiving module is used to receive signal data sent from multiple sub-nodes via satellite, and pre-process the received signal data to obtain parsed data;

[0011] The interference identification module is used to analyze the quality of the received signal in real time during the signal reception process, identify the type and strength parameters of the interference source through the automatically planned threshold and algorithm, and generate an interference set;

[0012] Interference analysis module, used to calculate the signal loss value based on the interference set by statistical methods and identify the cause of the loss;

[0013] The data compensation module is used to perform data repair and compensation processing on the corresponding loss source through data reconstruction technology according to the loss value and cause analyzed by the interference analysis module;

[0014] The event prediction module is used to build a comprehensive prediction model, analyze the historical data of each node, predict events on the current node sections within a preset period in the future, and generate prediction data;

[0015] A data forwarding module, used to synchronously send the prediction data to several associated child nodes;

[0016] The verification module is used to compare and analyze the actual collected data with the prediction results of the previous cycle at the end of the previous cycle to determine whether the accuracy coefficient of the evaluation prediction meets the preset accuracy threshold;

[0017] The parameter adjustment module is used to output the planning parameters of active interference identification to the interference identification module based on the accuracy coefficient feedback obtained by the verification module and the verification result.

[0018] Furthermore, in the process of automatically planning the thresholds and algorithms of the interference identification module to identify the types and strength parameters of interference sources, historical signal data is used to analyze the signal characteristics under normal operating conditions, and the mean and standard deviation of the normal signal strength are statistically used to set several dynamic thresholds for signal strength. The thresholds are updated through real-time data, and the received signal strength values ​​are monitored to locate the interference source. If it exceeds or is lower than the preset threshold, it is marked as a potential interference source. According to the results of the identification algorithm, different interference sources are classified, and the interference intensity is calculated by comparing the difference between the signal strength and the preset threshold, and a certain interference source is quantified into one or more indicators.

[0019] Furthermore, in the process of locating the interference source, the interference source signal is converted to the frequency domain by fast Fourier transform, the spectrum characteristics of the interference source signal are analyzed, and the abnormal signal in a specific frequency range is identified. The discrete Fourier transform calculation formula of the interference source signal is:

[0020]

[0021] Wherein, X(K) represents the Kth frequency component, K=0, 1, 2, ..., N-1; y(n) represents the discrete sample value of the input signal, n represents the time index, N represents the total number of samples of the input signal, j represents the imaginary unit, and e represents the base of the natural logarithm.

[0022] Furthermore, during the analysis of the frequency spectrum characteristics of the interference source signal, the intensity calculation formula for each frequency component is obtained as follows:

[0023]

[0024] In the formula, |X(K)| represents the intensity of the frequency K component, Re(X(K)) represents the real part of X(K), and Im(X(K)) represents the imaginary part of X(K). When the intensity of the frequency K component exceeds the preset threshold, it is marked as an interference signal.

[0025] Furthermore, the calculation formula of the phase O(K) of the frequency K component is:

[0026]

[0027] Furthermore, the attributes of the interference set of the interference identification module include: the identified interference type and its intensity, occurrence time and affected nodes.

[0028] Furthermore, the data compensation module extracts features from the non-lost data during the data reconstruction process to obtain features of the loss value. Based on the features of the non-lost data part and the features of the loss value, a reconstruction model is established and trained through interpolation. For each damaged data point, the reconstruction model obtains the predicted reconstruction value, and fills the predicted reconstruction value back into the original data to form a complete data set.

[0029] Furthermore, the construction and operation process of the comprehensive prediction model in the event prediction module is as follows:

[0030] Acquire and pre-process the historical reporting data, environmental information, historical event records and interference pattern data of each node, train them through machine learning algorithms, use the trained model to make predictions using the latest input data, and generate event expectations within a preset period in the future.

[0031] Furthermore, an update module is provided inside the parameter adjustment module, and the update module is used to continuously monitor the status and data of each node, update the content analysis reported by the verification module in real time, and periodically submit the accuracy coefficient of the updated data to the parameter adjustment module for feedback.

[0032] Furthermore, the management module is interactively connected to the data receiving module and the data forwarding module through a wireless network, the data receiving module is interactively connected to the interference identification module through a wireless network, the interference identification module is electrically connected to the parameter adjustment module through a medium, the interference identification module is interactively connected to the interference analysis module through a wireless network, the interference analysis module is interactively connected to the data compensation module through a wireless network, the data compensation module is interactively connected to the event prediction module through a wireless network, the event prediction module is interactively connected to the data forwarding module through a wireless network, the data forwarding module is interactively connected to the verification module through a wireless network, and the verification module is interactively connected to the parameter adjustment module through a wireless network.

[0033] (III) Beneficial effects

[0034] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0035] 1. Through proactive measures to identify interference to satellite communications, the interference source can be detected and located in real time, and the type and strength of the interference source can be analyzed, thereby improving the reliability of satellite communication signal reception and the integrity of the data. By analyzing the signal quality and interference type, it helps to reduce errors in communications. After obtaining actual data, the threshold and algorithm recognition planned for the type and strength of the interference source can be adaptively adjusted to ensure that the recognition capability can adapt to changes in various interference environments. In the process of long-link communication, it avoids excessive errors in the overall data due to local node failures or interference.

[0036] 2. By using historical signal data to set dynamic thresholds, monitor signal strength, identify interference sources in a timely manner, reduce the occurrence of data errors, and use data reconstruction technology to compensate for interfered data, it is possible to effectively repair lost data during transmission and avoid a decrease in prediction accuracy caused by missing data.

[0037] 3. By utilizing a comprehensive prediction model, combining historical data with existing signals, we can foresee future traffic events and make more reasonable decisions in traffic management. By receiving and processing data in real time, we can quickly monitor traffic signals. In this process, through a sufficient verification mechanism, the original setting parameters are automatically analyzed and corrected based on actual data performance, thereby improving the accuracy of traffic event predictions and reducing human intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 It is a schematic diagram of the framework of the present invention.

[0040] The numbers in the figure represent, respectively, 1. management module; 2. data receiving module; 3. interference identification module; 4. interference analysis module; 5. data compensation module; 6. event prediction module; 7. data forwarding module; 8. verification module; 9. parameter adjustment module; 91. update module. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] The present invention will be further described below in conjunction with the embodiments.

[0043] Example 1

[0044] A high-performance satellite communication signal transceiver device of this embodiment, such as Figure 1 As shown, including:

[0045] Management module 1 is used to manage and control each functional module, register sub-nodes with upload and receiving permissions, ensure that only authorized nodes can send and receive signals, and improve security;

[0046] The data receiving module 2 is used to receive signal data sent from multiple sub-nodes via satellite, and pre-process the received signal data to obtain analytical data, thereby improving the quality of the received signal and facilitating subsequent processing and analysis;

[0047] Interference identification module 3 is used to analyze the quality of the received signal in real time during the signal reception process, identify the type and strength parameters of the interference source through the automatically planned threshold and algorithm, and generate an interference set; use historical signal data to analyze the signal characteristics under normal operating conditions, statistically calculate the mean and standard deviation of the normal signal strength to set several dynamic thresholds for signal strength, update the threshold through real-time data, monitor the received signal strength value, and locate the interference source. If it exceeds or falls below the preset threshold, it is marked as a potential interference source. According to the results of the identification algorithm, different interference sources are classified, and the interference intensity is calculated by comparing the difference between the signal strength and the preset threshold, and a certain interference source is quantified into one or more indicators; the attributes of the interference set include: the identified interference type and its intensity, the occurrence time and the affected node; real-time detection and identification of interference sources, and quantification of interference intensity, so that the interference source positioning is more accurate;

[0048] Interference analysis module 4, used to calculate the signal loss value by statistical method based on the interference set and identify the cause of the loss;

[0049] The data compensation module 5 is used to perform data repair and compensation processing on the corresponding loss source through data reconstruction technology according to the loss value and cause analyzed by the interference analysis module 4;

[0050] The data compensation module 5 extracts the features of the undamaged data and obtains the features of the loss value during the data reconstruction process. Based on the features of the undamaged data and the loss value, a reconstruction model is established and trained through interpolation. For each damaged data point, the reconstruction model obtains the predicted reconstruction value and fills the predicted reconstruction value back into the original data to form a complete data set, thereby improving the signal quality and credibility.

[0051] The event prediction module 6 is used to build a comprehensive prediction model, analyze the historical data of each node, predict events for each current node section within a preset period in the future, and generate prediction data; the construction and operation process of the comprehensive prediction model is as follows:

[0052] Obtain and pre-process the historical reporting data, environmental information, historical event records and interference pattern data of each node, train them through machine learning algorithms, use the trained model to make predictions using the latest input data, and generate event expectations within the future preset period; analyze historical data and environmental information, predict future events, and improve the ability to predict future signal interference;

[0053] The data forwarding module 7 is used to synchronously send the prediction data to several associated child nodes; the prediction data is timely synchronized to the relevant nodes so that they can take preventive measures to reduce the impact of future interference;

[0054] Verification module 8 is used to compare and analyze the actual collected data with the prediction results of the previous cycle at the end of the previous cycle to determine whether the accuracy coefficient of the evaluation prediction meets the preset accuracy threshold; regularly evaluate the accuracy of the prediction to ensure the effectiveness and reliability of the prediction model;

[0055] The parameter adjustment module 9 is used to feedback the accuracy coefficient obtained by the verification module 8, and output the planning parameters of active interference identification to the interference identification module 3 based on the verification result. The parameter adjustment module 9 is internally provided with an update module 91, and the update module 91 is used to continuously monitor the status and data of each node, update the content analysis reported by the verification module 8 in real time, and periodically submit the accuracy coefficient feedback of the updated data to the parameter adjustment module 9. Based on the prediction accuracy evaluation, the planning parameters of active interference identification are output to optimize the interference source identification and processing strategy.

[0056] As an implementation method of this embodiment, Figure 1 As shown, the management module 1 is interactively connected with the data receiving module 2 and the data forwarding module 7 through a wireless network, the data receiving module 2 is interactively connected with the interference identification module 3 through a wireless network, the interference identification module 3 is electrically connected with the parameter adjustment module 9 through a medium, the interference identification module 3 is interactively connected with the interference analysis module 4 through a wireless network, the interference analysis module 4 is interactively connected with the data compensation module 5 through a wireless network, the data compensation module 5 is interactively connected with the event prediction module 6 through a wireless network, the event prediction module 6 is interactively connected with the data forwarding module 7 through a wireless network, the data forwarding module 7 is interactively connected with the verification module 8 through a wireless network, and the verification module 8 is interactively connected with the parameter adjustment module 9 through a wireless network.

[0057] Compared with existing technologies, this device can quantify interference by analyzing signal quality in real time and identifying the type and strength of interference sources, providing a basis for locating and mitigating interference sources, calculating signal loss values ​​based on interference sets and identifying the causes of loss, and then using data reconstruction technology to perform data repair and compensation processing to maintain data integrity.

[0058] Build a comprehensive prediction model to predict future events using historical data and environmental information, so that managers can deploy resources and take mitigation measures in advance, conduct comparative analysis based on actual collected data and prediction results, evaluate prediction accuracy, and continuously optimize interference identification and event prediction algorithms based on feedback to improve prediction reliability;

[0059] Monitor the status and data of each node in real time, and periodically submit accurate coefficient feedback of updated data to ensure that interference identification and event prediction algorithms are always up-to-date and accurate.

[0060] Example 2

[0061] At other levels, this embodiment also provides a measure for locating interference sources, which can actively calculate the location and strength of the interference source. In the process of locating the interference source, the interference source signal is converted to the frequency domain through fast Fourier transform, the spectrum characteristics of the interference source signal are analyzed, and abnormal signals in a specific frequency range are identified. The discrete Fourier transform calculation formula of the interference source signal is:

[0062]

[0063] Wherein, X(K) represents the Kth frequency component, K=0, 1, 2, ..., N-1; y(n) represents the discrete sample value of the input signal, n represents the time index, N represents the total number of samples of the input signal, j represents the imaginary unit, and e represents the base of the natural logarithm. In the analysis of the intensity of the frequency K component, by setting a preset threshold, the sensitivity of interference source identification can be flexibly adjusted according to the needs of the actual application environment, thereby improving the applicability and reliability of the system.

[0064] As a preferred implementation in this embodiment, during the analysis of the spectrum characteristics of the interference source signal, the calculation formula for obtaining the intensity of each frequency component is:

[0065]

[0066] In the formula, |X(K)| represents the intensity of the frequency K component, Re(X(K)) represents the real part of X(K), and Im(X(K)) represents the imaginary part of X(K). When the intensity of the frequency K component exceeds the preset threshold, it is marked as an interference signal.

[0067] The calculation formula for the phase O(K) of the frequency K component is:

[0068]

[0069] Compared with the existing technology, frequency domain analysis can accurately identify and extract specific frequency ranges and their characteristics in the signal. Compared with time domain signals, frequency domain information is easier to reveal the characteristics and changes of signals, especially in the task of locating interference sources. It can clearly distinguish interference signals from normal signals. By analyzing the real and imaginary parts of the frequency components, the strength of each frequency component can be comprehensively evaluated, thereby ensuring high-sensitivity identification of interference signals and making the location of interference sources more accurate. Not only the intensity is analyzed, but also the phase of the frequency is calculated, which provides an important basis for the identification and subsequent processing of phase interference and enhances the ability to analyze complex signals.

[0070] By combining the strength, phase and spectrum characteristics, a more complex interference source location model can be constructed to improve the monitoring and location capabilities of multi-source interference and meet the needs of complex modern communication environments.

[0071] Working principle: edit and issue control instructions through management module 1, register sub-nodes that meet access requirements, obtain communication data of sub-nodes through data receiving module 2, identify interference information of communication data through interference identification module 3, analyze specific interference source types and location information through interference analysis module 4, compensate damaged sub-node data through data compensation module 5, predict traffic events through event prediction module 6 with compensated data, issue predicted data to designated sub-nodes through data forwarding module 7, verify error between predicted data and real data through verification module 8, if error data exists, adjust identification settings of interference identification module 3 through parameter adjustment module 9 based on error data, update module 91 reports updated data in real time, and performs data feedback periodically;

[0072] The present invention improves the satellite communication signal receiving and processing capabilities, reduces the impact of interference, identifies and locates interference sources in real time, quickly takes countermeasures, accurately predicts future interference, takes preventive measures in advance, optimizes interference identification and processing strategies, and improves the reliability and stability of signal transmission.

[0073] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A high-performance satellite communication signal transceiver, characterized in that: include: Management module (1), used to manage and control each functional module and register sub-nodes with upload and receiving permissions; A data receiving module (2) is used to receive signal data sent from multiple sub-nodes via satellite, and pre-process the received signal data to obtain parsed data; An interference identification module (3) is used to analyze the quality of the received signal in real time during the signal reception process, identify the type and strength parameters of the interference source through the automatically planned threshold and algorithm, and generate an interference set; An interference analysis module (4), used to calculate the signal loss value based on the interference set by using a statistical method and identify the cause of the loss; A data compensation module (5) is used to perform data repair and compensation processing on the corresponding loss source through data reconstruction technology according to the loss value and cause analyzed by the interference analysis module (4); An event prediction module (6) is used to construct a comprehensive prediction model, analyze the historical data of each node, predict events on the current node sections within a preset period in the future, and generate prediction data; A data forwarding module (7), used for synchronously sending the prediction data to a plurality of associated child nodes; A verification module (8) is used to compare and analyze the actual collected data with the prediction results of the previous cycle at the end of the previous cycle to determine whether the accuracy coefficient of the evaluation prediction meets the preset accuracy threshold; The parameter adjustment module (9) is used to output the planning parameters of active interference identification to the interference identification module (3) based on the accuracy coefficient feedback obtained by the verification module (8) and the verification result.

2. A high-performance satellite communication signal transceiver according to claim 1, characterized in that: In the process of automatically planning the threshold and algorithm for identifying the type and strength parameters of the interference source by the interference identification module (3), historical signal data is used to analyze the signal characteristics under normal operating conditions, and the mean and standard deviation of the normal signal strength are statistically calculated to set a number of dynamic thresholds for the signal strength. The threshold is updated through real-time data, and the received signal strength value is monitored to locate the interference source. If it exceeds or falls below a preset threshold, it is marked as a potential interference source. According to the result of the identification algorithm, different interference sources are classified, and the interference strength is calculated by comparing the difference between the signal strength and the preset threshold, and a certain interference source is quantified into one or more indicators.

3. A high-performance satellite communication signal transceiver according to claim 2, characterized in that: In the process of locating the interference source, the interference source signal is converted to the frequency domain by fast Fourier transform, the spectrum characteristics of the interference source signal are analyzed, and abnormal signals in a specific frequency range are identified. The discrete Fourier transform calculation formula of the interference source signal is: Wherein, X(K) represents the Kth frequency component, K=0, 1, 2, ..., N-1; y(n) represents the discrete sample value of the input signal, n represents the time index, N represents the total number of samples of the input signal, j represents the imaginary unit, and e represents the base of the natural logarithm.

4. A high-performance satellite communication signal transceiver device according to claim 3, characterized in that: During the analysis of the frequency spectrum characteristics of the interference source signal, the intensity calculation formula for each frequency component is obtained as follows: In the formula, |X(K)| represents the intensity of the frequency K component, Re(X(K)) represents the real part of X(K), and Im(X(K)) represents the imaginary part of X(K). When the intensity of the frequency K component exceeds the preset threshold, it is marked as an interference signal.

5. A high-performance satellite communication signal transceiver according to claim 4, characterized in that: The calculation formula of the phase O(K) of the frequency K component is:

6. A high-performance satellite communication signal transceiver according to claim 1, characterized in that: The attributes of the interference set of the interference identification module (3) include: the identified interference type and its intensity, occurrence time and affected nodes.

7. A high-performance satellite communication signal transceiver according to claim 1, characterized in that: The data compensation module (5) extracts features from the undamaged data during the data reconstruction process to obtain features of the loss value. Based on the features of the undamaged data and the features of the loss value, a reconstruction model is established and trained by interpolation. For each damaged data point, the reconstruction model obtains a predicted reconstruction value, and fills the predicted reconstruction value back into the original data to form a complete data set.

8. The high-performance satellite communication signal transceiver device according to claim 1, characterized in that: The construction and operation process of the comprehensive prediction model in the event prediction module (6) is as follows: Acquire and pre-process the historical reporting data, environmental information, historical event records and interference pattern data of each node, train them through machine learning algorithms, use the trained model to make predictions using the latest input data, and generate event expectations within a preset period in the future.

9. The high-performance satellite communication signal transceiver device according to claim 1, characterized in that: The parameter adjustment module (9) is internally provided with an update module (91), and the update module (91) is used to continuously monitor the status and data of each node, update the content analysis reported by the verification module (8) in real time, and periodically submit the accuracy coefficient of the updated data to the parameter adjustment module (9) for feedback.

10. The high-performance satellite communication signal transceiver device according to claim 1, characterized in that: The management module (1) is interactively connected to the data receiving module (2) and the data forwarding module (7) via a wireless network; the data receiving module (2) is interactively connected to the interference identification module (3) via a wireless network; the interference identification module (3) is electrically connected to the parameter adjustment module (9) via a medium; the interference identification module (3) is interactively connected to the interference analysis module (4) via a wireless network; the interference analysis module (4) is interactively connected to the data compensation module (5) via a wireless network; the data compensation module (5) is interactively connected to the event prediction module (6) via a wireless network; the event prediction module (6) is interactively connected to the data forwarding module (7) via a wireless network; the data forwarding module (7) is interactively connected to the verification module (8) via a wireless network; and the verification module (8) is interactively connected to the parameter adjustment module (9) via a wireless network.

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