Simulation assessment system and assessment method integrating theoretical knowledge and course training

Abstract

The invention relates to the technical field of education assessment simulation, in particular to a theoretical knowledge and course training integrated simulation assessment system and method, and the method comprises the steps: generating a standard feature sequence according to the operation steps of a physical model in a simulation assessment task; analyzing a marking result theoretically answered by the user, comparing the difference between the user operation sequence and the standard feature sequence, and obtaining a first score of the user in combination with the distribution and dispersion degree of elements in the user operation time sequence; analyzing the correlation between the user operation sequence and the user operation time sequence, and combining the randomness of element distribution in the user operation time sequence and the first score to obtain a second score of the user; and obtaining a simulation assessment result of the user based on the numerical value of the second score of the user. The objective of the invention is to improve the limitation of assessment dimension simplification and improve the accuracy of an assessment result.

Description

Technical Field

[0001] This application relates to the technical field of educational assessment simulation, and specifically relates to a simulation assessment system and assessment method that integrate theoretical knowledge and course training. Background Art

[0002] With the deepening of the digital transformation of education and the reform of core literacy evaluation, traditional educational evaluation has long faced the dilemma of "emphasizing knowledge memorization and neglecting practical application". Standardized tests are difficult to comprehensively evaluate students' higher-order thinking and problem-solving abilities. Since the 21st century, the maturity of technologies such as virtual reality, big data analysis, and artificial intelligence has provided technical support for building immersive and intelligent evaluation systems.

[0003] Currently, the simulation assessment system generally adopts a result-oriented assessment mode, only recording the final score or the correctness of the operation result, and completely ignoring the key behavior data of the user during the operation process. This assessment method has the limitation of single-dimensional assessment, resulting in the single assessment result of complex assessment scenarios deviating from the true skill level. In addition, the traditional system lacks the ability to accurately trace the source of incorrect steps, and can only feedback "failed the assessment", but cannot point out the specific defective steps and error types, resulting in the problem of fuzzy defect positioning, making the user's improvement direction unclear and wasting training resources on repetitive trial and error. Summary of the Invention

[0004] In view of the above, it is necessary to provide a simulation assessment system and assessment method that integrate theoretical knowledge and course training to solve the above problems.

[0005] The first aspect of this application provides a simulation assessment method that integrates theoretical knowledge and course training, and the method includes: Mark the correctness of the theoretical answer results of the user in each step of the simulation assessment; Analyze the marked results of the user's theoretical answers, compare the differences between the standard operation steps of the physical model in the simulation assessment task and the operation sequence of the user on the physical model, and combine the time distribution of the user to complete each step and the dispersion degree of the completion time to obtain the first score of the user; Analyze the correlation between the operation sequence of the user in the simulation assessment and the time of the user to complete each step, and combine the randomness of the time distribution of the user to complete each step and the first score to obtain the second score of the user; Based on the numerical value of the second score of the user, obtain the simulation assessment result of the user.

[0006] Among them, the standard operation steps of the physical model are determined by the pressing sequence of the sensors in the physical model.

[0007] Among them, marking the correctness of the theoretical answer results of the user in each step of the simulation assessment is specifically as follows: When the theoretical answer result of the user in each step is correct, the corresponding marking result is the first preset value; otherwise, the marking result is the second preset value; where the first preset value is not equal to the second preset value.

[0008] Among them, obtaining the first score of the user is specifically as follows: Generate a standard feature sequence according to the standard operation steps of the physical model in the simulation assessment task; Obtain the user operation sequence according to the operation order of the user in the simulation assessment; Obtain the user operation time sequence according to the time taken by the user to complete each step in the simulation assessment; Fuse the proportion of the number of marked values with incorrect theoretical answer results of the user in a positive direction with the difference between the user operation sequence and the standard feature sequence to obtain a first result; Calculate the sum of the element mean and the element dispersion of the user operation time sequence; Denote the ratio of the element mean to the sum value as the second result; Fuse the negative correlation mapping of the first result with the second result in a positive direction to obtain the first score of the user.

[0009] Among them, the difference between the user operation sequence and the standard feature sequence is determined by the edit distance between the two sequences.

[0010] Among them, the element dispersion is determined by the standard deviation of all elements in the user operation time sequence.

[0011] Among them, the steps for obtaining the second score of the user are as follows: Calculate the correlation between the user operation sequence and the user operation time sequence, and fuse it in a positive direction with the sample entropy of the user operation time sequence to obtain a third result; Fuse the negative correlation mapping of the third result with the first score in a positive direction to obtain the second score of the user operation; where the second score is negatively correlated with the positive fusion result and positively correlated with the first score.

[0012] Among them, the correlation between the user operation sequence and the user operation time sequence is determined by the mutual information analysis algorithm.

[0013] Among them, the process of obtaining the simulation assessment result of the user based on the numerical value of the second score of the user includes: Set a threshold range, and obtain the simulation assessment result of the user through the threshold range where the second score of the user is located; The simulation assessment results include: excellent, good, qualified, unqualified.

[0014] In a second aspect, embodiments of the present application further provide a simulation assessment system that integrates theoretical knowledge and course training to implement any one of the simulation assessment methods that integrate theoretical knowledge and course training. The system includes: A model configuration and sensor integration module, configured to obtain the standard operation steps for the physical model in the simulation assessment task; An operation behavior dynamic acquisition module, configured to mark the correctness of the theoretical answer results of the user in each step of the simulation assessment; obtain the operation sequence of the user in the simulation assessment; and obtain the time taken by the user to complete each step; A step and time quantization analysis module, configured to analyze the marked results of the user's theoretical answers, compare the differences between the standard operation steps for the physical model in the simulation assessment task and the operation sequence of the user for the physical model, and combine the time distribution of the user to complete each step and the degree of dispersion of the completion time to obtain the first score of the user; A dynamic coupling risk assessment module, configured to analyze the correlation between the operation sequence of the user in the simulation assessment and the time taken by the user to complete each step, and combine the randomness of the time distribution of the user to complete each step and the first score to obtain the second score of the user; A multi-level determination and feedback optimization module, configured to obtain the simulation assessment result of the user based on the numerical value of the second score of the user.

[0015] The present application has at least the following beneficial effects: 1. Aiming at the fact that traditional evaluations ignore the hidden impact of step sequence errors and time fluctuations on safety, based on the analysis of the differences between the user operation sequence and the standard feature sequence and the feature analysis of the user operation time series, it reflects the standardization of the step sequence and the stability of time management, solves the safety hazard problems caused by incorrect step sequences, and eliminates the speculative behavior of users sacrificing stability for speed, avoiding the one-sidedness of single-dimensional scoring.

[0016] 2. Aiming at the problem that existing technologies ignore the dynamic coupling risk of step sequence and time fluctuations, through the correlation between the user operation sequence and the user operation time series and the randomness of the user operation time element distribution, it reflects the correlation strength between step errors and time chaos, and eliminates the influence of "compliance cheating" relying only on a single dimension of steps or time.

[0017] 3. Set qualified thresholds, good thresholds, and excellent thresholds according to the task type, accurately evaluate the user's operation score, and solve the problem of one-sidedness in traditional evaluations; and generate an evaluation report that includes the positioning of weak links and improvement suggestions, forming a closed-loop of "evaluation - feedback - improvement", quantifying the user's skill shortfalls, reducing repetitive trial and error through precise defect positioning and closed-loop feedback, shortening the skill attainment cycle, constructing a quantifiable, traceable, and optimizable skill evaluation system, significantly improving the training effect and actual operation safety, and providing a more sound solution for standardized training in high-risk industries. Description of the Drawings

[0018] Figure 1 The flowchart of the steps of a simulation assessment method that integrates theoretical knowledge and course training provided by an embodiment of the present application; Figure 2 The block diagram of a simulation assessment system that integrates theoretical knowledge and course training provided by an embodiment of the present application. Detailed Implementation Modes

[0019] In the description of the embodiments of the present application, words such as "exemplary", "or", "for example", etc. are used to indicate examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary", "or", "for example", etc. is intended to present related concepts in a specific manner.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in the description of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0021] In addition, it should be noted that the terms "first" and "second" in this application and the drawings are used to distinguish similar objects and are not used to describe a specific order or sequence. For the methods disclosed in the embodiments of this application or shown in the flowcharts of the methods, which include one or more steps for implementing the method, without departing from the protection scope of this application, the execution order of multiple steps can be interchanged with each other, and some steps can also be deleted.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0023] The following specifically describes the specific solutions of a simulation assessment system and an assessment method that integrate theoretical knowledge and course training provided by this application with reference to the drawings.

[0024] Please refer toFigure 1 , which shows a step flow chart of a simulation assessment method that combines theoretical knowledge and course training provided by an embodiment of the present application. This method is implemented through a simulation assessment system that combines theoretical knowledge and course training as shown in Figure 2 . The system includes: a model configuration and sensor integration module, an operation behavior dynamic acquisition module, a step and time quantization analysis module, a dynamic coupling risk assessment module, and a multi-level determination and feedback optimization module.

[0025] The model configuration and sensor integration module is used to obtain the standard operation steps for the physical model in the simulation assessment task.

[0026] Adopting a modular design, it includes general physical components: detachable connectors, sensor interfaces, simulation tools, etc.; it supports replacing functional modules according to training needs: robotic arms, electronic components, fluid devices, etc. The model integrates multiple types of sensors. In this embodiment, it includes motion sensors and pressure sensors to collect data such as the user's operation trajectory, force, and accuracy in real time. Number all pressure sensors; according to the specific assessment task of the model, formulate the pressing sequence of the standard pressure sensors, and convert the determined pressing sequence into a standard feature sequence for uniquely identifying the correct pressing sequence.

[0027] It should be noted that when constructing the physical model, relevant theoretical knowledge such as mechanical design principles, electronic circuit theory, and fluid mechanics principles should be relied on to ensure the rationality and feasibility of the model. For example, when designing the robotic arm module, mechanical structure design and kinematics theory should be applied to ensure the flexible and accurate movement of the robotic arm; when designing the electronic component module, the working principles of electronic circuits and signal transmission laws should be followed to ensure the normal operation of electronic components and the accurate acquisition of data.

[0028] The operation behavior dynamic acquisition module is used to mark the correctness of the theoretical answer results of the user in each step of the simulation assessment; obtain the operation sequence of the user in the simulation assessment; obtain the time taken by the user to complete each step.

[0029] In the constructed physical model, pressure sensors are deployed at each key operation node (the part where the assessor must perform an operation). When the user reaches this node (for example, presses the fixing device in place), the sensor triggers a signal, and the system automatically records the completion of the step. Number the determined key operation nodes for subsequent identification and processing.

[0030] Before each operation step, the user needs to answer theoretical questions related to the current step, such as mechanical principles, circuit theory, etc. The system records the correctness of the answers. If the user answers correctly, it is marked as the first preset value, and if the answer is incorrect, it is marked as the second preset value. The marking results of the user's theoretical answers in all steps are combined to form a user answer sequence. In this embodiment, the first preset value is 1 and the second preset value is 0.

[0031] The system automatically records the pressing steps of the pressure sensor by the user during the simulation assessment process to generate a user operation sequence, and records the time when the user performs each step and sorts them in the order of the operation steps to obtain a user operation time sequence. It should be understood that the elements with the same serial number in the user operation sequence and the user operation time sequence are in one-to-one correspondence, so the number of elements in the user operation sequence and the user operation time sequence is the same.

[0032] The step and time quantization analysis module is used to analyze the marking results of the user's theoretical answers, compare the differences between the standard operation steps of the physical model in the simulation assessment task and the user's operation order of the physical model, and combine the time distribution of the user to complete each step and the dispersion degree of the completion time to obtain the first score of the user.

[0033] Since the existing simulation assessment system only relies on the final score for evaluation and ignores the key details of the user's operation process, the evaluation result is one-sided and cannot truly reflect the user's comprehensive skill level. If only the result correctness is concerned, the user may complete the operation in the wrong order. Although the final score is qualified, in actual operation, equipment damage or safety hazards may be caused by reversed steps. Secondly, there is a lack of quantitative analysis of time management. The user may have frequent time-consuming fluctuations due to the pursuit of speed, and such unstable operations cannot be recognized by the traditional system, resulting in the training effect remaining on the surface and it is difficult to improve the standardization and reliability of actual operations.

[0034] To solve the above problems, the ratio of the number of marked values with correct theoretical answer results of the user is positively fused with the difference between the user operation sequence and the standard feature sequence to obtain the first result; calculate the sum of the element mean and the element dispersion degree of the user operation time sequence; denote the ratio of the element mean to the sum value as the second result; and positively fuse the negative correlation mapping of the first result with the second result to obtain the first score of the user.

[0035] In this embodiment, the ratio of the number of marked values with correct theoretical answer results of the user is specifically the ratio of the number of steps marked as 0 in the user answer sequence to the total number of steps, denoted as H; the difference between the user operation sequence and the standard feature sequence is calculated using the edit distance, denoted as D; the element dispersion degree of the user operation time sequence is calculated using the standard deviation, denoted as , denote the element mean of the user operation time series as . Denote the first score of the user as A, and the specific calculation relation is: , where e is the natural constant.

[0036] It should be noted that the edit distance between the user operation sequence and the standard feature sequence represents the minimum number of edit operations between the user operation sequence and the standard feature sequence, and is used to quantify the degree of deviation of the operation step order. The larger its value, the more step omissions, redundancies or order errors the user has, and the more likely it is to cause potential safety hazards or operation failures; the mean of the user operation time series reflects the average time consumption of the operation, and the sum of the mean and the standard deviation reflects the comprehensive performance of the user between speed and stability by combining the "absolute time consumption" and "fluctuation risk" of the time cost, representing the comprehensive time cost of the operation. When the value is closer to 1, it indicates that the time management of the user operation is highly stable. When the value is closer to 0, it indicates that the operation time of the user fluctuates violently and the stability is poor.

[0037] The first score synthesizes the step normativeness and the time management quality, quantifies the comprehensive performance of the user operation. When the value of A is larger, it indicates that the user performs better.

[0038] The dynamic coupling risk assessment module is used to analyze the correlation between the operation order of the user in the simulation assessment and the time for the user to complete each step, and combine the randomness of the time distribution for the user to complete each step and the first score to obtain the second score of the user.

[0039] In this application, the first score of the user only has a single-dimensional weighted characteristic and a static evaluation logic, and cannot solve the problem of the lack of dynamic correlation between the user operation sequence and the user operation time series, resulting in the evaluation result being difficult to truly reflect the complexity and multi-faceted nature of the user's skill level. There is a dynamic association between the step order and the time management: for example, the user spending too much time on a key step may cover up the correctness of its order, and a step error may lead to the compression of the subsequent operation time, exacerbating the overall risk. Therefore, analyzing the user operation sequence or the user operation time series alone cannot comprehensively capture the complexity of the user's behavior, and it is necessary to conduct a joint analysis of the two.

[0040] Based on the above analysis, calculate the second score of the user operation, specifically: calculate the correlation between the user operation sequence and the user operation time series, and perform positive fusion with the sample entropy of the user operation time series to obtain a third result; perform positive fusion of the negative correlation mapping of the third result and the first score to obtain the second score of the user operation; where the second score is negatively correlated with the positive fusion result and positively correlated with the first score.

[0041] In this embodiment, the specific process of obtaining the correlation between two sequences is as follows: taking the user operation sequence and the user operation time sequence as inputs, using the mutual information analysis algorithm to calculate the mutual information score between the two sequences, denoted as S. The mutual information quantifies the dependence relationship between the operation steps and the time fluctuations. The higher the mutual information value, the stronger the correlation between the step errors and the operation time chaos. Subsequently, taking the user operation time sequence as the input, calculate the sample entropy E of the user operation time sequence.

[0042] In this embodiment, the positive fusion of multiple variables adopts a multiplication calculation method. Denote the second score of the user operation as B, and its formula form is: ; where e represents the natural constant.

[0043] The larger the value of the mutual information score between the user operation sequence and the user operation time sequence, the stronger the correlation between the step errors and the operation time fluctuations. For example, step errors may lead to subsequent time compression, or time chaos may cause step omissions. There is a negative impact of dynamic coupling between the steps and time management in the user operation, that is, incorrect operations and out-of-control time reinforce each other, and the user's operation standardization is damaged due to the strong correlation chaos between the steps and time. The larger the sample entropy of the user operation time sequence, the more irregular the user operation time sequence, and there is uncontrollable randomness in the user's time management, which may lead to chaotic time allocation due to step errors or unskilled operations. The comprehensive step sequence correctness and time stability indicate that the user's step sequence is correct and the time management is stable. The larger its value, the more the user's operation conforms to the specification and the time allocation is reasonable, and it has the basic skill compliance.

[0044] The dynamic coupling evaluation index comprehensively reflects the step standardization and the correlation risk between the steps and the time spent. The larger its value, the more correct the user operation steps are, and there is no strong correlation between the steps and the time, indicating that the user's operation is standardized, stable, low-risk, and has stronger safety, avoiding "cheating" in a single dimension and truly reflecting the comprehensive ability in complex operation scenarios.

[0045] The multi-level determination and feedback optimization module is used to obtain the user's simulated assessment result based on the numerical value of the user's second score.

[0046] Based on the user's second score obtained by the dynamic coupling risk assessment module, the simulated assessment system realizes the assessment determination through the following process.

[0047] 1. Threshold setting: For different assessment tasks, set the passing threshold , good threshold and excellent threshold , where the passing threshold has a value range of 0.5 to 0.7, representing that the user operation reaches the basic safety standard; the good threshold The value range is 0.7~0.85, which means that the user requires that the operation standardization significantly meets the requirements; the excellent threshold The value range of is 0.85~0.9, which means that the user's representative operation is highly accurate. In this embodiment, , and The values ​​of are 0.5, 0.7 and 0.85 respectively.

[0048] 2. Multi-dimensional classification: When the second score B of the user's operation is greater than or equal to the excellent threshold , it means that the user's operation steps are completely standardized, time management is highly stable, and the risk is extremely low, and the user's assessment is judged as "excellent"; when the second score B of the user's operation is greater than or equal to the good threshold And less than the excellent threshold , it means that the user's operation steps have slight step deviations or time fluctuations, but overall meet the safety regulations, and the user's assessment is judged as "good"; when the second score B of the user's operation is greater than or equal to the qualified threshold and less than the good threshold , it means that the user has met the basic requirements, but needs to strengthen the step sequence or time control training, and the user's assessment is judged as "qualified"; when the second score B of the user's operation is less than the qualified threshold , it means that the user's operating steps are wrong or the time is seriously out of control, there are significant safety hazards, and retraining is required. The user's assessment will be judged as "unqualified".

[0049] 3. Feedback report generation: The system automatically generates a detailed evaluation report, which includes the following contents: Based on the above two steps, it clearly marks whether the assessment is passed and the level, and combines the specific values ​​of S (correlation between steps and time spent), E (time randomness), and A (step and time stability) to point out the user's weak links in terms of incorrect step sequence, time fluctuations, dynamic coupling risks, etc., and recommends targeted training courses based on the analysis results.

[0050] Through the dynamic coupling evaluation of the second score B, the simulation assessment system not only quantifies whether the user meets the standards, but also more accurately locates skill deficiencies, realizes the "assessment-feedback-improvement" closed loop, and ensures the deep connection between training effects and actual operational safety.

[0051] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

[0052] For those skilled in the art, it is obvious that the present application is not limited to the details of the above-described exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic characteristics of the present application. Therefore, from any point of view, the above-described embodiments of the present application should be considered exemplary and non-limiting; modifications to the technical solutions described in the foregoing embodiments, or equivalent replacements of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A simulation assessment method that integrates theoretical knowledge and course training, characterized in that, The method includes: Marking the correctness of the theoretical answer results of the user in each step of the simulated assessment; Analyzing the marked results of the user's theoretical answers, comparing the differences between the standard operation steps of the physical model in the simulated assessment task and the operation sequence of the user on the physical model, and combining the time distribution of the user to complete each step and the degree of dispersion of the completion time to obtain the first score of the user; Analyzing the correlation between the operation sequence of the user in the simulated assessment and the time of the user to complete each step, and combining the randomness of the time distribution of the user to complete each step and the first score to obtain the second score of the user; Based on the numerical value of the second score of the user, obtaining the simulated assessment result of the user.

2. The simulation assessment method integrating theoretical knowledge and curriculum training according to claim 1, characterized in that The standard operation steps of the physical model are determined by the pressing sequence of the sensors in the physical model.

3. A simulation assessment method integrating theoretical knowledge and course training as claimed in claim 1, characterized in that, The marking of the correctness of the theoretical answer results of the user in each step of the simulated assessment is specifically: When the theoretical answer result of the user in each step is correct, the corresponding marked result is the first preset value; otherwise, the marked result is the second preset value; where the first preset value is not equal to the second preset value.

4. The simulation assessment method integrating theoretical knowledge and course training according to claim 1, wherein The obtaining of the first score of the user is specifically: Generating a standard feature sequence according to the standard operation steps of the physical model in the simulated assessment task; Obtaining the user operation sequence according to the operation sequence of the user in the simulated assessment; Obtaining the user operation time sequence according to the time of the user to complete each step in the simulated assessment; Fusing the proportion of the number of marked values with incorrect theoretical answer results of the user in a positive direction with the difference between the user operation sequence and the standard feature sequence to obtain the first result; Calculating the sum value of the element mean and the element dispersion of the user operation time sequence; denoting the ratio of the element mean to the sum value as the second result; Fusing the negative correlation mapping of the first result with the second result in a positive direction to obtain the first score of the user.

5. The simulation assessment method integrating theoretical knowledge and course training according to claim 4, characterized in that, The difference between the user operation sequence and the standard feature sequence is determined by the edit distance between the two sequences.

6. The simulation assessment method integrating theoretical knowledge and course training according to claim 4, characterized in that, The element dispersion is determined by the standard deviation of all elements in the user operation time sequence.

7. The simulation assessment method integrating theoretical knowledge and course training according to claim 4, characterized in that, The steps of obtaining the second score of the user are: Calculating the correlation between the user operation sequence and the user operation time sequence, and fusing it in a positive direction with the sample entropy of the user operation time sequence to obtain the third result; Fusing the negative correlation mapping of the third result with the first score in a positive direction to obtain the second score of the user operation; where the second score is negatively correlated with the positive fusion result and positively correlated with the first score.

8. The simulation assessment method integrating theoretical knowledge and course training according to claim 7, wherein The correlation between the user operation sequence and the user operation time sequence is determined by the mutual information analysis algorithm.

9. The simulation assessment method integrating theoretical knowledge and course training according to claim 1, wherein The process of obtaining the simulated assessment result of the user based on the numerical value of the second score of the user includes: Setting a threshold range, and obtaining the simulated assessment result of the user through the threshold range where the second score of the user is located; the simulated assessment results include: excellent, good, qualified, unqualified.

10. A simulation assessment system that integrates theoretical knowledge and course training, implementing a simulation assessment method for integrating theoretical knowledge and course training as described in any one of claims 1-9, characterized in that, The system includes: A model configuration and sensor integration module for obtaining the standard operation steps of the physical model in the simulated assessment task; An operation behavior dynamic acquisition module is used to mark the correctness of the theoretical answer results of users in each step of the simulation assessment; obtain the operation sequence of users in the simulation assessment; and obtain the time taken by users to complete each step. A step and time quantization analysis module is used to analyze the marked results of users' theoretical answers, compare the differences between the standard operation steps of the physical model in the simulation assessment task and the operation sequence of users on the physical model, and combine the time distribution of users to complete each step and the degree of dispersion of the completion time to obtain the first score of the user. A dynamic coupling risk assessment module is used to analyze the correlation between the operation sequence of users in the simulation assessment and the time taken by users to complete each step, and combine the randomness of the time distribution of users to complete each step and the first score to obtain the second score of the user. A multi-level determination and feedback optimization module is used to obtain the simulation assessment results of users based on the numerical value of the second score of the user.

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