High-concurrency test method for flight control system of unmanned aerial vehicle

Abstract

The invention discloses an unmanned aerial vehicle flight control system high-concurrency testing method, and belongs to the technical field of high-concurrency testing, and the method comprises the steps: building a multi-dimensional load model covering a sensor, a control instruction and communication based on a test target and a performance index. The load intensity is dynamically increased or decreased according to a formula, the resource state and the load adjustment history are displayed in real time in combination with an instrument panel, it is ensured that the load adjustment process is traceable, visualized and self-adaptively adjusted, test failure caused by overload collapse or resource idleness of the system is avoided, the test efficiency is improved, and the test cost is reduced. Typical fault scenes such as hardware faults, communication faults and software faults are injected, software and hardware faults are simulated through combination of a fault injection framework and electromagnetic interference equipment, fault injection time, fault injection types and system response behaviors are recorded, the fault-tolerant capability of the flight control system in complex fault scenes is comprehensively verified, and potential risks are exposed in advance.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-concurrency testing, and specifically refers to a high-concurrency testing method for a UAV flight control system. Background Art

[0002] Currently, high-concurrency testing of drone flight control systems relies heavily on traditional physical testing methods, which presents challenges such as high testing costs, low scenario reusability, and weak real-time data analysis capabilities. With the growing demand for drone swarm operations and mission execution in complex environments, traditional testing methods are unable to meet the requirements for rapid, iterative flight control system performance evaluation. New technologies are urgently needed to improve testing efficiency and accuracy.

[0003] However, the existing high-concurrency test of UAV flight control system still has certain defects. The existing load model is constructed based on a single dimension, or the accurate mapping relationship between load parameters and actual scenarios is not established, resulting in the model being unable to truly reflect the multi-dimensional load characteristics under high-concurrency scenarios. It adopts static load configuration and lacks real-time perception and dynamic adjustment mechanism of hardware / software resource status. It cannot automatically optimize the test intensity according to the real-time load of the system. It is mostly limited to software-level fault simulation and lacks comprehensive simulation of hardware faults and communication faults. In addition, the hardware-level fault tolerance capability is not verified by means such as electromagnetic interference equipment. Therefore, a high-concurrency test method for UAV flight control system is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-concurrency testing method for a UAV flight control system to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a high-concurrency test method for a UAV flight control system, comprising the following steps:

[0006] S1. Based on the test objectives and performance indicators, a multi-dimensional load model covering sensors, control instructions and communications is constructed;

[0007] S2, based on the multi-dimensional load model, generates virtual load and introduces a dynamic adjustment algorithm to adapt to the real-time resource status;

[0008] S3: Based on the dynamically adjusted virtual load, typical fault scenarios are injected and the system response behavior is monitored in real time.

[0009] S4. Analyze performance bottlenecks based on fault scenario monitoring data.

[0010] S5. Make optimization suggestions based on the analysis results;

[0011] S6. Based on the optimization suggestions, the flight control system is tested again according to the original process, and the optimization effect is verified through repeated testing;

[0012] S7. Generate a standardized report based on the final test results.

[0013] Preferably, the S1, based on the test objectives and performance indicators, constructs a multidimensional load model covering sensors, control instructions and communications; based on the test objectives and performance indicators, constructs a multidimensional load model covering sensors, control instructions and communications; determines the core test requirements according to the drone application scenario, and sets performance indicators including quantitative indicators and reliability indicators.

[0014] Preferably, the S1 defines multi-dimensional load types including sensor load, control instruction load and communication load, wherein the sensor load includes data type and load characteristics, the control instruction load includes instruction type and load characteristics, and the communication load includes data direction and load characteristics; establishes a mapping relationship between load parameters and scenarios, including sensor load mapping, control instruction mapping and communication load mapping, and constructs a multi-dimensional load model based on the scenario mapping results. The preliminarily constructed multi-dimensional load model is compared and verified with the actual operation data to check whether the model can accurately simulate the load characteristics of the system under high concurrency. If there is a deviation, analyze the cause and adjust and optimize the model.

[0015] Preferably, the S2 generates a virtual load based on a multi-dimensional load model and introduces a dynamic adjustment algorithm to adapt to the real-time resource status; according to the actual needs of the UAV flight control system, the dimensions of the virtual load are clarified, and the parameter range is set for each dimension. By configuring the virtual load generation tool, the concurrent behavior of the multi-dimensional load is simulated, the sensor data stream simulates the periodic data packets of the IMU, GNSS, and visual sensors, the control instructions generate high-frequency control signals, and the communication load: simulates the two-way traffic of telemetry data return and ground station command reception.

[0016] Preferably, in S2, the hardware and software resource status of the flight control system is collected in real time through a resource monitoring tool, and a dynamic adjustment rule is defined based on the real-time resource status. The dynamic load adjustment implementation formula is:

[0017]

[0018] In the formula, R ue Indicates the current resource occupancy rate, T td Indicates the preset resource occupation threshold, R max Indicates the maximum value of resources. When the resource occupancy rate R ue Exceeding the threshold T td When k ad Decrease, when R ue Below the threshold, k ad As it approaches 1, the load intensity increases.

[0019] Preferably, the S2 regularly collects resource status data, analyzes the matching degree between the current load and the system performance, dynamically adjusts the parameters of the virtual load generator according to the analysis results, verifies through the monitoring module whether the resource status after adjustment returns to the safe range, records the triggering conditions, execution strategies and effects of each dynamic adjustment in the log file, and displays the resource status and load adjustment history in real time through the dashboard.

[0020] Preferably, the S3, based on the dynamically adjusted virtual load, injects typical fault scenarios and monitors the system response behavior in real time; typical fault scenarios include hardware failure: simulating sensor data loss and motor loss of control, communication failure: simulating communication delay and data packet loss, software failure: simulating control algorithm abnormalities and task scheduling errors, injecting faults in the load stabilization phase after dynamic adjustment, setting the fault severity according to the system fault tolerance capability, simulating software-level faults through the fault injection framework, simulating hardware faults through electromagnetic interference equipment, starting fault injection according to preset rules, and recording the fault injection time, type and system response behavior.

[0021] Preferably, the S4 analyzes the performance bottleneck based on the fault scenario monitoring data; obtains the fault scenario monitoring data and performs preprocessing and feature extraction, determines the normal value range and operation mode of each performance parameter based on the historical data of the UAV flight control system under normal operation without fault, establishes a normal operation data benchmark, compares the monitoring data under the fault scenario with the normal operation data benchmark item by item, analyzes the changes of various performance parameters before and after the fault, finds out the parameters that obviously deviate from the normal range according to the fluctuations of key indicators such as instruction processing time, data transmission rate, system resource occupancy rate, and task completion success rate, and conducts in-depth analysis on the abnormal parts of the performance parameters based on the comparative analysis results and the architecture and working principle of the flight control system, and summarizes the key links, causes and change characteristics of the performance bottlenecks obtained by analysis.

[0022] Preferably, S5 proposes optimization suggestions based on the analysis results, and based on the performance bottleneck characteristics, causes and impact range summarized in S4, combined with the overall design goals and actual application requirements of the UAV flight control system, comprehensively evaluates the optimization ideas and measures proposed from different levels such as hardware, software, and system architecture, considers the implementation difficulty, cost investment, impact on the existing system, and expected optimization effect of each solution, screens out solutions with high feasibility and good cost-effectiveness, and integrates them into a complete set of optimization suggestions.

[0023] Preferably, in S6, according to the optimization suggestions, the flight control system is tested again according to the original process, and the optimization effect is verified by repeated testing; according to the optimization suggestions proposed in step S5, the flight control system is subjected to hardware upgrades, software code modifications, and system architecture adjustment optimization operations; after the optimization of the flight control system is completed, the test environment is restored to its initial state to ensure that it is consistent with the environmental conditions during the first test, the test site and test equipment are inspected and calibrated, and the optimized flight control system is subjected to high concurrency testing in full accordance with the test process from S1 to S5, and a multi-dimensional load model is constructed again, a virtual load is generated and dynamically adjusted according to the real-time resource status, typical fault scenarios are injected and the system response behavior is monitored in real time, various types of data generated during the test process are collected, and the optimized flight control system is subjected to multiple rounds of repeated testing. In each round of testing, the test parameters and scenarios can be appropriately adjusted to simulate different high-concurrency situations, and the performance of the flight control system under various conditions is comprehensively evaluated. The process and results of each round of testing are recorded to form a complete test data record.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This invention builds a multi-dimensional load model covering sensors, control instructions, and communications, and accurately associates scenario requirements through sensor load mapping, control instruction mapping, and communication load mapping. Model deviations are corrected by comparing and verifying actual operation data, ensuring that the load model can accurately simulate the actual load characteristics in high-concurrency scenarios. The simulation is closer to actual application scenarios, comprehensively covers the multi-dimensional load pressure of the flight control system, improves the credibility of test results, avoids missed or erroneous measurements due to model distortion, and reduces test blind spots.

[0026] 2. This invention uses a dynamic adjustment algorithm to perceive the status of hardware / software resources in real time, and dynamically increases or decreases the load intensity according to a formula. Combined with a dashboard that displays resource status and load adjustment history in real time, this ensures that the load adjustment process is traceable and visual, and adaptively adjusts the load intensity to avoid system crashes due to overload or test failures due to idle resources, thereby improving test efficiency and ensuring maximum test coverage with limited resources.

[0027] 3. This invention injects typical fault scenarios such as hardware failure, communication failure, and software failure. It uses a fault injection framework and electromagnetic interference equipment to simulate hardware and software failures, and records the fault injection time, type, and system response behavior. This fully verifies the fault tolerance of the flight control system in complex fault scenarios and exposes potential risks in advance.

[0028] 4. The present invention compares fault scenario monitoring data with normal operating data benchmarks, combines key indicators such as instruction processing time, data transmission rate, resource utilization, and task completion success rate to locate performance bottlenecks, and proposes optimization suggestions from multiple levels of hardware, software, and system architecture. It also comprehensively evaluates the implementation difficulty, cost, scope of impact, and expected effects, selects the most cost-effective solution, accurately locates the root cause of performance bottlenecks, avoids blind optimization, provides a systematic optimization path, reduces trial and error costs, and improves the feasibility and implementation efficiency of the optimization solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is the operating process of a high-concurrency test method for a UAV flight control system of the present invention Figure 1 ;

[0030] Figure 2 This is the operating process of a high-concurrency test method for a UAV flight control system of the present invention Figure 2 ;

[0031] Figure 3 This is the operating process of a high-concurrency test method for a UAV flight control system of the present invention Figure 3 . DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] Example

[0034] See also Figure 1-3 As shown, the present invention provides a technical solution: comprising the following steps:

[0035] S1. Based on the test objectives and performance indicators, a multi-dimensional load model covering sensors, control instructions and communications is constructed;

[0036] S2, based on the multi-dimensional load model, generates virtual load and introduces a dynamic adjustment algorithm to adapt to the real-time resource status;

[0037] S3: Based on the dynamically adjusted virtual load, typical fault scenarios are injected and the system response behavior is monitored in real time.

[0038] S4. Analyze performance bottlenecks based on fault scenario monitoring data.

[0039] S5. Make optimization suggestions based on the analysis results;

[0040] S6. Based on the optimization suggestions, the flight control system is tested again according to the original process, and the optimization effect is verified through repeated testing;

[0041] S7. Generate a standardized report based on the final test results.

[0042] Preferably, the S1, based on the test objectives and performance indicators, constructs a multidimensional load model covering sensors, control instructions and communications; based on the test objectives and performance indicators, constructs a multidimensional load model covering sensors, control instructions and communications; determines the core test requirements according to the drone application scenario, and sets performance indicators including quantitative indicators and reliability indicators.

[0043] Preferably, the S1 defines multi-dimensional load types including sensor load, control instruction load and communication load, wherein the sensor load includes data type and load characteristics, the control instruction load includes instruction type and load characteristics, and the communication load includes data direction and load characteristics; establishes a mapping relationship between load parameters and scenarios, including sensor load mapping, control instruction mapping and communication load mapping, and constructs a multi-dimensional load model based on the scenario mapping results. The preliminarily constructed multi-dimensional load model is compared and verified with the actual operation data to check whether the model can accurately simulate the load characteristics of the system under high concurrency. If there is a deviation, analyze the cause and adjust and optimize the model.

[0044] Preferably, the S2 generates a virtual load based on a multi-dimensional load model and introduces a dynamic adjustment algorithm to adapt to the real-time resource status; according to the actual needs of the UAV flight control system, the dimensions of the virtual load are clarified, and the parameter range is set for each dimension. By configuring the virtual load generation tool, the concurrent behavior of the multi-dimensional load is simulated, the sensor data stream simulates the periodic data packets of the IMU, GNSS, and visual sensors, the control instructions generate high-frequency control signals, and the communication load: simulates the two-way traffic of telemetry data return and ground station command reception.

[0045] Preferably, in S2, the hardware and software resource status of the flight control system is collected in real time through a resource monitoring tool, and a dynamic adjustment rule is defined based on the real-time resource status. The dynamic load adjustment implementation formula is:

[0046]

[0047] In the formula, R ue Indicates the current resource occupancy rate, T td Indicates the preset resource occupation threshold, R max Indicates the maximum value of resources. When the resource occupancy rate R ue Exceeding the threshold T td When k ad Decrease, when R ue Below the threshold, kad As it approaches 1, the load intensity increases.

[0048] Preferably, the S2 regularly collects resource status data, analyzes the matching degree between the current load and the system performance, dynamically adjusts the parameters of the virtual load generator according to the analysis results, verifies through the monitoring module whether the resource status after adjustment returns to the safe range, records the triggering conditions, execution strategies and effects of each dynamic adjustment in the log file, and displays the resource status and load adjustment history in real time through the dashboard.

[0049] Preferably, the S3, based on the dynamically adjusted virtual load, injects typical fault scenarios and monitors the system response behavior in real time; typical fault scenarios include hardware failure: simulating sensor data loss and motor loss of control, communication failure: simulating communication delay and data packet loss, software failure: simulating control algorithm abnormalities and task scheduling errors, injecting faults in the load stabilization phase after dynamic adjustment, setting the fault severity according to the system fault tolerance capability, simulating software-level faults through the fault injection framework, simulating hardware faults through electromagnetic interference equipment, starting fault injection according to preset rules, and recording the fault injection time, type and system response behavior.

[0050] Preferably, the S4 analyzes the performance bottleneck based on the fault scenario monitoring data; obtains the fault scenario monitoring data and performs preprocessing and feature extraction, determines the normal value range and operation mode of each performance parameter based on the historical data of the UAV flight control system under normal operation without fault, establishes a normal operation data benchmark, compares the monitoring data under the fault scenario with the normal operation data benchmark item by item, analyzes the changes of various performance parameters before and after the fault, finds out the parameters that obviously deviate from the normal range according to the fluctuations of key indicators such as instruction processing time, data transmission rate, system resource occupancy rate, and task completion success rate, and conducts in-depth analysis on the abnormal parts of the performance parameters based on the comparative analysis results and the architecture and working principle of the flight control system, and summarizes the key links, causes and change characteristics of the performance bottlenecks obtained by analysis.

[0051] Preferably, S5 proposes optimization suggestions based on the analysis results, and based on the performance bottleneck characteristics, causes and impact range summarized in S4, combined with the overall design goals and actual application requirements of the UAV flight control system, comprehensively evaluates the optimization ideas and measures proposed from different levels such as hardware, software, and system architecture, considers the implementation difficulty, cost investment, impact on the existing system, and expected optimization effect of each solution, screens out solutions with high feasibility and good cost-effectiveness, and integrates them into a complete set of optimization suggestions.

[0052] Preferably, in S6, according to the optimization suggestions, the flight control system is tested again according to the original process, and the optimization effect is verified by repeated testing; according to the optimization suggestions proposed in step S5, the flight control system is subjected to hardware upgrades, software code modifications, and system architecture adjustment optimization operations; after the optimization of the flight control system is completed, the test environment is restored to its initial state to ensure that it is consistent with the environmental conditions during the first test, the test site and test equipment are inspected and calibrated, and the optimized flight control system is subjected to high concurrency testing in full accordance with the test process from S1 to S5, and a multi-dimensional load model is constructed again, a virtual load is generated and dynamically adjusted according to the real-time resource status, typical fault scenarios are injected and the system response behavior is monitored in real time, various types of data generated during the test process are collected, and the optimized flight control system is subjected to multiple rounds of repeated testing. In each round of testing, the test parameters and scenarios can be appropriately adjusted to simulate different high-concurrency situations, and the performance of the flight control system under various conditions is comprehensively evaluated. The process and results of each round of testing are recorded to form a complete test data record.

[0053] Working Principle: First, based on the test objectives and performance indicators, combined with the drone application scenario, the core test requirements are determined, and performance indicators such as quantitative indicators and reliability indicators are set. Next, multi-dimensional load types are defined, covering sensor loads, control command loads, and communication loads. A mapping relationship between load parameters and scenarios is established. Based on the mapping results, a multi-dimensional load model is constructed. The preliminary model is then compared and verified with actual operation data. If there are any deviations, the cause is analyzed and the model is adjusted and optimized.

[0054] By clarifying the virtual load dimensions and setting the parameter range according to the actual needs of the UAV flight control system, the virtual load generation tool is configured to simulate the concurrent behavior of multi-dimensional loads, such as simulating the periodic data packets of IMU, GNSS, and visual sensors as sensor data streams, generating high-frequency control signals as control instructions, and simulating the two-way traffic of telemetry data return and ground station command reception as communication load; at the same time, the resource monitoring tool is used to collect the hardware and software resource status of the flight control system in real time, and dynamic adjustment rules are defined based on the real-time resource status; when the resource occupancy rate exceeds the preset threshold, the load intensity is reduced, and when it is below the threshold, the load intensity is increased; resource status data is collected regularly to analyze the current load and system performance Matching degree, based on which the parameters of the virtual load generator are dynamically adjusted, and the monitoring module is used to verify whether the resource status after adjustment returns to the safe range. At the same time, the triggering conditions, execution strategy and effect of each dynamic adjustment are recorded in the log file, and the resource status and load adjustment history are displayed in real time through the dashboard; in the stable stage of the virtual load after dynamic adjustment, typical fault scenarios are injected, including hardware failure, communication failure, and software failure. The fault severity is set according to the system fault tolerance capability, and the software-level failure is simulated through the fault injection framework. The hardware failure is simulated through the electromagnetic interference equipment. The fault injection is started according to the preset rules, and the fault injection time, type and system response behavior are recorded; after obtaining the fault scenario monitoring data, the prediction is carried out Processing and feature extraction, based on the historical data of the UAV flight control system in normal operation without faults, determine the normal value range and operation mode of each performance parameter, and establish a normal operation data benchmark; compare the monitoring data under the fault scenario with the normal operation data benchmark item by item, analyze the changes of various performance parameters before and after the fault, and find out the parameters that are obviously deviated from the normal range based on the fluctuations of key indicators such as instruction processing time, data transmission rate, system resource occupancy rate, and task completion success rate; combine the architecture and working principle of the flight control system to conduct in-depth analysis of the abnormal parts of the performance parameters, summarize the key links, causes and change characteristics of related performance parameters of the performance bottleneck; according to the performance summarized by S4 The bottleneck characteristics, causes, and impact range are combined with the overall design goals and actual application requirements of the UAV flight control system. Optimization ideas and measures proposed from different levels, such as hardware, software, and system architecture, are comprehensively evaluated. The implementation difficulty, cost investment, impact on the existing system, and expected optimization effect of each solution are considered. The feasible and cost-effective solutions are selected and integrated into a complete set of optimization suggestions. Based on the optimization suggestions proposed in step S5, the flight control system is optimized by hardware upgrades, software code modifications, and system architecture adjustments. After the optimization is completed, the test environment is restored to its initial state to ensure that the environmental conditions are consistent with those during the first test. The test site and test equipment are inspected and calibrated.Following the S1 to S5 test process, the optimized flight control system is tested with high concurrency. A multi-dimensional load model is rebuilt, virtual loads are generated, and dynamically adjusted based on real-time resource status. Typical failure scenarios are injected and system response behavior is monitored in real time. Various data generated during the test process are collected. Multiple rounds of repeated testing are conducted on the optimized flight control system, with appropriate adjustments to test parameters and scenarios in each round. Different high-concurrency scenarios are simulated to comprehensively evaluate the flight control system's performance under various conditions. The process and results of each round of testing are recorded to form a complete test data record. A standardized report is generated based on the final test results. This report should include all data from the entire test process, analysis results, optimization suggestions, and the performance of the optimized system.

[0055] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0056] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A high-concurrency test method for a UAV flight control system, characterized in that: The following steps are involved: S1. Based on the test objectives and performance indicators, a multi-dimensional load model covering sensors, control instructions and communications is constructed; S2, based on the multi-dimensional load model, generates virtual load and introduces a dynamic adjustment algorithm to adapt to the real-time resource status; S3: Based on the dynamically adjusted virtual load, typical fault scenarios are injected and the system response behavior is monitored in real time. S4. Analyze performance bottlenecks based on fault scenario monitoring data. S5. Make optimization suggestions based on the analysis results; S6. Based on the optimization suggestions, the flight control system is tested again according to the original process, and the optimization effect is verified through repeated testing; S7. Generate a standardized report based on the final test results.

2. The high-concurrency test method for a UAV flight control system according to claim 1, characterized in that: S1, based on the test objectives and performance indicators, builds a multi-dimensional load model covering sensors, control instructions and communications; Based on test objectives and performance indicators, a multi-dimensional load model covering sensors, control instructions and communications is constructed; core test requirements are determined according to the drone application scenarios, and performance indicators are set including quantitative indicators and reliability indicators.

3. The high-concurrency test method for a UAV flight control system according to claim 2, characterized in that: S1 defines multi-dimensional load types including sensor load, control instruction load and communication load, wherein the sensor load includes data type and load characteristics, the control instruction load includes instruction type and load characteristics, and the communication load includes data direction and load characteristics; Establish the relationship between load parameters and scenario mapping, including sensor load mapping, control instruction mapping and communication load mapping. Based on the scenario mapping results, build a multi-dimensional load model. Compare and verify the preliminarily constructed multi-dimensional load model with the actual operation data to check whether the model can accurately simulate the load characteristics of the system under high concurrency. If there is any deviation, analyze the cause and adjust and optimize the model.

4. The high-concurrency test method for a UAV flight control system according to claim 1, characterized in that: The S2 generates a virtual load based on a multi-dimensional load model and introduces a dynamic adjustment algorithm to adapt to the real-time resource status. According to the actual needs of the UAV flight control system, the dimensions of the virtual load are clarified and the parameter range is set for each dimension. By configuring the virtual load generation tool, the concurrent behavior of the multi-dimensional load is simulated. The sensor data stream simulates the periodic data packets of the IMU, GNSS, and vision sensors. The control instructions generate high-frequency control signals. The communication load simulates the two-way traffic of telemetry data return and ground station command reception.

5. The high-concurrency test method for a UAV flight control system according to claim 4, characterized in that: In S2, the hardware and software resource status of the flight control system is collected in real time through a resource monitoring tool. Based on the real-time resource status, dynamic adjustment rules are defined. The dynamic load adjustment implementation formula is: In the formula, R ue Indicates the current resource occupancy rate, T td Indicates the preset resource occupation threshold, R max Indicates the maximum value of resources. When the resource occupancy rate R ue Exceeding the threshold T td When k ad Decrease, when R ue Below the threshold, k ad As it approaches 1, the load intensity increases.

6. The high-concurrency test method for a UAV flight control system according to claim 5, characterized in that: The S2 periodically collects resource status data, analyzes the matching degree between the current load and system performance, dynamically adjusts the parameters of the virtual load generator based on the analysis results, verifies through the monitoring module whether the resource status after adjustment returns to the safe range, records the triggering conditions, execution strategies and effects of each dynamic adjustment in the log file, and displays the resource status and load adjustment history in real time through the dashboard.

7. The high-concurrency test method for a UAV flight control system according to claim 1, characterized in that: S3, based on the dynamically adjusted virtual load, injects typical fault scenarios and monitors the system response behavior in real time; Typical failure scenarios include hardware failure: simulating sensor data loss and motor loss of control; communication failure: simulating communication delay and packet loss; software failure: simulating control algorithm anomalies and task scheduling errors; injecting faults during the load stabilization phase after dynamic adjustment; setting the fault severity based on the system's fault tolerance; simulating software-level failures through a fault injection framework; simulating hardware failures through electromagnetic interference equipment; starting fault injection according to preset rules; and recording the fault injection time, type, and system response behavior.

8. The high-concurrency test method for a UAV flight control system according to claim 1, characterized in that: S4: Analyze performance bottlenecks based on fault scenario monitoring data; Acquire fault scenario monitoring data and perform preprocessing and feature extraction. Based on the historical data of the UAV flight control system under normal operation without faults, determine the normal value range and operation mode of various performance parameters, establish a normal operation data benchmark, compare the monitoring data under the fault scenario with the normal operation data benchmark item by item, analyze the changes in various performance parameters before and after the fault, and find out the parameters that obviously deviate from the normal range based on the fluctuations of key indicators such as instruction processing time, data transmission rate, system resource occupancy rate, and task completion success rate. According to the comparative analysis results, combined with the architecture and working principle of the flight control system, conduct in-depth analysis of the abnormal parts of the performance parameters, and summarize the key links, causes and change characteristics of the performance bottlenecks obtained from the analysis.

9. The high-concurrency test method for a UAV flight control system according to claim 1, characterized in that: S5 proposes optimization suggestions based on the analysis results. According to the performance bottleneck characteristics, causes and impact range summarized in S4, combined with the overall design goals and actual application requirements of the UAV flight control system, a comprehensive evaluation is conducted on the optimization ideas and measures proposed from different levels such as hardware, software, and system architecture. Considering the implementation difficulty, cost investment, impact on the existing system and expected optimization effect of each solution, the feasible and cost-effective solutions are screened out and integrated into a complete set of optimization suggestions.

10. The high-concurrency test method for a UAV flight control system according to claim 1, characterized in that: In S6, based on the optimization suggestions, the flight control system is tested again according to the original process, and the optimization effect is verified by repeated testing; according to the optimization suggestions proposed in step S5, the flight control system is subjected to hardware upgrades, software code modifications, and system architecture adjustment optimization operations. After the flight control system optimization is completed, the test environment is restored to its initial state to ensure that the environmental conditions are consistent with those during the first test. The test site and test equipment are inspected and calibrated. The optimized flight control system is subjected to high-concurrency testing in full accordance with the test process from S1 to S5. A multi-dimensional load model is constructed again, a virtual load is generated and dynamically adjusted according to the real-time resource status, typical fault scenarios are injected and the system response behavior is monitored in real time, various types of data generated during the test process are collected, and the optimized flight control system is subjected to multiple rounds of repeated testing. In each round of testing, the test parameters and scenarios can be appropriately adjusted to simulate different high-concurrency situations, and the performance of the flight control system under various conditions is comprehensively evaluated. The process and results of each round of testing are recorded to form a complete test data record.

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