Electric aircraft simulation training platform

Abstract

The invention relates to the technical field of aircraft training, and discloses an electric aircraft simulation practical training platform which comprises an acquisition module, a simulation module, a control module and an iteration module which are in communication connection in sequence. According to the invention, comprehensive optimization of simulation training of the electric aircraft is realized; the acquisition module obtains the performance and fault parameters of the aircraft, and provides a data basis for subsequent accurate simulation. The simulation module constructs flight and fault models, simulates various working conditions, and enhances the authenticity of a training scene; the control module feeds back an operation instruction of the trainee to the simulation module to realize man-machine interaction training; the iteration module calculates the reduction degree and the trained quality, correlates the reduction degree and the trained quality to generate an evaluation result, and adjusts the next round of simulation parameters and training tasks based on evaluation to form closed-loop optimization; the simulation training quality of the electric aircraft is effectively improved, so that the training better meets the actual flight requirement, and powerful support is provided for cultivating high-quality electric aircraft operators.

Description

Technical Field

[0001] The present application relates to the field of aircraft training technology, and specifically to an electric aircraft simulation training platform. Background Art

[0002] In modern aviation, electric aircraft are becoming increasingly common, and simulation training is crucial for ensuring flight safety and improving operator skills. However, existing electric aircraft simulation training technology faces numerous challenges. Some platforms lack sufficient accuracy in simulating aircraft performance, and are ineffective in simulating the impact of complex environments (such as extreme weather and unique geographical conditions) on aircraft performance, making it difficult to accurately reflect the state of electric aircraft in actual flight. Furthermore, most platforms have relatively limited functionality and lack effective simulation of complex fault scenarios, making them unable to meet diverse training needs. In terms of teaching applicability, they are not closely integrated with professional courses, and cannot provide personalized training based on students' learning progress and abilities.

[0003] Chinese invention application publication number CN119414729A discloses a hardware-in-the-loop simulation system and method for a glide-extended-range guided aircraft. While this system offers some innovations in the field of hardware-in-the-loop aircraft simulation, it also has limitations. It primarily focuses on the parameters of glide-extended-range guided aircraft, lacks scalability, and inadequately simulates the unique performance parameters of electric aircraft (such as battery performance variations and motor efficiency degradation). Furthermore, the system focuses solely on aircraft evaluation and testing, lacking a quantitative evaluation system related to trainee operations. This system fails to meet the need for improving trainee training quality and lacks diversity and specificity in practical training scenarios.

[0004] In summary, a new technical solution for electric aircraft simulation training is urgently needed to solve these problems and promote the development of electric aircraft simulation training technology. Summary of the Invention

[0005] The purpose of this application is to provide an electric aircraft simulation training platform to solve the technical problems raised in the above background technology.

[0006] To achieve the above-mentioned purpose, the present application discloses the following technical solutions: an electric aircraft simulation training platform, the platform comprising an acquisition module, a simulation module, a control module and an iteration module that are communicatively connected in sequence; The acquisition module is used to obtain performance parameters and fault parameters of the electric aircraft, wherein the performance parameters are used to characterize flight-related parameters, and the fault parameters are used to characterize fault-related parameters; The simulation module is used to construct a flight model and a fault model based on the performance parameters and the fault parameters, and the flight model and the fault model are used to simulate the flight state and fault scenarios of the electric aircraft under various working conditions; The control module is used to receive the trainee's operating instructions and feed them back to the simulation module to adjust the simulated flight state; The iterative module is used to calculate the degree of restoration of the performance based on the simulated flight state and the training quality of the trainees, associate the degree of restoration and the training quality to generate an evaluation result, and adjust the parameters and training tasks of the next round of simulation based on the evaluation results of the previous round.

[0007] Preferably, the performance parameters include at least flight speed, altitude, attitude angle, battery life and motor power output characteristics; The fault parameters include at least information related to motor fault, battery fault, sensor fault and software fault.

[0008] Preferably, in the simulation module, the construction of the flight model includes: generating a corresponding flight index based on the flight speed, the altitude, and the attitude angle, the flight index being used to characterize a flight state during simulated flight; Based on the battery life and in combination with preset temperature parameters, a corresponding battery indicator is generated, where the battery indicator is used to characterize the battery status during the simulated flight; generating a corresponding motor index based on the motor power output characteristic, wherein the motor index is used to characterize the motor state during simulated flight; The flight model is constructed by preset weighting based on the flight index, the battery index and the motor index, and different weighting weights correspond to different degrees of restoration.

[0009] Preferably, in the simulation module, the construction of the fault model includes: Obtain preset fault trigger logics based on performance parameters and fault parameters, which are set for motor faults, battery faults, sensor faults, and software faults respectively; when the corresponding fault trigger logic is met, adjust the relevant parameters in the flight model based on the preset fault trigger logic, and the fault trigger logic corresponding to different restoration degrees is different. The adjustment also includes adjustments based on the restoration degree and training quality evaluation results of the previous round.

[0010] Preferably, in the control module, the adjustment of the simulated flight state includes: The trainee's operating instructions are converted into control signals of the flight model and the fault model, the control signals are corrected based on the current degree of restoration, and the flight model and the fault model are synchronously adjusted based on the corrected control signals. The correction process also includes correction based on the previous round of degree of restoration and training quality assessment results.

[0011] Preferably, in the iterative module, the calculation of the restoration degree includes: The degree of deviation between the simulated value and the actual reference value of each performance indicator in the flight model and the fault model is calculated respectively, and a weight is assigned to each performance indicator in the flight model and the fault model. The degree of deviation of the performance indicator is calculated by weighted calculation to obtain the degree of restoration, which is used to evaluate the degree of fit between the current simulated flight state and the actual flight state.

[0012] Preferably, in the iterative module, the calculation of the trained quality includes: Conduct a multi-dimensional quantitative assessment of the trainees' operations. This multi-dimensional quantitative assessment includes at least an assessment based on the degree of operational standardization, task completion progress, and the timeliness and effectiveness of fault response. Scoring criteria and weights are set for each assessment dimension, and the weights are related to the current degree of restoration. The training quality is calculated by comprehensively considering the scores of each dimension and the corresponding weights. The training quality is used to characterize the training effect corresponding to the current degree of restoration of the trainees. The training quality is compared with the preset threshold to determine whether to enter the training at the next degree of restoration.

[0013] Preferably, in the iteration module, generating the evaluation result includes: The calculated degree of restoration and the trained quality are compared with their respective preset thresholds. Based on the comparison result, an evaluation result is generated in combination with a preset evaluation rule. The evaluation rule is used to quantify the comparison result of the calculated degree of restoration and the trained quality with their respective preset thresholds. The categories of the evaluation results include "the degree of restoration and the trained quality meet the standards", "the degree of restoration needs to be improved", "the quality of training needs to be improved" and "the overall needs to be improved". The simulation parameters and training tasks for the next round are adjusted based on the evaluation result.

[0014] Preferably, in the iteration module, adjusting the parameters and training tasks of the next round of simulation based on the evaluation results of the previous round includes: When the evaluation result is "the degree of restoration needs to be improved", adjusting the flight model and the fault model; When the evaluation result is "training quality needs to be improved", the difficulty and type of the preset training task are adjusted; When the evaluation result is "overall needs to be improved", the difficulty and type of the flight model, the fault model and the training task are adjusted at the same time, wherein the magnitude of the adjustment is determined based on the gap between the degree of restoration and the training quality and their corresponding thresholds.

[0015] Preferably, the platform further comprises a display module, which is communicatively connected with the acquisition module, the simulation module, the control module and the iteration module; The display module is used to display the performance parameters, fault parameters, simulated flight status, current restoration degree, training quality, evaluation results and adjustment direction of the next round of simulation parameters and training tasks of the electric aircraft in real time.

[0016] Beneficial effects: The electric aircraft simulation training platform of the present application realizes comprehensive optimization of electric aircraft simulation training by utilizing the acquisition module, simulation module, control module and iteration module that are connected in sequence by communication; the acquisition module obtains the performance and fault parameters of the aircraft, providing a data basis for subsequent precise simulation; the simulation module constructs flight and fault models, simulates various working conditions, and enhances the authenticity of the training scene; the control module feeds back the trainee's operating instructions to the simulation module to realize human-computer interaction training; the iteration module calculates the degree of restoration and the quality of training, associates the two to generate an evaluation result, and adjusts the next round of simulation parameters and training tasks based on the evaluation to form a closed-loop optimization; it solves the problems of insufficient simulation accuracy, single function and inability to personalize training in the existing technology, effectively improves the quality of electric aircraft simulation training, makes the training more in line with actual flight needs, and provides strong support for the cultivation of high-quality electric aircraft operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a structural block diagram of the electric aircraft simulation training platform provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] In this document, the term "comprising" is intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0021] This embodiment discloses Figure 1 An electric aircraft simulation training platform is shown, which includes an acquisition module, a simulation module, a control module and an iteration module that are communicatively connected in sequence; The acquisition module is used to obtain the performance parameters and fault parameters of the electric aircraft. The performance parameters are used to characterize flight-related parameters, and the fault parameters are used to characterize fault-related parameters. The simulation module is used to build flight models and fault models based on performance parameters and fault parameters. The flight models and fault models are used to simulate the flight status and fault scenarios of electric aircraft under various working conditions. The control module is used to receive the trainee's operating instructions and feed them back to the simulation module to adjust the simulated flight status; The iteration module is used to calculate the degree of restoration of the performance based on the simulated flight state and the training quality of the trainees, associate the degree of restoration and the training quality to generate an evaluation result, and adjust the parameters and training tasks of the next round of simulation based on the evaluation results of the previous round.

[0022] Based on the above, this embodiment uses the acquisition module, simulation module, control module and iteration module that are connected in sequence through communication to achieve comprehensive optimization of electric aircraft simulation training; the acquisition module obtains the performance and fault parameters of the aircraft, providing a data basis for subsequent precise simulation; the simulation module constructs flight and fault models, simulates various working conditions, and enhances the authenticity of the training scene; the control module feeds back the trainee's operating instructions to the simulation module to realize human-computer interaction training; the iteration module calculates the degree of restoration and training quality, associates the two to generate an evaluation result, and adjusts the next round of simulation parameters and training tasks based on the evaluation to form a closed-loop optimization; it solves the problems of insufficient simulation accuracy, single function, and inability to personalize training in the existing technology, effectively improves the quality of electric aircraft simulation training, makes the training more in line with actual flight needs, and provides strong support for cultivating high-quality electric aircraft operators.

[0023] Specifically, performance parameters include at least flight speed, altitude, attitude angle, battery life, and motor power output characteristics; The fault parameters include at least information related to motor fault, battery fault, sensor fault and software fault.

[0024] Through the above, this embodiment utilizes clear performance and fault parameters to achieve a comprehensive simulation of the electric aircraft's state. The performance parameters accurately reflect the aircraft's critical flight states, allowing the simulation module to more accurately construct a flight model and simulate flight conditions. The fault parameters provide triggering and simulation basis for the fault model, allowing trainees to train in a variety of fault scenarios and improve their ability to handle complex situations. The determination of these parameters provides critical data support for the efficient operation of subsequent modules, enhancing the practicality and training effectiveness of the simulation training platform, and enabling trainees to more comprehensively master electric aircraft operating skills in various situations.

[0025] Specifically, in the simulation module, the construction of the flight model includes: A corresponding flight index is generated based on the flight speed, altitude, and attitude angle. The flight index is used to characterize the flight state during the simulated flight. As a preferred implementation of this embodiment, the flight index is calculated based on a flight index calculation formula. The flight index calculation formula is:

[0026] in, is the flight speed, is the altitude corresponding to the flight speed, is the attitude angle corresponding to the flight speed, is the calculated flight index, 、 and The corresponding preset weights are set based on, but not limited to, the degree of influence of different parameters on flight indicators obtained through regression analysis of multiple flight records. These weights are adjusted based on training requirements and actual flight characteristics at different levels of fidelity. For example, at the initial fidelity, if more emphasis is placed on the impact of flight speed on flight status, the value can be appropriately increased. Based on this, as the fidelity increases, the weights are dynamically adjusted based on the feedback from the previous round of training and actual flight conditions to more accurately represent the simulated flight status. Based on the battery life and combined with the preset temperature parameters, a corresponding battery indicator is generated. The battery indicator is used to characterize the battery status during the simulated flight. As a preferred implementation of this embodiment, the battery status is calculated based on a battery status calculation formula. The battery status calculation formula is:

[0027] in, For battery life, is the temperature influence coefficient, is the standard temperature, is the current temperature, The calculated battery status is: It is understood that when the temperature deviates from the standard temperature, the battery performance will change. This formula can reflect the impact of temperature on battery life, thereby generating more accurate battery indicators. At different restoration levels, the battery status value will be adjusted based on the actual battery performance in different temperature environments. For example, at high restoration levels, the performance changes of the battery at extreme temperatures are more accurately simulated, and the value range may be increased to more realistically reflect the battery status. A corresponding motor index is generated based on the motor power output characteristics, and the motor index is used to characterize the motor state during simulated flight. As a preferred implementation of this embodiment, this embodiment uses a weighted synthesis of the comparison between the current motor torque and the current motor power and the corresponding standard values to obtain the motor state. For example, if the sum of the weights of the comparison results of the current motor torque and the current motor power and the corresponding standard values is 1, during the takeoff phase of the aircraft, more attention is paid to the motor torque, and its weight can be set to 0.6; during the cruising phase, the stability of the motor power is more important, and its weight can be set to 0.6 to accurately characterize the motor state in different flight phases. The flight model is constructed based on a preset weighted combination of flight indicators, battery indicators, and motor indicators, and different weighted weights correspond to different degrees of restoration. As a preferred implementation of this embodiment, based on the above 、 and At different degrees of restoration, based on the principle of adjusting according to training requirements and actual flight characteristics, after obtaining the current flight indicators, battery indicators, and motor indicators, different degrees of restoration are obtained and the weights of the flight indicators, battery indicators, and motor indicators in the flight model are adjusted.

[0028] Based on the above, this embodiment generates flight indicators based on flight speed, altitude, and attitude angle, generates battery indicators based on battery life and preset temperature parameters, generates motor indicators based on motor power output characteristics, and constructs a flight model with different weights at different degrees of restoration, thereby achieving accurate construction of the flight model. Flight indicators, battery indicators, and motor indicators reflect the state of the aircraft from different dimensions, and the weighted construction of the flight model can more realistically simulate the flight state. The weighted weight adjustment at different degrees of restoration can optimize the model according to training needs and actual flight characteristics, making the training more targeted. For example, at the initial degree of restoration, focus on the weight of basic indicators. As the degree of restoration increases, adjust the weight according to training feedback to more accurately characterize the simulated flight state and improve the accuracy and effectiveness of training.

[0029] Specifically, in the simulation module, the construction of the fault model includes: Obtain preset fault trigger logic based on performance parameters and fault parameters for motor failure, battery failure, sensor failure, and software failure. When the corresponding fault trigger logic is met, adjust the relevant parameters in the flight model based on the preset fault trigger logic. The fault trigger logic corresponding to different restoration degrees is different. The adjustment also includes adjustments based on the restoration degree and training quality assessment results of the previous round.

[0030] Through the above, this embodiment uses a method of presetting trigger logic for different faults, adjusting relevant parameters of the flight model when the logic is met, and changing the fault trigger logic according to different restoration levels and the results of the previous round of evaluation, thereby achieving flexible simulation of fault scenarios and training optimization. The fault trigger logic preset based on common sense known to those skilled in the art ensures that various types of faults are simulated under appropriate conditions, and adjusting the flight model parameters can truly reflect the impact of the fault on the aircraft. The changes in the fault trigger logic under different restoration levels, as well as the adjustments combined with the restoration level of the previous round and the training quality evaluation results, make the fault simulation more in line with the actual training. For example, as the training progresses, the difficulty and impact of the fault trigger are adjusted according to the performance of the trainees, so as to improve the trainees' ability to deal with faults and enhance the training effect and pertinence.

[0031] Specifically, in the control module, the adjustment of the simulated flight status includes: The trainee's operating instructions are converted into control signals for the flight model and fault model. The control signals are corrected based on the current degree of restoration. The flight model fault model is synchronously adjusted based on the corrected control signals. The correction process also includes corrections based on the previous round of restoration and training quality assessment results.

[0032] As described above, this embodiment utilizes a mechanism that converts operational instructions into control signals, modifies the control signals based on the current and previous rounds of fidelity and training quality assessment results, and simultaneously adjusts the flight and fault models, achieving dynamic adjustment of the simulated flight state. The conversion of operational instructions into control signals enables trainee operations to influence simulated flight. Control signals are modified based on fidelity and assessment results, allowing for optimized adjustments based on the training stage and trainee performance. For example, at lower fidelity levels, the control signal correction amplitude is small. As fidelity increases and trainee capabilities improve, the correction amplitude is increased based on assessment results, and flight and fault models are simultaneously adjusted to make the simulated flight state more aligned with the trainee's operational and training needs, enhancing the interactivity and adaptability of training.

[0033] Specifically, in the iteration module, the calculation of the restoration degree includes: The degree of deviation between the simulated value and the actual reference value of each performance indicator in the flight model and the fault model is calculated, and a weight is assigned to each performance indicator in the flight model and the fault model. The degree of deviation of the performance indicators is weighted to obtain a degree of restoration, which is used to evaluate the consistency between the current simulated flight state and the actual flight state. As a preferred implementation of this embodiment, when assigning weights to the various performance indicators in the flight model and the fault model, the importance of each performance indicator to flight safety and mission completion is adjusted at different degrees of restoration. For example, at a lower degree of restoration, some basic performance indicators (such as flight speed and altitude) may be given a higher weight. As the degree of restoration increases, the weights are reallocated based on the results of the previous round of training and the changes in the correlation between the various indicators in actual flight. For example, the weights of relevant performance indicators in complex fault scenarios may be increased to more accurately evaluate the consistency between the simulated flight state and the actual flight state.

[0034] Based on the above, this embodiment utilizes a method of calculating the degree of deviation between the simulated values of the flight and fault model performance indicators and the actual reference values, assigning weights to each indicator, and calculating the degree of deviation by weighted calculation to obtain the degree of restoration, thereby achieving a quantitative assessment of the degree of fit between the simulated flight state and the actual flight state. By calculating the degree of deviation, the accuracy of the model simulation can be accurately analyzed; weights are assigned to each indicator, and the weights are adjusted based on the importance of each indicator to flight safety and mission completion at different degrees of restoration, making the degree of restoration calculation more scientific. For example, at a lower degree of restoration, the weight of basic performance indicators is higher. As the degree of restoration increases, the weight of indicators related to complex fault scenarios increases, thereby more accurately evaluating the simulated flight state, providing a reliable basis for iterative optimization, and improving the quality of simulation training.

[0035] Specifically, in the iteration module, the calculation of the training quality includes: Conduct a multi-dimensional quantitative assessment of trainees' operations. This multi-dimensional quantitative assessment includes at least an assessment based on the degree of operational standardization, task completion progress, and the timeliness and effectiveness of fault response. Set scoring criteria and weights for each assessment dimension. The weights are related to the current degree of restoration. In a simple example, at a lower degree of restoration, the value of the degree of operational standardization may be larger due to the greater emphasis on standardization of basic operations. As the degree of restoration increases, if the training focus gradually shifts to complex tasks and fault response, the weights of the timeliness and effectiveness of fault response can be appropriately increased based on feedback from the previous round of training to more reasonably calculate the training quality and accurately represent the training effect. The training quality is calculated by comprehensively considering the scores of each dimension and the corresponding weights. The training quality is used to characterize the training effect corresponding to the current degree of restoration of the trainees. The training quality is compared with the preset threshold to determine whether to enter the training at the next degree of restoration.

[0036] As a preferred implementation of this embodiment, when it is determined to enter the next level of restoration training, based on Calculate the next reduction degree ,in, To restore The quality of training for trainees, and Meet the training threshold You can enter the next level of restoration Simulation training, The restoration adjustment coefficient is set based on the actual training situation and training goals, for example, , indicating that according to and The degree of difference is adjusted according to a certain ratio. Compare The bigger it gets, relatively The greater the improvement, the faster the training difficulty increases; on the contrary, if near , and The difference is small and the training difficulty increases slowly.

[0037] Through the above, this embodiment utilizes a multi-dimensional quantitative evaluation of the trainees' operations, sets scoring standards and weights related to the current degree of restoration for each evaluation dimension, and comprehensively calculates the training quality and compares it with the preset threshold, thereby achieving an accurate evaluation of the trainees' training effect and a reasonable division of the training stages. The multi-dimensional quantitative evaluation comprehensively reflects the trainees' performance, and the scoring standards and weight settings related to the degree of restoration can adjust the evaluation focus according to the difficulty of training. For example, at a lower degree of restoration, the focus is on operating specifications, and as the degree of restoration increases, more attention is paid to fault response capabilities. By comparing with the preset threshold, it can be determined whether the trainee has entered the next degree of restoration training, realizing personalized training, and improving the trainees' training effect and the speed of skill improvement.

[0038] Specifically, in the iteration module, the generation of evaluation results includes: The calculated degree of restoration and training quality are compared with their respective preset thresholds. Based on the comparison results, an evaluation result is generated in combination with the preset evaluation rules. The evaluation rules are used to quantify the comparison results of the calculated degree of restoration and training quality with their respective preset thresholds. The categories of evaluation results include "the degree of restoration and training quality meet the standards", "the degree of restoration needs to be improved", "the training quality needs to be improved" and "the overall needs to be improved". The simulation parameters and training tasks for the next round are adjusted based on the evaluation results.

[0039] As a preferred implementation of this embodiment, after determining the next reduction degree, ,based on 、 and ,pass Calculation in reduction degree The quality of training ,in It is a quality adjustment coefficient with a preset value range of [0,1], which reflects the impact of the change in restoration degree on the training quality.

[0040] Based on the above, this embodiment achieves dynamic optimization of simulation training by comparing the degree of restoration and training quality with their respective preset thresholds, combining them with pre-set evaluation rules using any existing evaluable technology to generate evaluation results, and adjusting the next round of simulation parameters and training tasks based on the evaluation results. By comparing thresholds and applying evaluation rules, training results are classified into different categories, clearly displaying the training status. Simulation parameters and training tasks are adjusted based on the evaluation results, making training more targeted.

[0041] Specifically, in the iteration module, the parameters and training tasks for the next round of simulation are adjusted based on the evaluation results of the previous round, including: When the evaluation result is "restored to a certain degree", the flight model and fault model will be adjusted; If the evaluation result is "training quality needs to be improved", adjust the difficulty and type of the preset training tasks; When the evaluation result is "overall needs improvement", the difficulty and type of the flight model, fault model and training tasks will be adjusted at the same time. The extent of the adjustment is determined based on the gap between the degree of restoration and training quality and their corresponding thresholds.

[0042] Based on the above, this embodiment achieves precise optimization of simulation training by adjusting the flight model, fault model, and training task difficulty and type based on the evaluation results. The adjustment range is determined based on the gap between the degree of restoration and the quality of training and a threshold value. When the evaluation result is "the degree of restoration needs to be improved," the flight and fault models are adjusted to improve simulation accuracy; when the evaluation result is "the quality of training needs to be improved," the difficulty and type of training tasks are changed to enhance the trainee's ability; when the evaluation result is "the overall quality needs to be improved," the models and tasks are adjusted simultaneously. The adjustment range is determined based on the gap between the degree of restoration and the quality of training and a threshold value, avoiding over- or under-adjustment, making the training more consistent with the trainee's actual situation, improving training effectiveness, and helping trainees better master electric aircraft operating skills.

[0043] Specifically, the platform further includes a display module, which is communicatively connected to the acquisition module, the simulation module, the control module, and the iteration module; The display module is used to display the electric aircraft's performance parameters, fault parameters, simulated flight status, current restoration degree, training quality, evaluation results, and the adjustment direction of the next round of simulation parameters and training tasks in real time.

[0044] As described above, this embodiment utilizes a display module that communicates with various functional modules to display in real time the electric aircraft's performance parameters, fault parameters, simulated flight status, current fidelity, training quality, evaluation results, and adjustments to the next round of simulation parameters and training tasks, thus achieving visualization and transparency in the training process. The display module intuitively presents key training information to trainees and instructors, allowing trainees to understand their training performance in real time and adjust their operational strategies promptly. Instructors can monitor the training process based on the displayed content and provide targeted guidance to trainees. They can also manually intervene to adjust simulation parameters and training tasks based on actual conditions, further optimizing the training process, improving training efficiency and quality, and enhancing the practicality and interactivity of the simulation training platform.

[0045] In summary, the electric aircraft simulation training platform of this embodiment utilizes the acquisition module, simulation module, control module and iteration module that are connected in sequence by communication to achieve comprehensive optimization of electric aircraft simulation training; the acquisition module obtains the performance and fault parameters of the aircraft, providing a data basis for subsequent precise simulation; the simulation module constructs flight and fault models, simulates various working conditions, and enhances the authenticity of the training scene; the control module feeds back the trainee's operating instructions to the simulation module to realize human-computer interaction training; the iteration module calculates the degree of restoration and the quality of training, associates the two to generate an evaluation result, and adjusts the next round of simulation parameters and training tasks based on the evaluation to form a closed-loop optimization; it solves the problems of insufficient simulation accuracy, single function, and inability to personalize training in the existing technology, effectively improves the quality of electric aircraft simulation training, makes the training more in line with actual flight needs, and provides strong support for cultivating high-quality electric aircraft operators.

[0046] In the embodiments provided herein, it should be understood that the embodiments described herein can be implemented using hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, or other electronic units designed to implement the functionality described herein, or any combination thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the relevant hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of a computer program from one location to another. The storage medium may be any available medium that can be accessed by a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing the desired program code in the form of instructions or data structures and accessible by a computer.

[0047] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. An electric aircraft simulation training platform, characterized in that: The platform includes an acquisition module, a simulation module, a control module and an iteration module which are sequentially connected in communication; The acquisition module is used to obtain performance parameters and fault parameters of the electric aircraft, wherein the performance parameters are used to characterize flight-related parameters, and the fault parameters are used to characterize fault-related parameters; The simulation module is used to construct a flight model and a fault model based on the performance parameters and the fault parameters, and the flight model and the fault model are used to simulate the flight state and fault scenarios of the electric aircraft under various working conditions; The control module is used to receive the trainee's operating instructions and feed them back to the simulation module to adjust the simulated flight state; The iterative module is used to calculate the degree of restoration of the performance based on the simulated flight state and the training quality of the trainees, associate the degree of restoration and the training quality to generate an evaluation result, and adjust the parameters and training tasks of the next round of simulation based on the evaluation results of the previous round.

2. The electric aircraft simulation training platform according to claim 1, characterized in that: The performance parameters include at least flight speed, altitude, attitude angle, battery life and motor power output characteristics; The fault parameters include at least information related to motor fault, battery fault, sensor fault and software fault.

3. The electric aircraft simulation training platform according to claim 2, characterized in that: In the simulation module, the construction of the flight model includes: generating a corresponding flight index based on the flight speed, the altitude, and the attitude angle, the flight index being used to characterize a flight state during simulated flight; Based on the battery life and in combination with preset temperature parameters, a corresponding battery indicator is generated, where the battery indicator is used to characterize the battery status during the simulated flight; generating a corresponding motor index based on the motor power output characteristic, wherein the motor index is used to characterize the motor state during simulated flight; The flight model is constructed by preset weighting based on the flight index, the battery index and the motor index, and different weighting weights correspond to different degrees of restoration.

4. The electric aircraft simulation training platform according to claim 1, characterized in that: In the simulation module, the construction of the fault model includes: Obtain preset fault trigger logics based on performance parameters and fault parameters, which are set for motor faults, battery faults, sensor faults, and software faults respectively; when the corresponding fault trigger logic is met, adjust the relevant parameters in the flight model based on the preset fault trigger logic, and the fault trigger logic corresponding to different restoration degrees is different. The adjustment also includes adjustments based on the restoration degree and training quality evaluation results of the previous round.

5. The electric aircraft simulation training platform according to claim 1, characterized in that: In the control module, the adjustment of the simulated flight state includes: The trainee's operating instructions are converted into control signals of the flight model and the fault model, the control signals are corrected based on the current degree of restoration, and the flight model and the fault model are synchronously adjusted based on the corrected control signals. The correction process also includes correction based on the previous round of degree of restoration and training quality assessment results.

6. The electric aircraft simulation training platform according to claim 1, characterized in that: In the iterative module, the calculation of the restoration degree includes: The degree of deviation between the simulated value and the actual reference value of each performance indicator in the flight model and the fault model is calculated respectively, and a weight is assigned to each performance indicator in the flight model and the fault model. The degree of deviation of the performance indicator is calculated by weighted calculation to obtain the degree of restoration, which is used to evaluate the degree of fit between the current simulated flight state and the actual flight state.

7. The electric aircraft simulation training platform according to claim 6, characterized in that: In the iterative module, the calculation of the trained quality includes: Conduct a multi-dimensional quantitative assessment of the trainees' operations. This multi-dimensional quantitative assessment includes at least an assessment based on the degree of operational standardization, task completion progress, and the timeliness and effectiveness of fault response. Scoring criteria and weights are set for each assessment dimension, and the weights are related to the current degree of restoration. The training quality is calculated by comprehensively considering the scores of each dimension and the corresponding weights. The training quality is used to characterize the training effect corresponding to the current degree of restoration of the trainees. The training quality is compared with the preset threshold to determine whether to enter the training at the next degree of restoration.

8. The electric aircraft simulation training platform according to claim 7, characterized in that: In the iterative module, the generation of the evaluation result includes: The calculated degree of restoration and the trained quality are compared with their respective preset thresholds. Based on the comparison result, an evaluation result is generated in combination with preset evaluation rules. The evaluation rule is used to quantify the comparison result of the calculated degree of restoration and the trained quality with their respective preset thresholds. The categories of the evaluation results include "the degree of restoration and the trained quality meet the standards", "the degree of restoration needs to be improved", "the trained quality needs to be improved" and "the overall needs to be improved". The simulation parameters and training tasks for the next round are adjusted based on the evaluation results.

9. The electric aircraft simulation training platform according to claim 8, characterized in that: In the iteration module, adjusting the parameters and training tasks of the next round of simulation based on the evaluation results of the previous round includes: When the evaluation result is "the degree of restoration needs to be improved", adjusting the flight model and the fault model; When the evaluation result is "training quality needs to be improved", adjust the difficulty and type of the preset training task; When the evaluation result is "overall needs to be improved", the difficulty and type of the flight model, the fault model and the training task are adjusted at the same time, wherein the magnitude of the adjustment is determined based on the gap between the degree of restoration and the training quality and their corresponding thresholds.

10. The electric aircraft simulation training platform according to claim 1, characterized in that: The platform further includes a display module, which is communicatively connected with the acquisition module, the simulation module, the control module and the iteration module; The display module is used to display the performance parameters, fault parameters, simulated flight status, current restoration degree, training quality, evaluation results and adjustment direction of the next round of simulation parameters and training tasks of the electric aircraft in real time.

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