A high-performance satellite communication transceiver

Abstract

The application discloses a kind of high-performance satellite communication signal transceiver equipment, it is related to communication receiving field, including: management module, for controlling each function module, registration has upload and receives the subnode of authority;Data receiving module is used to receive the signal data sent from multiple subnodes through satellite, and the received signal data is preprocessed, and analysis data is obtained;Interference identification module is used to analyze the received signal quality in signal receiving process, and the type and intensity parameters of interference source are identified by automatic planning threshold and algorithm, and interference set is generated;By the measures of active satellite communication interference identification, real-time detection and positioning interference source, analyzing the type and intensity of interference source, the threshold and algorithm identification planned for the type and intensity of interference source are adaptively adjusted, in the communication process of long chain, avoid because local node failure or interference causes overall data to appear too large error.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication transceiver, in particular to a high-performance satellite communication signal transceiver. BACKGROUND

[0002] With the acceleration of urbanization, traffic flow increases, and traffic management becomes more complex and difficult. Real-time and accurate traffic monitoring can help improve traffic efficiency and 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 support. Through satellite data transmission, real-time monitoring and feedback can be achieved, and event prediction can be performed. Therefore, the application of satellites in the field of traffic monitoring shows great potential and advantages.

[0003] However, the existing satellite communication signal transceiver has the following problems: in the communication process of long-chain multi-node, due to the existence of various types and intensity of interference sources, the data error of any transmission node in the communication process of this link is easy to cause the deviation of the final prediction result in the process of traffic monitoring, which cannot effectively predict the possible events in the future, and the system lacks reasonable error data compensation behavior, which is not conducive to improving the correctness of event prediction, and lacks a verification mechanism for predicted events and a correction mechanism for identified data. SUMMARY

[0004] (I) Technical problems to be solved

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

[0006] (II) Technical solutions

[0007] To achieve the above purpose, the present application is realized by the following technical solutions,

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

[0009] The management module is used for managing and controlling each functional module, and registering the sub-nodes with upload and receiving authority;

[0010] The data receiving module is used for receiving signal data sent from multiple sub-nodes through a satellite, and pre-processing the received signal data to obtain parsed data;

[0011] An interference identification module is configured to analyze the quality of received signals in real time during signal receiving, identify the type and intensity parameters of interference sources through automatically programmed threshold values and algorithms, and generate an interference set;

[0012] An interference analysis module is configured to calculate signal loss values through statistical methods based on the interference set, and identify loss causes;

[0013] A data compensation module is configured to perform data repair and compensation processing on corresponding loss sources through data reconstruction techniques according to the loss values and causes analyzed by the interference analysis module;

[0014] An event prediction module is configured to construct a comprehensive prediction model, analyze historical data of each node, and perform event prediction on current nodes and road sections in a future preset time period to generate prediction data;

[0015] A data forwarding module is configured to synchronously send the prediction data to a plurality of associated sub-nodes;

[0016] A verification module is configured to compare and analyze actual collected data and prediction results of a previous period at the end of the previous period, and determine whether an accuracy coefficient of the prediction meets a preset accuracy threshold;

[0017] A parameter adjustment module is configured to output programmed parameters of active interference identification to the interference identification module based on the verification results according to the accuracy coefficient feedback obtained by the verification module.

[0018] Further, in the process of identifying the type and intensity parameters of interference sources by the interference identification module through automatically programmed threshold values and algorithms, historical signal data is used to analyze signal characteristics in a normal operating state, the mean and standard deviation of normal signal intensity are calculated to set a plurality of dynamic threshold values of signal intensity, the threshold values are updated through real-time data, the received signal intensity values are monitored, the interference source is located, if the threshold values exceed or are lower than preset threshold values, the interference source is marked as a potential interference source, different interference sources are classified according to the results of the identification algorithm, the interference intensity is calculated by comparing the differences between the signal intensity and the preset threshold values, and a certain interference source is quantified into one or more indexes.

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

[0020]

[0021] In the formula, X(K) represents the Kth frequency component, D represents the total number of signal sample points, 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 natural logarithm.

[0022] Further, in the analysis process of the spectral characteristics of the interference source signal, the intensity calculation formula of each frequency component is:

[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 a preset threshold, it is marked as an interference signal.

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

[0026]

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

[0028] Further, in the data reconstruction process of the data compensation module, the features in the non-loss data are extracted, the loss value features are obtained, and based on the features of the non-loss data part and the loss value features, an interpolation method is used to establish and train a reconstruction model. For each damaged data point, the reconstruction model obtains a predicted reconstruction value, fills the predicted reconstruction value back into the original data, and forms a complete data set.

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

[0030] The historical reporting data, environmental information, historical event records, and interference mode data of each node are obtained and preprocessed, a machine learning algorithm is used for training, the latest input data is used for prediction through the trained model, and an event expectation in a future preset period is generated.

[0031] Further, the parameter adjustment module is internally provided with an update module, which is used to continuously monitor the state and data of each node, real-time update the content analysis reported by the verification module, and periodically feed back the accuracy coefficient of the updated data to the parameter adjustment module.

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

[0033] (III) Beneficial Effects

[0034] Compared with the known prior art, the technical scheme provided by the application has the following beneficial effects,

[0035] 1. By actively identifying satellite communication interference, real-time detection and positioning of the interference source, analysis of the type and intensity of the interference source, the reliability of satellite communication signal reception and the integrity of data are improved, and by analyzing signal quality and interference type, errors in communication are reduced, and after obtaining actual data, the threshold and algorithm identification planned by the type and intensity of the interference source are adaptively adjusted to ensure that the identification capability can adapt to changes in various interference environments, and in the communication process of a long chain, local node failure or interference is avoided to cause large errors in the overall data.

[0036] 2. By setting a dynamic threshold value using historical signal data, monitoring signal strength, and identifying interference sources in a timely manner, data error occurrence is reduced, and by using data reconstruction technology to compensate for interfered data, lost data in transmission can be effectively repaired to avoid a decrease in prediction accuracy caused by data loss.

[0037] 3. By using a comprehensive prediction model, combining historical data and existing signals, and predicting future traffic events, more reasonable decisions are made in traffic management, and by real-time reception and processing of data, rapid monitoring of traffic signals is realized, and in this process, the original setting parameters are automatically analyzed and corrected according to actual data performance under a sufficient verification mechanism, thereby improving the accuracy of traffic event prediction and reducing human intervention. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical scheme in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0039] Figure 1 A schematic diagram of the framework of the present application.

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

[0041] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0042] The present application will be further described below in combination with the embodiments.

[0043] Embodiment 1

[0044] A high-performance satellite communication signal transceiving device of the present embodiment, as shown in the figure, comprises: Figure 1

[0045] The management module 1 is used for managing and controlling each functional module, registering a child node with upload and receiving authority, ensuring that only authorized nodes can send and receive signals, and improving security.

[0046] The data receiving module 2 is used for receiving signal data sent from multiple child nodes through a satellite, pre-processing the received signal data, obtaining parsed data, improving the quality of received signals, and facilitating subsequent processing and analysis.

[0047] ​An interference identification module 3 is configured to analyze the quality of received signals in real time during signal receiving, identify the type and intensity parameters of interference sources through automatically programmed threshold values and algorithms, and generate an interference set; historical signal data is used to analyze signal characteristics in a normal operation state, the mean and standard deviation of normal signal intensity are calculated to set several dynamic threshold values of signal intensity, the threshold values are updated in real time, the received signal intensity values are monitored, and the interference source is located; if the preset threshold value is exceeded or lowered, it is marked as a potential interference source; different interference sources are classified according to the results of the identification algorithm, the difference between the signal intensity and the preset threshold value is compared, the interference intensity is calculated, and a certain interference source is quantified as one or more indicators; the attributes of the interference set include the identified interference type and intensity, occurrence time, and affected nodes; real-time detection and identification of interference sources and quantification of interference intensity make the interference source positioning more accurate;

[0048] An interference analysis module 4 is configured to calculate signal loss values based on the interference set through statistical methods, and identify loss causes;

[0049] A data compensation module 5 is configured to analyze loss values and causes through the interference analysis module 4, and perform data repair and compensation processing on corresponding loss sources through data reconstruction technology;

[0050] The data compensation module 5 extracts features from non-loss data in the process of data reconstruction, obtains features of loss values, and establishes and trains a reconstruction model based on the features of the non-loss data part and the features of the loss values; for each damaged data point, the reconstruction model obtains a predicted reconstruction value, fills the predicted reconstruction value back into the original data, and forms a complete data set; the signal quality and reliability are improved;

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

[0052] Historical reported data, environmental information, historical event records, and interference mode data of each node are obtained and preprocessed, trained through a machine learning algorithm, and predicted using the latest input data through the trained model to generate expected events in a future preset period; historical data and environmental information are analyzed to predict future events and improve the prediction ability of future signal interference;

[0053] A data forwarding module 7 is configured to synchronously send prediction data to a plurality of associated sub-nodes; the prediction data is timely synchronized to related nodes so that they can take preventive measures to reduce the impact of future interference;

[0054] The verification module 8 is used for comparing and analyzing the actual data collection condition with the prediction result of the previous period at the end of the previous period, judging whether the accuracy coefficient of the prediction meets the preset accuracy threshold, regularly evaluating the prediction accuracy, and ensuring the effectiveness and reliability of the prediction model.

[0055] The parameter adjustment module 9 is used for outputting the planning parameter of the active interference identification to the interference identification module 3 based on the verification result according to the accuracy coefficient feedback obtained by the verification module 8. The parameter adjustment module 9 is internally provided with an updating module 91. The updating module 91 is used for continuously monitoring the state and data of each node, updating the content reported by the verification module 8 in real time, periodically submitting the accuracy coefficient feedback of the updated data to the parameter adjustment module 9, outputting the planning parameter of the active interference identification based on the prediction accuracy evaluation, and optimizing the interference source identification and processing strategy.

[0056] As one of the embodiments in the present embodiment, as shown in Figure 1 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 the prior art, by analyzing the signal quality in real time and identifying the type and intensity of the interference source, the device can quantify the interference, provide a basis for interference source positioning and mitigation, calculate the signal loss value based on the interference set and identify the loss reason, and then use the data reconstruction technology for data repair and compensation processing to maintain the data integrity.

[0058] A comprehensive prediction model is constructed to predict future events using historical data and environmental information, so that the management personnel can deploy resources in advance and take mitigation measures. The actual collected data and the prediction result are compared and analyzed to evaluate the prediction accuracy, and the interference identification and event prediction algorithm is continuously optimized according to the feedback to improve the prediction reliability.

[0059] The state and data of each node are monitored in real time, and the accuracy coefficient feedback of the updated data is periodically submitted to ensure that the interference identification and event prediction algorithm always remains up-to-date and accurate.

[0060] Embodiment 2

[0061] In other aspects, the embodiment also provides a measure for interference source positioning, which can actively calculate the position and intensity of the interference source. In the process of interference source positioning, the interference source signal is converted to the frequency domain through fast Fourier transform, the spectral characteristics of the interference source signal are analyzed, and the abnormal signal in the frequency range is identified. The discrete Fourier transform calculation formula of the interference source signal is:

[0062]

[0063] In the formula, X(K) represents the Kth frequency component, D represents the total number of signal sample points, 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 natural logarithm. In the analysis of the intensity of the frequency K component, the sensitivity of interference source identification can be flexibly adjusted according to the needs of the actual application environment by setting a preset threshold, and the applicability and reliability of the system are improved.

[0064] As a preferred embodiment in the embodiment, in the analysis process of the spectral characteristics of the interference source signal, the intensity calculation formula 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 of the phase O(K) of the frequency K component is:

[0068]

[0069] Compared with the prior art, through frequency domain analysis, the specific frequency range and its characteristics in the signal can be accurately identified and extracted. Compared with the time domain signal, the frequency domain information is more likely to reveal the characteristics and changes of the signal. In particular, in the interference source positioning task, the interference signal and the normal signal can be clearly distinguished. By analyzing the real part and the imaginary part of the frequency component, the intensity of each frequency component can be comprehensively evaluated, thereby ensuring high sensitivity identification of the interference signal and making the positioning of the interference source 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 analysis ability of complex signals.

[0070] By combining the intensity, phase and spectral characteristics, a more complex interference source positioning model can be constructed to improve the monitoring and positioning ability of multiple source interference and adapt to the needs of modern communication complex environment.

[0071] Working principle: through the management module 1 control instruction editing and issuing, register to meet the access requirements of the child node, through the data receiving module 2 obtains the communication data of the child node, through the interference identification module 3 to the communication data carries out the identification of interference information, through the interference analysis module 4 carries out the analysis of the specific interference source kind and position information, through the data compensation module 5 to the damaged child node data carries out data compensation, with the compensated data through the event prediction module 6 carries out traffic event prediction, through the data forwarding module 7 the predicted data is issued to the designated child node, through the verification module 8 verifies the error of predicted data and true data, if there is error data, then based on the error data, through the parameter adjustment module 9 the identification setting of interference identification module 3 is adjusted, update module 91 real-time reporting update data, according to the period carries out data feedback;

[0072] The application improves the satellite communication signal receiving and processing capability, reduces the interference influence, identifies and locates the interference source in real time, takes countermeasures quickly, accurately predicts future interference, takes preventive measures in advance, optimizes the interference identification and processing strategy, and improves the reliability and stability of signal transmission.

[0073] The above examples are only used to illustrate the technical solutions of the application, but not limit it; although the application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the application.

Claims

1. A high performance satellite communication transceiver apparatus, characterized by, The application relates to a satellite signal transmission system, which comprises the following modules: a management module (1) for managing and controlling various functional modules, registering sub-nodes with uploading and receiving permissions; a data receiving module (2) for receiving signal data sent by multiple sub-nodes through a satellite, pre-processing the received signal data, and obtaining analysis data; an interference identification module (3) for analyzing the quality of received signals in real time during signal receiving, identifying the type and intensity parameters of interference sources through an automatically planned threshold and algorithm, and generating an interference set; an interference analysis module (4) for calculating signal loss values through a statistical method based on the interference set, and identifying loss reasons; a data compensation module (5) for performing data repair compensation processing on corresponding loss sources through data reconstruction technology according to the loss values and reasons analyzed by the interference analysis module (4); an event prediction module (6) for constructing a comprehensive prediction model, analyzing historical data of various nodes, predicting events of current node sections in a future preset time period, and generating prediction data; a data forwarding module (7) for synchronously sending the prediction data to a plurality of associated sub-nodes; a verification module (8) for comparing and analyzing actual collected data and prediction results of a previous period at the end of the previous period, and judging whether an accuracy coefficient of the prediction meets a preset accuracy threshold; a parameter adjustment module (9) for outputting planning parameters of active interference identification to the interference identification module (3) based on a verification result according to an accuracy coefficient feedback obtained by the verification module (8).

2. A high performance satellite communication transceiver device according to claim 1, characterized in that, In the process of the interference identification module (3) identifying the type and intensity parameters of interference sources through an automatically planned threshold and algorithm, historical signal data is used to analyze signal characteristics in a normal operation state, the mean value and standard deviation of normal signal intensity are calculated to set a plurality of dynamic threshold values of signal intensity, the threshold values are updated through real-time data, the received signal intensity values are monitored, interference source positioning is performed, if the threshold values are exceeded or lowered, the interference source is marked as a potential interference source, different interference sources are classified according to the results of the identification algorithm, the interference intensity is calculated by comparing the differences between the signal intensity and the preset threshold values, and the interference source is quantified into one or more indexes.

3. A high performance satellite communication transceiver device according to claim 2, wherein, In the process of interference source positioning, the interference source signal is converted to a frequency domain through fast Fourier transform, the frequency spectrum characteristics of the interference source signal are analyzed, and abnormal signals in the frequency range are identified. The discrete Fourier transform calculation formula of the interference source signal is as follows: In the formula, X(K) represents the Kth frequency component, D represents the total number of signal sample points, 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 input signal samples, j represents the imaginary unit, and e represents the base number of natural logarithm.

4. A high performance satellite communication transceiver device according to claim 3, wherein, In the analysis process of the frequency spectrum characteristics of the interference source signal, the intensity calculation formula of each frequency component is 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 value, the interference signal is marked.

5. A high performance satellite communication transceiver device according to claim 4, wherein, The formula for calculating the phase O(K) of the frequency K component is:

6. The high performance satellite communication transceiver of claim 1, wherein, The attributes of the interference set of the interference identification module (3) include the type and intensity of the identified interference, the occurrence time and the affected nodes.

7. The high performance satellite communication transceiver of claim 1, wherein, In the data reconstruction process, the data compensation module (5) extracts features from the non-loss data, obtains the loss value features, and based on the features of the non-loss data part and the loss value features, establishes and trains a reconstruction model through interpolation method. For each damaged data point, the reconstruction model obtains a predicted reconstruction value, fills the predicted reconstruction value back into the original data, and forms a complete data set.

8. The high performance satellite communication transceiver of claim 1, wherein, The construction and operation process of the comprehensive prediction model in the event prediction module (6) is: The historical reporting data, environmental information, historical event records and interference mode data of each node are obtained and preprocessed, trained through machine learning algorithm, and the latest input data is used to predict through the trained model to generate the expected events in the future preset period.

9. The high performance satellite communication transceiver of claim 1, wherein, The parameter adjustment module (9) is internally provided with an update module (91), which is used to continuously monitor the state and data of each node, real-time update the reporting content analysis of the verification module (8), and periodically feed back the accurate coefficient of the updated data to the parameter adjustment module (9).

10. The high performance satellite communication transceiver of claim 1, wherein, The management module (1) is connected with the data receiving module (2) and the data forwarding module (7) through wireless network interaction, the data receiving module (2) is connected with the interference identification module (3) through wireless network interaction, the interference identification module (3) is connected with the parameter adjustment module (9) through medium electrical connection, the interference identification module (3) is connected with the interference analysis module (4) through wireless network interaction, the interference analysis module (4) is connected with the data compensation module (5) through wireless network interaction, the data compensation module (5) is connected with the event prediction module (6) through wireless network interaction, the event prediction module (6) is connected with the data forwarding module (7) through wireless network interaction, the data forwarding module (7) is connected with the verification module (8) through wireless network interaction, the verification module (8) is connected with the parameter adjustment module (9) through wireless network interaction.

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