CN114743432B - Simulation training evaluation method, device and storage medium - Google Patents
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Abstract
The application discloses a simulation training evaluation method, a simulation training evaluation device and a storage medium, relates to the technical field of driving simulation, and solves the technical problem that the precision evaluation requirement for complex test items cannot be met at present. The simulation training evaluation method comprises the following steps: acquiring a test item; determining an evaluation rule and a data acquisition mode based on the type of the test item; acquiring observation parameters according to the determined data acquisition mode; judging whether the observation parameters are within a preset allowable error range according to the determined evaluation rule; and if the judgment result is within the preset allowable error range, determining that the test item passes. The simulation training evaluation method can adjust the evaluation rule and the data acquisition mode based on the test item category in a self-adaptive manner, so that the evaluation of each test item is more targeted, and the test process can be evaluated accurately.
Description
Technical Field
The present disclosure relates to the field of driving simulation technologies, and in particular, to a method, an apparatus, and a storage medium for evaluating simulation training.
Background
The driving simulator is a complex ground simulation device, is mainly used for ground simulation training of pilots, can reproduce air flight environment, and can provide various training subjects such as basic driving technology flight training, special situation handling training and the like. The pilot training by using the driving simulator is not influenced by natural environment, meteorological conditions and time, is generally adopted by various countries as an efficient, safe, economic and repeatable auxiliary training mode, and becomes an important mode for training pilots.
When a pilot drives an airplane, the operation process of the airplane is complex and various, and the key of flight training is how to accurately evaluate each different operation link. However, the current evaluation index is single, and the requirement of accurate evaluation for complex test items cannot be met.
Disclosure of Invention
The embodiment of the application provides a simulation training evaluation method, a simulation training evaluation device and a storage medium, and solves the technical problem that the precision evaluation requirement for complex test items cannot be met at present.
In a first aspect, an embodiment of the present application provides a simulated training evaluation method, where the simulated training evaluation method includes: acquiring a test item; determining an evaluation rule and a data acquisition mode based on the test item type; acquiring observation parameters according to the determined data acquisition mode; judging whether the observation parameters are within a preset allowable error range according to a determined evaluation rule; and if the judgment result is within the preset allowable error range, determining that the test item passes.
With reference to the first aspect, in a possible implementation manner, the determining an evaluation rule and a data obtaining manner based on the test item type includes: when the test item type is judged to be an instruction input type, acquiring a plurality of groups of parameters output by the operation object model by adopting an input opportunity evaluation rule and storing the parameters to form a parameter sequence; and when the test item type is judged to be the operation control type, acquiring the current parameters output by the operation object model by adopting a posture control evaluation rule.
With reference to the first aspect, in a possible implementation manner, when the test item type is an instruction input type, the acquiring an observation parameter according to the determined data acquisition manner includes: acquiring a plurality of groups of parameters output by an operation object model at a preset frequency and storing the parameters to form a parameter sequence; receiving a command to be tested; and selecting a group of parameters corresponding to the moment of receiving the preset digit of the instruction to be detected as the observation parameters.
With reference to the first aspect, in a possible implementation manner, the acquiring the observation parameter according to the determined data acquisition manner further includes: and stopping acquiring the parameters after the parameters at the moment of receiving the instruction to be detected are acquired, and storing the parameters corresponding to the moment of receiving the instruction to be detected.
With reference to the first aspect, in a possible implementation manner, the acquired parameters are stored in a queue form, an enqueue operation is performed on the acquired parameter at the latest time to form a parameter sequence, and an dequeue operation is performed on the parameter at the earliest time in the queue.
With reference to the first aspect, in a possible implementation manner, before determining whether the observation parameter is within a preset allowable error range according to a determined evaluation rule, the method further includes: judging whether the received instruction to be tested is correct or not based on a preset sequence of the instruction to be tested corresponding to the test item; and if the instruction to be tested is an error instruction, judging that the simulation training fails and ending the simulation training.
With reference to the first aspect, in a possible implementation manner, when the test item type is an instruction input type, the acquiring an observation parameter according to the determined data acquisition manner includes: and acquiring the current parameters output by the operation object model as the observation parameters.
With reference to the first aspect, in a possible implementation manner, the preset allowable error includes: the observation parameters are within a preset standard range; and starting to count down in a preset time period when the observation parameters exceed the preset standard range, and returning the observation parameters to the preset standard range before the counting down is finished.
In a second aspect, the present application provides a simulated training evaluation apparatus, including: the project acquisition module is used for acquiring test projects; the rule determining module is used for determining an evaluation rule and a data acquisition mode based on the test item type; the observation parameter determining module is used for acquiring observation parameters according to the determined data acquisition mode; the first judgment module is used for judging whether the observation parameters are within a preset allowable error range according to the determined evaluation rule; and the result determining module is used for determining that the test item passes when the judgment result is within a preset allowable error range.
With reference to the second aspect, in a possible implementation manner, the rule determining module is specifically configured to: when the test item type is judged to be an instruction input type, acquiring a plurality of groups of parameters output by the operation object model by adopting an input opportunity evaluation rule and storing the parameters to form a parameter sequence; and when the test item type is judged to be the operation control type, acquiring the current parameters output by the operation object model by adopting a posture control evaluation rule.
With reference to the second aspect, in a possible implementation manner, when the test item type determined by the rule determining module is an instruction input type, the observation parameter determining module is specifically configured to: acquiring a plurality of groups of parameters output by an operation object model at a preset frequency and storing the parameters to form a parameter sequence; receiving a command to be tested; and selecting a group of parameters corresponding to the moment of receiving the preset digit of the instruction to be detected as the observation parameters.
With reference to the second aspect, in a possible implementation manner, the observation parameter determining module is further configured to: and stopping acquiring the parameters after the parameters at the moment of receiving the instruction to be detected are acquired, and storing the parameters corresponding to the moment of receiving the instruction to be detected.
With reference to the second aspect, in a possible implementation manner, the acquired parameters are stored in a queue form, an enqueue operation is performed on the acquired parameter at the latest time to form a parameter sequence, and a dequeue operation is performed on the parameter at the earliest time in the queue.
With reference to the second aspect, in a possible implementation manner, the simulation training evaluation apparatus further includes a second determining module and an ending module; before the first judging module judges whether the observation parameters are within a preset allowable error range according to the determined evaluation rule: the second judging module is used for judging whether the received instruction to be detected is correct or not based on the preset sequence of the instruction to be detected corresponding to the test item; and the ending module judges that the simulation training fails and ends the simulation training when the instruction to be tested is an error instruction.
With reference to the second aspect, in a possible implementation manner, when the test item type determined by the rule determining module is an operation control type, the observation parameter determining module is specifically configured to: and acquiring the current parameters output by the operation object model as the observation parameters.
With reference to the second aspect, in a possible implementation manner, the preset allowable error includes: the observation parameters are within a preset standard range; and when the observation parameters exceed the preset standard range, counting down in a preset time period, and returning the observation parameters to the preset standard range before counting down is finished.
In a third aspect, an embodiment of the present application provides a computer-readable storage medium, where computer-readable instructions are stored, and when executed by a processor, the computer-readable instructions implement the simulation training evaluation method according to the first aspect or any one of the possible implementation manners of the first aspect.
The technical scheme provided in the embodiment of the application has at least the following technical effects or advantages:
according to the simulation training evaluation method provided by the embodiment of the application, after a test item is obtained, an evaluation rule and a data obtaining mode are determined based on the type of the test item, an observation parameter is obtained according to the determined data obtaining mode, whether the observation parameter is within a preset allowable error range is judged according to the determined evaluation rule, and if the judgment result is within the preset allowable error range, the test item is determined to pass. The simulation training evaluation method adaptively adjusts the evaluation rule and the data acquisition mode based on the test item category, so that the evaluation of each test item is more targeted, and the test process can be evaluated accurately.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments of the present application or the technical solutions in the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a simulation training evaluation method provided in an embodiment of the present application;
fig. 2 is a flowchart of acquiring an observation parameter when a test item type is an instruction input type according to an embodiment of the present application;
FIG. 3 is a flow chart of ending simulation training provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of a simulated training evaluation device provided in an embodiment of the present application;
fig. 5 is a schematic diagram of a queue structure provided in the embodiment of the present application;
fig. 6 is a command input sequence chart for driving the engine according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It should be apparent that the described embodiments are only some of the embodiments of the present invention, and not all of them. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a simulation training evaluation method, which can be applied to operation object models such as a flight driving simulator, a ship driving simulator, an automobile driving simulator and the like. As shown in fig. 1, the method includes steps S101 to S105.
S101: and acquiring a test item. The test items can be divided into a command input type test item and an operation control type test item.
Illustratively, fig. 6 shows a test item of the type of command input, which is a "driving" simulation training, in which a trainer needs to input the following commands in order: the system comprises an external control unit, an engine power supply unit, an on enrichment unit, a left fuel pump unit, a right fuel pump unit, a left magnetic unit, a right magnetic unit, a start unit, an off enrichment unit, a clutch tensioning unit, a warm unit, a slow unit, a rated unit, a take-off preparation unit, a manual take-off unit, a flat flight unit, a manual landing unit, a slow unit, a warm unit, an idle unit, a clutch contraction unit, a pre-stop unit, a clutch contraction unit, a stop unit, a recovery unit, a 12v battery forced disconnection unit and a 24v battery forced disconnection unit.
The operation control type test items are explained by taking left flight simulation training as an example, and a trainer completes the left flight simulation training by operating and controlling the vertical movement distance, the longitudinal movement distance, the rolling angle, the pitch angle, the course angle and the like of the flight driving simulator.
S102: and determining an evaluation rule and a data acquisition mode based on the test item type.
And when the test item type is judged to be the instruction input type, acquiring a plurality of groups of parameters output by the operation object model by adopting an input opportunity evaluation rule and storing the parameters to form a parameter sequence. When a trainer inputs an instruction in the driving process, one or more observation parameters need to be observed, the instruction is input when the parameters to be observed meet the requirements, the opportunity for the trainer to input the instruction is checked by inputting the opportunity evaluation rule, and whether the input opportunity of each instruction is correct or not can be judged.
And when the test item type is judged to be the operation control type, acquiring the current parameters output by the operation object model by adopting a posture control evaluation rule. The posture control evaluation rule examines the operation of the trainer on the operation object model, and further can accurately position the misoperation or weak link of the trainer in the simulation training process, so that the subsequent targeted training can be carried out, and the operation skill of the trainer can be rapidly improved.
S103: and acquiring the observation parameters according to the determined data acquisition mode.
S104: and judging whether the observation parameters are within a preset allowable error range according to the determined evaluation rule.
If the test item is of the instruction input type, the instruction to be tested is an instruction input by a trainer, and the observation parameter may be one or more parameters, and accordingly, the preset allowable error also has one or more intervals. Such as: when the command to be detected is an input 'driving' command, the preset standard of the observation parameters is as follows: the engine speed is 0, the enrichment is 19, the fuel pressure is 0.45, the lubricating oil pressure is 0 to 0.3, the main lubricating oil pressure reduction is 20, the exhaust temperature is-22, the cylinder temperature is 20 to 21, the air door is 0.2 to 1 percent, and the air box temperature is 20; the preset allowable error of the engine speed may be ± 10%, the preset allowable error of the enrichment may be 19 ± 2, the preset allowable error of the fuel pressure may be 0.45 ± 0.1, the preset allowable error of the slip oil pressure may be 0 to 0.4, the preset allowable error of the main slip oil pressure may be 20 ± 2, the preset allowable error of the exhaust temperature may be-22 ± 2, the preset allowable error of the cylinder temperature may be 19 to 22, and the preset allowable error of the damper may be 0.15% to 1.1%.
If the test item is of an operation control type, and when the trainer performs operation control on the operation object model, presetting an allowable error comprises the following steps: the observation parameters are within a preset standard range; and starting to count down in a preset time period when the observation parameters exceed the preset standard range, and returning the observation parameters to the preset standard range before the counting down is finished.
The preset standard range is a range of each observation parameter set in advance. If the observation parameter is a parameter, the preset standard range is the range of the parameter; if the observation parameters are a plurality of parameters, the preset standard range is a plurality of ranges corresponding to the plurality of parameters. For example, in the process of 'left flight' simulation training, it is required to ensure that the vertical motion error and the longitudinal motion error are kept within +/-0.5 m, and the motion errors of a roll angle, a pitch angle and a course angle are within +/-1 degree.
When one of the observation parameters exceeds the preset standard range, counting down is started in a preset time period, for example, counting down is started for 5s, and if all the observation parameters return to the respective preset standard range before counting down is finished, the observation parameters are regarded as being within the preset allowable error.
Of course, when the test item is an operation control type, the preset allowable error is not limited to the above specific case, and may be other types of cases, such as: the preset allowable error may only include that the observation parameter is within a preset standard range, and when there is an observation parameter exceeding the preset standard range, it is determined that the observation parameter exceeds the preset allowable error; the preset allowable error may also include only the observation parameter being equal to the preset standard value, and when there is an observation parameter unequal to the preset standard value, the observation parameter is determined to exceed the preset allowable error.
If the determination result of S104 is within the preset allowable error range, then S105 is executed: and determining that the test item passes.
If the test item is of the instruction input type, and the judgment result of the S104 is yes, determining that the test item passes, and specifically indicating that the input time of the instruction is correct; if the result of the determination in S104 is negative, it indicates that the input timing of the command is incorrect. In addition, when the judgment result of the S104 is negative, namely the observation parameters exceed the preset allowable error range, the score corresponding to the instruction with wrong input time can be directly deducted, and further the quantitative assessment on the instruction input time of the trainer is realized.
If the test item is of the operation control type and the judgment result of the S104 is yes, the fact that the test item is correct through the operation specifically representing the trainer is determined; if the result of the determination in S104 is negative, it indicates that the operation performed by the trainer is erroneous. In addition, when there is an error in the operation performed by the trainer, a penalty rule is executed and the operation type is recorded. Specific penalty rules may be deducting the score of the operation, restarting the operation, etc. And by recording punished operation types, the link of insufficient operation of the trainer is accurately positioned, so that the trainer can carry out special training aiming at the defects in the subsequent simulated training process, and the training efficiency is improved.
When the type of the test item acquired in S101 is an instruction input type, the embodiment of the present application provides a specific implementation manner of S103, and as shown in fig. 2, includes steps S201 to S203.
S201: and acquiring multiple groups of parameters output by the operation object model at a preset frequency and storing the parameters to form a parameter sequence.
As shown in FIG. 5, the retrieved parameters may be stored in a queue, from t 0 Time to t m-1 The parameters acquired at a time can be represented in the form of a sequence, forming a sequence of parameters t 0 ，t 1 ，t 2 ，t 3 ，……，t m-1 Performing enqueue operation on the acquired parameter at the latest moment to form a parameter sequence, and performing dequeue operation on the parameter at the earliest moment in the queue to form a new parameter sequence, namely when the latest moment t is acquired m By performing enqueue and dequeue operations, the parameter sequence becomes { t } 1 ，t 2 ，t 3 ，……，t m-1 ，t m }。
Because the input time of the instruction is judged without using parameters at all times, and the parameters separated from the time of receiving the instruction to be detected by a plurality of times do not have use value and can be discarded, the acquired parameters are stored in a queue form shown in fig. 5, the requirement of acquiring observation parameters is met, the storage space is saved, and the effective utilization of storage resources is realized.
Of course, the storage of the parameters at multiple time points is not limited to the queue form shown in fig. 5, and other forms of structures, such as a stack structure, may be used to store the parameters at multiple time points.
S202: and receiving a command to be tested.
S203: and selecting a group of parameters corresponding to the moment of receiving the preset digit of the instruction to be detected as observation parameters.
Illustratively, the time when the instruction to be tested is received is t m (ii) a When the pre-set digit is 1, the time of the pre-set digit is t m-1 (ii) a When the pre-set digit is 2, the moment of the pre-set digit is t m-2 (ii) a The specific value of the pre-set number of bits may be determined according to the time interval for acquiring each set of parameters, practical experience, and the like.
According to the embodiment of the application, the acquisition of the parameters is suspended after the parameters at the moment of receiving the instruction to be detected are acquired, and the parameters corresponding to the moment of the instruction to be detected are stored. Through the pause and storage processes, the observation time can be quickly determined, and the time of receiving the instruction to be detected does not need to be marked and stored separately, so that the execution efficiency of the method is improved.
When the type of the test item acquired in S101 is an instruction input type, the embodiment of the present application further provides another specific implementation manner of S103, which specifically includes: and acquiring a parameter sequence at the moment when the instruction to be detected is received, and determining the parameter sequence as an observation parameter. Take the example of the trainer inputting the 'driving' instruction: the trainer inputs a 'driving' instruction when the operation object model is at t m After the 'driving' instruction is received, the parameter sequence is stored, and the stored t is stored m The sequence of time instants is determined as an observation parameter. In this specific implementation, the time when the instruction to be measured is received is approximately used as the observation time of the instruction to be measuredThe parameter sequence at the moment when the instruction to be detected is received is taken as an observation parameter, only the parameter sequence at one moment needs to be stored, the required storage space is smaller, the processing speed is higher, and the specific implementation mode can be adopted in a scene with lower precision requirement.
Specifically, the following manner may be adopted to obtain the parameter sequence at the time when the instruction to be detected is received: and a timer is adopted to obtain the simulation parameters at the current moment in real time, and a group of parameter sequences are stored after a driving command is received. In addition, the approximation accuracy can be improved by increasing the refresh rate of the timer.
The method provided in the present application further includes steps S301 and S302 shown in fig. 3 before executing S104.
S301: and judging whether the received instruction to be tested is correct or not based on the preset sequence of the instruction to be tested corresponding to the test item.
If the judgment result in S301 is negative, that is, the received instruction to be tested is an error instruction, which indicates that the trainer inputs the error instruction, the method executes S302: judging that the simulation training fails and finishing the simulation training. If the determination result in S301 is yes, that is, the received command to be tested is a correct command, which indicates that the trainer has input the correct command, the steps S104 to S105 are continuously executed.
The judgment on whether the instruction to be detected input by the trainer is correct or not and the processing on the wrong instruction are realized through S301 and S302, so that the evaluation is avoided when the trainer inputs the wrong instruction, and the evaluation efficiency of the method is improved.
When the test item type is the operation control type, S103 specifically includes: and acquiring the current parameters output by the operation object model as observation parameters. For example, in the process of left flight simulation training, the flight driving simulator can output a vertical movement distance, a longitudinal movement distance, a rolling angle, a pitch angle, a course angle and the like in real time, and a trainer needs to observe parameters output by the flight driving simulator in real time so as to perform operation control.
Through the above detailed description, it can be seen that the simulation training evaluation method provided by the embodiment of the present application can adaptively adjust the evaluation rule and the data acquisition mode based on the test item category, so that the evaluation of each test item is more targeted, and the method is beneficial to accurately evaluating the test process.
While the embodiments of the present application provide for the above-described method steps, additional or fewer steps may be included based on routine or non-inventive labor. In addition, the step sequence listed in the embodiments of the present application is only one manner of execution sequence of many steps, and does not represent a unique execution sequence. In actual driving simulator execution, the driving simulator may be executed sequentially or in parallel (for example, in the context of a parallel processor or a multi-thread process) according to the method shown in the present embodiment or the drawings.
Referring to fig. 4, the embodiment of the present application further provides a simulated training evaluation apparatus 400, where the simulated training evaluation apparatus 400 includes a project acquisition module 401, a rule determination module 402, an observation parameter determination module 403, a first judgment module 404, and a result determination module 405.
Specifically, the item obtaining module 401 is configured to obtain a test item; the rule determining module 402 is configured to determine an evaluation rule and a data obtaining manner based on the test item type; the observation parameter determining module 403 is configured to obtain an observation parameter according to the determined data obtaining manner; the first judging module 404 is configured to judge whether the observation parameter is within a preset allowable error range according to a determined evaluation rule; the result determining module 405 is configured to determine that the test item passes through when the determination result is within a preset allowable error range.
The rule determining module 402 is specifically configured to: when the test item type is judged to be the instruction input type, acquiring a plurality of groups of parameters output by the operation object model by adopting an input opportunity evaluation rule and storing the parameters to form a parameter sequence; and when the test item type is judged to be the operation control type, acquiring the current parameters output by the operation object model by adopting a posture control evaluation rule.
When the test item type determined by the rule determining module 402 is an instruction input type, the observation parameter determining module 403 is specifically configured to: acquiring a plurality of groups of parameters output by an operation object model at a preset frequency and storing the parameters to form a parameter sequence; receiving a command to be tested; and selecting a group of parameters corresponding to the moment of receiving the preset digit of the instruction to be detected as observation parameters.
The observation parameter determination module 403 is further configured to: and stopping acquiring the parameters after the parameters at the moment of receiving the instruction to be detected are acquired, and storing the parameters corresponding to the moment of receiving the instruction to be detected.
And storing the acquired parameters in a queue form, performing enqueuing operation on the acquired parameters at the latest moment to form a parameter sequence, and performing dequeuing operation on the parameters at the earliest moment in the queue to realize dynamic update of the stored parameter sequence.
The simulation training evaluation device 400 further comprises a second judgment module and an end module; before the first determining module 404 determines whether the observation parameter is within the preset allowable error range according to the determined evaluation rule: the second judging module is used for judging whether the received instruction to be detected is correct or not based on the preset sequence of the instruction to be detected corresponding to the test item; and the ending module judges that the simulation training fails and ends the simulation training when the instruction to be tested is an error instruction.
When the test item type determined by the rule determining module 402 is an operation control type, the observation parameter determining module 403 is specifically configured to: and acquiring the current parameters output by the operation object model as observation parameters.
The preset allowable error includes: the observation parameters are within a preset standard range; and starting to count down in a preset time period when the observation parameters exceed the preset standard range, and returning the observation parameters to the preset standard range before the counting down is finished.
Program modules in the above-described devices of the embodiments of the present application include programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The apparatus of embodiments of the present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiment of the present application further provides a computer-readable storage medium, where computer-readable instructions are stored, and when the computer-readable instructions are executed by a processor, the simulated training evaluation method provided in the embodiment of the present application is implemented.
In particular, the computer-readable storage medium includes, but is not limited to, a random access memory, a read-only memory, a cache, a hard disk, or a memory card.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary hardware. Based on such understanding, the technical solution of the present application, which essentially or contributes to the prior art, may be embodied in the form of a software product, and may also be embodied in the implementation process of data migration. The computer software product may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to perform the method of the embodiments of the present invention.
The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. All or portions of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the present application; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the present disclosure.
Claims (8)
1. A simulation training evaluation method is characterized by comprising the following steps:
acquiring a test item;
determining an evaluation rule and a data acquisition mode based on the test item type;
acquiring observation parameters according to the determined data acquisition mode;
judging whether the observation parameters are within a preset allowable error range according to a determined evaluation rule;
if the judgment result is within a preset allowable error range, determining that the test item passes;
the determining of the evaluation rule and the data acquisition mode based on the test item type comprises the following steps: when the test item type is judged to be an instruction input type, acquiring a plurality of groups of parameters output by the operation object model by adopting an input opportunity evaluation rule and storing the parameters to form a parameter sequence; when the test item type is judged to be the operation control type, acquiring the current parameters output by the operation object model by adopting a posture control evaluation rule;
when the test item type is an instruction input type, the acquiring observation parameters according to the determined data acquisition mode includes: acquiring a plurality of groups of parameters output by an operation object model at a preset frequency and storing the parameters to form a parameter sequence; receiving a command to be tested; and selecting a group of parameters corresponding to the moment of receiving the preset digit of the instruction to be detected as the observation parameters.
2. The simulation training evaluation method of claim 1, wherein the obtaining observation parameters according to the determined data obtaining manner further comprises:
and stopping acquiring the parameters after the parameters at the moment of receiving the instruction to be detected are acquired, and storing the parameters corresponding to the moment of receiving the instruction to be detected.
3. The simulation training evaluation method according to claim 1 or 2, wherein the acquired parameters are stored in a queue form, an enqueue operation is performed on the acquired parameters at the latest moment to form a parameter sequence, and a dequeue operation is performed on the parameters at the earliest moment in the queue.
4. The simulation training evaluation method according to claim 1, before determining whether the observation parameter is within a preset allowable error range according to the determined evaluation rule, further comprising:
judging whether the received instruction to be tested is correct or not based on a preset sequence of the instruction to be tested corresponding to the test item;
and if the instruction to be tested is an error instruction, judging that the simulation training fails and ending the simulation training.
5. The simulation training evaluation method of claim 1, wherein when the test item type is an operation control type, the obtaining observation parameters according to the determined data obtaining manner includes:
and acquiring the current parameters output by the operation object model as the observation parameters.
6. The simulated training evaluation method of claim 5, wherein the preset allowable error comprises:
the observation parameters are within a preset standard range;
and starting to count down in a preset time period when the observation parameters exceed the preset standard range, and returning the observation parameters to the preset standard range before the counting down is finished.
7. A simulated training evaluation device, comprising:
the project acquisition module is used for acquiring test projects;
the rule determining module is used for determining an evaluation rule and a data acquisition mode based on the test item type;
the observation parameter determining module is used for acquiring observation parameters according to the determined data acquisition mode;
the first judgment module is used for judging whether the observation parameters are within a preset allowable error range according to a determined evaluation rule;
the result determining module is used for determining that the test item passes when the judgment result is within a preset allowable error range;
the rule determining module is specifically configured to: when the test item type is judged to be an instruction input type, acquiring a plurality of groups of parameters output by the operation object model by adopting an input opportunity evaluation rule and storing the parameters to form a parameter sequence; when the test item type is judged to be the operation control type, acquiring the current parameters output by the operation object model by adopting a posture control evaluation rule;
when the test item type determined by the rule determining module is an instruction input type, the observation parameter determining module is specifically configured to: acquiring a plurality of groups of parameters output by an operation object model at a preset frequency and storing the parameters to form a parameter sequence; receiving a command to be tested; and selecting a group of parameters corresponding to the moment of receiving the preset digit of the instruction to be detected as the observation parameters.
8. A computer-readable storage medium storing computer-readable instructions which, when executed by a processor, implement the simulated training evaluation method of any one of claims 1-6.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111047945A (en) * | 2020-01-15 | 2020-04-21 | 中仿智能科技（上海）股份有限公司 | Flight training evaluation system of simulation aircraft |
| CN112116844A (en) * | 2020-09-21 | 2020-12-22 | 北京中航科电测控技术股份有限公司 | Reconfigurable aircraft avionics simulation training system |
| CN114529177A (en) * | 2022-02-10 | 2022-05-24 | 北方天途航空技术发展（北京）有限公司 | Training result intelligent judgment method and system for training machine |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2644350B2 (en) * | 1989-12-05 | 1997-08-25 | 株式会社東芝 | Driving training simulator |
| JP6150553B2 (en) * | 2013-02-22 | 2017-06-21 | 三菱重工業株式会社 | Driving operation evaluation device, driving operation evaluation method, and driving operation evaluation program |
| CN110147941A (en) * | 2019-04-28 | 2019-08-20 | 中国银行股份有限公司 | Content of examination acquisition methods, Stakeholder Evaluation method and device |
| CN111932583A (en) * | 2020-06-05 | 2020-11-13 | 西安羚控电子科技有限公司 | Space-time information integrated intelligent tracking method based on complex background |
| US12039880B2 (en) * | 2020-06-15 | 2024-07-16 | Honeywell International Inc. | Systems and methods for training aircraft operators |
| CN112466181A (en) * | 2020-12-03 | 2021-03-09 | 郑州捷安高科股份有限公司 | Evaluation method, device and medium based on simulation driving simulation system |
-
2022
- 2022-06-14 CN CN202210666035.3A patent/CN114743432B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111047945A (en) * | 2020-01-15 | 2020-04-21 | 中仿智能科技（上海）股份有限公司 | Flight training evaluation system of simulation aircraft |
| CN112116844A (en) * | 2020-09-21 | 2020-12-22 | 北京中航科电测控技术股份有限公司 | Reconfigurable aircraft avionics simulation training system |
| CN114529177A (en) * | 2022-02-10 | 2022-05-24 | 北方天途航空技术发展（北京）有限公司 | Training result intelligent judgment method and system for training machine |
Non-Patent Citations (1)
| Title |
|---|
| 某飞行仿真系统虚拟教员软件研究;刘永易等;《火力与指挥控制》;20160615(第06期);第157-160页 * |
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