Simulation training examination evaluation method and system, electronic equipment and storage medium

Abstract

The invention provides a simulated training examination evaluation method and system, electronic equipment and a storage medium. The method comprises the following steps: performing training configuration on the training; acquiring student operation data and video images and flight data of the unmanned aerial vehicle; according to the operation data, the video image and the flight data, obtaining the training score and generating a training curve; and on the basis of previous training scores, obtaining comprehensive evaluation scores and rankings of the individuals and the units. The beneficial effects of the invention are that in the process of simulation training, operation pictures and operation data of training can be visually and accurately obtained, evaluation of single-person and cooperative operation capability can be carried out according to general operation habits, and evaluation results of training and examining comprehensive level capability of trainees can be clearly displayed; the preciseness of simulated training evaluation is enhanced, the training result of the trainee and the quality level of comprehensive training can be more clearly obtained, and the obtained score and evaluation are convenient and clear.

Description

Technical Field

[0001] This invention belongs to the field of simulation training technology, and in particular relates to a simulation training assessment method, system, electronic device and storage medium. Background Technology

[0002] In the existing technology, when calculating training scores, a certain simulator training and assessment system cannot clearly display the results of the comprehensive assessment of the trainee's training and assessment ability, cannot intuitively and accurately obtain the operation screen and operation data of the training, and lacks a clear assessment of the individual and collaborative operation ability of common operation habits. Summary of the Invention

[0003] This invention provides a simulation training assessment method, system, electronic device, and storage medium, which effectively solves the above-mentioned technical problems and overcomes the shortcomings of the prior art.

[0004] The technical solution adopted in this invention is: a simulation training assessment method, comprising the following steps:

[0005] Configure the training for this training session;

[0006] Collect student operation data and video images, as well as drone flight data;

[0007] Based on the operational data, video images, and flight data, the training results for this training session are obtained and a training curve is generated.

[0008] Based on the results of each training session, a comprehensive evaluation and ranking of individuals and organizations will be obtained.

[0009] Furthermore, the step of obtaining the training score and generating a training curve based on the operation data, video images, and flight data includes:

[0010] Calculate the horizontal and vertical deviations per second based on the video images to identify the number of hits;

[0011] By comparing the operational data and flight data, the path deviation, distance deviation, speed deviation, and pitch deviation per second are calculated.

[0012] Based on the horizontal deviation, vertical deviation, route deviation, distance deviation, speed deviation, and pitch deviation, calculate the number of times each item deviates and the continuous tracking time;

[0013] Calculate the training results and generate a training curve.

[0014] Furthermore, the step of calculating the training score includes:

[0015] Define the time T for the target to enter the firing range;

[0016] Based on the continuous tracking time of each project, calculate the duration t for each project to meet the scope conditions. 高低 、 t 水平、 t 距离 、 t 航路、 t 速度、 t 俯仰 ;

[0017] Calculate elevation score, firing score, level score, distance score, course score, speed score, and pitch score;

[0018] Calculate the scores of the left operator, the right operator, and the middle operator.

[0019] Furthermore, the left operator's score = high / low score + firing score, where high / low score = (t... 高低 / T)*k1, firing score = (number of hits / number of shots)*k2;

[0020] The right operator's score = level score, where the level score = (t) 水平 / T)*k3;

[0021] The operator's score is calculated as follows: distance score + route score + speed score + pitch score, where distance score = (t... 距离 / T)*k4, Route score = (t) 航路 / T)*k5, speed score = (t) 速度 / T)*k6, Pitch score = ( t 俯仰 / T)*k7;

[0022] Among them, k1, k2, k3, k4, k5, k6, and k7 are the preset weight scores for each item.

[0023] Furthermore, the steps described above, based on the results of each training session, yield a comprehensive evaluation score and ranking for individuals and organizations:

[0024] The comprehensive evaluation score for the left operator is calculated as follows: (Variance of left operator score * K1) + (Average number of high / low departures * K2) + (Average high / low deviation * K3) + (Fire hit rate * K4)

[0025] K1, K2, K3, and K4 are preset values ​​for the weight percentage of each item.

[0026] Furthermore, the comprehensive evaluation score of the right operator is calculated as follows: right operator score variance * K5 + average number of disengagements * K6 + average deviation * K7.

[0027] K5, K6, and K7 are preset values ​​for the weight percentage of each item.

[0028] Furthermore, the comprehensive evaluation score of the operator is calculated as follows: Operator score variance * K8 + Average distance disengagement count * K9 + Average speed disengagement count * K10 + Average route disengagement count * K11 + Average pitch disengagement count * K12.

[0029] K8, K9, K10, K11, and K12 are preset values ​​for the weight percentage of each item.

[0030] This invention also provides a simulation training assessment system, comprising,

[0031] The training management module is used to configure the training for this training session.

[0032] The training acquisition module is used to collect student operation data, video images, and drone flight data.

[0033] The training monitoring module is used to obtain the training results and generate a training curve based on the operation data, video images, and flight data.

[0034] The training evaluation module is used to calculate the comprehensive evaluation scores and rankings of individuals and units based on the results of each training session.

[0035] The present invention also provides an electronic device, including a memory, a processor, and one or more programs stored in the memory and executable on the processor. When the one or more programs are executed by the processor, the electronic device performs the simulation training assessment method as described above.

[0036] The present invention also provides a computer-readable storage medium storing computer instructions, which, when executed on a processor of an electronic device, cause the device containing the computer storage medium to perform the simulation training and assessment method described above.

[0037] The advantages and positive effects of this invention are as follows: By adopting the above technical solution, the operation screen and operation data of the training can be obtained intuitively and accurately during the simulation training process. The individual and collaborative operation capabilities can be evaluated based on common operating habits, and the evaluation results of the trainees' comprehensive training level can be clearly displayed. The rigor of the simulation training evaluation is enhanced, and the trainees' training results and comprehensive training quality level can be more clearly obtained. The results and evaluations are convenient and clear. Attached Figure Description

[0038] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0039] Figure 1 This is a flowchart illustrating a simulation training assessment method according to an embodiment of the present invention.

[0040] Figure 2 This is a schematic diagram of data transmission in a simulation training assessment method according to an embodiment of the present invention.

[0041] Figure 3 This is an architecture diagram of a simulation training assessment system according to an embodiment of the present invention.

[0042] Figure 4 This is a training flowchart of a simulation training assessment system according to an embodiment of the present invention. Detailed Implementation

[0043] This invention provides a simulation training assessment method, system, electronic device, and storage medium. The embodiments of this invention will be described below with reference to the accompanying drawings.

[0044] In the description of the embodiments of this invention, it should be understood that the terms "top," "bottom," etc., indicating orientation or positional relationships are based on the orientation or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. In the description of this invention, it should be noted that unless otherwise explicitly specified and limited, the terms "set" and "connected" should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention through specific circumstances.

[0045] like Figure 1 As shown in the figure, an embodiment of the present invention provides a simulation training assessment method, which includes the following steps: configuring the training for this training; collecting operator operation data, video images, and UAV flight data; obtaining the training score and generating a training curve based on the operation data, video images, and flight data; and obtaining the comprehensive evaluation score and ranking of individuals and units based on the training scores of previous training sessions.

[0046] S1. Configure the training for this training session;

[0047] This includes selecting training subjects and personnel information, checking whether the simulation terminal and drone are currently connected online, and issuing a start training command to the simulation terminal after confirming that the training conditions are met.

[0048] S2. Collect operator operation data, video images, and drone flight data;

[0049] Image and data transmission, such as Figure 2 As shown, it includes the following steps:

[0050] (1) At the start of training, the left and right operators manually confirm the position of the gradation center point, and the gradation center is fixed after confirmation;

[0051] (2) The operator operates the simulation terminal to collect sensor data every second and report data such as horizontal angle, vertical angle, flight path, speed, firing status, distance, pitch angle, etc., and uploads the data of each item to the main control computer through the router.

[0052] (3) After training begins, the flight control software reports data such as the UAV's heading, longitude, latitude, speed, distance, and pitch angle to the training software every second, and transmits the data from the flight control computer to the main control computer.

[0053] S3. Based on the operational data, video images, and flight data, obtain the training results and generate a training curve;

[0054] Includes the following steps:

[0055] (1) Calculate the horizontal and vertical deviations per second based on the video images to identify the number of hits;

[0056] The calculation methods for horizontal and vertical deviation data per second are as follows:

[0057] Horizontal deviation ( / mil) = Distance between the horizontal deviation of the target in the video image and the initial reticle center - D1;

[0058] Elevation deviation ( / mil) = Distance between the target in the video image and the initial reticle center in terms of elevation deviation - D1;

[0059] D1 is set as the allowable error range; preferably, D1=2.

[0060] Taking horizontal deviation as an example, when the horizontal deviation is greater than 0, it means the horizontal deviation distance is greater than 2 mils, exceeding the allowable error range, and is judged as having missed the target; when the horizontal deviation is less than or equal to 0, it means the horizontal deviation distance is less than or equal to 2 mils, within the allowable error range, and is judged as continuing to track. The same logic applies to vertical deviation, which will not be elaborated here. Each time the left operating hand presses the firing button, as long as both the horizontal and vertical deviations are less than or equal to 0, it means the target has not been missed, and a hit is determined.

[0061] (2) Compare the operational data and flight data, and calculate the path deviation, distance deviation, speed deviation and pitch deviation per second;

[0062] Flight path deviation ( / mil) = Equipment flight path data - UAV flight path data - D2;

[0063] Distance deviation ( / mil) = Device distance data - UAV distance data - D2;

[0064] Speed ​​deviation ( / mil) = Equipment speed data - UAV speed data - D2;

[0065] Pitch deviation ( / mil) = Equipment pitch data - UAV pitch data - D2;

[0066] D2 is set as the allowable error range for each item; preferably, D2=2.

[0067] Taking route deviation as an example, when the route deviation is greater than 0, it means that the deviation from the target is more than 2 mils, which exceeds the allowable error range and is judged as a departure from the target; when the route deviation is less than or equal to 0, it means that the deviation from the target is less than or equal to 2 mils, which is within the allowable error range and is judged as continuous tracking; the same applies to other items, which will not be elaborated here.

[0068] (3) Calculate the number of times each item deviates and the continuous tracking time based on the horizontal deviation, vertical deviation, route deviation, distance deviation, speed deviation and pitch deviation;

[0069] Number of deviations = Number of times the deviation of each item is greater than 0;

[0070] Continuous tracking time ( / second) = current detachment time - previous detachment time.

[0071] (4) Calculate the training results and generate the training curve;

[0072] Includes the following steps:

[0073] a. Define the time T for the target to enter the firing range;

[0074] b. Calculate the duration t for each project to meet the scope criteria based on the continuous tracking time for each project. 高低 、 t 水平、 t距离 、 t 航路、 t 速度、 t 俯仰 ;

[0075] c. Calculate elevation score, firing score, level score, distance score, route score, speed score, and pitch score;

[0076] High and low scores = (t) 高低 / T)*k1,

[0077] Score = (Number of hits / Number of shots) * k2

[0078] Level score = (t) 水平 / T)*k3,

[0079] Distance score = (t) 距离 / T)*k4,

[0080] Route score = (t) 航路 / T)*k5,

[0081] Speed ​​score = (t) 速度 / T)*k6,

[0082] Pitch score = (t) 俯仰 / T)*k7

[0083] Among them, k1, k2, k3, k4, k5, k6, and k7 are the preset weight scores for each item.

[0084] The total score for each operator is set to 100, meaning the sum of the total scores for all training items for each operator is 100. Multiple weighted scores can be set according to the number of training items for each operator, and the sum of all weighted scores is 100.

[0085] The left-hand operator's score consists of high and low scores and firing score. Preferably, the weight score of high and low scores is set to 80, i.e., k1=80, and the weight score of firing score is set to 20, i.e., k2=20.

[0086] The right operator's score consists of a level score, with a weight of 100, i.e., k3=100.

[0087] The operator's score consists of distance score, route score, speed score, and pitch score. Preferably, the weight score of distance score is set to 25, i.e., k4=25; the weight score of route score is set to 25, i.e., k5=25; the weight score of speed score is set to 25, i.e., k6=25; and the weight score of pitch score is set to 25, i.e., k7=25.

[0088] d. Calculate the scores of the left operator, the right operator, and the middle operator.

[0089] Left-hand score = High / Low score + Firing score;

[0090] Right operator's score = skill level score;

[0091] The operator's score is calculated as follows: distance score + route score + speed score + pitch score.

[0092] Preferably, in order to facilitate the assessment of the trainees' stability during the training, the horizontal deviation, altitude deviation, flight path deviation, pitch deviation, distance deviation, and speed deviation of the training are fitted to generate corresponding curves. The stability of the trainees' operation in each item is judged by the degree of fluctuation of the curves.

[0093] S4. Based on the results of each training session, obtain the comprehensive evaluation scores and rankings for individuals and organizations;

[0094] For multiple training sessions over a period of time, the overall level of individuals and units can be evaluated separately according to the operator's position. For individual evaluation, the evaluation can be filtered by evaluation scope and subject to obtain the number of training sessions, pass rate, average tracking and disengagement times, average deviation value, shooting accuracy, and individual ranking in the class, platoon, company, and battalion for a certain operating position. For class evaluation, the evaluation can be filtered by evaluation scope and subject to obtain the number of training sessions, pass rate, average tracking and disengagement times, average deviation value, firing accuracy, and class ranking in the platoon, company, and battalion for a certain operating position.

[0095] The comprehensive evaluation of the left operator's performance is divided into average number of high and low departures, average high and low deviation, firing accuracy, and performance variance.

[0096] Average number of high-low separations = Total number of high-low separations / Number of training sessions;

[0097] Average high-low deviation = Sum of all high-low deviations / Number of training sessions;

[0098] Hit rate = (Number of high and low misses / Number of training sessions) * 100;

[0099] Variance of left-hand performance = [(x1-x)] 平均分 ) 2 +(x2-x 平均分 ) 2 +…+(x n -x 平均分 ) 2 ] / n; (n = number of training iterations)

[0100] The overall evaluation score = variance of left-hand performance * K1 + average number of high and low departures * K2 + average high and low deviation * K3 + firing accuracy * K4, where K1, K2, K3 and K4 are preset weight percentages for each item. Preferably, K1=40%, K2=10%, K3=10% and K4=40%.

[0101] The ranking of the left operator or class is determined based on the overall evaluation scores.

[0102] The performance of the right-hand operator is comprehensively evaluated, including the average number of times the operator disengages, the average deviation value, and the performance variance value.

[0103] Average number of disengagements = Total number of disengagements / Number of training sessions;

[0104] Average level deviation = Sum of all level deviations / Number of training sessions;

[0105] Variance of right-hand performance = [(x1-x)] 平均分 ) 2 +(x2-x 平均分 ) 2 +…+(x n -x 平均分 ) 2 ] / n; (n = number of training iterations)

[0106] The overall evaluation score = variance of right-hand operator's performance * K5 + average number of times the operator leaves the field * K6 + average deviation of the operator's performance * K7, where K5, K6 and K7 are preset weighting values ​​for each item. Preferably, K5 = 40%, K6 = 30% and K7 = 30%.

[0107] The ranking of the right operator or class is determined based on the overall evaluation scores.

[0108] The overall evaluation of the operator's performance is divided into average distance disengagement times, average speed disengagement times, average route disengagement times, average pitch disengagement times, and performance variance.

[0109] Average number of disengagements = Total number of disengagements / Number of training sessions;

[0110] Average speed escape count = Total speed escape count / Number of training sessions;

[0111] Average number of route departures = Total number of route departures / Number of training sessions;

[0112] Average number of pitch exits = Total number of pitch exits / Number of training sessions;

[0113] Variance of parameters = [(x1-x)] 平均分 ) 2+(x2-x 平均分 ) 2 +…+(x n -x 平均分 ) 2 ] / n; (n = number of training iterations)

[0114] The overall evaluation score is calculated as follows: Variance of the operator's performance * K8 + Average distance-based exit attempts * K9 + Average speed-based exit attempts * K10 + Average route-based exit attempts * K11 + Average pitch-based exit attempts * K12. K8, K9, K10, K11, and K12 are preset weightings for each item. Preferably, K8 = 40%, K9 = 15%, K10 = 15%, K11 = 15%, and K12 = 15%.

[0115] The ranking of individual operators or classes is determined based on the comprehensive evaluation scores.

[0116] A simulation training assessment system, such as Figure 3 and Figure 4 As shown, the system includes a training management module, a training data acquisition module, a training monitoring module, and a training evaluation module. The training management module configures the training session; the training data acquisition module collects student operation data, video images, and drone flight data; the training monitoring module obtains the training results and generates a training curve based on the operation data, video images, and flight data; and the training evaluation module calculates the overall evaluation score and ranking for individuals and units based on previous training results. Preferably, a flight control module is also included, primarily for receiving drone data.

[0117] Specifically, the training management module includes the selection of training subjects and personnel information, querying whether the current connection of the simulation terminal and the drone is online, and issuing a start training command to the simulation terminal after confirming that the training conditions are met.

[0118] Specifically, the training acquisition module uses an attitude sensor to collect attitude and operation data, a camera for video monitoring, an attitude acquisition and transmission module to transmit attitude and operation data, an image recognition module to transmit images, and a video push module to transmit video streams.

[0119] Specifically, the training monitoring module receives real-time data from the simulated terminal, including horizontal angle, vertical angle, flight path, speed, firing status, distance, pitch angle, horizontal deviation mil, and vertical deviation mil. It also receives and broadcasts real-time data such as the UAV's heading, longitude, latitude, speed, distance, and pitch angle. The video monitoring module allows trainees to monitor hit situations and track the UAV, and simultaneously calculates the tracking and shooting data of the left, right, and center operators. After training, it displays the training results, basic operational data, intelligent evaluation, and operational curves for various data.

[0120] Specifically, the training assessment module mainly includes evaluating and displaying the performance of the left operator, right operator, and middle operator using tables, records, and graphs; and conducting comprehensive evaluation and ability assessment of the individual and class training performance of the left operator, right operator, and middle operator.

[0121] An electronic device includes a memory, a processor, and one or more programs stored in the memory and executable on the processor. When the processor executes the one or more programs, the electronic device performs the simulation training assessment method described above.

[0122] A computer-readable storage medium storing computer instructions, which, when executed on a processor of an electronic device, cause the device containing the computer storage medium to perform the simulation training and assessment method described above.

[0123] The aforementioned functional modules can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or as a software functional unit.

[0124] When integrated modules are implemented as software functional units, they can be stored in a computer-readable storage medium. The technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the simulation training and assessment method described in this application.

[0125] The aforementioned storage media include various media that can store program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0126] The advantages and positive effects of this invention are:

[0127] By adopting the above technical solution, the operation screen and operation data of the training can be obtained intuitively and accurately during the simulation training process. It can evaluate the individual and collaborative operation capabilities based on common operating habits, and clearly display the evaluation results of the trainees' comprehensive training level. It enhances the rigor of the simulation training assessment, and can more clearly determine the trainees' training results and comprehensive training quality level. The results and evaluations are convenient and clear.

[0128] The embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. It should be noted that implementations not illustrated or described in the drawings or the main text of the specification are forms known to those skilled in the art and have not been described in detail. Furthermore, the definitions of the various components described above are not limited to the specific structures, shapes, or methods mentioned in the embodiments, and those skilled in the art can easily modify or substitute them.

[0129] The embodiments of the present invention have been described in detail above, but the content described is only a preferred embodiment of the present invention and should not be considered as limiting the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the patent coverage of the present invention.

Claims

1. A simulation training assessment method, characterized in that, Includes the following steps: Configure the training for this training session; Collect student operation data and video images, as well as drone flight data; Based on the operational data, video images, and flight data, the training results for this training session are obtained and a training curve is generated. Based on the results of each training session, a comprehensive evaluation and ranking of individuals and organizations will be obtained.

2. The simulation training assessment method according to claim 1 or 2, characterized in that, The steps of obtaining the training results and generating a training curve based on the operation data, video images, and flight data include: Calculate the horizontal and vertical deviations per second based on the video images to identify the number of hits; By comparing the operational data and flight data, the path deviation, distance deviation, speed deviation, and pitch deviation per second are calculated. Based on the horizontal deviation, vertical deviation, route deviation, distance deviation, speed deviation, and pitch deviation, calculate the number of times each item deviates and the continuous tracking time; Calculate the training results and generate a training curve.

3. The simulation training assessment method according to claim 3, characterized in that, The steps for calculating the training score include: Define the time T for the target to enter the firing range; Based on the continuous tracking time of each project, calculate the duration t for each project to meet the scope conditions. 高低 、 t 水平、 t 距离 、 t 航路、 t 速度、 t 俯仰 ; Calculate elevation score, firing score, level score, distance score, course score, speed score, and pitch score; Calculate the scores of the left operator, the right operator, and the middle operator.

4. The simulation training assessment method according to claim 4, characterized in that: The left operator's score = high / low score + firing score, where high / low score = (t) 高低 / T)*k1, firing score = (number of hits / number of shots)*k2; The right operator's score = level score, where the level score = (t) 水平 / T)*k3; The operator's score is calculated as follows: distance score + route score + speed score + pitch score, where distance score = (t... 距离 / T)*k4, Route score = (t) 航路 / T)*k5, speed score = (t) 速度 / T)*k6, Pitch score = ( t 俯仰 / T)*k7; Among them, k1, k2, k3, k4, k5, k6, and k7 are the preset weight scores for each item.

5. The simulation training assessment method according to claim 4, characterized in that, The steps described above are based on previous training results, to obtain comprehensive evaluation scores and rankings for individuals and organizations: The comprehensive evaluation score for the left operator is calculated as follows: (Variance of left operator score * K1) + (Average number of high / low departures * K2) + (Average high / low deviation * K3) + (Fire hit rate * K4) K1, K2, K3, and K4 are preset values ​​for the weight percentage of each item.

6. The simulation training assessment method according to claim 4, characterized in that: The comprehensive evaluation score for the right operator is calculated as follows: right operator score variance * K5 + average number of disengagements * K6 + average deviation * K7. K5, K6, and K7 are preset values ​​for the weight percentage of each item.

7. The simulation training assessment method according to claim 4, characterized in that: The overall operator evaluation score is calculated as follows: Operator score variance * K8 + Average distance-based disengagement count * K9 + Average speed-based disengagement count * K10 + Average route-based disengagement count * K11 + Average pitch-based disengagement count * K12. K8, K9, K10, K11, and K12 are preset values ​​for the weight percentage of each item.

8. A simulation training assessment system, characterized in that: include, The training management module is used to configure the training for this training session. The training acquisition module is used to collect student operation data, video images, and drone flight data. The training monitoring module is used to obtain the training results and generate a training curve based on the operation data, video images, and flight data. The training evaluation module is used to calculate the comprehensive evaluation scores and rankings of individuals and units based on the results of each training session.

9. An electronic device, characterized in that: The device includes a memory, a processor, and one or more programs stored in the memory and executable on the processor. When the processor executes the one or more programs, the electronic device performs the simulation training assessment method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions that, when executed on the processor of the electronic device, cause the device containing the computer storage medium to perform the simulation training assessment method as described in any one of claims 1-7.

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