CN116249164A - Qos parameter adjusting method and device for satellite communication, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a Qos parameter adjusting method, a device, electronic equipment and a storage medium for satellite communication, wherein the Qos parameter adjusting method for the satellite communication comprises the following steps: acquiring first satellite communication data, wherein the first satellite communication data comprises network environment information; analyzing the first satellite communication data based on a machine learning algorithm to obtain an analysis result; and adjusting Qos parameters for satellite communication based on the analysis result. The method and the device can overcome the defect of low communication efficiency in the prior art and realize automatic Qos accurate adjustment.
Description
Technical Field
The present application relates to the field of 5G field of mobile communications, and in particular, to a Qos parameter adjustment method, apparatus, electronic device, and storage medium for satellite communications.
Background
The technology of satellite-to-ground communication has been greatly developed in recent years, and with the continuous improvement of mobile communication technology, the demand for satellite communication technology is also increasing. Satellite communication has an advantage of wider coverage than terrestrial communication, and can provide communication services in areas where terrestrial communication cannot be achieved, such as remote areas, offshore, aviation, etc. Therefore, the satellite communication technology has important significance in the aspects of dealing with emergency events, marine rescue, aviation communication and the like.
The evolution of communication technology has made satellite communication more technically possible, for example by the satellite issuing Qos policies to UEs. Qos (Quality of Service) refers to the quality of service in a communication system, which is an important indicator of whether the communication system can meet the service requirements of users. Therefore, for satellite communication systems, the issue of Qos policies to UEs through satellites is a key point for improving communication efficiency and ensuring communication quality. But may encounter difficulties and problems due to such communications, such as cloud interference, terrain blocking, harsh conditions of the space environment, and so forth. At this time, the Qos of the UE cannot be updated well in time.
In the prior art, in the communication between the satellite and the UE, the updating of Qos has some drawbacks and imperfect places. First, since the propagation path of the satellite signal is complex and is affected by the earth surface environment, such as cloud layer and fog, the communication environment between the satellite and the UE is changed, so that the Qos updating speed is relatively slow. Secondly, there are many interference factors, such as electromagnetic interference, solar interference, etc., in the communication between the satellite and the UE, and these interference factors affect the communication efficiency and quality between the satellite and the UE, so as to affect the Qos updating effect. Therefore, improving the stability and reliability of the communication environment between the satellite and the UE, and enhancing the update efficiency and accuracy of Qos are important directions for improvement and optimization at present.
Disclosure of Invention
An objective of the embodiments of the present application is to provide a Qos parameter adjustment method, apparatus, electronic device and storage medium for satellite communication, which are used for overcoming the defect of low communication efficiency in the prior art, and implementing automatic Qos accurate adjustment.
In a first aspect, the present invention provides a Qos parameter adjustment method for satellite communications, where the method includes:
acquiring first satellite communication data, wherein the first satellite communication data comprises network environment information;
analyzing the first satellite communication data based on a machine learning algorithm to obtain an analysis result;
and adjusting Qos parameters for satellite communication based on the analysis result.
In the first aspect of the application, by acquiring the first satellite communication data, the first satellite communication data can be further analyzed based on a machine learning algorithm to obtain an analysis result, so that Qos parameters for satellite communication can be adjusted based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
In an alternative embodiment, the analyzing the first satellite communication data based on the machine learning algorithm to obtain an analysis result includes:
acquiring second satellite communication data, wherein the second satellite communication data is historical satellite communication data;
preprocessing the second satellite communication data to obtain preprocessed data;
generating a feature subset corresponding to the preprocessed data based on a feature extraction mode;
taking the feature subset as input of a decision tree model and training the decision tree model;
testing the trained decision tree model based on a test set and obtaining a test result;
evaluating the test result based on the evaluation index to obtain an evaluation result, and optimizing the decision tree model after training based on the evaluation result;
and analyzing the first satellite communication data based on the optimized decision tree model to obtain the analysis result.
In this optional embodiment, the second satellite communication data is obtained, so that the second satellite communication data can be preprocessed to obtain preprocessed data, then a feature subset corresponding to the preprocessed data can be generated based on a feature extraction mode, then the feature subset can be used as input of a decision tree model and training the decision tree model, then the decision tree model after training can be tested based on a test set to obtain a test result, then the test result can be evaluated based on an evaluation index to obtain an evaluation result, and the decision tree model after training can be optimized based on the evaluation result, so that the first satellite communication data can be analyzed based on the optimized decision tree model to obtain the analysis result.
In an alternative embodiment, the preprocessing the second satellite communication data to obtain preprocessed data includes:
and performing data cleaning, denoising, duplication removing, normalization and missing value processing on the second satellite communication data to obtain the preprocessed data.
In this optional embodiment, by performing data cleaning, denoising, deduplication, normalization, and missing value processing on the second satellite communication data, useless data can be removed and more useful data can be generated, so that better data is used as preprocessed data.
In an alternative embodiment, the feature extraction means includes a statistical means, a frequency domain means, and a time domain means.
In an optional implementation manner, the generating, based on the feature extraction manner, a feature subset corresponding to the preprocessed data includes:
extracting the characteristics of the preprocessed data based on the statistical mode, the frequency domain mode and the time domain mode, and obtaining first preselected characteristics;
performing dimension reduction on the first preselected feature based on a principal component analysis algorithm to obtain a second preselected feature;
the feature subset is constructed based on N principal components in the second preselected feature, where N is a positive integer.
In this optional embodiment, the features of the preprocessed data may be extracted based on the statistical manner, the frequency domain manner, and the time domain manner, and a first pre-selected feature may be obtained, so that the first pre-selected feature may be reduced in dimension based on a principal component analysis algorithm, and a second pre-selected feature may be obtained, so that the feature subset may be constructed based on N principal components in the second pre-selected feature.
In an alternative embodiment, the evaluation-based metrics include confusion matrix, accuracy, precision, and recall.
In an alternative embodiment, the network environment information includes at least weather information, satellite location information, and signal strength information.
In the embodiment of the application, since the network environment information comprises weather information, satellite position information and signal strength information, the machine learning algorithm can analyze and learn the data, so that Qos parameters can be automatically adjusted based on weather, satellite position and signal strength.
In a second aspect, the present invention provides a Qos parameter adjustment device for satellite communications, where the device includes:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring first satellite communication data, and the first satellite communication data comprises network environment information;
the analysis module is used for analyzing the first satellite communication data based on a machine learning algorithm to obtain an analysis result;
and the parameter adjusting module is used for adjusting Qos parameters for satellite communication based on the analysis result.
The device of the second aspect of the present application, by performing Qos parameter adjustment of satellite communications, can obtain first satellite communications data, and further analyze the first satellite communications data based on a machine learning algorithm, so as to obtain an analysis result, thereby being capable of adjusting Qos parameters for satellite communications based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
In a third aspect, the present invention provides an electronic device comprising:
a processor; and
a memory configured to store machine readable instructions that, when executed by the processor, perform a Qos parameter adjustment method for satellite communications as in any of the preceding embodiments.
The electronic device of the third aspect of the present application, by performing Qos parameter adjustment of satellite communications, may be capable of obtaining first satellite communications data, and further analyzing the first satellite communications data based on a machine learning algorithm, to obtain an analysis result, so that Qos parameters for satellite communications may be adjusted based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
In a fourth aspect, the present invention provides a storage medium storing a computer program, where the computer program is executed by a processor to perform the Qos parameter adjustment method for satellite communication according to any one of the foregoing embodiments.
The storage medium of the fourth aspect of the present application, by performing Qos parameter adjustment of satellite communications, can obtain first satellite communications data, and further analyze the first satellite communications data based on a machine learning algorithm, so as to obtain an analysis result, thereby being capable of adjusting Qos parameters for satellite communications based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a Qos parameter adjustment method for satellite communication according to an embodiment of the present application;
fig. 2 is a schematic diagram of NWDAF analysis modifying Qos according to an embodiment of the present application;
fig. 3 is a schematic diagram of a registration procedure of a UE in 5G according to an embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a first NWDAF-collected data source as disclosed in embodiments of the present application;
FIG. 5 is a process diagram of transmitting Qos between satellites according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a Qos parameter adjustment device for satellite communications according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a Qos parameter adjustment method for satellite communication according to an embodiment of the present disclosure, where the method is applied to a satellite. As shown in fig. 1, the method of the embodiment of the present application includes the following steps:
101. acquiring first satellite communication data, wherein the first satellite communication data comprises network environment information;
102. analyzing the first satellite communication data based on a machine learning algorithm to obtain an analysis result;
103. and adjusting Qos parameters for satellite communication based on the analysis result.
In the embodiment of the application, the first satellite communication data is acquired, so that the first satellite communication data can be analyzed based on a machine learning algorithm to obtain an analysis result, and Qos parameters for satellite communication can be adjusted based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
In this embodiment of the present application, as an example, since the satellite signal is subjected to electromagnetic interference during the satellite communication process, it is difficult to reach the user terminal within a preset time, at this time, by adjusting the Qos parameter, the strength of the satellite signal may be enhanced, so that the satellite signal may reach the user terminal within the preset time. More specifically, by acquiring the network environment information, it is possible to comprehensively determine whether the condition Qos parameter is required and how to adjust the Qos parameter specifically, for example, when the network environment information of the first scene is collected, the strength of the satellite signal is enhanced to be L1, and when the network environment information of the second scene is collected, the strength of the satellite signal is enhanced to be L2.
In this embodiment of the present application, for step 101, the first satellite communication data represents a real-time communication situation between the user terminal and the satellite. Further, the network environment information includes at least weather information, satellite position information, and signal strength information, so that since the network environment information includes the weather information, the satellite position information, and the signal strength information, the machine learning algorithm can analyze and learn such data, thereby automatically adjusting Qos parameters based on the weather, the satellite position, and the signal strength.
In this embodiment of the present application, for step 101, the network environment information may be collected by the NWDAF, where the NWDAF may collect the environment information by monitoring network traffic, or various network elements in the satellite communication network, such as a base station, a terminal, etc., actively report the network environment information to the NWDAF. Note that NWDAF (Network Data Analytics Function) is a functional module in the 5G core network, and is mainly responsible for network data analysis, including collecting, processing, and analyzing network data, and providing data analysis services.
In the embodiment of the present application, please refer to fig. 2, fig. 2 is a schematic diagram of NWDAF analysis modification Qos disclosed in the embodiment of the present application. As shown in fig. 2, after the NWDAF collects the network environment information, the network environment information may be reported as a machine learning platform, where the machine learning platform may be built in a satellite. Further, the decision tree model may be trained after the machine learning platform receives the network environment information. Further, the trained decision tree model may be used as a node of the NWDAF, and further after the NWDAF receives the first satellite communication data, the first satellite communication data may be analyzed, so as to send a notification to the PCF, and further suggest whether the PCF modifies Qos of the user terminal by notifying whether the PCF modifies Qos. It should be noted that, PCF (Policy and Charging Control) network elements collect Qos information of the controlling UE to determine different policy requirements, where the PCF may configure an appropriate Qos policy for the user terminal UE according to these requirements. On the other hand, according to the 3GPP protocol, the SMF (Session Management Function session management function) in the 5G network may request to issue Qos rules of the UE registered this time to the PCF, so as to achieve the purpose of limiting or restricting the UE behavior of the UE, where, with reference to fig. 3, fig. 3 is a schematic diagram of the UE registration flow in 5G in the embodiment of the present application.
In embodiments of the present application, the NWDAF may collect network environment information through different interfaces and protocols. For example, referring to fig. 4, fig. 4 is a schematic diagram of a first NWDAF collected data source as disclosed in the embodiments of the present application. As shown in fig. 4, NWDAF may collect data from AMF by receiving N1, may collect data from UPF by receiving N2, and may collect data from terrestrial gateway station by receiving N3, where AMF (Access and Mobility Management Function) refers to access and mobility management functions in 5G network and UPF (User Plane Function) refers to user plane functions in 5G network.
In the embodiment of the present application, qos parameter adjustment may be performed between satellites, for example, refer to fig. 5, and fig. 5 is a process diagram of Qos transmission between satellites disclosed in the embodiment of the present application. As shown in fig. 5, a satellite may push data up (Qos parameters for intelligent adjustment) to change satellite communication conditions.
In an alternative embodiment, the steps of: analyzing the first satellite communication data based on a machine learning algorithm to obtain an analysis result, wherein the analysis result comprises the following sub-steps:
acquiring second satellite communication data, wherein the second satellite communication data is historical satellite communication data;
preprocessing the second satellite communication data to obtain preprocessed data;
generating a feature subset corresponding to the preprocessed data based on the feature extraction mode;
taking the feature subset as input of a decision tree model and training the decision tree model;
testing the trained decision tree model based on the test set to obtain a test result;
evaluating the evaluation test result based on the evaluation index to obtain an evaluation result, and optimizing the decision tree model after training based on the evaluation result;
and analyzing the first satellite communication data based on the tuned decision tree model to obtain an analysis result.
In this optional embodiment, the second satellite communication data is obtained, so that the second satellite communication data can be preprocessed to obtain preprocessed data, then a feature subset corresponding to the preprocessed data can be generated based on a feature extraction mode, then the feature subset can be used as input of a decision tree model and training the decision tree model, so that the decision tree model can learn the data mode and rule better, then the trained decision tree model can be tested based on a test set to obtain a test result, further the test result can be evaluated based on an evaluation index to obtain an evaluation result, and the trained decision tree model can be optimized based on the evaluation result, so that the first satellite communication data can be analyzed based on the optimized decision tree model to obtain an analysis result.
In an alternative embodiment, the preprocessing of the second satellite communication data to obtain preprocessed data includes:
and carrying out data cleaning, denoising, duplication removing, normalization and missing value processing on the second satellite communication data to obtain preprocessed data.
In this optional embodiment, by performing data cleaning, denoising, deduplication, normalization, and missing value processing on the second satellite communication data, useless data can be removed and more useful data can be generated, so that more preferable data is used as preprocessed data.
In this optional embodiment, further optionally, preprocessing the second satellite communication data further includes randomizing and splitting the second satellite communication data to obtain a representative test set and a training set, where the training set is used for training the initial decision tree model and the test set is used for testing the trained decision tree model. In addition, by randomizing and splitting the second satellite communication data, the problems of over-fitting and under-fitting can be avoided.
In this alternative embodiment, during the process of training the decision tree model, the loss function of the decision tree model may be optimized based on a preset algorithm, so as to determine the optimal model parameters. In addition, training time and resource cost of the decision tree model can be set to avoid over-fitting and under-fitting problems.
In this optional embodiment, in the process of training the decision tree model, the optimal features may be selected for partitioning based on methods such as information gain and a coefficient of kunning.
In this alternative embodiment, the decision tree model obtained by training is a tree structure with the feature as a node and the category as a leaf node.
In alternative embodiments, the feature extraction means includes statistical means, frequency-domain means, and time-domain means.
In an alternative embodiment, the steps of: generating a feature subset corresponding to the preprocessed data based on the feature extraction mode, including:
extracting characteristics of the preprocessed data based on a statistical mode, a frequency domain mode and a time domain mode, and obtaining first preselected characteristics;
performing dimension reduction on the first preselected feature based on a principal component analysis algorithm to obtain a second preselected feature;
a feature subset is constructed based on N principal components in the second preselected feature, where N is a positive integer.
In the optional embodiment, features of the preprocessed data can be extracted based on a statistical mode, a frequency domain mode and a time domain mode, and a first preselected feature is obtained, so that the first preselected feature can be subjected to dimension reduction based on a principal component analysis algorithm, a second preselected feature is obtained, and a feature subset can be constructed based on N principal components in the second preselected feature.
In this alternative embodiment, the evaluation indexes include confusion matrix, accuracy, precision and recall, wherein the evaluation result obtained based on the evaluation indexes can be used for analyzing the merits of the decision tree model of the model and the performances under different data distributions.
In this optional embodiment, tuning the decision tree model after training based on the evaluation result may refer to tuning the super parameter, the feature extraction mode, and the like of the decision tree model based on the evaluation result, where tuning may be performed based on experimental results, domain knowledge, and domain experience, for example, may be based on factors such as bandwidth limitation, signal strength, and signal-to-noise ratio of satellite communications, and appropriate machine learning algorithm, feature extraction method, and QoS adjustment policy may be selected.
Example two
Referring to fig. 6, fig. 6 is a schematic structural diagram of a Qos parameter adjustment device for satellite communication according to an embodiment of the present application, and as shown in fig. 6, the device of the embodiment of the present application includes the following functional modules:
an acquisition module 201, configured to acquire first satellite communication data, where the first satellite communication data includes network environment information;
the analysis module 202 is configured to analyze the first satellite communication data based on a machine learning algorithm to obtain an analysis result;
a parameter adjustment module 203, configured to adjust Qos parameters for satellite communications based on the analysis result.
According to the device, through the Qos parameter adjustment of the satellite communication, the first satellite communication data can be obtained, and then the first satellite communication data is analyzed based on the machine learning algorithm, so that an analysis result is obtained, and the Qos parameter for the satellite communication can be adjusted based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
It should be noted that, for other detailed descriptions of the apparatus in the embodiments of the present application, please refer to the related descriptions in the first embodiment of the present application, which are not repeated herein.
Example III
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application, and as shown in fig. 7, the electronic device in the embodiment of the present application includes:
a processor 301; and
a memory 302 configured to store machine readable instructions that, when executed by the processor 301, perform a Qos parameter adjustment method for satellite communications as in any of the previous embodiments.
According to the electronic equipment, through the Qos parameter adjustment of satellite communication, the first satellite communication data can be obtained, and then the first satellite communication data is analyzed based on a machine learning algorithm to obtain an analysis result, so that the Qos parameter for satellite communication can be adjusted based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
Example IV
The present embodiment provides a storage medium storing a computer program that is executed by a processor to perform the Qos parameter adjustment method for satellite communication according to any one of the foregoing embodiments.
According to the storage medium, through executing Qos parameter adjustment of satellite communication, the first satellite communication data can be obtained, and then the first satellite communication data is analyzed based on a machine learning algorithm to obtain an analysis result, so that Qos parameters for satellite communication can be adjusted based on the analysis result. Compared with the prior art, the method and the device can analyze the first satellite communication data based on the machine learning algorithm, further can more accurately adjust and control Qos parameters based on the analysis result, can automatically perform Qos adjustment and result reporting, enhance the intellectualization and automation of the satellite communication system, and can effectively reduce the operation and maintenance cost. On the other hand, the communication efficiency can be improved by adjusting and controlling the Qos parameter, and particularly, the communication efficiency under a specific scene can be improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
It should be noted that the functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM) random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above is only an example of the present application, and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A Qos parameter adjustment method for satellite communications, the method comprising:
acquiring first satellite communication data, wherein the first satellite communication data comprises network environment information;
analyzing the first satellite communication data based on a machine learning algorithm to obtain an analysis result;
and adjusting Qos parameters for satellite communication based on the analysis result.
2. The method of claim 1, wherein the analyzing the first satellite communication data based on the machine learning algorithm to obtain the analysis result comprises:
acquiring second satellite communication data, wherein the second satellite communication data is historical satellite communication data;
preprocessing the second satellite communication data to obtain preprocessed data;
generating a feature subset corresponding to the preprocessed data based on a feature extraction mode;
taking the feature subset as input of a decision tree model and training the decision tree model;
testing the trained decision tree model based on a test set and obtaining a test result;
evaluating the test result based on the evaluation index to obtain an evaluation result, and optimizing the decision tree model after training based on the evaluation result;
and analyzing the first satellite communication data based on the optimized decision tree model to obtain the analysis result.
3. The method of claim 2, wherein preprocessing the second satellite communication data to obtain preprocessed data comprises:
and performing data cleaning, denoising, duplication removing, normalization and missing value processing on the second satellite communication data to obtain the preprocessed data.
4. The method of claim 2, wherein the feature extraction means comprises statistical means, frequency-domain means, and time-domain means.
5. The method of claim 4, wherein generating the feature subset corresponding to the preprocessed data based on feature extraction comprises:
extracting the characteristics of the preprocessed data based on the statistical mode, the frequency domain mode and the time domain mode, and obtaining first preselected characteristics;
performing dimension reduction on the first preselected feature based on a principal component analysis algorithm to obtain a second preselected feature;
the feature subset is constructed based on N principal components in the second preselected feature, where N is a positive integer.
6. The method of claim 2, wherein the evaluation-based metrics include confusion matrix, accuracy, precision, and recall.
7. The method of claim 1, wherein the network environment information includes at least weather information, satellite position information, and signal strength information.
8. A Qos parameter adjustment device for satellite communications, the device comprising:
the system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring first satellite communication data, and the first satellite communication data comprises network environment information;
the analysis module is used for analyzing the first satellite communication data based on a machine learning algorithm to obtain an analysis result;
and the parameter adjusting module is used for adjusting Qos parameters for satellite communication based on the analysis result.
9. An electronic device, comprising:
a processor; and
a memory configured to store machine readable instructions that, when executed by the processor, perform the Qos parameter adjustment method of satellite communications as claimed in any one of claims 1 to 7.
10. A storage medium storing a computer program for executing the Qos parameter adjustment method for satellite communication according to any one of claims 1 to 7 by a processor.
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