Unmanned aerial vehicle simulation test method and system

Abstract

The invention belongs to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle simulation test method and system. The method comprises the following steps: constructing a digital simulation model, and setting an analog sensor module and an instruction forwarding module to access an actual flight control system; inputting route data into the flight control system, and placing the digital simulation model in a preset environment field; simulation flight is carried out based on a digital simulation model so as to extract flight state data in the simulation flight through the simulation sensor module, and after current environment data in an environment field are obtained, the data are forwarded to a flight control system so as to trigger the flight control system to issue a control instruction; inputting the flight control state data, the current environment data and the control instruction into an instruction judgment model to judge that the instruction is abnormal, and stopping simulated flight to perform abnormality checking on the flight control system; otherwise, the control instruction is sent to the digital simulation model through the instruction forwarding module so as to continuously control the flight process of the digital simulation model in real time. The reliability of a baking machine test result can be effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle simulation test method and system. BACKGROUND

[0002] Before a medium-large unmanned aerial vehicle is put into use, baking machine test is performed, that is, each subsystem including a sensor system, a rudder surface control system, a power supply system and an engine system is connected to an integral machine cable under the control of a flight control system for joint debugging, which is a key to ensure safe flight and reliable flight of the unmanned aerial vehicle.

[0003] The existing baking machine test is divided into static baking machine test and dynamic baking machine test. Specifically, in the static baking machine test, all subsystems are powered on, and the engine system is controlled not to work, and other subsystem devices are kept working continuously, then flight control instructions such as actuator instructions, throttle instructions and device control instructions are input by manual operation to test the reliability of the cooperative work of each subsystem. In the dynamic baking machine test, all subsystems are powered on, and the engine system is controlled to start and keep running, and similarly, flight control instructions are input by manual operation to test the reliability of the cooperative work of each subsystem.

[0004] However, the inventors found that the way of manually inputting flight control instructions during baking machine test can partially examine the working reliability of each subsystem of the unmanned aerial vehicle, but cannot verify whether continuous control instructions in the actual flight process of the aircraft will cause abnormal work of the aircraft system. Although in part of the baking machine test process, the historical continuous control instructions (such as rudder deflection instructions, throttle control instructions or device switch instructions, etc.) of the flight control program are recorded and then input to each subsystem, but this kind of way can only simulate the continuous instructions in the historical flight state, and thus the baking machine test process still has certain limitations (such as being unable to simulate and analyze the instruction response situation after abnormal situation or interference in the actual operation process of the aircraft), resulting in difficulty in improving the reliability of the baking machine test result. SUMMARY

[0005] The present application aims to provide an unmanned aerial vehicle simulation test method and system to solve the technical problem that the reliability of the current baking machine test result is difficult to improve.

[0006] To achieve the above-mentioned purpose, the present application proposes the following technical solutions: In a first aspect, an unmanned aerial vehicle simulation test method is provided, comprising: constructing a digital simulation model corresponding to a to-be-tested unmanned aerial vehicle, and setting an analog sensor module and an instruction forwarding module associated with the digital simulation model to be connected to a flight control system of the to-be-tested unmanned aerial vehicle through a reserved maintenance channel; wherein the digital simulation model is a six-degree-of-freedom model; inputting route data corresponding to a preset flight task to the flight control system; obtaining target meteorological data based on a historical meteorological database to generate a preset environment field, and placing the digital simulation model in the environment field; performing simulated flight based on the digital simulation model to extract flight state data in the simulated flight through the simulated sensor module, and obtaining current environment data in the environment field, and then forwarding them to the flight control system at a preset frequency to trigger the flight control system to issue control instructions; when the control instructions are abnormal, inputting the flight control state data, the current environment data, and the control instructions to a pre-trained instruction judgment model to judge the abnormality of the control instructions, and stopping the simulated flight to troubleshoot the flight control system; otherwise, sending the control instructions to the digital simulation model through the instruction forwarding module to continuously control the flight process of the digital simulation model in real time; wherein the instruction judgment model is a neural network model.

[0007] Further, comprising: constructing an initial simulation model corresponding to the unmanned aerial vehicle to be tested, and placing the initial simulation model in a real-time environment field; wherein the real-time environment field is built based on real-time meteorological data; issuing the same control instructions to the unmanned aerial vehicle to be tested and the initial simulation model and obtaining corresponding actual flight state data and simulated flight state data; iteratively performing the previous step until the deviation between the simulated flight state data and the actual flight state data is less than a preset deviation threshold, and obtaining the digital simulation model.

[0008] Further, comprising: obtaining a plurality of historical data sets based on a navigation database, and adding labels to each historical data set to construct a plurality of data samples; wherein any historical data set includes historical meteorological data, historical flight state data, and historical control instructions; and the corresponding label is instruction normal or instruction abnormal; randomly dividing each data sample into a training set and a test set to iteratively train an initial judgment model until a preset iteration number is reached to obtain the instruction judgment model.

[0009] Further, comprising an interactive control module, which is in communication connection with the digital simulation model; comprising: injecting flight fault data into the digital simulation model based on the interactive control module.

[0010] Further, when the control instructions are abnormal, inputting the flight control state data, the current environment data, and the control instructions to a pre-trained instruction judgment model to judge the abnormality of the control instructions, and stopping the simulated flight to troubleshoot the flight control system; comprising: sending a suspension instruction to the data simulation model and the environment field to maintain the current state; sending a new control instruction to the instruction forwarding module to control the digital simulation model and the environment field to continue the simulation flight from the suspension state after the flight control system returns to normal.

[0011] In a second aspect, an unmanned aerial vehicle simulation test system is provided, comprising: a model construction module configured to construct a digital simulation model corresponding to the unmanned aerial vehicle to be tested, and set a simulation sensor module and an instruction forwarding module associated with the digital simulation model to access the flight control system of the unmanned aerial vehicle to be tested through a reserved maintenance channel; wherein the digital simulation model is a six-degree-of-freedom model; a task setting module configured to input route data corresponding to a preset flight task to the flight control system, obtain target meteorological data based on a historical meteorological database to generate a preset environment field, and place the digital simulation model in the environment field; a simulation flight module configured to perform a simulation flight based on the digital simulation model to extract flight state data in the simulation flight through the simulation sensor module, and after obtaining current environment data in the environment field, forward them to the flight control system at a preset frequency to trigger the flight control system to issue a control instruction; a continuous control module configured to input the flight control state data, the current environment data, and the control instruction to a pre-trained instruction judgment model to determine whether the control instruction is abnormal, and if so, suspend the simulation flight to troubleshoot the flight control system; otherwise, send the control instruction to the digital simulation model through the instruction forwarding module to perform real-time continuous control on the flight process of the digital simulation model; wherein the instruction judgment model is a neural network model.

[0012] Further comprising: a second construction module configured to construct an initial simulation model corresponding to the unmanned aerial vehicle to be tested, and place the initial simulation model in a real-time environment field; wherein the real-time environment field is built based on real-time meteorological data; an instruction issuing module configured to issue the same control instruction to the unmanned aerial vehicle to be tested and the initial simulation model, and obtain corresponding actual flight state data and simulation flight state data; an iterative optimization module configured to iteratively call the instruction issuing module until the deviation between the simulation flight state data and the actual flight state data is less than a preset deviation threshold, and obtain the digital simulation model.

[0013] Further comprising an interactive control module in communication with the digital simulation model; comprising: a fault injection module configured to inject flight fault data into the digital simulation model based on the interactive control module.

[0014] In a third aspect, the technical solution provides an electronic device, comprising at least one processor coupled with a memory, wherein the memory stores a computer program configured to be executed by the processor to implement the method.

[0015] In a fourth aspect, the technical solution provides a computer readable storage medium, which stores a computer program for being executed by a processor to implement the method.

[0016] Advantages: From the above technical solution, the technical solution of the present application provides a UAV simulation test method to solve the problem that the reliability of existing UAV baking test is difficult to improve due to the inability to perform flight test during baking test.

[0017] The technical solution comprises the following steps: first, a digital simulation model corresponding to the UAV to be tested is constructed, and an analog sensor module and an instruction forwarding module associated with the digital simulation model are set to be connected to the flight control system of the UAV to be tested through a reserved maintenance channel. Specifically, the digital simulation model is a six-degree-of-freedom model. Second, flight path data corresponding to a preset flight task is input into the flight control system; target meteorological data is obtained based on a historical meteorological database to generate a preset environment field, and the digital simulation model is placed in the environment field. Then, based on the digital simulation model, a simulated flight is performed to extract flight state data in the simulated flight through the analog sensor module, and after obtaining the current environment data in the environment field, they are forwarded to the flight control system at a preset frequency to trigger the issuance of control instructions. When the control instruction is abnormal, the simulated flight is stopped to perform abnormality checking on the flight control system; otherwise, the control instruction is sent to the digital simulation model through the instruction forwarding module to perform real-time continuous control on the flight process of the digital simulation model; wherein the instruction judgment model is a neural network model.

[0018] It can be seen that the technical scheme simulates the flight process of the unmanned aerial vehicle by using the digital simulation model, and introduces an environment field to simulate weather conditions in actual flight, so that the simulated flight process is as close to the actual flight as possible, thereby realizing flight simulation under any weather conditions, especially extreme conditions such as severe weather. At the same time, the simulation sensor module and the instruction forwarding module are set, and then the related data in the simulation flight can be collected by the simulation sensor module and input to the flight control system of the unmanned aerial vehicle to be tested, and the control instruction made by the flight control system is issued to the digital simulation model based on the instruction forwarding module to realize continuous simulation flight control and adjustment under continuous instructions, and then the reliability of the flight control system is verified. At the same time, before the control instruction is input to the digital simulation model, the instruction judgment model based on the neural network is also used to confirm the accuracy of the control instruction, so as to comprehensively simulate the flight process of the digital simulation model, and further ensure the accuracy of the verification result when verifying the reliability of the flight control system. At the same time, since the communication control between the digital simulation model and the flight control system is different from the communication control between the actual unmanned aerial vehicle actuator and the flight control system, the instruction judgment model can further avoid the single-point risk when relying only on the data simulation model.

[0019] It should be understood that all combinations of the aforementioned concepts and additional concepts described in greater detail below can be seen as part of the subject matter of the present disclosure provided such concepts are not mutually inconsistent.

[0020] The foregoing and other aspects, embodiments and features of the present teachings can be better understood from the following description of the present teachings when considered in connection with the accompanying drawings. Various embodiments of the present teachings will be presented in terms of methods, systems, and computer program products. The methods, systems, and computer program products presented herein can be implemented in software and / or hardware. As should be appreciated, the present teachings can be implemented in computer-readable storage medium having stored computer-executable program code that, when executed by one or more computer processors, carries out aspects of the present teachings. As should be appreciated, such computer-executable program code can be loaded into computer memory (e.g., RAM, ROM, cache, etc.) of one or more computer systems whereupon the computer-executable program code is executed under the control of the operating system and / or application programs. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical, or nearly identical, component that is illustrated in various figures is represented with a like numeral. For purposes of clarity, not every component is called out in every drawing. There is now being described by way of example various aspects of embodiments of the present teachings with reference to the attached drawings, in which: Figure 1 Flowchart of the unmanned aerial vehicle simulation test method; Figure 2 Flowchart of obtaining the digital simulation model; Figure 3 Flowchart of simulation process control based on the interactive control module; Figure 4 Flowchart of obtaining the instruction judgment model; Figure 5 Flowchart of processing the digital simulation model when the instruction is abnormal; Figure 6 This is the structural block diagram of the UAV simulation test system; Figure 7 This is a structural block diagram of the electronic device described in this embodiment. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the described embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used herein should be the common meanings understood by people with ordinary skills in the field to which the present invention belongs.

[0023] The terms "first", "second" and similar words used in the specification and claims of this application do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, unless the context clearly indicates otherwise, the singular forms of "a", "an" or "the" and similar words do not indicate a quantitative limitation, but rather indicate the presence of at least one. Words such as "include" or "comprise" mean that the elements or objects preceding "include" or "comprises" cover the features, wholes, steps, operations, elements and / or components listed after "include" or "comprises", and do not exclude the existence or addition of one or more other features, wholes, steps, operations, elements, components and / or their collections. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0024] Conducting burn-in tests before medium- and large-sized drones are put into operation is crucial for ensuring safe and reliable flight. However, existing burn-in tests fail to verify the aircraft's response to commands after anomalies or interference during actual operation, resulting in incomplete and limited burn-in tests. This embodiment addresses this issue by providing a drone simulation testing method.

[0025] The following is a detailed introduction to the UAV simulation test method described in this embodiment with reference to the accompanying drawings.

[0026] Combine Figure 1 As shown, the method includes: Step S202, a digital simulation model corresponding to the unmanned aerial vehicle to be tested is constructed, and a simulation sensor module and an instruction forwarding module associated with the digital simulation model are set by plugging a reserved maintenance navigation into the flight control system of the unmanned aerial vehicle to be tested.

[0027] The digital simulation model is a six-degree-of-freedom model, which is realized by modeling the aerodynamic data, power data and structural weight and center of gravity data of the unmanned aerial vehicle, can calculate the aerodynamic force and aerodynamic moment received by the unmanned aerial vehicle under the current speed, height, attitude and specific rudder deflection state in the flight process, and can calculate the speed, attitude and position change of the unmanned aerial vehicle in real time, and simulate the actual flight process. The simulation sensor module extracts key aircraft state data from the digital simulation model, and then simulates the transmission protocol of the sensor to send simulation sensor data in real time according to the actual sensor transmission frequency; the instruction forwarding module receives the control instructions sent by the flight control in real time by accessing the data interface of the flight control output instruction, and transmits the control instructions to the simulation model input interface, so as to realize the flight control in the digital simulation model.

[0028] In the unmanned aerial vehicle to be tested, in order to carry out simulation flight test under the control of the flight control system, a simulation system is designed in the simulation cable to realize the switch of the flight control system access and disconnection, so as to control the access of the simulation sensor data and the control of the control instruction. The switch switching mode is that a maintenance navigation is introduced between the navigation plug of the flight control system and the main cable navigation socket, and the maintenance navigation contains sensor input signal line and control instruction output signal line. When the unmanned aerial vehicle is actually running, the navigation is normally connected to the main cable, so that the input end is connected to the sensor system of the unmanned aerial vehicle to be tested, and the output end is connected to the executive mechanism control system of the unmanned aerial vehicle to be tested. When the simulation flight is carried out, the navigation is connected to the digital simulation system, and the corresponding navigation socket of the digital simulation system contains the simulation sensor data output interface corresponding to the simulation sensor module and the instruction data receiving reading interface corresponding to the instruction forwarding module.

[0029] As a specific embodiment, in order to ensure the consistency of the digital simulation model with the unmanned aerial vehicle to be tested and improve the reliability of the simulation result, the method further comprises the steps of: Figure 2 As shown in the figure, the method further comprises the steps of: Step S20102, an initial simulation model corresponding to the unmanned aerial vehicle to be tested is constructed, and the initial simulation model is placed in a real-time environment field.

[0030] In the specific implementation, the real-time environment field is built based on real-time weather data.

[0031] Step S20104, the above step is iteratively performed until the deviation between the simulation flight state data and the actual flight state data is less than a preset deviation threshold, and the digital simulation model is obtained.

[0032] At this time, the digital simulation model obtained based on steps S20102-S20104 has a response to the control instruction consistent with the response of the unmanned aerial vehicle to be tested, thereby ensuring the accuracy of the verification of the reliability of the flight control system of the unmanned aerial vehicle to be tested based on the simulation flight result.

[0033] Step S204: inputting route data corresponding to the preset flight task to the flight control system; obtaining target meteorological data based on a historical meteorological database to generate a preset environment field, and placing the digital simulation model in the environment field.

[0034] The historical meteorological database in the embodiment is used to store meteorological satellite data. In specific implementation, different data can be selected according to actual needs. For example, if the flight process in a bad situation needs to be simulated, data corresponding to a snowy day or meteorological data in a low-visibility situation can be selected.

[0035] As a specific implementation, an interactive control module is further included, which is in communication connection with the digital simulation model. At this time, the interactive control module is a software independent of the digital simulation model, which can provide a man-machine interactive interface for controlling the digital simulation model, support simulation parameter setting, simulation running control and fault injection functions. Thus, data modification related to simulation flight can be conveniently performed according to actual needs, so as to improve the controllability of the simulation process. Specifically, as shown in the figure, the interactive control module includes: Figure 3 Step S20402: injecting flight fault data into the digital simulation model based on the interactive control module.

[0036] Step S206: performing simulation flight based on the digital simulation model to extract flight state data in the simulation flight through the simulation sensor module, and after obtaining current environment data in the environment field, forwarding them to the flight control system at a preset frequency to trigger the flight control system to issue control instructions.

[0037] In specific implementation, the digital simulation model is first initialized. During the simulation flight, the control instructions are generated in real time by the flight control according to the flight state and the flight task, and the aircraft engine, rudder surface and rudder motor, and avionics switch all respond to the control instructions issued by the flight control in real time.

[0038] Step S208: when the control instruction is abnormal, inputting the flight control state data, the current environment data and the control instruction to a pre-trained instruction judgment model to judge the abnormality of the control instruction, and stopping the simulation flight to troubleshoot the flight control system; otherwise, sending the control instruction to the digital simulation model through the instruction forwarding module to continuously control the flight process of the digital simulation model in real time.

[0039] ​In this embodiment, the instruction judgment model is a neural network model. During the running of the instruction judgment model, a predicted instruction is calculated based on the flight control state data and the current environment data. When the deviation between the control instruction and the predicted instruction is large, it is considered that there is an instruction anomaly. In combination with Figure 4 As shown in the figure, the instruction judgment model is obtained by training as follows: Step S302: Obtain a plurality of historical data sets based on the navigation database, and add labels to each historical data set to construct a plurality of data samples.

[0040] In specific implementation, any historical data set includes historical meteorological data, historical flight state data and historical control instruction. The corresponding label is instruction normal or instruction anomaly.

[0041] Step S304: Randomly divide each data sample into a training set and a test set to iteratively train the initial judgment model until a preset iteration number is reached to obtain the instruction judgment model.

[0042] As a preferred embodiment, if it is judged that the control instruction is abnormal, in combination with Figure 5 As shown in the figure, it also includes: Step S20802: Send a suspension instruction to the data simulation model and the environment field to maintain the current state.

[0043] Step S20804: After the flight control system returns to normal, send a new control instruction to the instruction forwarding module to control the digital simulation model and the environment field to continue the simulated flight from the suspended state.

[0044] At this time, in combination with steps S20802-S20804, the simulated flight can be continued from the suspended state after the instruction is normal, thereby avoiding repeated operations and improving the efficiency of the simulated flight process.

[0045] In specific implementation, after step S208, it also includes: Step S210: Judge the correctness of the control instruction based on the simulated flight process, and if flight anomaly occurs after the digital simulation model responds to the control instruction, it is judged that the control instruction is abnormal, and then trigger the technical personnel to perform anomaly troubleshooting on the flight control system.

[0046] In specific implementation, the corresponding processing of the data simulation model during the corresponding anomaly troubleshooting can also adopt the mode shown in steps S20802-S20804.

[0047] In summary, the test method described in this embodiment is realized based on the interaction between the digital simulation model and the flight control system, so as to solve the problem that the test result reliability is difficult to improve due to the fact that actual flight cannot be performed during current oven test. Meanwhile, a machine learning model is introduced to further ensure the accuracy of the verification result based on simulation flight and avoid introducing single-point risk.

[0048] The above program can be run in a processor, or can also be stored in a memory (or called computer readable storage medium), and the computer readable medium includes permanent and non-permanent, removable and non-removable media, which can realize information storage by any method or technology. The information can be computer readable instructions, data structure, program module or other data. Examples of computer storage medium include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. According to the definition herein, the computer readable medium does not include the temporary computer readable medium such as modulated data signal and carrier wave.

[0049] These computer programs can also be loaded into a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to generate a computer implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the flow Figure 1 One flow or multiple flows and / or the functions specified in one block or multiple blocks can be implemented by different modules. Figure 1 The different steps can be implemented by different modules.

[0050] The embodiment also provides an unmanned aerial vehicle simulation test system, which combines the above-mentioned simulation test method and the flight control system. Figure 6 As shown in the figure, the system comprises: A model construction module is configured to construct a digital simulation model corresponding to the unmanned aerial vehicle to be tested, and set a simulation sensor module and an instruction forwarding module associated with the digital simulation model to access the flight control system of the unmanned aerial vehicle to be tested through a reserved maintenance channel.

[0051] A task setting module is configured to input route data corresponding to a preset flight task to the flight control system, acquire target meteorological data based on a historical meteorological database to generate a preset environment field, and place the digital simulation model in the environment field.

[0052] a simulation flight module, configured to perform a simulation flight based on the digital simulation model to extract flight state data in the simulation flight through the simulation sensor module, and to forward the flight state data and current environment data in the environment field to the flight control system at a preset frequency to trigger the flight control system to issue a control instruction.

[0053] a continuous control module, configured to input the flight control state data, the current environment data and the control instruction into a pre-trained instruction judgment model to determine whether the control instruction is abnormal, and to stop the simulation flight to troubleshoot the flight control system if the control instruction is abnormal, or to send the control instruction to the digital simulation model through the instruction forwarding module to perform real-time continuous control on the flight process of the digital simulation model if the control instruction is normal, wherein the instruction judgment model is a neural network model.

[0054] Since the system is built based on the method, the above has been described and will not be repeated here.

[0055] For example, the system further comprises: a second construction module, configured to construct an initial simulation model corresponding to the unmanned aerial vehicle to be tested, and to place the initial simulation model in a real-time environment field, wherein the real-time environment field is built based on real-time meteorological data.

[0056] an instruction issuing module, configured to issue the same control instruction to the unmanned aerial vehicle to be tested and the initial simulation model, and to obtain actual flight state data and simulation flight state data.

[0057] an iterative optimization module, configured to iteratively call the instruction issuing module until a deviation between the simulation flight state data and the actual flight state data is less than a preset deviation threshold, and to obtain the digital simulation model.

[0058] For example, the system comprises an interactive control module, which is communicatively connected to the digital simulation model, and based on this, comprises the following functional modules: a fault injection module, configured to inject flight fault data into the digital simulation model based on the interactive control module.

[0059] In combination with Figure 7 As shown in the drawings, the embodiment provides an electronic device, which comprises at least one processor, the processor is coupled to a memory, the memory stores a computer program, and the computer program is configured to be executed by the processor to perform the method.

[0060] Meanwhile, a computer readable storage medium is provided, which stores a computer program, and the computer program is used to be executed by a processor to implement the method.

[0061] Since the system, the electronic device and the storage medium are all based on the method to build or used to implement the method, the accuracy of the oven test result (i.e. the reliability verification of the flight control system) can be effectively improved, and the safety during the subsequent actual flight of the unmanned aerial vehicle can be ensured.

[0062] Although the present application has been disclosed in the above preferred embodiments, it is not intended to limit the present application. Those skilled in the art can make various modifications and improvements without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be subject to the scope defined by the claims.

Claims

1. A UAV simulation test method, characterized in that: include: Construct a digital simulation model corresponding to the UAV to be tested, and set an analog sensor module and a command forwarding module associated with the digital simulation model to be connected to the flight control system of the UAV to be tested through a reserved maintenance aviation plug; wherein the digital simulation model is a six-degree-of-freedom model; Inputting route data corresponding to a preset flight mission into the flight control system; acquiring target meteorological data based on a historical meteorological database to generate a preset environmental field, and placing the digital simulation model in the environmental field; Performing simulated flight based on a digital simulation model to extract flight status data during the simulated flight through the simulated sensor module, and simultaneously obtaining current environmental data within the environment field, and forwarding them to the flight control system at a preset frequency to trigger it to issue control instructions; The flight control status data, the current environment data and the control instructions are input into a pre-trained instruction judgment model to determine that the control instructions are abnormal. When this is determined, the simulated flight is terminated to check for abnormalities in the flight control system. Otherwise, the control instructions are sent to the digital simulation model via the instruction forwarding module to perform real-time and continuous control of its flight process. The instruction judgment model is a neural network model.

2. The UAV simulation test method according to claim 1, characterized in that: include: Constructing an initial simulation model corresponding to the UAV to be tested, and placing the initial simulation model in a real-time environment field; wherein the real-time environment field is constructed based on real-time meteorological data; Sending the same control instructions to the UAV to be tested and the initial simulation model and obtaining corresponding actual flight status data and simulated flight status data; The previous step is iterated until the deviation between the simulated flight status data and the actual flight status data is less than a preset deviation threshold, and the digital simulation model is obtained.

3. The UAV simulation test method according to claim 1, characterized in that: include: A plurality of historical data groups are obtained based on the navigation database, and labels are added to each historical data group to construct a plurality of data samples; wherein each historical data group includes historical meteorological data, historical flight status data, and historical control instructions; and the corresponding label is normal instruction or abnormal instruction; Each data sample is randomly divided into a training set and a test set to iteratively train the initial judgment model until a preset number of iterations is reached to obtain the instruction judgment model.

4. The UAV simulation test method according to claim 1, characterized in that: The method comprises an interactive control module, wherein the interactive control module is communicatively connected with the digital simulation model; and comprises: Injecting flight fault data into the digital simulation model based on the interactive control module.

5. The UAV simulation test method according to claim 1, characterized in that: The step of inputting the flight control state data, the current environment data, and the control instruction into a pre-trained instruction judgment model to determine that the control instruction is abnormal, and terminating the simulated flight to perform abnormality inspection on the flight control system includes: Sending abort instructions to the data simulation model and the environment field to maintain the current state; After the flight control system returns to normal, it sends new control instructions to the instruction forwarding module to control the digital simulation model and the environmental field to continue the simulated flight from the suspended state.

6. A UAV simulation test system, characterized in that: include: A model construction module is used to construct a digital simulation model corresponding to the UAV to be tested, and to set an analog sensor module and a command forwarding module associated with the digital simulation model to be connected to the flight control system of the UAV to be tested through a reserved maintenance aviation plug; wherein the digital simulation model is a six-degree-of-freedom model; A mission setting module is used to input route data corresponding to a preset flight mission into the flight control system; obtain target meteorological data based on a historical meteorological database to generate a preset environmental field, and place the digital simulation model in the environmental field; A flight simulation module is used to simulate flight based on a digital simulation model to extract flight status data during the simulated flight through the simulation sensor module, and simultaneously obtain current environmental data in the environment field, and forward them to the flight control system at a preset frequency to trigger it to issue control instructions; The continuous control module is used to input the flight control status data, the current environment data and the control instructions into a pre-trained instruction judgment model to determine if the control instructions are abnormal, and then terminate the simulated flight to check for abnormalities in the flight control system; otherwise, the control instructions are sent to the digital simulation model via the instruction forwarding module to perform real-time continuous control of its flight process; wherein the instruction judgment model is a neural network model.

7. The UAV simulation test system according to claim 6, characterized in that: include: The second construction module is used to construct an initial simulation model corresponding to the UAV to be tested, and place the initial simulation model in a real-time environment field; wherein the real-time environment field is constructed based on real-time meteorological data; An instruction issuing module is used to issue the same control instructions to the UAV to be tested and the initial simulation model and obtain corresponding actual flight status data and simulated flight status data; The iterative optimization module is used to iteratively call the instruction issuing module until the deviation between the simulated flight status data and the actual flight status data is less than a preset deviation threshold, thereby obtaining the digital simulation model.

8. The UAV simulation test system according to claim 6, characterized in that: The method comprises an interactive control module, wherein the interactive control module is communicatively connected with the digital simulation model; and comprises: A fault injection module is used to inject flight fault data into the digital simulation model based on the interactive control module.

9. An electronic device, characterized in that: The method comprises at least one processor, wherein the processor is coupled to a memory, wherein a computer program is stored in the memory, and wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed by the processor.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and the computer program is used to be executed by a processor to implement the method according to any one of claims 1 to 5.

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