Low-altitude aircraft engineering simulation system

Abstract

The invention relates to an engineering simulation system for a low-altitude aircraft. The engineering simulation system comprises an engineering simulator of the low-altitude aircraft, a simulator server and an upper computer, the engineering simulator comprises a cockpit; the cockpit comprises a driving seat, a central control column and an auxiliary control column; the central control stick is used for simulating accelerator operation and yaw operation of the low-altitude aircraft; the auxiliary joystick is used for simulating pitching operation and rolling operation of the low-altitude aircraft; sensors are respectively arranged in the central steering column and the auxiliary steering column, and are used for collecting operation data generated when a pilot operates the steering column; the simulator server receives the operation data and outputs a control instruction corresponding to the operation data; the upper computer receives the operation data and flight data corresponding to the flight process, and the operation data and the flight data are used for optimizing the simulator server. Therefore, the difference feeling in the process that a pilot uses the low-altitude aircraft engineering simulator to carry out simulation flight can be effectively reduced.

Description

Technical Field

[0001] This disclosure relates to the field of aircraft simulation engineering, and more particularly to a low-altitude aircraft engineering simulation system. Background Technology

[0002] Currently, engineering simulators are used for various tasks related to low-altitude aircraft cockpit evaluation, pilot control logic design verification, pilot flight training, and aircraft fault simulation verification. An engineering simulator is a device that includes a hardware-in-the-loop simulation system and a visual, acoustic, and vibration simulation system, capable of replicating the functions and operational characteristics of aircraft systems. Therefore, low-altitude aircraft engineering simulators are crucial tools for meeting the research and development, and airworthiness compliance testing and verification needs of low-altitude aircraft.

[0003] However, most engineering simulators currently used for low-altitude aircraft are derived from simulator technology for fixed-wing large aircraft and cannot fully adapt to the needs of low-altitude aircraft, with their design logic largely inherited from traditional large aircraft. Therefore, in the current context, low-altitude aircraft engineering simulators can create a certain degree of difference for pilots during use. Summary of the Invention

[0004] In view of this, this disclosure proposes a low-altitude aircraft engineering simulation system that can effectively reduce the sense of difference experienced by pilots during simulated flight using a low-altitude aircraft engineering simulator.

[0005] According to one aspect of this disclosure, a low-altitude aircraft engineering simulation system is provided, comprising: an engineering simulator for the low-altitude aircraft, a simulator server, and a host computer; wherein, the engineering simulator includes: a cockpit; the cockpit includes: a pilot's seat, a central control stick located on both sides of the pilot's seat, and an auxiliary control stick; the central control stick is used to simulate the throttle operation and yaw operation of the low-altitude aircraft; the auxiliary control stick is used to simulate the pitch operation and roll operation of the low-altitude aircraft; a pilot sits in the pilot's seat; sensors are respectively installed in the central control stick and the auxiliary control stick, the sensors being used to collect operation data generated by the pilot operating the control sticks; the simulator server receives the operation data and outputs control commands corresponding to the operation data to control the engineering simulator to simulate the flight process of the low-altitude aircraft; the host computer receives the operation data and flight data corresponding to the flight process, the operation data and flight data being used to optimize the simulator server.

[0006] In one possible implementation, the system further includes: a data recorder for recording pilot operation data and flight data during flight; wherein the operation data and flight data are recorded according to the batch and time of the pilot's simulated flight mission; wherein the host computer receives the operation data and flight data corresponding to the flight process, including: the host computer receiving the pilot's operation data and flight data sent by the data recorder in a specified batch and / or at a specified time.

[0007] In one possible implementation, a mode switching button is installed on the top of the central control stick, which is used to set the flight mode; and an instant-call button is installed on the top of the auxiliary control stick, which is used to perform an instant-call.

[0008] In one possible implementation, the cockpit further includes a cockpit display for displaying flight information during flight; the flight information includes flight status information and / or alarm information.

[0009] In one possible implementation, the simulator server includes: an environment modeling node composed of multiple computer devices, a simulation computing node composed of multiple computer devices, and a data interaction node composed of at least one computer device; the environment modeling node is used to construct a flight simulation environment for the engineering simulator to simulate the flight process of a low-altitude aircraft; the simulation computing node is used to output control commands corresponding to the operation data to control the engineering simulator to simulate the flight process of the low-altitude aircraft; and the data interaction node is used to realize data interaction.

[0010] In one possible implementation, the simulator server is equipped with flight control software, which is used to output control commands corresponding to the operation data based on the operation data, so as to control the engineering simulator to simulate the flight process of a low-altitude aircraft; the host computer also optimizes the flight control software based on the operation data and the flight data, so that the optimized flight control software is adapted to the pilot's operating habits.

[0011] In one possible implementation, optimizing the flight control software based on the operation data and the flight data includes: generating evaluation results for specified evaluation dimensions based on the operation data and the flight data, wherein the specified evaluation dimensions include at least one of: operation attitude feedback dimension, operation frequency dimension, over-operation dimension, and operation habit adaptation dimension; the operation attitude feedback dimension is used to evaluate at least one of attitude response speed, dynamic tracking error, steady-state error, and overshoot during the flight process of the engineering simulator simulating a low-altitude aircraft; the operation frequency dimension is used to evaluate high-frequency operations caused by the pilot operating the control stick due to control logic defects during flight; the over-operation dimension is used to evaluate over-operation caused by improper pilot operation during flight; the operation habit adaptation dimension is used to evaluate the pilot's operation habit type; and optimizing the flight control software based on the evaluation results of the specified evaluation dimensions.

[0012] In one possible implementation, the operational data includes: stick input data generated by the pilot manipulating the control stick during flight; the flight data includes: attitude data, speed data, and altitude data generated by an engineering simulator simulating the flight of a low-altitude aircraft; wherein, generating an evaluation result for a specified evaluation dimension based on the operational data and the flight data includes: obtaining reconstructed operational data by performing outlier removal and jitter elimination processing on the operational data; generating an association model based on the reconstructed operational data and the flight data, the association model representing the reconstructed operational data and flight data associated at any time point during flight; generating at least one of the evaluation results for the operational attitude feedback dimension, the operational frequency dimension, and the over-operation dimension based on the association model; and / or, generating an evaluation result for the operational habit adaptation dimension based on the reconstructed operational data.

[0013] In one possible implementation, the evaluation result of the operational attitude feedback dimension is generated based on the correlation model, including at least one of the following: based on the correlation model, determining the rise time when the pilot operates the control stick to generate a specified step operation, and taking the average rise time of all specified step operations as the attitude response speed; based on the correlation model, determining the attitude error at each time point in the response process of the engineering simulator simulating the low-altitude aircraft reaching the target attitude after the pilot operates the control stick to generate the specified operation, and taking the average attitude error at each time point in the response process as the dynamic tracking error; the target attitude is the aircraft attitude indicated by the specified operation; based on the correlation model, determining the error between the actual attitude and the corresponding target attitude in the attitude maintenance process of the engineering simulator simulating the low-altitude aircraft reaching the target attitude after the pilot operates the control stick to generate the specified operation, and taking the error between the actual attitude and the corresponding target attitude in the attitude maintenance process as the steady-state error; based on the correlation model, determining the proportion of the deviation between the attitude response peak and the corresponding target attitude relative to the target attitude as the overshoot, wherein the attitude response peak is the attitude peak generated when the actual attitude exceeds the target attitude during the flight of the engineering simulator simulating the low-altitude aircraft.

[0014] In one possible implementation, optimizing the flight control software based on the evaluation results of the specified evaluation dimension includes: optimizing the flight control software with the goal of minimizing the attitude response speed, the dynamic tracking error, the steady-state error, and the overshoot.

[0015] In one possible implementation, the evaluation result of the operation frequency dimension is generated based on the correlation model, including: determining the control stick operation frequency at different flight stages during flight based on the correlation model; confirming flight stages where the control stick operation frequency exceeds the expected value as unexpected stages; and determining the aircraft attitude fluctuation frequency corresponding to the unexpected stages; determining the ratio between the control stick operation frequency and the aircraft attitude fluctuation frequency of the unexpected stages as the fluctuation correlation coefficient of the unexpected stages; and determining that there are high-frequency operations during flight caused by the pilot operating the control stick due to control logic defects, when the fluctuation correlation coefficient is greater than a preset coefficient threshold and the control stick operation frequency is greater than a preset frequency threshold.

[0016] In one possible implementation, optimizing the flight control software based on the evaluation results of the specified evaluation dimension includes: optimizing the flight control software with the optimization objective of reducing the frequency of stick operation during unexpected phases to below the expected value and reducing the frequency of attitude fluctuations during unexpected phases.

[0017] In one possible implementation, the evaluation result of the over-operation dimension is generated based on the correlation model, including: determining whether amplitude-type over-operation and timing-type over-operation occur during flight based on the correlation model; wherein, amplitude-type over-operation is manifested when the pilot's single operation of the control stick exceeds a specified control stick amount threshold and causes the attitude change of the simulated low-altitude aircraft in the engineering simulator to exceed a specified attitude threshold; timing-type over-operation is manifested when, after the pilot operates the control stick to adjust the attitude, the actual attitude of the simulated low-altitude aircraft in the engineering simulator does not reach the target attitude, and the pilot operates the control stick again to adjust the attitude.

[0018] In one possible implementation, optimizing the flight control software based on the evaluation results of the specified evaluation dimension includes: optimizing the flight control software with the goal of suppressing the amplitude-type over-operation and the timing-type over-operation.

[0019] In one possible implementation, based on the reconstructed operation data, an evaluation result for the operation habit adaptation dimension is generated, including:

[0020] Based on the reconstructed operational data, the pilot's operational characteristics are determined. These characteristics include at least one of the following: jitter coefficient, mean stick curvature, standard deviation of stick movement, and peak operational rate. The jitter coefficient is the ratio between the standard deviation and the mean stick movement in the stick movement data. The mean stick curvature is the mean of the curvature of the curve corresponding to the stick movement data. The peak operational rate is the peak value of the first derivative of the stick movement data. Based on the pilot's operational characteristics, similarity matching is performed with the cluster centers of multiple preset operational habit types to obtain the operational habit type matching the pilot. The cluster centers of the multiple preset operational habit types are obtained by clustering multiple pilot operational characteristics.

[0021] In one possible implementation, different operating habit types have their own corresponding optimization goals. The step of optimizing the flight control software based on the evaluation results of the specified evaluation dimension includes: optimizing the flight control software according to the optimization goals that match the pilot's operating habit type.

[0022] In one possible implementation, optimizing the flight control software includes: optimizing the operation logic control parameters in the flight control software, wherein the operation logic control parameters determine the mapping rules between the operation input generated by the pilot operating the control stick and the attitude response generated during the simulation of low-altitude flight by the engineering simulator.

[0023] According to various aspects of this disclosure, various operations during the flight of a low-altitude aircraft can be simulated through the central control stick and auxiliary control stick on both sides of the pilot's seat. Then, operation data is collected by sensors, and control commands corresponding to the operation data are output through the simulator server. This can effectively realize the simulation flight of a low-altitude aircraft using an engineering simulator. Furthermore, the host computer can receive operation data and flight data to optimize the simulator server. This makes the optimized simulator server's control of the engineering simulator's simulation of the low-altitude aircraft's flight process more compatible with the pilot's operating habits, thereby effectively reducing the sense of difference for the pilot during the simulation flight of a low-altitude aircraft using an engineering simulator.

[0024] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0025] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this disclosure together with the specification and serve to explain the principles of this disclosure.

[0026] Figure 1 A schematic diagram of a low-altitude aircraft engineering simulation system according to an embodiment of the present disclosure is shown.

[0027] Figure 2 A schematic diagram of another low-altitude aircraft engineering simulation system according to an embodiment of the present disclosure is shown.

[0028] Figure 3 A schematic diagram of an operation acquisition level control optimization process according to an embodiment of the present disclosure is shown.

[0029] Figure 4 This diagram illustrates an operational habit type clustering optimization and adaptation process according to an embodiment of the present disclosure.

[0030] Figure 5 A schematic diagram illustrating an operational data and flight data parsing optimization process according to an embodiment of the present disclosure is shown.

[0031] Figure Labels

[0032] exist Figure 1 and Figure 2 In the diagram, 10 is the engineering simulator, 20 is the simulator server, 30 is the host computer, 40 is the data recorder, 50 is the cockpit support platform, 101 is the cockpit, 102 is the central control stick, 103 is the auxiliary control stick, 104 is the mode switching button, 105 is the push-to-talk button, 106 is the cockpit display, and 107 is the aviation interface. Detailed Implementation

[0033] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0034] As used herein, the terms “comprising,” “including,” “having,” or variations thereof are open-ended and include one or more of the stated features, integrals, elements, steps, components, or functions, but do not exclude the presence or addition of one or more other features, integrals, elements, steps, components, functions, or groups thereof.

[0035] When an element is referred to as “connected,” “coupled,” “responding,” or a variation thereof relative to another element, it may be directly connected, coupled, or responding to another element, or there may be an intermediate element present.

[0036] Although the terms first, second, third, etc., may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another. Therefore, without departing from the teachings of the inventive concept, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments.

[0037] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0038] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0039] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant regions.

[0040] Figure 1 A schematic diagram of a low-altitude aircraft engineering simulation system according to an embodiment of the present disclosure is shown. Figure 1 As shown, the system includes:

[0041] The engineering simulator 10, simulator server 20, and host computer 30 for low-altitude flight vehicles;

[0042] The engineering simulator 10 includes a cockpit 100; the cockpit 100 includes a pilot seat 101, a central control stick 102 located on both sides of the pilot seat, and an auxiliary control stick 103; the central control stick 102 is used to simulate the throttle and yaw operations of a low-altitude aircraft; the auxiliary control stick 103 is used to simulate the pitch and roll operations of a low-altitude aircraft; the pilot sits in the pilot seat 101;

[0043] Sensors (not shown in the figure) are respectively installed in the central control stick 102 and the auxiliary control stick 103. The sensors are used to collect the operation data generated by the pilot's operation of the control stick.

[0044] The simulator server 20 receives operation data and outputs control commands corresponding to the operation data to control the engineering simulator 10 to simulate the flight process of a low-altitude aircraft.

[0045] The host computer 30 receives operation data and flight data corresponding to the flight process. The operation data and flight data are used to optimize the simulator server 20.

[0046] According to the system of this disclosure, various operations during the flight of a low-altitude aircraft can be simulated by using the central control stick and auxiliary control stick on both sides of the pilot's seat. The system then collects operation data through sensors and outputs control commands corresponding to the operation data through the simulator server. This effectively realizes the simulation flight of a low-altitude aircraft using an engineering simulator. Furthermore, the host computer receives operation data and flight data and can optimize the simulator server. This makes the optimized simulator server's control of the engineering simulator's simulation of the low-altitude aircraft's flight process more compatible with the pilot's operating habits, thereby effectively reducing the sense of difference for the pilot during the simulation flight of a low-altitude aircraft using an engineering simulator.

[0047] In some embodiments, the cockpit 100 may be arranged according to the cockpit layout of the simulated low-altitude aircraft, replicating the seat arrangement of the real aircraft. The central control stick 102 and the auxiliary control stick 103 are arranged on both sides of the pilot seat 101 and can be fastened to the cockpit 100 by multiple hexagonal bolts. The pilot, sitting in the pilot seat 101, performs flight control operations of the low-altitude aircraft in the engineering simulator through the two control sticks. The central control stick 102 and the auxiliary control stick 103 are respectively responsible for performing pitch, roll, and yaw operations during the simulated flight process, and can determine the throttle position during the simulated flight process.

[0048] In some embodiments, the central control stick 102 and the auxiliary control stick 103 can both be two-axis control sticks, with each axis having a push-pull stroke of 10° to -10° and a center position of 0°. The two-axis control of the central control stick 102 corresponds to the throttle operation and yaw operation of the low-altitude aircraft, respectively, and the two-axis control of the auxiliary control stick 103 corresponds to the pitch operation and roll operation of the low-altitude aircraft, respectively.

[0049] In some embodiments, the sensors provided in the central control stick 102 and the auxiliary control stick 103 may be, for example, a rotary variable differential transformer or other sensors capable of measuring the amount of control stick operation, thereby enabling real-time monitoring of the amount of control stick operation. Furthermore, the central control stick 102 and the auxiliary control stick 103 may each be provided with a data output interface to connect to a transmission bus (such as an RS422 bus) through the data output interface, and then transmit the measured stick data (i.e., the stick operation amounts of the two control sticks in two axial directions and different flight modes) to the simulator server 20 through the transmission bus.

[0050] In some embodiments, such as Figure 2 As shown, a mode switching button 104 is installed on the top of the central control stick 102. The mode switching button 104 is used to set the flight mode. The flight mode may include attitude mode and speed mode, etc. The operation logic of different flight modes is different. The pilot can set the required flight mode according to the needs. A push-to-talk button 105 is installed on the top of the auxiliary control stick 103. The push-to-talk button 105 is used to perform push-to-talk communication.

[0051] In some embodiments, such as Figure 2 As shown, the cockpit 100 may further include a cockpit display 106, which displays flight information during flight; the flight information includes flight status information and / or alarm information. The flight status information may include, for example, real-time attitude, altitude, and speed during the simulation of low-altitude flight by an engineering simulator, and this embodiment of the present disclosure is not limited in this regard. The alarm information may include, for example, fault alarms and hazard alarms generated during the simulation of low-altitude flight by an engineering simulator, and this embodiment of the present disclosure is not limited in this regard.

[0052] In some embodiments, such as Figure 2 As shown, the cockpit 100 in the engineering simulator 10 can be installed on the cockpit support platform 50. The cockpit support platform 50 can drive the cockpit 100 to simulate the attitude changes such as yaw, pitch and roll during the flight of a low-altitude aircraft, so as to form the pilot's physical experience during the flight.

[0053] During the simulation of low-altitude flight using the aforementioned system, the pilot can control the flight process of the low-altitude aircraft by operating the central control stick 102 and the auxiliary control stick 103. Sensors (such as a rotary variable differential transformer) are installed inside the central control stick 102 and the auxiliary control stick 103. The movement of the central control stick 102 (pushing forward, pulling back, pushing left, and pushing right) is captured by the sensors, forming stick data. This stick data can be output to the simulator server 20 via a data output interface (such as an aviation interface) through a secondary RS422 bus. Similarly, the movement of the auxiliary control stick 103 (pushing forward, pulling back, pushing left, and pushing right) is also captured by the sensors, and the stick data is also output via a data output interface (such as an aviation interface). Figure 2 The aviation interface 107 in the simulator is output to the simulator server 20 via RS422 bus 1. The on / off states of the mode switching button 104 on the top of the center stick 102 and the push-to-play button 105 on the top of the auxiliary stick 103 can also be sent to the simulator server 20 via RS422 bus 1 and RS422 bus 2, respectively. Therefore, the above-mentioned operation data may include not only the stick data, but also operation frequency and button data (such as the on / off state of the mode switching button 104 and the push-to-play button 105, button frequency, etc.), which are not limited in this embodiment.

[0054] In some embodiments, in addition to the driver's cockpit, the engineering simulator 10 may also be equipped with other hardware devices as needed, such as driving instruments, instructor's console, etc. This disclosure does not limit the scope of the embodiments.

[0055] In some embodiments, such as Figure 2 As shown, the system may further include: a data recorder 40, used to record the pilot's corresponding operation data and flight data during flight; wherein the operation data and flight data are recorded according to the batch and time of the pilot's simulated flight mission; wherein the host computer 30 receives the operation data and flight data corresponding to the flight process, including: the host computer 30 receiving the pilot's operation data and flight data sent by the data recorder at a specified batch and time. That is, the data recorder 40 can receive and record the operation data of the central control stick 102 and the auxiliary control stick 103 during the operation of the simulator server, and the flight data during the simulator's simulated flight process. The data recorder 40 can, for example, encapsulate the operation data and flight data according to the time of the engineering simulator's simulated flight, and can send them to the host computer 30 through a network port. It should be understood that the flight process of different flight missions is different, and a batch of flight missions can be executed multiple times, thereby generating operation data and flight data of different batches and different times. The flight data may include, for example, attitude data (such as pitch angle, roll angle, and yaw angle), speed data (such as ground speed, airspeed, etc.), and altitude data.

[0056] In some embodiments, the flight process of a single flight mission may include multiple flight phases (such as takeoff, transition, cruise, reverse transition, landing, emergency, etc.). Therefore, the data recorder can record the flight phases corresponding to the flight data and operational data to facilitate the execution of subsequent optimization logic.

[0057] In some embodiments, the simulator server 20 can be used to control the startup, function reset, flight mission issuance, output control commands, etc. of the engineering simulator, and also run the flight simulation environment of the engineering simulator. In addition, it can receive the operation data of the central control stick 102 and the right control stick 102 during operation, generate flight data during the simulated flight of the engineering simulator, and send the operation data and flight data to the data recorder 40 for data recording and storage.

[0058] In some embodiments, the simulator server 20 can implement control commands based on operational data output through software, hardware, or a combination of both to control the engineering simulator 10 to simulate the flight process of a low-altitude aircraft and generate flight data. Therefore, the software, hardware, or a combination of both can be used to optimize the simulator server 20's software, hardware, or a combination of both, making the aircraft response to the control commands output by the simulator server 20 more suitable for the pilot's operating habits and needs.

[0059] In some embodiments, the simulator server 20 may consist of multiple computer devices installed in a server rack to establish the flight simulation environment for the engineering simulator 10, execute simulation control and related simulation calculations during the flight process, and form a hardware and software flight simulation loop with the engineering simulator 10. Specifically, the simulator server 20 may include: an environment modeling node composed of multiple computer devices (e.g., four high-performance computers can be used to form an environment modeling node), a simulation calculation node composed of multiple computer devices (e.g., eight multi-core computers can be used to form a simulation calculation node), and a data interaction node composed of at least one computer device; wherein, the environment modeling node is used to construct the flight simulation environment for the engineering simulator to simulate the flight process of a low-altitude aircraft (e.g., the environment modeling node can call a preset flight environment module to build a flight simulation environment, which can be matched with the flight mission, for example, if the flight mission is to pass through mountains or clouds at a specified altitude, the flight simulation environment can simulate the actual environment of the mountains or clouds at that altitude, such as temperature). The simulation calculation node is used to output control commands corresponding to the operation data to control the engineering simulator to simulate the flight process of the low-altitude aircraft (that is, to form a flight simulation process by performing simulation calculations based on the operation data); the data interaction node is used to realize data interaction. The simulator server 20 can send real-time flight environment data and simulation data (i.e., flight data) to the engineering simulator 10 via the server uplink bus through the data interaction node, and send simulated cockpit data (i.e., cockpit instrument display data in the simulated low-altitude aircraft, such as the current altitude and attitude of the aircraft) and some operation data (such as the above-mentioned stick data) to the engineering simulator 10 via the engineering simulator downlink bus, so as to display various data through the cockpit display 106. Among them, the nodes in the simulator server can realize data interaction and functional collaboration through a unified communication protocol and timing synchronization mechanism, which is not limited in this embodiment.

[0060] In some embodiments, the data recorder 40 may be connected to the simulator server 20 via, for example, a second UTP (Unshielded Twisted Pair) cable. During flight simulation in the engineering simulator, the simulator server 20 receives stick input data from the central control stick 102 and auxiliary control stick 103, as well as switch data from the mode switch button 104 and the push-to-play button 105, transmitted via the first and second RS422 buses. After receiving the data, the simulator server 20 converts it into internally common TCP / IP protocol data and forwards it to the data recorder 40 via the second UTP cable. The data recorder 40 records the operational data. Furthermore, during flight simulation in the engineering simulator, the simulator server 20 generates flight data corresponding to the flight process based on the pilot's control stick input data. This flight data is transmitted from the simulator server 20 to the data recorder 40 via the second UTP cable. The data recorder 40 receives the flight data and operational data transmitted from the simulator server 20 via the second UTP cable. The data recorder 40 records operational and flight data according to the batches and times of the simulated flight mission, generates data numbers, and stores them within the data recorder 40. The data recorder 40 and the host computer 30 can be connected via a No. 3 UTP twisted-pair cable. The host computer 30 can access flight and operational data of any batch and time at any time through the No. 3 UTP twisted-pair cable connected to the data recorder 30. For example, the user can specify the required batch and / or time through the host computer 30. The host computer can send a data access request carrying the specified batch and / or time to the data recorder 40 based on the user-specified batch and / or time. The data recorder 40 can package the operational and flight data of the specified batch and / or time in the data access request and return it to the host computer 30.

[0061] It should be understood that the data transmission between various devices via UTP twisted-pair cable and RS422 bus described in the embodiments of this disclosure are some possible implementation methods provided by the embodiments of this disclosure. In fact, those skilled in the art can use any type of line that can achieve data transmission according to actual needs, and the embodiments of this disclosure do not limit this.

[0062] In some embodiments, the host computer 30 can also load the actual flight control software used by the low-altitude aircraft (i.e., the operating logic control system software of the low-altitude aircraft), encapsulate the flight control software into software that can be run on the simulator server 20, and then send it to the simulator server 20. In practical applications, the flight control software is mainly used to control the entire low-altitude aircraft to make corresponding responses based on the pilot's operation input (including but not limited to pilot operation input, as well as various key operation inputs, etc.). In short, the flight control software mainly implements the flight operation logic. Therefore, the simulator server 20 can be equipped with the flight control software. This flight control software can be used to output control commands corresponding to the operation data based on the operation data, so as to control the engineering simulator 10 to simulate the flight process of the low-altitude aircraft, thereby generating flight data corresponding to the flight process. For example, the flight data can be obtained by the simulation computing node of the engineering simulator 10 through simulation calculation based on the control commands output by the flight control software and the flight simulation environment constructed by the environment modeling node. That is, the flight control software can be specifically deployed on the aforementioned simulation computing node. The simulation computing node executes the flight control software to output corresponding control commands based on the operation data input by the pilot. These control commands are used to control the low-altitude aircraft simulated by the engineering simulator to respond, such as attitude response, flight speed response, etc. Thus, the simulation computing node can obtain the flight data corresponding to the flight process through simulation calculation based on the control commands output by the flight control software and the flight simulation environment.

[0063] It should be understood that the embodiments of this disclosure do not limit the specific control logic of the flight control software, the simulation calculation process of the simulation calculation nodes, and the flight simulation environment constituted by the environment modeling nodes. These can be customized by those skilled in the art based on the actual situation of different low-altitude aircraft and different flight missions, and the embodiments of this disclosure do not limit them.

[0064] As mentioned above, in the current context of low-altitude aircraft engineering simulators, pilots may experience a certain degree of difference in their experience during flight. These differences can be addressed by continuously adjusting the operational logic. Furthermore, the operational performance of low-altitude aircraft in the verification phase cannot be directly optimized. Pilots may encounter situations requiring flight control software upgrades during simulator flights. Current flight control software upgrades are complex, requiring the conversion of pilot-initiated operational optimization requests into system software upgrades. After the software upgrade, it is then packaged and installed. This entire process is complex, lengthy, and time-consuming. Moreover, the upgrade results need to be further optimized and verified on the engineering simulator, potentially leading to repeated iterations due to unsatisfactory optimization, significantly extending the operational performance optimization cycle.

[0065] To overcome the shortcomings of existing low-altitude aircraft engineering simulator technologies, such as complex optimization and iteration processes for flight control software (including the operational logic within the flight control software) and low efficiency in verifying optimization effects, this disclosure provides a system capable of collecting simulator operation data and flight data, optimizing operational logic, and rapidly completing iterative upgrades of the flight control software. Based on the collected operation and flight data, the direction for operational logic optimization is determined. Immediate optimization is performed on the host computer 30, and the flight control software is packaged and distributed, achieving efficient iteration of the low-altitude aircraft engineering simulator flight control software. This allows the optimized flight control software to better adapt to the pilot's operating habits, making pilot operations in the engineering simulator smoother, and also balancing the overall response performance of the flight control software.

[0066] As described above, the aforementioned operational and flight data can be used to optimize the simulator server, making the control commands output by the optimized simulator server more compatible with the pilot's operating habits. In some embodiments, this can specifically optimize the flight control software deployed in the simulator server, which helps the pilot operate the low-altitude aircraft more smoothly and is more compatible with the pilot's operating habits. Therefore, the host computer 30 can also optimize the flight control software based on the operational and flight data, making the optimized flight control software compatible with the pilot's operating habits.

[0067] In some embodiments, such as Figure 3 As shown, the flight control software can be loaded first by the simulator server 20, and the flight environment (i.e., the flight simulation environment) can be run. The engineering simulator 10 then executes the simulated flight process, using the central control stick 102 and auxiliary control stick 103 within the engineering simulator 10 to change the pitch, roll, and yaw attitude during the flight simulation. Both the central control stick 102 and the auxiliary control stick 103 acquire control stick data by rotating a variable differential transformer and transmit it to the simulator server 20 via an RS422 bus. The simulator server 20 generates flight data based on the operational data and forwards the received operational data and generated flight data to the data recorder 40, which stores the flight data and operational data. Then, the host computer 30 reads and parses the flight data and operation data as the direction for flight control software optimization (i.e., including the direction for operation logic optimization), so as to edit and modify the flight control software, and package and send the optimized flight control software to the simulator server 20. The simulator server 20 receives and updates the flight control software, completes iterative flight simulation, and forms a closed-loop low-altitude aircraft engineering simulator pilot operation acquisition and control optimization system.

[0068] In some embodiments, the host computer 30 optimizes the flight control software based on operational data and flight data, which may include:

[0069] Step S11: Based on the operation data and flight data, generate evaluation results for specified evaluation dimensions. These dimensions include at least one of the following: operation attitude feedback dimension, operation frequency dimension, excessive operation dimension, and operation habit adaptation dimension. The operation attitude feedback dimension is used to evaluate at least one of the following during the flight of a simulated low-altitude aircraft in the engineering simulator: attitude response speed, dynamic tracking error, steady-state error, and overshoot. The operation frequency dimension is used to evaluate high-frequency operations caused by pilot stick manipulation due to control logic defects during flight. The excessive operation dimension is used to evaluate excessive operations caused by improper pilot operation during flight. The operation habit adaptation dimension is used to evaluate the type of pilot's operation habits.

[0070] Step S12: Optimize the flight control software based on the evaluation results of the specified evaluation dimensions.

[0071] As mentioned above, operational data includes: stick input data generated by the pilot's manipulation of the control stick during flight (i.e., the stick input in both axes of any flight mode). Flight data includes: attitude data (i.e., pitch angle, yaw angle, roll angle), speed data (such as ground speed, airspeed, etc.), and altitude data (i.e., flight altitude) generated during the simulation of low-altitude flight by the engineering simulator.

[0072] Considering that operational data may contain outliers (such as pilot errors) and noise (such as unintentional jitters superimposed on normal pilot control intentions), after the host computer 30 receives the encapsulated operational and flight data sent by the data recorder, it can first perform flight data parsing and operational data parsing, and then perform data cleaning and signal denoising on the operational data, thereby improving the accuracy of the evaluation results by utilizing the processed operational and flight data. Furthermore, considering that different data sources for operational and flight data may result in different timestamps and different units, in order to analyze and process the operational and flight data under the same dimension, time-series alignment and normalization can be performed on the operational and flight data for fusion processing, which is beneficial for using the processed data to achieve the evaluation of the specified evaluation dimension. Therefore, in some embodiments, step S11 above, generating an evaluation result for the specified evaluation dimension based on the operational and flight data, includes:

[0073] Step S111: By performing outlier removal and jitter elimination on the operation data, reconstructed operation data is obtained.

[0074] Step S112: Generate an association model based on the reconstruction operation data and flight data. The association model represents the stick position data and attitude data at any time point during the flight.

[0075] Step S113: Based on the association model, generate at least one of the following: evaluation results for the operation posture feedback dimension, evaluation results for the operation frequency dimension, and evaluation results for the over-operation dimension; and / or, based on the reconstructed operation data, generate evaluation results for the operation habit adaptation dimension.

[0076] In step S111, a rate threshold filter can be established for the operation data. This involves determining whether the first derivative of the lever position (i.e., operation rate = Δ lever position / Δ time) is less than a specified rate threshold (e.g., 100° / ms) to remove outliers, including extreme operation data (e.g., large-scale displacement of the lever position caused by driver misoperation). Specifically, the operation rate at any given time point can be the ratio between the difference between the lever position at the current time point and the lever position at the previous time point (i.e., Δ lever position) and the time difference between the current time point and the previous time point (i.e., Δ time). The lever position data at each time point in the operation data can be traversed, the operation rate corresponding to each time point can be calculated, and then it can be determined whether the operation rate at each time point is less than the specified rate threshold. If the operation rate at a certain time point is less than the specified rate threshold, the operation data at that time point is retained. If the operation rate at a certain time point is greater than or equal to the specified rate threshold, it can be considered an outlier and the operation data corresponding to that time point can be removed.

[0077] Then, the operational data after outlier removal can be processed using wavelet transform, i.e., by selecting a wavelet basis and performing multi-level wavelet decomposition (e.g., 3-level wavelet decomposition), retaining low-frequency approximation coefficients, and then reconstructing the signal to eliminate jitter generated during pilot stick operation. Specifically, technicians can select a suitable wavelet basis (i.e., the mother function used for wavelet decomposition) based on the characteristics of the operational data, and perform multi-level wavelet decomposition on the operational data. That is, the original operational data is decomposed layer by layer in the frequency domain according to the frequency from high to low into different components. For example, the first level of decomposition: decomposes the original operational data into high-frequency detail coefficients D1 (the finest jitter and noise) and low-frequency approximation coefficients A1 (the main outline of the signal). The second level of decomposition: further decomposes A1 from the previous step into D2 (second-highest frequency details) and A2 (a coarser outline). The third level of decomposition: decomposes A2 into D3 (mid-frequency details) and A3 (the most core and smoothest trend). Among them, A3 has the lowest frequency, representing the most macroscopic control intention; D1 has the highest frequency, representing the finest jitter and noise. Furthermore, the third-level low-frequency approximation coefficient A3, representing the macroscopic manipulation intention, can be retained, while the high-frequency detail coefficients D1, D2, and D3, representing high-frequency jitter and noise, can be discarded. Finally, using the retained low-frequency approximation coefficient A3, a new and smoother operational data is reconstructed through inverse wavelet transform, thus obtaining the reconstructed operational data.

[0078] In step S112, generating an association model based on the reconstructed operation data and flight data can be understood as not fusing the reconstructed operation data and flight data into an association model. Specifically, it can be that the operation data and flight data are time-series aligned and normalized to form an operation feature and flight attitude association model (i.e., an association model), which means that the reconstructed operation data and flight data are processed into association data with unified dimensions and time-series alignment. In practical applications, those skilled in the art can use known time synchronization and normalization algorithms to achieve the fusion processing of reconstructed operational data and flight data. For example, a synchronization event (such as the pulse generated when the "event marker" button on the flight recorder is pressed) can be used to align the two sets of data streams, or cross-correlation analysis can be used to find the time offset that maximizes the correlation between the two sets of signals, and then compensation can be performed to achieve time alignment. Then, a normalization algorithm (such as data normalization value = (original value - minimum value) / (maximum value - minimum value)) can be used to normalize each set of data (stick movements, roll angle, pitch angle, yaw angle, etc. in the two axis directions), for example, it can be scaled to a uniform range (such as [0, 1] or [-1, +1]), which can eliminate the influence of different units (such as data with a large numerical range (such as angle) dominating in subsequent processing, while parameters with a small numerical range (such as stick movements) are ignored).

[0079] It should be understood that after time alignment and normalization, the reconstructed operation data and flight data can be merged into a structured dataset. This structured dataset is itself a basic association model, namely a set of "operation-attitude" pairs arranged in chronological order. In this set (i.e., the association model), time points (i.e., timestamps) can be used to associate the normalized reconstructed operation data and flight data at a unified time point.

[0080] Furthermore, based on the correlation model, we can analyze dimensions such as control attitude feedback, control frequency, over-operation, and control habit adaptation to form optimization objectives. The optimization objectives mainly include control attitude feedback optimization, that is, optimizing the speed and error of control actions and attitude responses; control frequency capture, that is, removing source control defects that lead to unexpected high-frequency operations; over-operation suppression optimization, that is, identifying and suppressing over-operation scenarios; and control habit adaptation optimization, that is, clustering analysis of control characteristics (jitter coefficient, mean curvature, etc.) to adapt them to the pilot's control habits.

[0081] Specifically, in some embodiments, in step S113, an evaluation result for the operational posture feedback dimension is generated based on the correlation model, including at least one of the following:

[0082] Step S1131: Based on the correlation model, determine the rise time when the pilot operates the control stick to generate a specified step operation, and take the average rise time of all specified step operations as the attitude response speed.

[0083] Step S1132: Based on the correlation model, determine the attitude error at each time point in the response process of the engineering simulator simulating the actual attitude of the low-altitude aircraft to reach the target attitude after the pilot operates the control stick to generate a specified operation, and take the average of the attitude errors at each time point in the response process as the dynamic tracking error; the target attitude is the aircraft attitude indicated by the specified operation.

[0084] Step S1133: Based on the correlation model, determine the error between the actual attitude and the corresponding target attitude of the low-altitude aircraft simulated by the engineering simulator after the pilot operates the control stick to generate the specified operation, and take the error between the actual attitude and the corresponding target attitude during the attitude maintenance process as the steady-state error.

[0085] Step S1134: Based on the correlation model, determine the percentage of the deviation between the attitude response peak and the corresponding target attitude relative to the target attitude as the overshoot. The attitude response peak is the attitude peak generated when the actual attitude exceeds the target attitude during the simulation of low-altitude flight by the engineering simulator.

[0086] In step S1131, the pilot can use the engineering simulator to perform specified step operations of roll, pitch, and yaw ±10° during flight (i.e., quickly maneuvering the control stick to achieve the target stick amount of ±10°). This allows the average rise time of the corresponding step operation input to be extracted from the correlation model as the attitude response speed. The rise time can be understood as the time required for the aircraft's actual attitude angle (e.g., roll angle) to change from its initial value to its target value (e.g., 90% or 100% of the target value). For example, the roll angle changes from 0° to 10°. Specifically, operational and flight data for a complete "specified step operation input" process can be extracted from the correlation model. For example, the rise time from a stable roll angle of -5° to the pilot inputting a +10° roll step operation command, until the engineering simulator simulates the low-altitude aircraft stabilizing at +5° (i.e., the time from issuing the command to the aircraft achieving 90% or 100% response). It should be understood that the rise time generated by each execution of multiple ±10° roll, pitch, yaw and other step operations can be found from the operation-attitude correlation data. Then, the average rise time of all the specified step operations can be used as the attitude response speed. The smaller the attitude response speed, the more agile and responsive the low-altitude aircraft simulated by the engineering simulator is to the operation commands. Therefore, the optimization goal is to make the attitude response speed as small as possible.

[0087] In step S1132, comparison data between operational commands and actual responses during flight can be found from the correlation model. The average error is used as the dynamic tracking error. Alternatively, the correlation model can be used to continuously compare and calculate the difference (i.e., error) between the target attitude angle and the actual attitude angle at each moment during the entire dynamic change process from issuing the operational command to the final stabilization of the attitude. Then, the average of these error values ​​throughout the entire dynamic process is taken to obtain the dynamic tracking error. It can be understood that when a pilot inputs a specified operational command (such as rolling to 10°) through the control stick, the engineering simulator simulates the low-altitude aircraft during the attitude change process (such as the roll angle changing from the current attitude of 0° to the target attitude of 10°). The actual attitude may not smoothly reach 10°; it may fluctuate and adjust around the target value. The dynamic tracking error describes the degree to which the actual attitude follows the commanded attitude throughout the entire process, or the average deviation. The smaller the dynamic tracking error, the more precise the control of the flight control system during the dynamic transition process, and the higher the fit between the actual response of the aircraft and the expected response output by the flight control system. Therefore, reducing the dynamic tracking error can be an optimization objective.

[0088] In step S1133, it is understood that steady-state error can be understood as the fixed deviation between the output value and the target value after the aircraft response enters a steady state. For example, after maintaining a typical operating command (such as maintaining a roll angle of 10°) for a period of time (such as 30 seconds), it is considered to have entered the attitude maintenance phase. In this phase, the engineering simulator may not be able to accurately maintain the actual attitude of the low-altitude aircraft at 10 degrees, but rather stabilize at 9.8 degrees or 10.2 degrees (this degree is the fixed attitude angle, i.e., the actual attitude during the attitude maintenance process). The deviation between the fixed attitude angle and the target attitude angle (such as 0.2 degrees or -0.2 degrees) is the steady-state error (which can also be understood as the steady-state deviation value of maintaining the fixed attitude angle). It can be understood that after the aircraft completes any operating command (such as roll) and stabilizes, the steady-state error describes whether the aircraft finally stops accurately. Steady-state error is crucial for the attitude maintenance of low-altitude aircraft (such as autopilots), so reducing steady-state deviation can be an optimization goal.

[0089] In step S1134, the peak value generated when the actual attitude exceeds the target attitude can be found from the correlation model as the attitude response peak value. Then, the ratio of the deviation between the attitude response peak value and the target attitude to the target attitude is calculated as the overshoot. For example, if the pilot inputs a roll command of 10 degrees through the control stick, but the engineering simulator simulating a low-altitude aircraft may cause the aircraft attitude to roll to 12 degrees first due to inertia or overly aggressive control, and then swing back to stabilize at 10 degrees, then the overshoot is (12-10) / 10 = 20%. The overshoot can describe whether the aircraft response has overshot and by how much. Excessive overshoot is undesirable, as it means inaccurate response, wasted energy, and may cause the pilot to frequently operate the control stick. Therefore, the optimization goal for overshoot is to minimize it.

[0090] After obtaining the evaluation results of at least one of the attitude response speed, dynamic tracking error, steady-state error, and overshoot in the operational attitude feedback dimension through steps S1131 to S1134, step S12, based on the evaluation results of the specified evaluation dimension, optimizes the flight control software. This may include optimizing the flight control software with the goal of minimizing the attitude response speed, dynamic tracking error, steady-state error, and overshoot. This optimization directly represents the optimization of the mapping rules (i.e., flight operation logic) between the stick operation input and the aircraft response. In practical applications, this may specifically optimize the operational logic control parameters in the flight control software that affect the attitude response speed, dynamic tracking error, steady-state error, and overshoot. These operational logic control parameters determine the mapping rules between the operational input generated by the pilot's stick operation and the attitude response generated during the simulation of low-altitude aircraft flight by the engineering simulator. It should be understood that different flight control software may have a large number of different control parameters, and this disclosure does not limit which operational logic control parameters in the flight control software are specifically optimized. Furthermore, optimization algorithms known in the art, such as genetic algorithms and simulated annealing algorithms, can be used to optimize the operation logic control parameters in the flight control software with the goal of minimizing attitude response speed, dynamic tracking error, steady-state error, and overshoot. This disclosure does not limit the scope of the embodiments.

[0091] In some embodiments, in step S113 above, generating the evaluation result for the operation frequency dimension based on the correlation model includes:

[0092] Step S1135: Based on the correlation model, determine the control stick operation frequency during different flight phases, confirm the flight phases in which the control stick operation frequency exceeds the expected value as unexpected phases, and determine the aircraft attitude fluctuation frequency corresponding to the unexpected phases.

[0093] Step S1136: The ratio between the control stick operation frequency and the aircraft attitude fluctuation frequency during the unexpected phase is determined as the fluctuation correlation coefficient during the unexpected phase.

[0094] Step S1137: If the fluctuation correlation coefficient is greater than a preset coefficient threshold and the control stick operation frequency is greater than a preset frequency threshold, it is determined that there are high-frequency operations caused by the pilot operating the control stick during the flight due to control logic defects.

[0095] Understandably, evaluating the operational frequency dimension primarily aims to identify and address issues arising from design flaws in the flight control system itself, forcing pilots to perform unnecessary high-frequency, low-amplitude maneuvers to maintain aircraft attitude. Such high-frequency maneuvers exacerbate pilot workload and can even lead to danger. When pilots simulate different flight phases using engineering simulators, such as vertical takeoff / landing, transitions (e.g., from vertical to horizontal flight), cruise (i.e., high-speed horizontal flight), reverse transitions (e.g., returning from cruise to vertical flight), landing, and emergencies (e.g., engine failure, system malfunctions, etc.), the flight dynamics, operational logic, and pilot workload differ drastically across these phases. Therefore, the expected operational frequency (i.e., expected value) can vary between different flight phases. For example, transition phases require frequent and large-amplitude maneuvers, while cruise phases should primarily involve small, low-frequency corrections. This allows for defining a reasonable and normal operational frequency range (i.e., preset expected value) for each flight phase.

[0096] Then, the correlation model can be used to find the control stick operation frequency (i.e., the number of times the control stick is operated per unit time, reflecting the speed of the pilot's control stick operation) for different flight phases. It can then be determined whether the control stick operation frequency for each flight phase exceeds the corresponding expected value. Flight phases where the control stick operation frequency exceeds the expected value are confirmed as unexpected phases. Based on the correlation model, the aircraft attitude fluctuation frequency (i.e., the number of times the aircraft attitude fluctuates per unit time, reflecting the speed of the aircraft attitude fluctuation) corresponding to the unexpected phase can be calculated. Furthermore, for high-frequency operations in the unexpected phase, the correlation between operation frequency and attitude fluctuation can be calculated, i.e., the fluctuation correlation coefficient = attitude fluctuation frequency / operation frequency. If the two are highly correlated, it means that every high-frequency operation by the pilot immediately causes high-frequency swaying of the aircraft attitude.

[0097] It should be understood that if the fluctuation correlation coefficient = 1, it means that the frequency of aircraft attitude fluctuations is almost the same as the pilot's operation frequency. This is the most typical pilot-induced oscillation, where the pilot's operation and the aircraft's response resonate at the same frequency, mutually aggravating each other. If the fluctuation correlation coefficient > 1 (especially > 2), it means that the frequency of attitude fluctuations is higher than the operation frequency. This means that one operation by the pilot triggers two or more oscillations in the aircraft, indicating a significant problem with the aircraft response controlled by the flight control system. If the fluctuation correlation coefficient < 1, it means that the operation frequency is higher than the fluctuation frequency, possibly indicating that the pilot is trying to suppress fluctuations, but the method is inappropriate. Therefore, the judgment condition for flight control software control logic defects can be set as follows: if the fluctuation correlation coefficient is greater than a preset coefficient threshold (e.g., > 2) and the control stick operation frequency is greater than a preset frequency threshold (e.g., > 4Hz), it is determined that there are high-frequency operations caused by the pilot's control stick operation due to control logic defects during flight. Among them, 4Hz is an extremely high operation frequency, far exceeding the normal fine control ability of humans (usually 1-2Hz is the limit). Reaching 4Hz almost certainly means that the pilot is subconsciously and forced to compensate, rather than intentionally maneuvering. A correlation coefficient greater than 2 indicates a serious problem with the aircraft's dynamic response. If both conditions are met simultaneously, it's almost certain that the issue isn't pilot skill, but rather a flaw in the flight control system's logic. Examples include poor attitude command filtering, excessive feedback gain, and excessive control surface overshoot. Therefore, point-to-point optimization of control logic defects in the flight control software can effectively improve the handling feel.

[0098] Therefore, in some embodiments, optimizing the flight control software in step S12 based on the evaluation results of the specified evaluation dimension may include: optimizing the flight control software with the optimization objective of reducing the stick operation frequency during unexpected phases to below the expected value and reducing the attitude fluctuation frequency during unexpected phases. In practical applications, this may specifically optimize the operation logic control parameters in the flight control software that affect the stick operation frequency and attitude fluctuation frequency. As mentioned above, these operation logic control parameters determine the mapping rule between the operation input generated by the pilot operating the stick and the attitude response generated during the simulation of low-altitude aircraft flight by the engineering simulator. Furthermore, different flight control software may have a large number of different control parameters. Therefore, it is possible to pinpoint the specific flight phase where the problem occurs (i.e., the unexpected phase, such as "cruise"), the specific operating axis (such as the pitch axis in the auxiliary control stick used to achieve pitch control of low-altitude aircraft), and the specific flight conditions (such as specific speed and altitude). This allows for targeted adjustment of the operating logic control parameters of that axis under those specific flight conditions and during that phase. For example, adjusting damping parameters can help stop aircraft attitude swaying more quickly; adjusting filtering parameters can filter out unintentional high-frequency components in stick commands; and adjusting feedforward parameters can improve response delay or overshoot. This disclosure does not limit the scope of these adjustments. Additionally, optimization algorithms known in the art, such as genetic algorithms and simulated annealing algorithms, can be used to optimize the operating logic control parameters in the flight control software with the goal of reducing the frequency of stick operations during unexpected phases to the expected value and reducing the frequency of attitude fluctuations during unexpected phases. This disclosure also does not limit the scope of these adjustments.

[0099] In some embodiments, generating the evaluation result of the over-operation dimension based on the correlation model in step S113 may include:

[0100] Step S1138: Based on the correlation model, determine whether amplitude-type over-operation and timing-type over-operation occur during flight. Amplitude-type over-operation is characterized by the pilot's single operation of the control stick exceeding a specified control stick threshold, causing the attitude change of the simulated low-altitude aircraft in the engineering simulator to exceed a specified attitude threshold. Timing-type over-operation is characterized by the pilot operating the control stick again to adjust the attitude after the pilot adjusts the attitude by operating the control stick, when the actual attitude of the simulated low-altitude aircraft in the engineering simulator does not reach the target attitude.

[0101] Excessive operation can refer to unreasonable amplitude, frequency, or timing of pilot input to the control stick, resulting in excessively drastic changes in aircraft attitude (pitch, roll, yaw), approaching or exceeding safe limits (e.g., excessive angle of attack, excessive G-force, excessive bank angle), or causing the aircraft to enter an unstable state that is difficult to recover from. In other words, excessive operation is defined as pilots making excessive inputs, excessively frequent inputs, or inappropriate timing during flight, causing the aircraft attitude to exceed the reasonable control range. Excessive operation can be further divided into amplitude-based excessive operation (e.g., a single extremely large input causing the elevator, yaw, or rudder attitude to change by more than 80%) and timing-based excessive operation (e.g., re-operating before the attitude is stable, causing the steady-state error to persist; that is, applying a new operation command before the aircraft has completed the previous attitude operation command and the attitude is not yet stable, such as when the aircraft is transitioning from roll to level flight, and the pilot applies an operation command opposite to level flight before it is fully level). Specifically, the correlation model can capture consecutive amplitude-type over-operations in unexpected scenarios (i.e., flight phases where large-scale manipulation should not occur), such as large-scale turning operations during the vertical takeoff phase without turning (i.e., the pilot should maintain vertical motion during the vertical takeoff phase but instead gives a large-scale yaw operation command by operating the control stick), and timing-type over-operations caused by the pilot's misjudgment of the operation effect during attitude correction (i.e., the pilot incorrectly judges the effect of the previous operation when adjusting the attitude through the control stick, thus applying the reverse operation too early), in order to identify and suppress these over-operations.

[0102] In some embodiments, optimizing the flight control software in step S12 based on the evaluation results of the specified evaluation dimension may include: optimizing the flight control software with the optimization objective of suppressing amplitude-based and timing-based over-manipulation. This optimization can be achieved by adding identification and suppression logic for over-manipulation to the flight control software. For example, the optimized flight control software can identify a large roll command input by the pilot via the control stick during the takeoff / vertical phase when there is no need for turning (i.e., identifying amplitude-based over-manipulation). Once amplitude-based over-manipulation is identified, the flight control software can intervene and limit the amplitude of the operation command (e.g., attenuate the operation command to a safe range), or combine with other control surfaces (such as adjusting thrust) to smoothly achieve a safe attitude change and prevent danger from occurring. For example, optimized flight control software can identify frequent, alternating, small-amplitude, high-frequency or gradually increasing operational commands by comparing the pilot's input commands, the aircraft's actual response, and the expected response (i.e., identifying over-operation). Once over-operation is identified, commands can be filtered to smooth out the pilot's high-frequency operations; attitude-holding damping can be enhanced to prevent the aircraft from being easily swayed by frequent commands; and tactile / visual cues can be provided, such as stick force changes or screen warnings, to indicate "over-operation" or "the aircraft is responding, please wait," thus suppressing over-operation.

[0103] In some embodiments, in step S113 above, generating an evaluation result for the operation habit adaptation dimension based on the reconstructed operation data may include:

[0104] Step S1139: Based on the reconstructed operation data, determine the pilot's operation characteristics, which include at least one of the following: jitter coefficient, mean value of stick quantity curvature, standard deviation of stick quantity, and peak value of operation rate; wherein, the jitter coefficient is the ratio between the standard deviation of stick quantity and the mean value of stick quantity in the stick quantity data, the mean value of stick quantity curvature is the mean value of the curvature of the curve corresponding to the stick quantity data, and the peak value of operation rate is the peak value of the first derivative of the stick quantity data;

[0105] Step S1140: Based on the pilot's operational characteristics, perform similarity matching with the cluster centers of multiple preset operational habit types to obtain the pilot's operational habit type; wherein, the cluster centers of multiple preset operational habit types are obtained by clustering multiple pilots' operational characteristics.

[0106] In step S1139, based on the reconstructed operation data, the standard deviation and mean of the stick input data (i.e., calculating the standard deviation and mean of the stick input at each time point), the mean of the stick curvature (i.e., calculating the mean of the curvature of the stick input curve based on the stick input data at each time point), and the peak operation rate (i.e., calculating the maximum operation rate at different time points in the formula Δstick input / Δtime) can be calculated. This yields at least one operational characteristic among the jitter coefficient, mean stick curvature, standard deviation of stick input, and peak operation rate. Among these operational characteristics, a smaller jitter coefficient indicates smoother pilot operation; a smaller mean stick curvature indicates a smoother pilot operation trajectory; a smaller standard deviation of stick input indicates less pilot operation fluctuation; and a smaller peak operation rate indicates more aggressive pilot operation. Therefore, by extracting these operational characteristics, pilot operating habits can be identified, and the flight control software can be optimized based on the identified operating habit types, making the optimized flight control software adaptable to different pilot operating habits.

[0107] In practical applications, reconstructed operational data from each pilot can be collected in advance to analyze operational characteristics (operational characteristics can represent operational habits). Then, clustering algorithms (such as K-means) can be used to cluster the operational characteristics of each pilot, obtaining clustering results (i.e., multiple groups of operational characteristics clustered together). This allows for the identification of cluster centers for these multiple clusters of operational characteristics, essentially dividing each pilot into multiple groups. Each group shares similar operational characteristics (i.e., similar operational habits), and the cluster centers can be the most representative operational characteristics. For example, such as... Figure 4 As shown, pilots can generally be categorized into four groups, or four types of operating habits: Smooth Operation: Characterized by low jitter coefficient, high mean stick curvature, small standard deviation of stick movement, and low peak operating rate; Precise Operation: Characterized by medium jitter coefficient, moderate mean stick curvature, small standard deviation of stick movement, and low peak operating rate; Frequent Adjustment Operation: Characterized by high jitter coefficient, moderate mean stick curvature, small standard deviation of stick movement, and moderate peak operating rate; Aggressive Operation: Characterized by moderate jitter coefficient, low mean stick curvature, large standard deviation of stick movement, and high peak operating rate.

[0108] Then, based on the pilot's operational characteristics, similarity matching can be performed with cluster centers of multiple preset operational habit types. That is, the similarity between the pilot's operational characteristics and the cluster centers of each operational habit type can be calculated, and the operational habit type corresponding to the cluster center with the highest similarity is taken as the matched operational habit type for that pilot. This is equivalent to classifying the current pilot's operational characteristics into four cluster groups to locate the operational habit type, thereby forming corresponding flight control software optimization schemes for targeted control optimization of the four pilot cluster groups.

[0109] In some embodiments, different operating habit types may have their own corresponding optimization objectives. Based on the evaluation results of a specified evaluation dimension, the flight control software is optimized, including: optimizing the flight control software according to the optimization objectives corresponding to the pilot's operating habit type. For example, as shown... Figure 4 As shown, based on the operational adaptation characteristics of the smooth operation type, the optimization objective can be designed to achieve a more balanced control response, that is, not pursuing extreme requirements for single aspects such as response speed and overshoot rate, but rather obtaining a balanced control response. Based on the operational adaptation characteristics of the precise operation type, the optimization objective can be designed to maintain operational flexibility while improving attitude stability and avoiding excessive overshoot. Based on the operational adaptation characteristics of the frequent adjustment type, the optimization objective can be designed to filter out invalid inputs from operational jitter, suppress attitude fluctuations, and avoid cycles of jittery operation, attitude fluctuations, and more frequent corrections. Based on the operational adaptation characteristics of the aggressive operation type, the optimization objective can be designed to retain operational flexibility while suppressing overshoot and oscillation.

[0110] In practical applications, different optimization objectives can lead to the design of corresponding operational logic control parameters. These parameters can specifically optimize the operational logic control parameters in the flight control software that affect jitter coefficient, mean value of stick curvature, standard deviation of stick movement, and peak value of operational rate. The aim is to modify the aircraft's attitude response to suit the operating habits of different pilots. For example, some pilots prefer a fast and aggressive response, which can be achieved through parameter adjustments in the flight control software. It should be understood that different flight control software may have a large number of different control parameters. The specific operational logic control parameters in the optimized flight control software, as well as the optimized parameter values, can be pre-designed, and this disclosure does not limit this approach.

[0111] In summary, as can be seen, Figure 5As shown, after the host computer 30 acquires the flight data and operation data recorded by the data recorder 40, it can perform outlier detection and filtering to obtain reconstructed operation data, and perform feature extraction to obtain operation features. Then, the reconstructed operation data and flight data can be fused to obtain a correlation model. Based on the correlation model and operation features, evaluations can be performed on dimensions such as operation attitude feedback, operation frequency, over-operation, and operation habit adaptation. Based on the evaluation results, operation logic optimization can be performed, specifically optimizing the operation logic control parameters in the flight control software. In this way, after the host computer 30 modifies and optimizes the flight control software to form a new version of the flight control software, the host computer 30 can send the packaged optimized flight control software to the simulator server 20 via a UTP twisted-pair cable. The simulator server 20 then updates the flight control software, completing the iteration.

[0112] In some embodiments, the triggering timing for the aforementioned flight control software optimization can be after the pilot provides feedback or after multiple simulated flights. That is, after the pilot provides feedback or after multiple simulated flights, the host computer 30 can send a request to the data recorder 40 to obtain the operational and flight data generated by the pilot during any batch and / or any time of flight missions performed by the engineering simulator 10, in order to optimize the flight control software. If other pilots also use the engineering simulator, they can first conduct experience flights based on the basic parameter data of the flight software, or they can use the optimized flight control software parameter data of a particular pilot according to their personal preferences. Of course, the parameters of the flight control software can also be optimized based on the operational and flight data generated by other pilots performing flight missions using the engineering simulator, to better suit the operating habits of other pilots. This disclosure does not limit this aspect.

[0113] The system according to the embodiments of this disclosure effectively simplifies the intermediate steps required for upgrading the operating logic of low-altitude aircraft engineering simulators, without interrupting the verification process. It allows for rapid updates of flight control software, shortening the software optimization iteration cycle during the simulation verification phase. Furthermore, the control stick inputs of the central control stick and auxiliary control stick are collected by sensors, and the pilot's control stick operation data and flight data are recorded by a data recorder. This provides direction for optimizing the flight control software, resulting in optimized software that is more compatible with the pilot's operating habits and enhances the ergonomics of the low-altitude aircraft engineering simulator.

[0114] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of the system according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0115] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A low-altitude aircraft engineering simulation system, characterized in that, include: Engineering simulators, simulator servers, and host computers for low-altitude aircraft; The engineering simulator includes a cockpit; the cockpit includes a pilot's seat, a central control stick located on both sides of the pilot's seat, and an auxiliary control stick; the central control stick is used to simulate the throttle and yaw operations of a low-altitude aircraft; the auxiliary control stick is used to simulate the pitch and roll operations of a low-altitude aircraft; and the pilot sits in the pilot's seat. Sensors are installed in the central control stick and the auxiliary control stick respectively, and the sensors are used to collect operation data generated by the pilot's operation of the control stick; The simulator server receives the operation data and outputs control commands corresponding to the operation data to control the engineering simulator to simulate the flight process of a low-altitude aircraft. The host computer receives the operation data and the flight data corresponding to the flight process, and the operation data and flight data are used to optimize the simulator server.

2. The system according to claim 1, characterized in that, The system also includes a data recorder for recording the pilot's operational data and flight data during flight; wherein the operational data and flight data are recorded according to the batches and times in which the pilot performs simulated flight missions. The host computer receives the operation data and the flight data corresponding to the flight process, including: the host computer receives the operation data and flight data of the pilot sent by the data recorder in a specified batch and / or at a specified time.

3. The system according to claim 1, characterized in that, A mode switching button is installed at the top of the central control stick, and the mode switching button is used to set the flight mode. The top of the auxiliary driving stick is equipped with an instant call button, which is used to make an instant call.

4. The system according to claim 1, characterized in that, The cockpit also includes a cockpit display for displaying flight information during flight; the flight information includes flight status information and / or alarm information.

5. The system according to claim 1, characterized in that, The simulator server includes: an environment modeling node consisting of multiple computer devices, a simulation computing node consisting of multiple computer devices, and a data interaction node consisting of at least one computer device. The environment modeling node is used to construct a flight simulation environment for the engineering simulator to simulate the flight process of a low-altitude aircraft; the simulation calculation node is used to output control commands corresponding to the operation data to control the engineering simulator to simulate the flight process of the low-altitude aircraft; the data interaction node is used to realize data interaction.

6. The system according to claim 1, characterized in that, The simulator server is equipped with flight control software, which is used to output control commands corresponding to the operation data based on the operation data, so as to control the engineering simulator to simulate the flight process of a low-altitude aircraft. The host computer also optimizes the flight control software based on the operation data and the flight data, so that the optimized flight control software is adapted to the pilot's operating habits.

7. The system according to claim 6, characterized in that, The optimization of the flight control software based on the operational data and the flight data includes: Based on the operational data and the flight data, an evaluation result is generated for a specified evaluation dimension, which includes at least one of the following: operational attitude feedback dimension, operational frequency dimension, excessive operation dimension, and operational habit adaptation dimension. The operational attitude feedback dimension is used to evaluate at least one of the following during the flight process of the engineering simulator simulating a low-altitude aircraft: attitude response speed, dynamic tracking error, steady-state error, and overshoot. The operational frequency dimension is used to evaluate high-frequency operations caused by pilot stick operation due to control logic defects during flight. The excessive operation dimension is used to evaluate excessive operations caused by pilot improper operation during flight. The operational habit adaptation dimension is used to evaluate the type of pilot's operational habits. The flight control software is optimized based on the evaluation results of the specified evaluation dimensions.

8. The system according to claim 7, characterized in that, The operational data includes: stick movement data generated by the pilot manipulating the control stick during flight; the flight data includes: attitude data, speed data, and altitude data generated by the engineering simulator simulating the flight of a low-altitude aircraft. The step of generating an evaluation result for a specified evaluation dimension based on the operation data and the flight data includes: By performing outlier removal and jitter elimination on the operation data, reconstructed operation data is obtained; Based on the reconstruction operation data and the flight data, an association model is generated, which represents the reconstruction operation data and flight data associated at any point in time during the flight. Based on the association model, at least one of the following is generated: the evaluation result of the operation posture feedback dimension, the evaluation result of the operation frequency dimension, and the evaluation result of the over-operation dimension; and / or, based on the reconstructed operation data, the evaluation result of the operation habit adaptation dimension is generated.

9. The system according to claim 8, characterized in that, Based on the aforementioned correlation model, an evaluation result for the operational posture feedback dimension is generated, including at least one of the following: Based on the aforementioned correlation model, the rise time when the pilot operates the control stick to generate a specified step operation is determined, and the average rise time of all specified step operations is taken as the attitude response speed. Based on the aforementioned correlation model, the attitude error at each time point in the response process of the engineering simulator simulating the actual attitude of a low-altitude aircraft to reach the target attitude after the pilot operates the control stick to generate a specified operation is determined, and the average of the attitude errors at each time point in the response process is used as the dynamic tracking error; the target attitude is the aircraft attitude indicated by the specified operation. Based on the aforementioned correlation model, the error between the actual attitude and the corresponding target attitude of the low-altitude aircraft simulated by the engineering simulator after the pilot operates the control stick to generate a specified operation is determined, and the error between the actual attitude and the corresponding target attitude during the attitude maintenance process is taken as the steady-state error. Based on the aforementioned correlation model, the percentage of the deviation between the attitude response peak and the corresponding target attitude relative to the target attitude is determined as the overshoot. The attitude response peak is the attitude peak generated when the actual attitude exceeds the target attitude during the simulation of low-altitude flight by the engineering simulator.

10. The system according to claim 9, characterized in that, Optimizing the flight control software based on the evaluation results of the specified evaluation dimensions includes: The flight control software is optimized with the goal of minimizing the attitude response speed, dynamic tracking error, steady-state error, and overshoot.

11. The system according to claim 8, characterized in that, Based on the aforementioned correlation model, an evaluation result for the operation frequency dimension is generated, including: Based on the aforementioned correlation model, the control stick operation frequency during different flight phases is determined, and flight phases in which the control stick operation frequency exceeds the expected value are indeed considered unexpected phases. The corresponding aircraft attitude fluctuation frequency for the unexpected phases is also determined. The ratio between the frequency of stick operation during unexpected phases and the frequency of aircraft attitude fluctuations is defined as the fluctuation correlation coefficient during unexpected phases. If the fluctuation correlation coefficient is greater than a preset coefficient threshold and the stick operation frequency is greater than a preset frequency threshold, it is determined that there are high-frequency operations during flight caused by the pilot operating the stick due to control logic defects.

12. The system according to claim 11, characterized in that, Optimizing the flight control software based on the evaluation results of the specified evaluation dimensions includes: The flight control software is optimized with the goal of reducing the frequency of stick operation during unexpected phases to below the expected value and reducing the frequency of attitude fluctuations during unexpected phases.

13. The system according to claim 8, characterized in that, Based on the aforementioned correlation model, the evaluation results for the over-operation dimension are generated, including: Based on the aforementioned correlation model, it is determined whether amplitude-type over-operation and timing-type over-operation occur during flight. The amplitude-type over-operation is characterized by the pilot's single operation of the control stick exceeding a specified control stick threshold, causing the attitude change of the simulated low-altitude aircraft in the engineering simulator to exceed a specified attitude threshold; the timing-type over-operation is characterized by the pilot operating the control stick again to adjust the attitude after the pilot has adjusted the attitude by operating the control stick, when the actual attitude of the simulated low-altitude aircraft in the engineering simulator has not reached the target attitude.

14. The system according to claim 13, characterized in that, Optimizing the flight control software based on the evaluation results of the specified evaluation dimensions includes: The flight control software is optimized with the goal of suppressing the amplitude-type over-operation and the timing-type over-operation.

15. The system according to claim 8, characterized in that, Based on the reconstructed operation data, an evaluation result for the operation habit adaptation dimension is generated, including: Based on the reconstructed operational data, the pilot's operational characteristics are determined, including at least one of the following: jitter coefficient, mean stick curvature, standard deviation of stick volume, and peak operational rate; wherein, the jitter coefficient is the ratio between the standard deviation of stick volume and the mean stick volume in the stick volume data, the mean stick curvature is the mean of the curvature of the curve corresponding to the stick volume data, and the peak operational rate is the peak value of the first derivative of the stick volume data; Based on the pilot's operational characteristics, similarity matching is performed with the cluster centers of multiple preset operational habit types to obtain the operational habit type that matches the pilot; wherein, the cluster centers of multiple preset operational habit types are obtained by clustering multiple pilots' operational characteristics.

16. The system according to claim 15, characterized in that, Different operating habit types have their own corresponding optimization goals. The optimization of the flight control software based on the evaluation results of the specified evaluation dimensions includes: The flight control software is optimized according to the optimization objective of matching the pilot's operating habits.

17. The system according to any one of claims 7 to 16, characterized in that, Optimizing the flight control software includes: optimizing the operation logic control parameters in the flight control software, wherein the operation logic control parameters determine the mapping rules between the operation input generated by the pilot operating the control stick and the attitude response generated during the simulation of low-altitude flight by the engineering simulator.

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