A method, system and medium for evaluating performance of an airborne computing platform

Abstract

The present application provides a method, system and medium for evaluating the performance of an airborne computing platform. The method includes: obtaining historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and training to obtain an airborne resource demand prediction model, the airborne computing platform performs resource allocation according to the airborne resource demand prediction model, and obtains the performance monitoring data of the airborne computing platform, obtains the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index according to the performance monitoring data processing, and further processes to obtain the comprehensive performance evaluation index, draws a comprehensive performance change curve according to the comprehensive performance evaluation index within a preset time period, and performs performance judgment according to the comprehensive performance change curve; thereby achieving the purpose of the performance evaluation of the airborne computing platform.

Description

Technical Field

[0001] The present application relates to the technical field of big data and airborne computing platform performance testing, and specifically, to an airborne computing platform performance evaluation method, system and medium. Background Art

[0002] With the development of aviation technology, airborne computing platforms are responsible for handling various complex tasks, such as flight control, navigation, and communication, which require effective resource allocation and performance evaluation. Traditional airborne computing platform performance evaluation methods are performed under fixed resource configurations and cannot truly reflect the impact of resource allocation on platform performance. In actual applications, the resource allocation of airborne computing platforms needs to be adjusted according to different mission scenarios and requirements. However, existing performance evaluation methods often only focus on one aspect of the computing platform, such as computing performance or storage performance, and cannot process and evaluate the correlation between various performances in the actual airborne environment.

[0003] Therefore, there is currently a lack of technology that can evaluate the various performance of airborne computing platforms, allocate resources for different scenarios, and test system performance. The above-mentioned technology gaps need to be addressed urgently. Summary of the invention

[0004] The purpose of the present application is to provide a method, system and medium for evaluating the performance of an airborne computing platform. First, the computing resources, network resources and storage resources of the airborne computing platform are allocated according to different flight phases and airborne mission types. Then, for the airborne computing platform after resource allocation, computing performance tests, network performance tests and storage performance tests are performed respectively. Then, a comprehensive performance evaluation of the airborne computing platform is performed, and performance judgment is made based on the comprehensive performance change curve, so as to effectively evaluate the impact of resource allocation on platform performance and achieve the purpose of comprehensive and systematic evaluation of the airborne computing platform.

[0005] The present application also provides a method for evaluating the performance of an airborne computing platform, comprising the following steps:

[0006] Obtain historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and train an airborne resource demand prediction model based on the historical resource allocation data and historical flight environment data;

[0007] The onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data;

[0008] Processing the network performance monitoring data to obtain a network performance evaluation index, and processing the computing performance monitoring data to obtain a computing performance evaluation index;

[0009] Processing the storage performance monitoring data to obtain a storage performance evaluation index, and processing the network performance evaluation index, computing performance evaluation index, and storage performance evaluation index to obtain a comprehensive performance evaluation index;

[0010] Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, and make performance judgments based on the comprehensive performance change curve.

[0011] Optionally, in the airborne computing platform performance evaluation method described in the present application, the acquiring of historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and training to obtain an airborne resource demand prediction model based on the historical resource allocation data and the historical flight environment data, includes:

[0012] Extract historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft from a preset airborne computing platform database;

[0013] The historical resource allocation data includes network resource allocation data, computing resource allocation data and storage resource allocation data, and the historical flight environment data includes meteorological data and electromagnetic interference data;

[0014] The airborne resource demand prediction model is obtained by training based on flight phase data, airborne mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data and electromagnetic interference data.

[0015] Optionally, in the airborne computing platform performance evaluation method described in the present application, the airborne computing platform performs resource allocation according to the airborne resource demand prediction model, and obtains performance monitoring data of the airborne computing platform, including network performance monitoring data, computing performance monitoring data and storage performance monitoring data, including:

[0016] Acquire current aircraft flight phase data, current airborne mission type data, and current flight environment data, input the current aircraft flight phase data, current airborne mission type data, and current flight environment data into the airborne resource demand prediction model for processing, and obtain resource allocation data;

[0017] The onboard computing platform performs resource allocation according to the resource allocation data, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data;

[0018] The network performance monitoring data includes network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data;

[0019] The computing performance monitoring data includes CPU usage data, execution speed data, concurrent request quantity data, throughput data and error rate data;

[0020] The storage performance monitoring data includes memory usage data, memory read and write speed data, storage capacity usage data, storage read and write speed data and storage power consumption data.

[0021] Optionally, in the airborne computing platform performance evaluation method described in the present application, the processing according to the network performance monitoring data to obtain the network performance evaluation index, and the processing according to the computing performance monitoring data to obtain the computing performance evaluation index include:

[0022] Processing the network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data to obtain a network performance evaluation index;

[0023] The CPU usage rate data, execution speed data, concurrent request quantity data, throughput data and error rate data are processed to obtain a computing performance evaluation index.

[0024] Optionally, in the airborne computing platform performance evaluation method described in the present application, the processing according to the storage performance monitoring data to obtain a storage performance evaluation index, and the processing according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index include:

[0025] Processing the memory usage rate data, the memory read and write speed data, the storage capacity usage rate data, the storage read and write speed data, and the storage power consumption data to obtain a storage performance evaluation index;

[0026] Processing is performed according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index.

[0027] Optionally, in the airborne computing platform performance evaluation method described in the present application, obtaining a comprehensive performance evaluation index within a preset time period, drawing a comprehensive performance change curve, and performing performance determination according to the comprehensive performance change curve includes:

[0028] Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, identify feature points of the comprehensive performance change curve, and obtain the comprehensive performance change rate corresponding to the feature points;

[0029] Comparing the comprehensive performance change rate with a preset comprehensive performance change rate threshold to obtain a comprehensive performance change rate comparison result;

[0030] Calculating the comprehensive performance evaluation index mean value according to the comprehensive performance evaluation index within the preset time period, comparing the comprehensive performance evaluation index mean value with a preset comprehensive performance evaluation index mean value threshold value, and obtaining a comprehensive performance evaluation index mean value comparison result;

[0031] If the comprehensive performance change rate comparison result and the comprehensive performance evaluation index mean comparison result do not meet the preset threshold comparison result requirements, a performance abnormality reminder is issued.

[0032] In a second aspect, the present application provides an airborne computing platform performance evaluation system, the system comprising: a memory and a processor, the memory storing a program of an airborne computing platform performance evaluation method, and the program of the airborne computing platform performance evaluation method is executed by the processor to implement the following steps:

[0033] Obtain historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and train an airborne resource demand prediction model based on the historical resource allocation data and historical flight environment data;

[0034] The onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data;

[0035] Processing the network performance monitoring data to obtain a network performance evaluation index, and processing the computing performance monitoring data to obtain a computing performance evaluation index;

[0036] Processing the storage performance monitoring data to obtain a storage performance evaluation index, and processing the network performance evaluation index, computing performance evaluation index, and storage performance evaluation index to obtain a comprehensive performance evaluation index;

[0037] Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, and make performance judgments based on the comprehensive performance change curve.

[0038] Optionally, in the airborne computing platform performance evaluation system described in the present application, the acquiring of historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and training to obtain an airborne resource demand prediction model based on the historical resource allocation data and the historical flight environment data, includes:

[0039] Extract historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft from a preset airborne computing platform database;

[0040] The historical resource allocation data includes network resource allocation data, computing resource allocation data and storage resource allocation data, and the historical flight environment data includes meteorological data and electromagnetic interference data;

[0041] The airborne resource demand prediction model is obtained by training based on flight phase data, airborne mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data and electromagnetic interference data.

[0042] In a third aspect, the present application also provides a computer-readable storage medium, which stores an airborne computing platform performance evaluation method program. When the airborne computing platform performance evaluation method program is executed by a processor, the steps of the airborne computing platform performance evaluation method as described in any one of the above items are implemented.

[0043] From the above, it can be seen that the present application provides an airborne computing platform performance evaluation method, system and medium, which first allocate the computing resources, network resources and storage resources of the airborne computing platform according to different flight phases and airborne mission types, and then conduct computing performance tests, network performance tests and storage performance tests on the airborne computing platform after resource allocation, and then conduct a comprehensive performance evaluation of the airborne computing platform, and perform performance judgment based on the comprehensive performance change curve, so as to effectively evaluate the impact of resource allocation on platform performance and achieve the purpose of comprehensive and systematic evaluation of the airborne computing platform.

[0044] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flow chart of a method for evaluating the performance of an airborne computing platform provided in an embodiment of the present application;

[0047] Figure 2 A flowchart of obtaining an airborne resource demand prediction model for an airborne computing platform performance evaluation method provided in an embodiment of the present application;

[0048] Figure 3A flow chart of obtaining performance monitoring data of an airborne computing platform according to a method for evaluating the performance of an airborne computing platform provided in an embodiment of the present application;

[0049] Figure 4 A flowchart of obtaining a network performance evaluation index and a computing performance evaluation index in the airborne computing platform performance evaluation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0051] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0052] Please refer to Figure 1 , Figure 1 : is a flow chart of an onboard computing platform performance evaluation method in some embodiments of the present application. The onboard computing platform performance evaluation method is used in terminal devices, such as computers, mobile phone terminals, etc. The onboard computing platform performance evaluation method includes the following steps:

[0053] S11, obtaining historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and training an airborne resource demand prediction model based on the historical resource allocation data and the historical flight environment data;

[0054] S12, the onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data;

[0055] S13, processing according to the network performance monitoring data to obtain a network performance evaluation index, and processing according to the computing performance monitoring data to obtain a computing performance evaluation index;

[0056] S14, processing according to the storage performance monitoring data to obtain a storage performance evaluation index, and processing according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index;

[0057] S15. Obtain a comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, and perform performance determination based on the comprehensive performance change curve.

[0058] It should be noted that the purpose of the present application is to provide a method, system and medium for evaluating the performance of an airborne computing platform. First, the computing resources, network resources and storage resources of the airborne computing platform are allocated according to different flight phases and airborne mission types. Then, for the airborne computing platform after resource allocation, computing performance tests, network performance tests and storage performance tests are performed respectively. Then, a comprehensive performance evaluation of the airborne computing platform is performed, and performance judgment is made based on the comprehensive performance change curve, so as to effectively evaluate the impact of resource allocation on platform performance and achieve the purpose of comprehensive and systematic evaluation of the airborne computing platform.

[0059] Please refer to Figure 2 , Figure 2 The flowchart of the airborne resource demand prediction model of the airborne computing platform performance evaluation method in some embodiments of the present application. According to the embodiment of the present invention, the acquisition of historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and the airborne resource demand prediction model obtained by training according to the historical resource allocation data and historical flight environment data include:

[0060] S21, extracting historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft from a preset airborne computing platform database;

[0061] S22, the historical resource allocation data includes network resource allocation data, computing resource allocation data and storage resource allocation data, and the historical flight environment data includes meteorological data and electromagnetic interference data;

[0062] S23. Perform training based on flight phase data, airborne mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data, and electromagnetic interference data to obtain an airborne resource demand prediction model.

[0063] It should be noted that during the flight, different flight phases (such as take-off, cruising, landing, etc.) will have different requirements for computing, network and storage resources, and the flight environment will also affect resource allocation. In addition, different airborne mission types (such as navigation, communication, flight control, etc.) also have different requirements for resource allocation. Therefore, in order to make accurate predictions of resource allocation, it is necessary to train based on historical flight phase data, airborne mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data and electromagnetic interference data to obtain an airborne resource demand prediction model.

[0064] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of obtaining performance monitoring data of an airborne computing platform in a performance evaluation method of an airborne computing platform in some embodiments of the present application. According to an embodiment of the present invention, the airborne computing platform performs resource allocation according to the airborne resource demand prediction model, and obtains performance monitoring data of the airborne computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data, including:

[0065] S31, obtaining current aircraft flight phase data, current airborne mission type data and current flight environment data, inputting the current aircraft flight phase data, current airborne mission type data and current flight environment data into the airborne resource demand prediction model for processing, and obtaining resource allocation data;

[0066] S32, the onboard computing platform performs resource allocation according to the resource allocation data, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data;

[0067] S33, the network performance monitoring data includes network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data;

[0068] S34, the computing performance monitoring data includes CPU usage data, execution speed data, concurrent request quantity data, throughput data and error rate data;

[0069] S35. The storage performance monitoring data includes memory usage data, memory read and write speed data, storage capacity usage data, storage read and write speed data, and storage power consumption data.

[0070] It should be noted that the airborne computing platform allocates resources according to the airborne resource demand prediction model and obtains performance monitoring data of the airborne computing platform, including network performance monitoring data, computing performance monitoring data and storage performance monitoring data.

[0071] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of obtaining a network performance evaluation index and a computing performance evaluation index of an airborne computing platform performance evaluation method in some embodiments of the present invention. According to an embodiment of the present invention, the processing according to the network performance monitoring data to obtain the network performance evaluation index and the processing according to the computing performance monitoring data to obtain the computing performance evaluation index include:

[0072] S41, processing the network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data to obtain a network performance evaluation index;

[0073] S42: Process the CPU usage data, execution speed data, concurrent request quantity data, throughput data, and error rate data to obtain a computing performance evaluation index.

[0074] It should be noted that the network performance evaluation index is obtained by processing the network performance monitoring data, and the computing performance evaluation index is obtained by processing the computing performance monitoring data;

[0075] The calculation formula of the network performance evaluation index is:

[0076] ;

[0077] in, is the network performance evaluation index, is the network bandwidth data, For transmission speed data, Transmission delay data, To monitor the anti-interference performance data, For network security performance monitoring data, , , , and is a preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database);

[0078] The calculation formula of the computing performance evaluation index is:

[0079] ;

[0080] in, To calculate the performance evaluation index, For CPU usage data, For execution speed data, For the number of concurrent requests, is the throughput data, is the error rate data, , , , and is the preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database).

[0081] According to an embodiment of the present invention, the processing according to the storage performance monitoring data to obtain a storage performance evaluation index, and the processing according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index include:

[0082] Processing the memory usage rate data, the memory read and write speed data, the storage capacity usage rate data, the storage read and write speed data, and the storage power consumption data to obtain a storage performance evaluation index;

[0083] Processing is performed according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index.

[0084] It should be noted that the storage performance evaluation index is obtained by processing the storage performance monitoring data, and the comprehensive performance evaluation index is obtained by processing the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index;

[0085] The calculation formula of the storage performance evaluation index is:

[0086] ;

[0087] in, is the storage performance evaluation index, For memory usage data, It is the memory read and write speed data. To store capacity usage data, To store read and write speed data, To store power consumption data, , , , and is a preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database);

[0088] The calculation formula of the comprehensive performance evaluation index is:

[0089] ;

[0090] in, is the comprehensive performance evaluation index, is the network performance evaluation index, To calculate the performance evaluation index, is the storage performance evaluation index, , and is the preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database).

[0091] According to an embodiment of the present invention, the step of obtaining a comprehensive performance evaluation index within a preset time period, drawing a comprehensive performance change curve, and performing performance determination according to the comprehensive performance change curve includes:

[0092] Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, identify feature points of the comprehensive performance change curve, and obtain the comprehensive performance change rate corresponding to the feature points;

[0093] Comparing the comprehensive performance change rate with a preset comprehensive performance change rate threshold to obtain a comprehensive performance change rate comparison result;

[0094] Calculating the comprehensive performance evaluation index mean value according to the comprehensive performance evaluation index within the preset time period, comparing the comprehensive performance evaluation index mean value with a preset comprehensive performance evaluation index mean value threshold value, and obtaining a comprehensive performance evaluation index mean value comparison result;

[0095] If the comprehensive performance change rate comparison result and the comprehensive performance evaluation index mean comparison result do not meet the preset threshold comparison result requirements, a performance abnormality reminder is issued.

[0096] It should be noted that, in this embodiment, if the comprehensive performance change rate of the feature point is greater than the preset threshold, and the comprehensive performance average over a period of time is lower than the preset threshold, it means that the comprehensive performance is abnormal.

[0097] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0098] Processing is performed according to the computing performance evaluation index, the storage performance evaluation index and the network performance evaluation index to obtain a resource allocation evaluation index;

[0099] Compare the resource allocation evaluation index with a preset resource allocation evaluation index threshold, and if the resource allocation evaluation index is greater than or equal to the preset resource allocation evaluation index threshold, issue a warning of resource allocation imbalance;

[0100] The calculation formula of the resource allocation evaluation index is:

[0101] ;

[0102] in, Assess the index for resource allocation, is the network performance evaluation index, To calculate the performance evaluation index, is the storage performance evaluation index, , and is the preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database).

[0103] It should be noted that if the resource allocation evaluation index is greater than or equal to the preset resource allocation evaluation index threshold, it means that there are large differences in the computing performance evaluation index, storage performance evaluation index and network performance evaluation index, which further indicates that there is an unbalanced allocation of computing, storage and network resources. If the resource allocation evaluation index is less than the preset resource allocation evaluation index threshold, it means that the computing, storage and network resources are allocated in a balanced manner.

[0104] The present invention also discloses an airborne computing platform performance evaluation system, comprising a memory and a processor, wherein the memory stores an airborne computing platform performance evaluation method program, and when the airborne computing platform performance evaluation method program is executed by the processor, the following steps are implemented:

[0105] Obtain historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and train an airborne resource demand prediction model based on the historical resource allocation data and historical flight environment data;

[0106] The onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data;

[0107] Processing the network performance monitoring data to obtain a network performance evaluation index, and processing the computing performance monitoring data to obtain a computing performance evaluation index;

[0108] Processing the storage performance monitoring data to obtain a storage performance evaluation index, and processing the network performance evaluation index, computing performance evaluation index, and storage performance evaluation index to obtain a comprehensive performance evaluation index;

[0109] Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, and make performance judgments based on the comprehensive performance change curve.

[0110] It should be noted that the purpose of the present application is to provide a method, system and medium for evaluating the performance of an airborne computing platform. First, the computing resources, network resources and storage resources of the airborne computing platform are allocated according to different flight phases and airborne mission types. Then, for the airborne computing platform after resource allocation, computing performance tests, network performance tests and storage performance tests are performed respectively. Then, a comprehensive performance evaluation of the airborne computing platform is performed, and performance judgment is made based on the comprehensive performance change curve, so as to effectively evaluate the impact of resource allocation on platform performance and achieve the purpose of comprehensive and systematic evaluation of the airborne computing platform.

[0111] According to an embodiment of the present invention, the acquiring of historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and training to obtain an airborne resource demand prediction model based on the historical resource allocation data and the historical flight environment data, includes:

[0112] Extract historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft from a preset airborne computing platform database;

[0113] The historical resource allocation data includes network resource allocation data, computing resource allocation data and storage resource allocation data, and the historical flight environment data includes meteorological data and electromagnetic interference data;

[0114] The airborne resource demand prediction model is obtained by training based on flight phase data, airborne mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data and electromagnetic interference data.

[0115] It should be noted that during the flight, different flight phases (such as take-off, cruising, landing, etc.) will have different requirements for computing, network and storage resources, and the flight environment will also affect resource allocation. In addition, different airborne mission types (such as navigation, communication, flight control, etc.) also have different requirements for resource allocation. Therefore, in order to make accurate predictions of resource allocation, it is necessary to train based on historical flight phase data, airborne mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data and electromagnetic interference data to obtain an airborne resource demand prediction model.

[0116] According to an embodiment of the present invention, the onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data, including:

[0117] Acquire current aircraft flight phase data, current airborne mission type data, and current flight environment data, input the current aircraft flight phase data, current airborne mission type data, and current flight environment data into the airborne resource demand prediction model for processing, and obtain resource allocation data;

[0118] The onboard computing platform performs resource allocation according to the resource allocation data, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data;

[0119] The network performance monitoring data includes network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data;

[0120] The computing performance monitoring data includes CPU usage data, execution speed data, concurrent request quantity data, throughput data and error rate data;

[0121] The storage performance monitoring data includes memory usage data, memory read and write speed data, storage capacity usage data, storage read and write speed data and storage power consumption data.

[0122] It should be noted that the airborne computing platform allocates resources according to the airborne resource demand prediction model and obtains performance monitoring data of the airborne computing platform, including network performance monitoring data, computing performance monitoring data and storage performance monitoring data.

[0123] According to an embodiment of the present invention, the processing according to the network performance monitoring data to obtain the network performance evaluation index, and the processing according to the computing performance monitoring data to obtain the computing performance evaluation index include:

[0124] Processing the network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data to obtain a network performance evaluation index;

[0125] The CPU usage rate data, execution speed data, concurrent request quantity data, throughput data and error rate data are processed to obtain a computing performance evaluation index.

[0126] It should be noted that the network performance evaluation index is obtained by processing the network performance monitoring data, and the computing performance evaluation index is obtained by processing the computing performance monitoring data;

[0127] The calculation formula of the network performance evaluation index is:

[0128] ;

[0129] in, is the network performance evaluation index, is the network bandwidth data, For transmission speed data, Transmission delay data, To monitor the anti-interference performance data, For network security performance monitoring data, , , , and is a preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database);

[0130] The calculation formula of the computing performance evaluation index is:

[0131] ;

[0132] in, To calculate the performance evaluation index, For CPU usage data, For execution speed data, For the number of concurrent requests, is the throughput data, is the error rate data, , , , and is the preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database).

[0133] According to an embodiment of the present invention, the processing according to the storage performance monitoring data to obtain a storage performance evaluation index, and the processing according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index include:

[0134] Processing the memory usage rate data, the memory read and write speed data, the storage capacity usage rate data, the storage read and write speed data, and the storage power consumption data to obtain a storage performance evaluation index;

[0135] Processing is performed according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index.

[0136] It should be noted that the storage performance evaluation index is obtained by processing the storage performance monitoring data, and the comprehensive performance evaluation index is obtained by processing the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index;

[0137] The calculation formula of the storage performance evaluation index is:

[0138] ;

[0139] in, is the storage performance evaluation index, For memory usage data, It is the memory read and write speed data. To store capacity usage data, To store read and write speed data, To store power consumption data, , , , and is a preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database);

[0140] The calculation formula of the comprehensive performance evaluation index is:

[0141] ;

[0142] in, is the comprehensive performance evaluation index, is the network performance evaluation index, To calculate the performance evaluation index, is the storage performance evaluation index, , and is the preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database).

[0143] According to an embodiment of the present invention, the step of obtaining a comprehensive performance evaluation index within a preset time period, drawing a comprehensive performance change curve, and performing performance determination according to the comprehensive performance change curve includes:

[0144] Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, identify feature points of the comprehensive performance change curve, and obtain the comprehensive performance change rate corresponding to the feature points;

[0145] Comparing the comprehensive performance change rate with a preset comprehensive performance change rate threshold to obtain a comprehensive performance change rate comparison result;

[0146] Calculating the comprehensive performance evaluation index mean value according to the comprehensive performance evaluation index within the preset time period, comparing the comprehensive performance evaluation index mean value with a preset comprehensive performance evaluation index mean value threshold value, and obtaining a comprehensive performance evaluation index mean value comparison result;

[0147] If the comprehensive performance change rate comparison result and the comprehensive performance evaluation index mean comparison result do not meet the preset threshold comparison result requirements, a performance abnormality reminder is issued.

[0148] It should be noted that, in this embodiment, if the comprehensive performance change rate of the feature point is greater than the preset threshold, and the comprehensive performance average over a period of time is lower than the preset threshold, it means that the comprehensive performance is abnormal.

[0149] It is worth mentioning that according to an embodiment of the present invention, it also includes:

[0150] Processing is performed according to the computing performance evaluation index, the storage performance evaluation index and the network performance evaluation index to obtain a resource allocation evaluation index;

[0151] Compare the resource allocation evaluation index with a preset resource allocation evaluation index threshold, and if the resource allocation evaluation index is greater than or equal to the preset resource allocation evaluation index threshold, issue a warning of resource allocation imbalance;

[0152] The calculation formula of the resource allocation evaluation index is:

[0153] ;

[0154] in, Assess the index for resource allocation, is the network performance evaluation index, To calculate the performance evaluation index, is the storage performance evaluation index, , and is the preset characteristic coefficient (which can be obtained by querying the preset airborne computing platform database).

[0155] It should be noted that if the resource allocation evaluation index is greater than or equal to the preset resource allocation evaluation index threshold, it means that there are large differences in the computing performance evaluation index, storage performance evaluation index and network performance evaluation index, which further indicates that there is an unbalanced allocation of computing, storage and network resources. If the resource allocation evaluation index is less than the preset resource allocation evaluation index threshold, it means that the computing, storage and network resources are allocated in a balanced manner.

[0156] A third aspect of the present invention provides a readable storage medium, which stores an airborne computing platform performance evaluation method program. When the airborne computing platform performance evaluation method program is executed by a processor, the steps of the airborne computing platform performance evaluation method as described in any one of the above items are implemented.

[0157] The present invention discloses an airborne computing platform performance evaluation method, system and medium. The method first allocates computing resources, network resources and storage resources of the airborne computing platform according to different flight phases and airborne mission types. Then, computing performance test, network performance test and storage performance test are respectively performed on the airborne computing platform after resource allocation. Then, a comprehensive performance evaluation of the airborne computing platform is performed, and performance judgment is performed according to the comprehensive performance change curve. This can effectively evaluate the impact of resource allocation on platform performance and achieve the purpose of comprehensive and systematic evaluation of the airborne computing platform.

[0158] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0159] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0160] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0161] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0162] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A method for evaluating the performance of an airborne computing platform, characterized in that: The following steps are involved: Obtain historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and train an airborne resource demand prediction model based on the historical resource allocation data and historical flight environment data; The onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data; Processing the network performance monitoring data to obtain a network performance evaluation index, and processing the computing performance monitoring data to obtain a computing performance evaluation index; Processing the storage performance monitoring data to obtain a storage performance evaluation index, and processing the network performance evaluation index, computing performance evaluation index, and storage performance evaluation index to obtain a comprehensive performance evaluation index; Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, and make performance judgments based on the comprehensive performance change curve; The obtaining of historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and training an airborne resource demand prediction model based on the historical resource allocation data and the historical flight environment data, includes: Extract historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft from a preset airborne computing platform database; The historical resource allocation data includes network resource allocation data, computing resource allocation data and storage resource allocation data, and the historical flight environment data includes meteorological data and electromagnetic interference data; The onboard resource demand prediction model is obtained by training based on flight phase data, onboard mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data, and electromagnetic interference data; The onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data, including: Acquire current aircraft flight phase data, current airborne mission type data, and current flight environment data, input the current aircraft flight phase data, current airborne mission type data, and current flight environment data into the airborne resource demand prediction model for processing, and obtain resource allocation data; The onboard computing platform performs resource allocation according to the resource allocation data, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data; The network performance monitoring data includes network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data; The computing performance monitoring data includes CPU usage data, execution speed data, concurrent request quantity data, throughput data and error rate data; The storage performance monitoring data includes memory usage data, memory read and write speed data, storage capacity usage data, storage read and write speed data and storage power consumption data; The processing according to the network performance monitoring data to obtain a network performance evaluation index, and the processing according to the computing performance monitoring data to obtain a computing performance evaluation index, include: Processing the network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data to obtain a network performance evaluation index; Processing the CPU usage data, execution speed data, concurrent request quantity data, throughput data, and error rate data to obtain a computing performance evaluation index; The processing according to the storage performance monitoring data to obtain a storage performance evaluation index, and the processing according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index include: Processing the memory usage rate data, the memory read and write speed data, the storage capacity usage rate data, the storage read and write speed data, and the storage power consumption data to obtain a storage performance evaluation index; Processing is performed according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index; The calculation formula of the network performance evaluation index is: ； in, is the network performance evaluation index, is the network bandwidth data, For transmission speed data, Transmission delay data, To monitor the anti-interference performance data, For network security performance monitoring data, , , , and is the preset characteristic coefficient; The calculation formula of the computing performance evaluation index is: ； in, To calculate the performance evaluation index, For CPU usage data, For execution speed data, For the number of concurrent requests, is the throughput data, is the error rate data, , , , and is the preset characteristic coefficient; The calculation formula of the storage performance evaluation index is: ； in, is the storage performance evaluation index, For memory usage data, It is the memory read and write speed data. To store capacity usage data, To store read and write speed data, To store power consumption data, , , , and is the preset characteristic coefficient; The calculation formula of the comprehensive performance evaluation index is: ； in, is the comprehensive performance evaluation index, is the network performance evaluation index, To calculate the performance evaluation index, is the storage performance evaluation index, , and is the preset characteristic coefficient.

2. The airborne computing platform performance evaluation method according to claim 1, characterized in that: The obtaining of the comprehensive performance evaluation index within the preset time period, drawing a comprehensive performance change curve, and performing performance determination according to the comprehensive performance change curve includes: Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, identify feature points of the comprehensive performance change curve, and obtain the comprehensive performance change rate corresponding to the feature points; Comparing the comprehensive performance change rate with a preset comprehensive performance change rate threshold to obtain a comprehensive performance change rate comparison result; Calculating the comprehensive performance evaluation index mean value according to the comprehensive performance evaluation index within the preset time period, comparing the comprehensive performance evaluation index mean value with a preset comprehensive performance evaluation index mean value threshold value, and obtaining a comprehensive performance evaluation index mean value comparison result; If the comprehensive performance change rate comparison result and the comprehensive performance evaluation index mean comparison result do not meet the preset threshold comparison result requirements, a performance abnormality reminder is issued.

3. An airborne computing platform performance evaluation system, characterized in that: The system comprises a memory and a processor, wherein the memory stores a program of a method for evaluating the performance of an airborne computing platform, and when the method program for evaluating the performance of an airborne computing platform is executed by the processor, the following steps are implemented: Obtain historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and train an airborne resource demand prediction model based on the historical resource allocation data and historical flight environment data; The onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data; Processing the network performance monitoring data to obtain a network performance evaluation index, and processing the computing performance monitoring data to obtain a computing performance evaluation index; Processing the storage performance monitoring data to obtain a storage performance evaluation index, and processing the network performance evaluation index, computing performance evaluation index, and storage performance evaluation index to obtain a comprehensive performance evaluation index; Obtain the comprehensive performance evaluation index within a preset time period, draw a comprehensive performance change curve, and make performance judgments based on the comprehensive performance change curve; The obtaining of historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft, and training an airborne resource demand prediction model based on the historical resource allocation data and the historical flight environment data, includes: Extract historical resource allocation data and historical flight environment data corresponding to different flight phases and different airborne tasks of the aircraft from a preset airborne computing platform database; The historical resource allocation data includes network resource allocation data, computing resource allocation data and storage resource allocation data, and the historical flight environment data includes meteorological data and electromagnetic interference data; The onboard resource demand prediction model is obtained by training based on flight phase data, onboard mission type data, network resource allocation data, computing resource allocation data, storage resource allocation data, meteorological data, and electromagnetic interference data; The onboard computing platform performs resource allocation according to the onboard resource demand prediction model, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data, including: Acquire current aircraft flight phase data, current airborne mission type data, and current flight environment data, input the current aircraft flight phase data, current airborne mission type data, and current flight environment data into the airborne resource demand prediction model for processing, and obtain resource allocation data; The onboard computing platform performs resource allocation according to the resource allocation data, and obtains performance monitoring data of the onboard computing platform, including network performance monitoring data, computing performance monitoring data, and storage performance monitoring data; The network performance monitoring data includes network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data; The computing performance monitoring data includes CPU usage data, execution speed data, concurrent request quantity data, throughput data and error rate data; The storage performance monitoring data includes memory usage data, memory read and write speed data, storage capacity usage data, storage read and write speed data and storage power consumption data; The processing according to the network performance monitoring data to obtain a network performance evaluation index, and the processing according to the computing performance monitoring data to obtain a computing performance evaluation index, include: Processing the network bandwidth data, transmission speed data, transmission delay data, anti-interference performance monitoring data and network security performance monitoring data to obtain a network performance evaluation index; Processing the CPU usage data, execution speed data, concurrent request quantity data, throughput data, and error rate data to obtain a computing performance evaluation index; The processing according to the storage performance monitoring data to obtain a storage performance evaluation index, and the processing according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index include: Processing the memory usage rate data, the memory read and write speed data, the storage capacity usage rate data, the storage read and write speed data, and the storage power consumption data to obtain a storage performance evaluation index; Processing is performed according to the network performance evaluation index, the computing performance evaluation index and the storage performance evaluation index to obtain a comprehensive performance evaluation index; The calculation formula of the network performance evaluation index is: ； in, is the network performance evaluation index, is the network bandwidth data, For transmission speed data, Transmission delay data, To monitor the anti-interference performance data, For network security performance monitoring data, , , , and is the preset characteristic coefficient; The calculation formula of the computing performance evaluation index is: ； in, To calculate the performance evaluation index, For CPU usage data, For execution speed data, For the number of concurrent requests, is the throughput data, is the error rate data, , , , and is the preset characteristic coefficient; The calculation formula of the storage performance evaluation index is: ； in, is the storage performance evaluation index, For memory usage data, It is the memory read and write speed data. To store capacity usage data, To store read and write speed data, To store power consumption data, , , , and is the preset characteristic coefficient; The calculation formula of the comprehensive performance evaluation index is: ； in, is the comprehensive performance evaluation index, is the network performance evaluation index, To calculate the performance evaluation index, is the storage performance evaluation index, , and is the preset characteristic coefficient.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an airborne computing platform performance evaluation program, and when the airborne computing platform performance evaluation program is executed by the processor, the steps of the airborne computing platform performance evaluation method as described in any one of claims 1 to 2 are implemented.

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