UAV system testing method, device, electronic device and storage medium

Abstract

The present disclosure relates to a method, device, electronic device and storage medium for testing an unmanned aerial vehicle system, and relates to the field of unmanned aerial vehicle system testing. The method includes: obtaining attribute information of the unmanned aerial vehicle system; determining test subjects corresponding to different automation levels according to the attribute information and a preset set of operational risks; determining test tasks according to the test subjects corresponding to different automation levels; and testing the unmanned aerial vehicle system based on the test tasks. The present disclosure can be applied to perform an automation function decomposition method based on the attribute information of the unmanned aerial vehicle system and based on operational risks, and determine automation level test subjects in combination with the automation level division elements of the unmanned aerial vehicle system, which can provide a semi-physical simulation test of the automation capability of the unmanned aerial vehicle system and realize the automation level evaluation of the unmanned aerial vehicle system.

Description

Technical Field

[0001] The present application relates to the field of drone technology, and in particular to a drone system testing method, device, electronic device and storage medium. Background Art

[0002] At present, civil unmanned aviation is developing rapidly and evolving iteratively. The commonly used drone system verification method mostly uses drone actual flight tests, which are costly, inefficient, and the test scenarios are simple and cannot be reproduced. In addition, factors such as airspace use restrictions, incomplete insurance mechanisms, and imperfect laws and regulations also limit the implementation of large-scale drone actual flight verification. Summary of the invention

[0003] Embodiments of the present disclosure provide a method, device, electronic device, and storage medium for testing an unmanned aerial vehicle system.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for testing a drone system, including: obtaining attribute information of the drone system; determining test subjects corresponding to different automation levels based on the attribute information and a preset set of operational risks; determining test tasks based on the test subjects corresponding to different automation levels; and testing the drone system based on the test tasks.

[0005] In a second aspect, an embodiment of the present disclosure provides a UAV testing device, including: obtaining attribute information of a UAV; determining test subjects corresponding to systems at different automation levels based on the attribute information and a preset set of operating risks; determining test tasks based on the test subjects corresponding to different automation levels; and testing the UAV system based on the test tasks.

[0006] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the drone system testing method described in the first aspect is implemented.

[0007] In a fourth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the drone system testing method as described in the first aspect.

[0008] By applying the technical solution disclosed in the present invention, an automation function decomposition method can be performed based on the attribute information of the drone system and the operational risk, and the automation level test subjects can be determined in combination with the drone automation level classification elements. This can provide a semi-physical simulation test of the drone system's automation capability and realize the drone system's automation level assessment.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 An exemplary system architecture diagram is shown in which an embodiment of the drone system testing method disclosed herein can be applied;

[0012] Figure 2 A flowchart of an embodiment of a method for testing a drone system disclosed herein;

[0013] Figure 3 A flowchart of another embodiment of the UAV system testing method disclosed herein;

[0014] Figure 4 A schematic diagram of the structure of a UAV system semi-physical simulation test platform and a UAV system of the UAV system test method disclosed in the present invention;

[0015] Figure 5 It is a structural schematic diagram of an embodiment of the UAV system testing device disclosed in the present invention;

[0016] Figure 6 A schematic diagram of the structure of an embodiment of an electronic device disclosed herein. DETAILED DESCRIPTION

[0017] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0018] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0019] In the absence of conflict, the embodiments in the present disclosure and the features in the embodiments may be combined with each other.

[0020] In order to make the technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] Figure 1 An exemplary system architecture 100 is shown to which an embodiment of the UAV system test method or UAV system test device of the present disclosure may be applied.

[0022] like Figure 1 As shown, the system architecture 100 may include a drone system for controlling a drone 101, a terminal device 102, and a database 103. The drone system of the drone 101, the terminal device 102, and the database 103 perform data transmission through a variety of communication methods.

[0023] The drone system may be any unmanned aerial vehicle with automatic flight capability. It may receive instructions or data sent by the terminal device 102, and provide feedback on the instructions or process the data. The drone system may also send its own data to the terminal device 102, so that the terminal device 102 may provide feedback on or save the data.

[0024] The terminal device 102 can manage or process the data of the drone system. Specifically, during the test of the drone system, the terminal device 102 can obtain various data of the drone system and send data to the drone system to achieve real-time monitoring and interaction with the drone system.

[0025] The database 103 can store the data of the drone system, and can also store the data generated by the drone system during the test process, or the feedback data. The technicians can read the above data to further process the drone system.

[0026] It should be noted that the drone system testing method provided in the embodiment of the present disclosure is generally executed by the terminal device 102. Accordingly, the drone system testing device is generally disposed in the terminal device 102.

[0027] Figure 2 FIG. 2 shows a process 200 of an embodiment of the UAV system testing method disclosed in the present invention. Figure 2 As shown, the drone system testing method of this embodiment may include the following steps:

[0028] Step 201, obtaining attribute information of the drone system.

[0029] In this embodiment, the execution subject of the UAV system testing method (for example Figure 1The terminal device 102 shown in the figure can obtain the attribute information of the drone system in various ways. For example, the information of the preset platform can be obtained through a preset interface. The tester of the drone system can pre-register the attribute information of the drone system on the above preset platform. The above attribute information may include information such as the model of the drone, the unique identification code, the operation scene, the dynamic model parameters, etc. It is understandable that the above attribute information may include but is not limited to the information required to construct the dynamic model and the three-dimensional model of the drone.

[0030] Step 202: Determine test subjects corresponding to different automation levels based on the attribute information and a preset set of operational risks.

[0031] In 2022, the Civil Aviation Administration of China issued the industry standard "Classification of Distributed Operations of Civil Unmanned Aircraft Systems" (MH / T 2013-2022). This standard divides the automation levels of automatic flight systems according to four elements: flight mission execution, risk factor detection and response, flight mission takeover, and designed operating scope, and proposes a six-level automation classification scheme for unmanned aerial vehicle systems (AL-0 to AL-5).

[0032] Since the UAV system that operates automatically needs to prove that it has the corresponding automation capabilities in specific operating scenarios to ensure the safety of UAV operation, the UAV system must undergo a wider range of automation capability tests than before before it is put into operation.

[0033] In this embodiment, the attribute information of the drone system can be analyzed to determine the automation capabilities of the drone system. Then, the above-mentioned automation capabilities are matched and combined with the preset operation risk set to determine the test subjects corresponding to different automation levels. Here, the preset operation risk set may include internal risks and external risks of the drone system. Internal risks refer to random failures of the drone system itself encountered during operation, mainly including failures of internal systems such as power systems, energy systems, avionics systems, mission payloads, and communication links. External risks refer to risk factors from the operating environment encountered by the drone system during operation, mainly including meteorological environment, external system communications, ground risks, air flight conflict risks, etc.

[0034] Each risk in the above-mentioned operation risk set may correspond to different automation levels. The operation scenario in the attribute information may be analyzed to determine the automation capability of the drone. Then, the above-mentioned automation capability is matched with the test items and automation level to obtain the test subjects corresponding to different automation levels.

[0035] Step 203, determining the test tasks according to the test subjects corresponding to the different automation levels.

[0036] After determining the test subjects corresponding to different automation levels, test tasks can be generated. Specifically, each test subject can be used as a test task. Alternatively, several related test subjects can be used as a test task. Alternatively, the test subject corresponding to a single automation level can be used as a test task.

[0037] Step 204: testing the UAV system based on the test task.

[0038] After the test task is generated, the test task can be imported into a simulation test system. The simulation test system may include a simulation model of the drone system, and the test task can be completed by the simulation model to achieve the test of the drone system. Alternatively, the test task can be converted into a control command, and the control command can be sent to the drone system for testing.

[0039] The drone system testing method provided by the above-mentioned embodiments of the present disclosure can test the drone system using test subjects corresponding to different automation levels, thereby improving the convenience of drone system testing.

[0040] Continue to see Figure 3 , which shows a process 300 of another embodiment of the drone system testing method according to the present disclosure. Figure 3 As shown, the method in this embodiment may include the following steps:

[0041] Step 301, obtaining attribute information of the drone system.

[0042] Technicians can fill in the basic information of the drone system, assign test tasks, view automation level test subjects, etc. through the preset website. Furthermore, technicians can also register and log in to an account through the above website to maintain and update the attribute information of the drone system. In some specific practices, the drone system includes an automatic flight system, and the attribute information may include the modules included in the automatic flight system. The automatic flight system may be an operation control system set on the drone, and the flight control of the drone can be achieved through the above operation control system. The automatic flight system may include multiple modules, for example, a power monitoring module, a health monitoring module, an energy monitoring module, and the like.

[0043] Step 302, determining the automation functions of the UAV system during operation according to the modules included in the automatic flight system; determining the test subjects corresponding to the different automation levels according to the automation functions of the UAV system during operation, the preset operation risk set and the elements corresponding to the different automation levels.

[0044] The modules included in the automatic flight system can be analyzed to determine the automation functions of the drone system during operation. Specifically, the automation function can be determined based on the name of the module. Alternatively, the automation function can be determined based on the identification of the module.

[0045] After determining the automation function of the UAV system during operation, the test subjects corresponding to the non-automation level can be further determined in combination with the preset operation risk set and the elements corresponding to different automation levels. Specifically, a mapping relationship between the automation function and the operation risk and the elements corresponding to different automation levels can be preset, and the test subjects corresponding to different automation levels can be determined based on the above mapping relationship.

[0046] Specifically, the mapping relationship can be shown in the following table:

[0047]

[0048]

[0049] Step 303, according to the test subjects corresponding to different automation levels, determine the type of test items, the take-off and landing airports of the UAV, and the test routes.

[0050] After determining the test subjects corresponding to different automation levels, the type of test items, the take-off and landing airports of the drone, and the test routes can be further determined. Here, the test items may include: flight mission execution, internal operation risk factor detection and response, external operation risk factor detection and response, flight mission takeover, and designed operation range. In this embodiment, there is a corresponding relationship between different test subjects and test items. Through the above correspondence, the type of test item corresponding to the test subject can be determined.

[0051] At the same time, the take-off and landing airports and test routes of the drone can also be determined. It is understandable that the determination of the take-off and landing airports can be determined by using existing algorithms, or by determining according to the test scenario. The test route can be determined based on the correspondence between the test scenario and the test route, or it can be determined by receiving external input. For example, when the type of test project is "flight mission execution simulation verification", the test route can include a spiral ascent route, a five-pointed star route, a spiral descent route, a turning route, and a straight route. The determination of each type of route can be determined by an algorithm, and the order of each type of route can be determined by external input.

[0052] Step 304, determining the test task according to the type of test item, take-off and landing airports, and test routes.

[0053] After determining the type of test item, take-off and landing airports, and test routes, the test tasks can be further determined on this basis. Specifically, the test tasks can be determined according to the correspondence between the types of different test items and the test tasks. Alternatively, based on the test scenario, the risk factors or control commands required during the flight of the drone can be determined to determine the test tasks.

[0054] In some optional implementations of this embodiment, when the type of the test item is the first type, the test task can be determined according to the take-off and landing airports and the test route. Here, the first type can be flight task execution simulation verification.

[0055] The automatic flight system flight mission execution test project mainly verifies the flight control performance of the UAV system, including the health status check of the UAV equipment, the ability to maintain position and speed, the ability to fly on a predetermined route, etc. By comparing and analyzing the simulated flight test data with the preset route data, the adaptability and robustness of the automatic flight system control parameters are verified, and the rationality and correctness of the flight control scheme and control parameters are verified.

[0056] At this time, there is no need to inject internal and external risks into the flight process of the drone. Therefore, the test tasks can be determined according to the take-off and landing airports and test routes.

[0057] In some optional implementations of this embodiment, when the type of the test item is the second type, the internal risk or external risk to be injected can be determined from the preset operation risk set. Here, the second type can be internal operation risk detection simulation verification or external operation risk detection simulation verification.

[0058] If it is an internal operation risk detection simulation verification, it is necessary to inject internal risks into the UAV flight process. The internal operation risk detection project mainly verifies the detection and response capabilities of the UAV system related to internal operation risk factors, mainly including the detection and response capabilities of the power system, energy system, avionics system, mission payload system, and communication system. The simulation test system can flexibly set internal operation risks according to the requirements of the automation level test, such as loss of inertial navigation device signal, loss of altitude sensor signal, servo stuck, engine throttle controller failure, unstable power supply voltage, etc., to achieve fault injection and testing. The test system has a playback function and can save fault injection dynamic test data in the form of files for the tester to analyze in order to judge the fault response and handling capabilities of the automatic flight system.

[0059] If it is an external operation risk detection simulation verification, it is necessary to inject external risks into the UAV flight process. The external operation risk factor detection test project mainly verifies the detection and response capabilities of the UAV system related to external operation risk factors, mainly including meteorological environment over-limit warning, communication with external systems, ground risk factors, airspace flight conflicts and other detection and response capabilities. The biggest function of dynamic simulation testing is used for external risk setting. According to the test requirements, various external risk scenarios can be set during the UAV flight, such as abnormal wind environment, electronic fence, obstacle environment, crowd risk, route flight conflict, etc., to test the emergency response capabilities of sensors to abnormal environments, and then verify the external environment fault handling capabilities of the UAV system.

[0060] It is understandable that in order to enrich the diversity of test tasks, internal risks and external risks can also be injected into a single test task at the same time.

[0061] In some optional implementations of the present embodiment, when the type of the test item is the third type, the takeover command to be injected is determined from the preset takeover command set. Here, the third type may be a flight mission takeover simulation verification. The flight mission takeover test item mainly verifies the human-machine co-driving capability of the unmanned aerial vehicle system in both automatic control and manual operation modes when the unmanned aerial vehicle system exceeds the design operating range or the dynamic flight mission-related system fails, including tests such as control authority switching, operator takeover control, and system priority takeover control. That is, a takeover command is sent to the unmanned aerial vehicle during its flight. Therefore, the test task is determined based on the test route and the takeover command.

[0062] Step 305, build a test scenario; send instructions to the drone system to make the drone fly along the test route in the test scenario; inject internal risks, external risks and / or takeover commands during the flight, and monitor the flight status and parameters generated by the drone system; determine the test results based on the flight status and parameters generated by the drone system.

[0063] When executing a test task, you can build a test scenario that matches the test task. For example, if the test task is to test the response of the drone system to an abnormal wind environment, you need to build a wind speed limit test scenario. Then, send instructions to the drone system to make the drone fly along the test route in the test scenario. And inject simulated internal risks or external risks or takeover commands during the flight. In some specific practices, for testing needs, internal risks, external risks and takeover commands can also be injected into the simulated test system when executing a single test task.

[0064] During the test of the drone system, the flight status and parameters generated by the drone system can be monitored in real time. The above flight status may include take-off, hovering, landing, etc. Parameters may include flight altitude, flight speed, position and other parameters. According to the flight status and parameters generated by the drone system, the test results of the drone system can be determined. Specifically, the flight trajectory can be determined according to the position generated by the drone system at each moment. And compare the above flight trajectory with the test route. If the consistency between the two is high, it means that the flight capability is strong. If the consistency between the two is low, it means that the flight capability is poor.

[0065] In this embodiment, when testing the UAV system, a semi-physical simulation test platform for the UAV system can be used. The platform includes a simulation test system and a simulation engine. Through the simulation data interaction interface, the UAV system automatic function test can be carried out. The structural diagram of the UAV system semi-physical simulation test platform and the UAV system is shown in FIG. Figure 4 shown.

[0066] The simulation engine can establish a drone flight test scenario database and a drone model library. According to the requirements of the automation level verification subject, the test safety officer can use the simulation engine to build a simulated flight test scenario and verify the corresponding subject. The test safety officer can use the simulation engine to build a realistic drone dynamics model and three-dimensional model based on the user's test drone system and conduct drone simulation testing. It can include: virtual scene module, dynamics model construction module, sensor model module and three-dimensional model module.

[0067] (1) Virtual scenario module: This module can be used to build the required test scenarios and inject operational risks into the scenarios.

[0068] (2) Dynamic model construction module: This module can be used to construct the dynamic model of the UAV system under test and execute the control instructions of the UAV system.

[0069] (3) Sensor model module: This module can build a drone sensor model to perceive the simulation environment.

[0070] (4) Three-dimensional model module: This module can construct a three-dimensional model of the UAV system being tested and intuitively display the response status of the UAV.

[0071] The simulation test system can realize the full-process test management of the UAV system automation level simulation test. It can control the UAV system in simulated flight in real time, and can also inject operational risks into the UAV simulation environment according to the test subjects, monitor the flight parameters and response status generated during the test, and record the flight data in the system database. It can include: test management module, flight control module, operational risk injection module and data recording module.

[0072] (1) Test management module: This module manages the automation level verification process of the UAV system. Through this module, you can register and log in to your account, fill in basic information about the UAV, assign test tasks, view automation level verification subjects, determine automation levels, and generate and download verification reports.

[0073] (2) Flight control module: This module can establish take-off and landing airports, plan routes, and monitor the flight status and parameters generated by the UAV system.

[0074] (3) Operational risk injection module: This module can inject internal and external operational risks required by the verification subject, such as power failure, excessive weather environment, ground obstacles, etc.

[0075] (4) Data recording module: This module can record and store flight route information, calculate drone route deviation, and perform test data analysis.

[0076] The simulation test platform can communicate data with the UAV system under test through the developed simulation data interaction interface, realizing simulation tests of multiple types of UAV systems.

[0077] When testing the UAV system, if the test project type is flight mission execution simulation verification, the UAV test can be implemented through the following steps:

[0078] (a) Verify the health status of the simulation test system and ensure that the performance and interface of the simulation test system are working properly. Connect the automatic flight system to the semi-physical simulation test system, power the automatic flight system, and establish communication between the automatic flight system and the simulation test platform.

[0079] (b) Carry out the health status and pre-flight check of the automatic flight system, plan the airport and route using the simulation test system, and set the spiral ascent route, five-pointed star route, spiral descent route, turning route, and straight route in sequence.

[0080] (c) The UAV system completes the flight simulation according to the pre-set test route.

[0081] (d) Download the simulated flight route data and the test route data, compare and analyze the two, and verify the flight performance and flight mission execution capability of the UAV system.

[0082] If the test project type is internal operation risk detection simulation verification, the UAV system test can be implemented through the following steps:

[0083] (a) Connect the autoflight system to the simulation test system, power the autoflight system, and establish communication between the autoflight system and the simulation test platform.

[0084] (b) Select a take-off and landing airport, and the UAV system flies smoothly along the test route.

[0085] (c) Use the simulation test system to inject internal risks, that is, inject power system failures, such as multiple motor stalls, energy system failures, avionics system top event failures, mission payload system failures, and communication system failures.

[0086] (d) The flight parameters and control status generated by the UAV system are observed through the simulation test system, and the system's response capabilities are verified when the power system (fault tolerance and response strategy when the motor stops), energy system, avionics system, mission payload system, and communication system fail.

[0087] If the type of test project is external operation risk detection simulation verification, the UAV system test can be achieved through the following steps:

[0088] (a) According to the test requirements, the simulation engine software is used to build wind speed limit test scenarios, electronic fence test scenarios, ground obstacle test scenarios, and airspace conflict test scenarios.

[0089] (b) Power the automatic flight system and establish communication between the automatic flight system and the simulation test platform.

[0090] (c) Run the scenario simulation software and UAV dynamics model, complete relevant scenario tests, and record the detection and response results.

[0091] (d) The UAV system is able to sense test environments such as excessive wind speed, electronic fences, obstacles, and route conflicts, and respond according to pre-set fault handling strategies.

[0092] If the test project type is flight mission takeover simulation verification, the UAV system test can be implemented through the following steps:

[0093] (a) Power the autoflight system and establish communication between the autoflight system and the simulation test platform.

[0094] (b) The UAV system's route flight mode flies smoothly. The UAV is controlled to switch freely between automatic flight and manual control modes. It is observed whether the UAV switches smoothly and whether there are any out-of-control phenomena such as falling or flying off course.

[0095] (c) In the line flight mode, issue manual operation takeover instructions to the test UAV system in an appropriate manner, and record the manual operation takeover request reminder method of the test UAV system.

[0096] (d) After exiting the automatic flight mode, the test safety officer performs intervention operations, such as hovering, route replanning, etc., to enable the operator to take over control. The UAV system should not automatically restore the automatic flight mode.

[0097] (e) Sending an erroneous command to the test UAV system, which recognizes the erroneous command and refuses to respond.

[0098] Step 306: Determine the automation level corresponding to the UAV system based on the test results.

[0099] After the UAV system test is completed, the test results can be compared with the result requirements corresponding to each automation level to determine the automation level corresponding to the UAV system. In this way, the UAV system level can be divided through simulation testing of the UAV system, providing a data basis for further accurate classification and control of the UAV system.

[0100] In some optional implementations of this embodiment, the test results may also be evaluated to generate an evaluation report. Specifically, the evaluation information may be determined by comparing the automation level corresponding to the drone system with the actual automation level determined subsequently. Then, the evaluation information is combined with the drone information and the test information to obtain an evaluation report.

[0101] The drone system testing method provided in the above-mentioned embodiment of the present disclosure can determine the automation level corresponding to the drone system after performing a simulation test on the drone system, thereby providing a data basis for the subsequent operation of the drone system.

[0102] Further references Figure 5 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a drone system test device, the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0103] like Figure 5 As shown, the drone system testing device 500 of this embodiment includes: an information acquisition unit 501, a subject determination unit 502, a task determination unit 503 and a system testing unit 504.

[0104] The information acquisition unit 501 is configured to acquire attribute information of the drone system.

[0105] The subject determination unit 502 is configured to determine the test subjects corresponding to different automation levels according to the attribute information and a preset operation risk set.

[0106] The task determination unit 503 is configured to determine the test task according to the test subjects corresponding to different automation levels.

[0107] The system testing unit 504 is configured to test the drone system based on the test task.

[0108] In addition, in the technical solution of the present application, an electronic device is also proposed.

[0109] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present disclosure is shown.

[0110] like Figure 6 As shown, the electronic device may include a processor 601, a memory 602, a bus 603, and a computer program stored in the memory 602 and executable on the processor 601, wherein the processor 601 and the memory 602 communicate with each other via the bus 603. When the processor 601 executes the computer program, the steps of the above method are implemented, for example, including: obtaining attribute information of the UAV system; determining test subjects corresponding to different automation levels according to the attribute information and a preset set of operational risks; determining test tasks according to the test subjects corresponding to different automation levels; and testing the UAV system based on the test tasks.

[0111] In addition, in one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented, for example, including: obtaining attribute information of the drone system; determining test subjects corresponding to different automation levels based on the attribute information and a preset set of operating risks; determining test tasks based on the test subjects corresponding to different automation levels; and testing the drone system based on the test tasks.

[0112] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A UAV system testing method, include: Obtain attribute information of the drone system; Determine test subjects corresponding to different automation levels according to the attribute information and a preset set of operational risks; Determine the test tasks according to the test subjects corresponding to different automation levels; Based on the test task, testing the drone system; Determining the test tasks according to the test subjects corresponding to different automation levels includes: Determine the type of test item, the take-off and landing airport of the UAV, and the test route according to the test subjects corresponding to different automation levels; The test task is determined according to the type of the test item, the take-off and landing airports, and the test route.

2. The method according to claim 1, in, The method further comprises: According to the test results, the automation level corresponding to the UAV system is determined.

3. The method according to claim 1, in, The drone system includes an automatic flight system, and the attribute information includes modules included in the automatic flight system; and Determining test subjects corresponding to different automation levels according to the attribute information and a preset set of operational risks includes: Determining the automated functions of the UAV during operation according to the modules included in the automatic flight system; According to the automation functions of the UAV during operation, the preset operation risk set and the elements corresponding to different automation levels, the test subjects corresponding to the different automation levels are determined.

4. The method according to claim 1, in, Determining the test task according to the type of the test item, the take-off and landing airport, and the test route includes: In response to determining that the type of the test item is the first type, a test task is determined according to the take-off and landing airports and the test route.

5. The method according to claim 1, in, Determining the test task according to the type of the test item, the take-off and landing airport, and the test route includes: In response to determining that the type of the test item is the second type, determining an internal risk or an external risk to be injected from the preset operational risk set; A test task is determined according to the test route, the internal risk and / or the external risk.

6. The method according to claim 5, in, Determining the test task according to the type of the test item, the take-off and landing airport, and the test route includes: In response to determining that the type of the test item is the third type, determining a takeover command to be injected from a preset takeover command set; A test task is determined according to the test route and the takeover command.

7. The method according to claim 6, in, The testing of the UAV system based on the test task includes: Set up a test scenario; Sending instructions to the drone system to make the drone fly along the test route in the test scenario; Injecting the internal risk, the external risk and / or the takeover command during the flight, and recording and monitoring the flight status and parameters generated by the UAV system; The test results are determined based on the flight status and parameters.

8. A UAV system testing device, include: an information acquisition unit configured to acquire attribute information of the drone system; A subject determination unit is configured to determine test subjects corresponding to different automation levels according to the attribute information and a preset set of operational risks; The task determination unit is configured to determine the test tasks according to the test subjects corresponding to the different automation levels; An unmanned aerial vehicle system testing unit, configured to test the unmanned aerial vehicle system based on the test task; The task determination unit is further configured to: Determine the type of test item, the take-off and landing airport of the UAV, and the test route according to the test subjects corresponding to different automation levels; The test task is determined according to the type of the test item, the take-off and landing airports, and the test route.

9. An electronic device comprising a memory, a processor, a bus, and a computer program stored in the memory and executable on the processor, in, When the processor executes the computer program, the drone system testing method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the drone system testing method according to any one of claims 1 to 7 is implemented.

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