Teaching evaluation simulation training system based on digital twinning, Internet of Things and big data

Abstract

The invention discloses a teaching evaluation simulation training system based on digital twinning, Internet of Things and big data, relates to the field of evaluation simulation, solves the problem that training personnel cannot monitor the training process in real time during training evaluation at the present stage, and comprises a site simulation module, a first training module, a second training module and a training evaluation module, the field simulation module is used for constructing a corresponding digital twin field model according to real-time field data and real-time environment data of a field training field, and the first training module is used for performing training of a first training task on trainees to obtain real-time advancing speeds and training counterweights of the trainees; the first training module is used for executing a first training task and a second training task on the training personnel, the second training module is used for executing a second training task and a third training task on the training personnel, the training evaluation module is used for evaluating training results of the training personnel to obtain comprehensive training levels of the training personnel, and comprehensive evaluation and comprehensive evaluation of teaching evaluation simulation training are achieved.

Description

Technical Field

[0001] The present invention relates to the field of assessment simulation, and specifically to a teaching assessment simulation training system based on digital twins, the Internet of Things and big data. Background Art

[0002] The teaching and assessment simulation training system uses digital twin technology to simulate highly realistic teaching scenes and environments, making students feel as if they are in a real situation; this immersive learning experience helps students better understand and master knowledge, and enhances the practical applicability of learning; the system can simulate a variety of training scenarios, including different environments, tasks and challenges, and trainers can train in a variety of environments, enhance their ability to deal with different situations, and cultivate more flexible response methods; significantly improve teaching efficiency, ensure safety, reduce costs, and promote the standardization and scientificization of education assessment.

[0003] At present, when training and assessing trainees, the traditional training method of actual equipment exercises is used, which cannot monitor the training process in real time, resulting in the inability to monitor the physical condition of trainees in time during the training process, causing safety hazards and leading to low training efficiency; To this end, the present invention proposes a teaching and evaluation simulation training system based on digital twins, the Internet of Things and big data. Summary of the invention

[0004] The purpose of the present invention is to propose a teaching and evaluation simulation training system based on digital twins, the Internet of Things and big data to solve the problems raised in the above-mentioned background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A teaching and evaluation simulation training system based on digital twins, the Internet of Things and big data, comprising an environment acquisition module, a site simulation module, a task classification module, a first training module, a second training module and a training evaluation module, wherein the environment acquisition module is used to collect real-time site data and real-time environmental data of a field training site and send them to the site simulation module; the site simulation module is used to construct a corresponding digital twin site model based on the real-time site data and real-time environmental data of the field training site and send it to the first training module and the second training module; The task classification module is used to divide the assessment training tasks of the training personnel into a first training task, a second training task and a third training task, send the first training task to the first training module, and send the second training task and the third training task to the second training module; The first training module is used to train the trainee on the first training task, and obtain the real-time travel speed and training weight of the trainee and send them to the training evaluation module; The second training module is used to perform the second training task and the third training task on the trainee, obtain the total number of offset shots, the total number of stable shots and the shooting score corresponding to the stable shots when the trainee performs the second training task, and send them to the training evaluation module, and also obtain the completion status of the trainee's individual tasks and teamwork tasks in the third training task and send them to the training evaluation module; The training evaluation module is used to evaluate the training results of the trainees and obtain the comprehensive training level of the trainees.

[0006] Furthermore, the real-time site data includes the ground elevation, slope, aspect and soil and vegetation coverage in the field training site; Real-time environmental data includes real-time temperature, real-time humidity, real-time wind speed and direction of the field training site.

[0007] Furthermore, the training process of the first training module is as follows: The training personnel performs the first training task and obtains the real-time position coordinates AB (Xa, Ya) of the task destination corresponding to the first training task; The trainee hangs a weight with a training weight PZn to start the first training task, and records the time when the first training task is started as zero time and records the current time in real time; According to the assessment equipment, the real-time position coordinates RBn (Xnr, Ynr) of the trainee are identified, where n is the trainee's number, n=1, 2, ..., z, and z is a positive integer; the real-time position coordinates of the trainee at time zero are marked as LBn (Xnl, Ynl).

[0008] Furthermore, the training process of the first training module further includes: The initial distance CJLn between the trainee and the mission destination at time zero is calculated by the formula as follows: ; Similarly, the real-time distance SJLn between the training personnel and the mission destination at the current moment is calculated using the following formula: ; The real-time training time of the trainee is obtained by subtracting the zero time from the current time, and the real-time travel distance of the trainee is obtained by subtracting the real-time distance from the initial distance; the real-time travel speed XJSn of the trainee is obtained by subtracting the real-time training time from the real-time travel distance.

[0009] Furthermore, the training process of the second training module is as follows: The training personnel performs the second training task, and the bull's eye coordinates of the second training task are marked as BXB (X0, Y0); Measure the coordinates of the muzzle position YQBn (Xq, Yq) of the trainee in the ready-to-shoot state before each shooting; q is the number of the ready-to-shoot state, and the ready-to-shoot state means that the trainee has completed the aiming operation; Measure the muzzle position coordinates SQBn (Xp, Yp) of the trainee at each shooting; where p represents the number of each shooting, the upper limit of p is u, and u is a positive integer; Connect the training personnel's preparatory muzzle position coordinates YQBn and the bull's eye coordinate mark BXB when they are in the preparatory shooting state, and record them as the standard connecting line; connect the training personnel's shooting muzzle position coordinates SQBn and the bull's eye coordinate mark BXB at each shooting, and record them as the shooting connecting line.

[0010] Furthermore, the training process of the second training module further includes: The offset angle α between the shooting connection line and the standard connection line is calculated by the formula each time, and the formula is as follows: ; In the formula, arccos is the inverse cosine function; The offset angle of each shot is compared with the offset angle threshold. If the offset angle is less than or equal to the offset angle threshold, the shot is recorded as a stable shot. If the offset angle is greater than the offset angle threshold, the shooting will be recorded as offset shooting; the total number of offset shootings, the total number of stable shootings and the corresponding shooting scores of the stable shooting of the trainees will be counted.

[0011] Furthermore, the training process of the second training module further includes: The trainers perform the third training task, which is performed by multiple trainers at the same time; each trainer has a separate personal task and a unified team collaboration task; the specific task content is directly issued by the command center to the trainers' assessment equipment; If the trainees meet the completion conditions of the individual task or team collaboration task within the specified time, the corresponding task is deemed completed; otherwise, the task is deemed failed.

[0012] Furthermore, the evaluation process of the training evaluation module includes: Obtain the real-time travel speed XJSn and training weight PZn of the trainee in the first training task; The total score AFn of the first training of the trainee corresponding to the first training task is calculated by the formula as follows: AFn=A1×XJSn 2 +A2×PZn; where A1 and A2 are weight coefficients; Obtain the total number of offset shots PYn, the total number of stable shots WDn and the shooting score SFp of each shooting of the trainee in the second training task; The second training total score BFn of the trainee in the second training task is calculated by the formula, and the formula is as follows: ; Among them, the total number of offset shots and the total number of stable shots are added to obtain the total number of shots of the trainee.

[0013] Furthermore, the evaluation process of the training evaluation module also includes: Obtain the completion status of the trainees' individual tasks and teamwork tasks in the third training task; If the trainee fails both the individual task and the teamwork task in the third training task, the trainee's third training total score CFn in the third training task = 0; If the trainee only completes the personal task in the third training task, the total score of the third training of the trainee in the third training task is CFn=a1; If the trainee only completes the teamwork task in the third training task, the trainee's third training total score CFn=a2 in the third training task; If the trainee successfully completes both the individual task and the teamwork task in the third training task, the trainee's third training total score CFn=a1+a2 in the third training task; where a1 and a2 are constants, 0<a1<a2; The comprehensive training score value ZFSn of the trainer is calculated by the formula, the formula is as follows: ZFSn=b1×AFn+b2×BFn+b3×CFn; where b1, b2 and b3 are weight coefficients; The comprehensive training score value of the trainee is compared with the training score threshold to determine whether the comprehensive training level of the trainee is level one training, level two training or level three training.

[0014] Furthermore, the training results of the trainees corresponding to the first-level training are lower than the training results of the trainees corresponding to the second-level training; The training results of trainees corresponding to level 2 training are lower than those of trainees corresponding to level 3 training.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. The present invention divides the assessment and training tasks of the trainees, and constructs a corresponding digital twin site model based on the real-time site data and real-time environmental data of the field training site, so that the trainees can perform simulation training in the digital twin site model; 2. The present invention combines the digital twin site model to train the trainees in the first training task, obtains the real-time travel speed and training weight of the trainees, and at the same time combines the digital twin site model to train the trainees to perform the second training task and the third training task, obtains the total number of offset shots, the total number of stable shots and the corresponding shooting score of stable shots when the trainees perform the second training task, and obtains the completion status of the trainees' individual tasks and teamwork tasks in the third training task, thereby realizing data analysis and data extraction of the trainees in different training projects; 3. The present invention evaluates the training results of the trainees based on the training situations of the trainees in performing different assessment training tasks, obtains the comprehensive training level of the trainees, and realizes the comprehensive and overall evaluation of the trainees' corresponding simulation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 It is the overall system block diagram of the present invention.

[0018] Figure 2 It is a schematic diagram of the offset angle in the present invention.

[0019] Figure 3 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0020] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1, please refer to Figure 1 and Figure 2 As shown, the technical solution provided by the present invention is: a teaching and evaluation simulation training system based on digital twins, the Internet of Things and big data, including an environment acquisition module, a site simulation module, a task classification module, a first training module, a second training module, a training evaluation module, a display terminal and a database; In this embodiment, the environment acquisition module is used to collect real-time site data and real-time environment data of the field training site. The real-time site data includes the height, slope and slope direction of the ground in the field training site and the coverage area of ​​soil vegetation; the real-time environment data includes the real-time temperature, real-time humidity, real-time wind speed and direction of the field training site; the real-time site data is obtained through satellite remote sensing or drone aerial photography, and the real-time environment data is obtained through a meteorological station or a sensor network; the environment acquisition module sends the real-time site data and real-time environment data of the field training site to the site simulation module.

[0022] Furthermore, the site simulation module is used to construct a corresponding digital twin site model based on the real-time site data and real-time environmental data of the field training site. Constructing a digital twin site model is an existing mature technology. The specific construction process includes: Acquire real-time site data and real-time environment data, and remove outlier data of the real-time site data and the real-time environment data; specifically, outliers can be identified by Z-score or IQR; Use the Cloth Simulation Filter (CSF) algorithm to separate ground and non-ground points, then use the SfM (Structure from Motion) algorithm to generate a 3D texture model, and implement ground object classification through the Internet of Things; Assign corresponding materials and dynamic models according to the categories of 3D texture models and objects; align real-time environmental data through dynamic time warping (DTW) and map the real-time environmental data to the environmental parameters in the 3D texture model; select a visualization engine (Unity3D or Unreal Engine 5) and implement interactive functions to complete the construction of the digital twin site model; The site simulation module sends the digital twin site model of the field training site to the first training module and the second training module.

[0023] In this embodiment, the task classification module is used to divide the assessment training tasks of the trainees, and divide the assessment training tasks into the first training task, the second training task and the third training task according to the assessment criteria of the assessment training tasks corresponding to the trainees; the task classification module sends the first training task to the first training module, and sends the second training task and the third training task to the second training module; the assessment training task is the assessment content pre-set in the database; Among them, the assessment criteria for the first training task is related to physical fitness, such as long-distance running training or weighted marching training; the assessment criteria for the second training task is related to accuracy, such as shooting training or throwing training; the assessment criteria for the third training task is related to teamwork ability, such as collective tactical drills or team coordinated operations.

[0024] During the assessment, the trainees wear the assessment equipment and enter the training ground. The assessment equipment collects the trainees' posture information in real time and projects it into the digital twin field model. The assessment equipment consists of a wearable pressure sensor, an optical motion capture device and a head-mounted VR device. In specific implementation, the first training module is used to train the trainee on the first training task, and the training process is as follows: The training personnel performs the first training task and obtains the real-time position coordinates AB (Xa, Ya) of the task destination corresponding to the first training task; The trainee hangs a weight with a training weight PZn to start the first training task, and records the time when the first training task is started as zero time and records the current time in real time; According to the assessment equipment, the real-time position coordinates of the trainee are identified as RBn (Xnr, Ynr), where n is the trainee's number, n=1, 2, ..., z, and z is a positive integer; the real-time position coordinates of the trainee at time zero are marked as LBn (Xnl, Ynl); The initial distance CJLn between the trainee and the mission destination at time zero is calculated by the formula as follows: ; Similarly, the real-time distance SJLn between the training personnel and the mission destination at the current moment is calculated using the following formula: ; The real-time training time of the trainee is obtained by subtracting the zero time from the current time, and the real-time distance is obtained by subtracting the real-time distance from the initial distance; the real-time speed XJSn of the trainee is obtained by subtracting the real-time training time from the real-time distance; The first training module sends the real-time traveling speed and training weight of the trainee to the training evaluation module.

[0025] Furthermore, the second training module is used to perform the second training task and the third training task on the training personnel, and the training process is specifically as follows: The training personnel perform the second training task. Taking the shooting task as an example, the bull's eye coordinates of the second training task are marked as BXB (X0, Y0). Measure the coordinates of the muzzle position YQBn (Xq, Yq) of the trainee in the ready-to-shoot state before each shooting; q is the number of the ready-to-shoot state, and the ready-to-shoot state means that the trainee has completed the aiming operation; Measure the muzzle position coordinates SQBn (Xp, Yp) of the trainee at each shooting; where p represents the number of each shooting, the upper limit of p is u, and u is a positive integer; like Figure 2As shown, the preparation muzzle position coordinate YQBn of the trainee in the preparation shooting state is connected with the bull's eye coordinate mark BXB, which is recorded as the standard connection line; the shooting muzzle position coordinate SQBn of the trainee in each shooting is connected with the bull's eye coordinate mark BXB, which is recorded as the shooting connection line; The offset angle α between the shooting connection line and the standard connection line is calculated by the formula each time, and the formula is as follows: ; In the formula, arccos is the inverse cosine function; The offset angle of each shot is compared with the offset angle threshold. If the offset angle is less than or equal to the offset angle threshold, the shot is recorded as a stable shot. If the offset angle is greater than the offset angle threshold, the shooting is recorded as offset shooting; the total number of offset shootings, the total number of stable shootings and the shooting scores corresponding to stable shootings of the trainees are counted; The trainers perform the third training task, which is performed by multiple trainers at the same time; each trainer has a separate personal task and a unified team collaboration task; the specific task content is directly issued by the command center to the trainers' assessment equipment; If the trainees achieve the completion conditions of the individual task or teamwork task within the specified time, the corresponding task is deemed completed; otherwise, the task is deemed failed; The second training module sends the total number of offset shots, the total number of stable shots and the corresponding shooting scores of the stable shots when the trainees perform the second training task to the training evaluation module. At the same time, the second training module also sends the completion status of the trainees' individual tasks and team collaboration tasks in the third training task to the training evaluation module.

[0026] In this embodiment, the training evaluation module is used to evaluate the training results of the trainees. The evaluation process is as follows: Obtain the real-time travel speed XJSn and training weight PZn of the trainee in the first training task; The total score AFn of the first training of the trainee corresponding to the first training task is calculated by the formula as follows: AFn=A1×XJSn 2 +A2×PZn; where A1 and A2 are weight coefficients; Obtain the total number of offset shots PYn, the total number of stable shots WDn and the shooting score SFp of each shooting of the trainee in the second training task; The second training total score BFn of the trainee in the second training task is calculated by the formula, and the formula is as follows: ; The total number of offset shots and the total number of stable shots are added together to obtain the total number of shots of the trainee; Obtain the completion status of the trainees' individual tasks and teamwork tasks in the third training task; If the trainee fails both the individual task and the teamwork task in the third training task, the trainee's third training total score CFn in the third training task = 0; If the trainee only completes the personal task in the third training task, the total score of the third training of the trainee in the third training task is CFn=a1; If the trainee only completes the teamwork task in the third training task, the trainee's third training total score CFn=a2 in the third training task; If the trainee successfully completes both the individual task and the teamwork task in the third training task, the trainee's third training total score CFn=a1+a2 in the third training task; where a1 and a2 are constants, 0<a1<a2; The comprehensive training score value ZFSn of the trainer is calculated by the formula, the formula is as follows: ZFSn=b1×AFn+b2×BFn+b3×CFn; where b1, b2 and b3 are weight coefficients; Comparing the comprehensive training score value of the trainee with the training score threshold; If the comprehensive training score value of the trainee is less than or equal to the first training score threshold, the comprehensive training level of the trainee is recorded as first-level training; if the comprehensive training score value of the trainee is greater than the first training score threshold, and the comprehensive training score value of the trainee is less than or equal to the second training score threshold, the comprehensive training level of the trainee is recorded as second-level training; if the comprehensive training score value of the trainee is greater than the second training score threshold, the comprehensive training level of the trainee is recorded as third-level training; Among them, the first training score threshold is less than the second training score threshold; the training results of the trainees corresponding to the first-level training are lower than the training results of the trainees corresponding to the second-level training; the training results of the trainees corresponding to the second-level training are lower than the training results of the trainees corresponding to the third-level training; The training evaluation module sends the comprehensive training level of the trainee to the display terminal and the database; the display terminal is used to receive and display the comprehensive training level of the trainee; the database is used to receive and store the comprehensive training level of the trainee.

[0027] As a further solution of this embodiment, the system further includes a safety monitoring module, which is used to monitor the physical condition of the trainees when performing different assessment training tasks. The monitoring process is as follows: When the trainee is performing the assessment training task, the assessment equipment will monitor the trainee's corresponding heartbeat status in real time and mark the trainee's heartbeat time interval sequence as XTi={XT1, XT2, ..., XTx}, where x is the upper limit value of i; The real-time heart rate index XLZn of the trainee is calculated by the formula, and the specific formula is: ; Among them, the larger the real-time heart rate index is, the better the trainee's autonomic nervous system's ability to control the heart is at this time, and the smaller the real-time heart rate index is, the worse the trainee's autonomic nervous system's ability to control the heart is at this time; If the real-time heart rate index of the trainee is less than the real-time heart rate index threshold for a continuous period reaching the dangerous period, the warning operation is triggered; the dangerous period is different for trainees with different physical conditions, and is specifically calculated based on the heart rate data of the trainee during the most recent physical examination; The specific early warning operations are: generating an alarm signal, notifying the trainees to stop training, delivering emergency supplies to the trainees' location coordinates via drones, and dispatching emergency personnel to conduct on-site inspections of the trainees' physical conditions.

[0028] In this application, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficient and the proportional coefficient can be determined as long as it does not affect the proportional relationship between the parameter and the result value.

[0029] Embodiment 2, as Figure 3 As shown, based on another concept of the same invention, a teaching assessment simulation training method based on digital twins, the Internet of Things and big data is proposed, comprising the following steps: Step S101, collecting real-time site data and real-time environmental data of a field training site, and constructing a corresponding digital twin site model according to the real-time site data and real-time environmental data of the field training site; Step S102, dividing the assessment training tasks of the trainees, training the trainees on the first training task, and obtaining the real-time travel speed and training weight of the trainees; Step S103, the trainer performs the second training task and the third training task, obtains the total number of offset shots, the total number of stable shots, and the shooting score corresponding to the stable shots in the second training task, and obtains the completion status of the trainer's individual tasks and teamwork tasks in the third training task; Step S104, evaluating the training results of the trainees in combination with the training situations of the trainees in performing different assessment training tasks, and obtaining the comprehensive training level of the trainees.

[0030] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A teaching and assessment simulation training system based on digital twins, the Internet of Things and big data, characterized by: It includes an environment acquisition module, a site simulation module, a task classification module, a first training module, a second training module and a training evaluation module. The environment acquisition module is used to collect real-time site data and real-time environmental data of the field training site and send them to the site simulation module; the site simulation module is used to build a corresponding digital twin site model based on the real-time site data and real-time environmental data of the field training site and send it to the first training module and the second training module; The task classification module is used to divide the assessment training tasks of the training personnel into a first training task, a second training task and a third training task, send the first training task to the first training module, and send the second training task and the third training task to the second training module; The first training module is used to train the trainees on the first training task, and obtain the real-time travel speed and training weight of the trainees and send them to the training evaluation module; the second training module is used to perform the second training task and the third training task on the trainees, and obtain the total number of offset shots, the total number of stable shots and the corresponding shooting scores of stable shots when the trainees perform the second training task and send them to the training evaluation module, and also obtain the completion status of the trainees' individual tasks and team collaboration tasks in the third training task and send them to the training evaluation module; the training evaluation module is used to evaluate the training results of the trainees, and obtain the comprehensive training level of the trainees through evaluation.

2. The teaching evaluation simulation training system based on digital twin, Internet of Things and big data according to claim 1 is characterized in that: Real-time site data includes the ground elevation, slope, aspect and soil and vegetation coverage of the field training site; Real-time environmental data includes real-time temperature, real-time humidity, real-time wind speed and direction of the field training site.

3. The teaching assessment simulation training system based on digital twin, Internet of Things and big data according to claim 1 is characterized in that: The training process of the first training module is as follows: The training personnel performs the first training task and obtains the real-time position coordinates AB (Xa, Ya) of the task destination corresponding to the first training task; The trainee hangs a weight with a training weight PZn to start the first training task, and records the time when the first training task is started as zero time and records the current time in real time; According to the assessment equipment, the real-time position coordinates RBn (Xnr, Ynr) of the trainee are identified, where n is the trainee's number, n=1, 2, ..., z, and z is a positive integer; the real-time position coordinates of the trainee at time zero are marked as LBn (Xnl, Ynl).

4. The teaching assessment simulation training system based on digital twin, Internet of Things and big data according to claim 3 is characterized in that: The training process of the first training module also includes: The initial distance CJLn between the trainee and the mission destination at time zero is calculated by the formula as follows: ， Similarly, the real-time distance SJLn between the training personnel and the mission destination at the current moment is calculated using the following formula: ； The real-time training time of the trainee is obtained by subtracting the zero time from the current time, and the real-time travel distance of the trainee is obtained by subtracting the real-time distance from the initial distance; the real-time travel speed XJSn of the trainee is obtained by subtracting the real-time training time from the real-time travel distance.

5. The teaching evaluation simulation training system based on digital twin, Internet of Things and big data according to claim 4 is characterized in that: The training process of the second training module is as follows: The training personnel performs the second training task, and the bull's eye coordinates of the second training task are marked as BXB (X0, Y0); Measure the coordinates of the muzzle position of the trainee in the ready-to-shoot state YQBn (Xq, Yq) before each shooting; q is the number of the ready-to-shoot state; Measure the muzzle position coordinates SQBn (Xp, Yp) of the trainee at each shooting; where p represents the number of each shooting, the upper limit of p is u, and u is a positive integer; Connect the training personnel's ready muzzle position coordinate YQBn and the bull's eye coordinate mark BXB when they are in the ready shooting state, and record it as the standard connection line; Connect the muzzle position coordinates SQBn and the bull's eye coordinate mark BXB of the trainee at each shooting, and record it as the shooting connection line.

6. The teaching assessment simulation training system based on digital twin, Internet of Things and big data according to claim 5 is characterized in that: The training process of the second training module also includes: The offset angle α between the shooting connection line and the standard connection line is calculated by the formula each time, and the formula is as follows: ; In the formula, arccos is the inverse cosine function; The offset angle of each shot is compared with the offset angle threshold; If the offset angle is less than or equal to the offset angle threshold, the shooting is recorded as stable shooting; If the offset angle is greater than the offset angle threshold, the shooting is recorded as offset shooting; The total number of offset shots, the total number of stable shots, and the shooting scores corresponding to the stable shots of the trainees are counted.

7. The teaching assessment simulation training system based on digital twin, Internet of Things and big data according to claim 6 is characterized in that: The training process of the second training module also includes: The trainers perform the third training task, which is performed by multiple trainers at the same time; each trainer has a separate personal task and a unified team collaboration task; If the trainees meet the completion conditions of the individual task or team collaboration task within the specified time, the corresponding task is deemed completed, otherwise the task is deemed failed.

8. The teaching assessment simulation training system based on digital twin, Internet of Things and big data according to claim 1 is characterized in that: The evaluation process of the training evaluation module includes: Obtain the real-time travel speed XJSn and training weight PZn of the trainee in the first training task; The total score AFn of the first training of the trainee corresponding to the first training task is calculated by the formula as follows: AFn=A1×XJSn 2 +A2×PZn; where A1 and A2 are weight coefficients; Then, the total number of offset shots PYn, the total number of stable shots WDn and the shooting score SFp of each shooting of the trainee in the second training task are obtained; The second training total score BFn of the trainee in the second training task is calculated by the formula, and the formula is as follows: ; Among them, the total number of offset shots and the total number of stable shots are added to obtain the total number of shots of the trainee.

9. The teaching assessment simulation training system based on digital twin, Internet of Things and big data according to claim 8 is characterized in that: The evaluation process of the training evaluation module also includes: Obtain the completion status of the trainees' individual tasks and teamwork tasks in the third training task; If the trainee fails both the individual task and the teamwork task in the third training task, the trainee's third training total score CFn in the third training task = 0; If the trainee only completes the personal task in the third training task, the total score of the third training of the trainee in the third training task is CFn=a1; If the trainee only completes the teamwork task in the third training task, the trainee's third training total score CFn=a2 in the third training task; If the trainee successfully completes both the individual task and the teamwork task in the third training task, the trainee's third training total score CFn=a1+a2 in the third training task; where a1 and a2 are constants, 0<a1<a2; The comprehensive training score value ZFSn of the trainer is calculated by the formula, the formula is as follows: ZFSn=b1×AFn+b2×BFn+b3×CFn; where b1, b2 and b3 are weight coefficients; The comprehensive training score value of the trainee is compared with the training score threshold to determine whether the comprehensive training level of the trainee is level one training, level two training or level three training.

10. The teaching assessment simulation training system based on digital twin, Internet of Things and big data according to claim 9 is characterized in that: The training results of trainees corresponding to the first-level training are lower than those of trainees corresponding to the second-level training; The training results of trainees corresponding to level 2 training are lower than those of trainees corresponding to level 3 training.

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