Intelligent teaching method, system, medium and electronic equipment based on virtual simulation

Abstract

The application relates to a smart teaching method and system based on virtual simulation, a medium and an electronic device, and relates to the technical field of smart teaching. The method comprises the following steps: acquiring the number of occurrences of an operation movement path of a target user from a current position to a non-target operation position in a target virtual simulation teaching scene; determining an operation stagnation degree of the target user for actual experiment operation according to the number of occurrences; when the operation stagnation degree exceeds a preset stagnation threshold, constructing a target virtual auxiliary force field with a directional traction characteristic between the current position and the target operation position; based on the target virtual auxiliary force field, performing traction processing on an operated object in the target virtual simulation teaching scene from the current position to the target operation position; detecting whether the operated object reaches the target operation position from the current position, and if the operated object reaches the target operation position from the current position, the target virtual auxiliary force field is cancelled. The application can improve the teaching effect.

Description

Technical Field

[0001] This application relates to the field of smart teaching technology, specifically to a smart teaching method, system, medium, and electronic device based on virtual simulation. Background Technology

[0002] Virtual simulation refers to a technical means of constructing digital virtual scenes that highly replicate real objects, environments, or operational laws based on computer graphics, mathematical modeling, physics engines, and real-time interactive technologies. It reproduces the structural characteristics, motion states, dynamic responses, and interactive logic of things in virtual space, thereby simulating, deducing, training, and verifying real processes, operational behaviors, system performance, or teaching experiments. With its intuitive and interactive characteristics, virtual simulation technology has shown great potential in the field of education. Through virtual simulation, abstract knowledge points can be presented in an intuitive form, making them easier for students to understand and master. At the same time, virtual simulation technology can also provide rich interactive learning experiences, stimulating students' learning interest and enthusiasm.

[0003] Currently, virtual simulation-based teaching methods typically involve using a fixed and uniform teaching control mechanism to present abstract knowledge points to learners in a visual way. However, different users have different levels of participation in the teaching process, and the uniform teaching control mechanism cannot adapt well to different users, resulting in poor teaching effectiveness. Summary of the Invention

[0004] To improve teaching effectiveness, this application provides a smart teaching method, system, medium, and electronic device based on virtual simulation.

[0005] The first aspect of this application provides a smart teaching method based on virtual simulation, specifically including: The actual experimental operation to be performed by the target user in the target virtual simulation teaching scenario is obtained, and the number of occurrences of the operation movement path of the target user from the current position to the non-target operation position in the target virtual simulation teaching scenario is obtained. The non-target operation position is the operation position other than the target operation position involved in the actual experimental operation. The target virtual simulation teaching scenario is the virtual simulation teaching scenario of the experimental teaching of the target experiment. Based on the number of occurrences, determine the degree of operational stagnation of the target user in relation to the actual experimental operation; When the degree of operational stagnation exceeds a preset stagnation threshold, a target virtual auxiliary force field with directional traction characteristics is constructed between the current position and the target operating position. The target virtual auxiliary force field is used to provide directional traction force from the current position to the target operating position. Based on the target virtual auxiliary force field, the manipulated object in the target virtual simulation teaching scenario is subjected to traction processing from the current position to the target operation position; Detect whether the operated object has reached the target operation position from the current position. If the operated object has reached the target operation position from the current position, then cancel the target virtual auxiliary force field.

[0006] By employing the above technical solution, the number of operation movement paths appearing from the user's current location to non-target operation locations (all operable locations other than the target operation location corresponding to the actual experimental operation) is acquired in real time. This number directly reflects the user's familiarity with the actual experimental operation and the degree of operational confusion, accurately capturing the experimental operation status of the target user. Subsequently, based on this number, the user's operational stagnation level is determined, quantifying the target user's operational proficiency and the degree to which they cannot accurately locate the target operation location, providing an objective and accurate basis for the subsequent initiation of auxiliary guidance. When the operational stagnation level exceeds a preset stagnation threshold, it indicates that the user has experienced significant operational stagnation. When a user stagnates or struggles to complete an operation independently, a virtual auxiliary force field with directional traction characteristics is constructed between the current position and the target operation position. This field provides directional traction support without interfering with the user's willingness to operate autonomously, achieving precise adaptation between assisted guidance and the user's operational state. Then, based on this virtual auxiliary force field, directional traction is applied to the manipulated object in the scene, guiding the user to quickly locate the target operation position and smoothly advance the experimental operation. Finally, the position of the manipulated object is monitored in real time. Once the target operation position is confirmed, the virtual auxiliary force field is immediately withdrawn to avoid excessive assistance leading to user dependence, balancing the effectiveness of assisted guidance with the cultivation of the user's autonomous operation ability. This solution, through a complete closed loop of "operation state capture—stagnation degree quantification—dynamic auxiliary construction—directional traction—auxiliary withdrawal," precisely adapts to the real-time operational state of the target user in virtual simulation teaching, providing differentiated and dynamic assisted guidance for different levels of stagnation, thereby improving teaching effectiveness.

[0007] In one implementation, the step of performing a traction process on the manipulated object in the target virtual simulation teaching scenario from its current position to the target operation position based on the target virtual auxiliary force field specifically includes: Obtain the cumulative number of times the target user has constructed a virtual auxiliary force field in historical experimental teaching; A first traction correction factor is determined based on the cumulative number of constructions. The more cumulative constructions, the smaller the first traction correction factor. The first traction correction factor is a positive number not greater than 1. Multiply the initial traction force of the target virtual auxiliary force field by the first traction force correction factor to obtain the first traction force of the target virtual auxiliary force field, and adjust the initial traction force to the first traction force; The duration of continuous stay of the operated object at non-target operation positions during the process from the current position to the target operation position is calculated. Based on the duration of continuous stay, a second traction force correction factor for the target virtual auxiliary force field is determined. The second traction force correction factor is not less than 1. The longer the duration of continuous stay, the larger the second traction force correction factor becomes. The first traction force is multiplied by the second traction force correction factor to obtain the second traction force, and the first traction force is adjusted to the second traction force.

[0008] In one embodiment, constructing a target virtual auxiliary force field with directional traction characteristics between the current position and the target operating position specifically includes: Obtain multiple historical knowledge points that triggered operational errors in the target experiment, and filter multiple reference knowledge points associated with the operational errors from these historical knowledge points; Obtain multiple historical experimental operations in the target experiment that resulted in operational errors due to the reference knowledge points, and filter multiple reference experimental operations that are prone to operational errors from the multiple historical experimental operations; Assess a first risk value for operational errors caused by the reference knowledge points, and assess a second risk value for operational errors in each of the reference experimental operations; Based on the actual experimental operation, the first risk value, and each of the second risk values, determine the initial traction force on the object being operated on during the process from the current position to the target operation position; Based on the initial traction force, a target virtual auxiliary force field with directional traction characteristics is constructed.

[0009] In one implementation, determining the initial traction force on the manipulated object during the process from the current position to the target operation position based on the actual experimental operation, the first risk value, and each of the second risk values ​​specifically includes: When the actual experimental operation exists in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is determined as the actual knowledge point. Based on the first risk value of the actual knowledge point and the second risk value of the actual experimental operation, an initial risk index is determined for the actual knowledge point to cause an error in the actual experimental operation. The initial risk index is adjusted based on the target user's weakness in mastering the actual knowledge points to obtain the final risk index. The final risk indices corresponding to each of the actual knowledge points are summed to obtain the comprehensive risk index of errors occurring in the actual experimental operation. Based on the comprehensive risk index, the initial traction force on the object being operated is determined during the process from the current position to the target operation position. The larger the comprehensive risk index, the larger the initial traction force.

[0010] In one implementation, determining the initial traction force on the manipulated object during the process from the current position to the target operation position based on the comprehensive risk index specifically includes: In the target experiment, the associated experimental operation that follows the actual experimental operation is obtained, and when the associated experimental operation exists in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is determined as the associated knowledge point. Based on the first risk value of the associated knowledge point and the second risk value of the associated experimental operation, determine the initial risk coefficient of the associated knowledge point causing the associated experimental operation to fail; Based on the target user's weakness in mastering the related knowledge points, the initial risk coefficient is corrected to obtain the final risk coefficient; The final risk coefficients corresponding to each of the aforementioned related knowledge points are summed to obtain the comprehensive risk coefficient for errors in the related experimental operations. If the comprehensive risk coefficient is not greater than a preset coefficient threshold, then an index correction factor is determined based on the comprehensive risk coefficient, and the initial traction force on the operated object during the process from the current position to the target operation position is determined based on the product of the index correction factor and the comprehensive risk index. The smaller the comprehensive risk coefficient, the larger the index correction factor, and the index correction factor is not less than 1.

[0011] In one implementation, before obtaining the actual experimental operation to be performed by the target user in the target virtual simulation teaching scenario, the method further includes: When there is a single target experimental operation in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is identified as an important knowledge point, and based on the first risk value of the important knowledge point and the second risk value of the target experimental operation, the risk index of the important knowledge point causing the target experimental operation to fail is determined, and the target experimental operation is a single experimental operation covered in the target experiment; Select the highest risk index from the risk indices corresponding to each of the important knowledge points, and determine the important knowledge point corresponding to the highest risk index as the knowledge point to be noted in the target experimental operation. According to the order of operation of each of the target experimental operations, the corresponding learning videos of the knowledge points to be noted are displayed in turn on the terminal of the target user.

[0012] In one embodiment, the method further includes: After the experimental teaching of the target experiment is completed, at least one experimental operation of interest that has triggered the virtual auxiliary force field is obtained; When the experimental operation to be concerned exists in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is identified as the knowledge point to be concerned, and based on the first risk value of the knowledge point to be concerned and the second risk value of the experimental operation to be concerned, the risk value of the knowledge point to be concerned causing the experimental operation to be concerned to fail is determined. If the risk value exceeds a preset threshold, then the specific exercises corresponding to the knowledge points to be monitored are selected from the preset question bank, and the specific exercises are sent to the target user's terminal.

[0013] A second aspect of this application provides a smart teaching system based on virtual simulation, specifically including: The information acquisition module is used to acquire the actual experimental operation to be performed by the target user in the target virtual simulation teaching scenario, and to acquire the number of occurrences of the operation movement path of the target user from the current position to the non-target operation position in the target virtual simulation teaching scenario. The non-target operation position is the operation position other than the target operation position involved in the actual experimental operation. The target virtual simulation teaching scenario is the virtual simulation teaching scenario of the experimental teaching of the target experiment. The status assessment module is used to determine the degree of operational stagnation of the target user in relation to the actual experimental operation based on the number of occurrences. A force field construction module is used to construct a target virtual auxiliary force field with directional traction characteristics between the current position and the target operation position when the operation stagnation exceeds a preset stagnation threshold. The target virtual auxiliary force field is used to provide directional traction force from the current position to the target operation position. An operation assistance module is used to perform traction processing on the manipulated object in the target virtual simulation teaching scenario from the current position to the target operation position based on the target virtual auxiliary force field; The auxiliary cancellation module is used to detect whether the operated object has reached the target operation position from the current position. If the operated object has reached the target operation position from the current position, the target virtual auxiliary force field is cancelled.

[0014] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.

[0015] A fourth aspect of this application provides an electronic device, specifically comprising: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0016] In summary, this application includes at least one of the following beneficial technical effects: Based on the occurrence quantity, the degree of user stagnation in the actual experimental operation is determined, quantifying the target user's operational proficiency and the extent to which they cannot accurately locate the target operation position, providing an objective and accurate basis for subsequent assisted guidance; when the degree of stagnation exceeds a preset stagnation threshold, it indicates that the user has experienced significant operational stagnation and difficulty in completing the operation independently. At this time, a target virtual auxiliary force field with directional traction characteristics is constructed between the current position and the target operation position to provide directional traction support without interfering with the user's willingness to operate independently, achieving precise adaptation between assisted guidance and the user's operational state; then, based on this virtual auxiliary force field, directional traction processing is performed on the operated object in the scene, guiding the user to quickly locate the target operation position and smoothly advance the experimental operation; finally, the position of the operated object is detected in real time, and when it is confirmed that it has reached the target operation position, the virtual auxiliary force field is immediately withdrawn to avoid excessive assistance leading to user dependence, balancing the effectiveness of assisted guidance with the cultivation of the user's independent operation ability. This solution, through a complete closed loop of "operation status capture—stagnation degree quantification—dynamic auxiliary construction—directional guidance—auxiliary cancellation," accurately adapts to the real-time operation status of target users in virtual simulation teaching, and provides differentiated and dynamic auxiliary guidance for different stagnation levels, thereby improving teaching effectiveness. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a smart teaching method based on virtual simulation provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a smart teaching system based on virtual simulation provided in an embodiment of this application; Figure 3 This is a schematic diagram of another intelligent teaching system based on virtual simulation provided in the embodiments of this application.

[0018] Explanation of reference numerals in the attached diagram: 11. Information acquisition module; 12. State assessment module; 13. Force field construction module; 14. Operation assistance module; 15. Assisted cancellation module; 16. Knowledge learning module; 17. Targeted practice module. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0020] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0021] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0022] See Figure 1 This application discloses a flowchart of a smart teaching method based on virtual simulation, which can be implemented using a computer program or run on a virtual simulation-based smart teaching system based on the von Neumann architecture. The computer program can be integrated into an application or run as a standalone utility application, specifically including: S101: Obtain the actual experimental operation to be performed by the target user in the target virtual simulation teaching scenario, and obtain the number of occurrences of the operation movement path of the target user from the current position to the non-target operation position in the target virtual simulation teaching scenario.

[0023] Specifically, in this application, the execution entity of a smart teaching method based on virtual simulation is a server. The server is wirelessly connected to the target user's terminal, which can be a personal computer or a tablet computer. The terminal has a smart teaching-related client installed, and the server is the backend server for the client, which can be a standalone physical server or a cluster of multiple physical servers. Furthermore, the teaching scenario in this application is an experimental teaching scenario.

[0024] Non-target operation locations refer to operation locations other than the target operation locations involved in the actual experimental operation. The target virtual simulation teaching scenario is the virtual simulation teaching scenario for the experimental teaching of the target experiment. The target experiment can be a physics experiment or a chemistry experiment, such as a chemical acid-base neutralization experiment, a physical circuit connection experiment, or a biological cell culture experiment. The target virtual simulation teaching scenario is built based on the Unity 3D engine. The scenario includes operation locations consistent with real experiments (such as the placement of experimental instruments and the operation points corresponding to the operation steps), the objects to be operated on (such as virtual test tubes, virtual wires, and virtual petri dishes), and the operation interaction logic. It supports target users to perform experimental operations via mouse, touchscreen, or VR controller. The system obtains the actual experimental operations to be performed by the target user in the target virtual simulation teaching scenario. These actual experimental operations are determined by matching the system's preset experimental step library, including the operation name (such as "take test tube" or "connect wires"), operation requirements, the corresponding target operation location (preset three-dimensional coordinate points within the scenario, in the format X, Y, Z, such as (100, 200, 300)), and the operation sequence. Simultaneously, it utilizes Unity 3D Trajectory... The Recorder custom plugin tracks the target user's movement trajectory (i.e., the movement coordinate sequence of the operating device) in real time within the scene. It counts the number of movement paths from the current position (the 3D coordinates corresponding to the target user's current operating device) to non-target operation positions. Non-target operation positions are defined as all preset operable positions within the scene, excluding all target operation positions involved in the actual experimental operation (such as the placement of irrelevant experimental instruments or invalid operation points at the scene edge). The criteria for determining a movement path are: the user's operating device starts from the current position, the trajectory ends at a non-target operation position, and no valid experimental operation is triggered during the movement (e.g., no clicking or dragging of the operated object). Each occurrence of this type of complete movement trajectory is counted as one occurrence. Instantaneous movements caused by accidental touches (movement distance less than 5 scene units, movement duration less than 1 second) are excluded during the counting process. The real-time occurrence count is obtained after the statistics are completed. It should be noted that the target operation positions involved in the actual experimental operation can be understood as the explicitly pointed-to, operable positions within the target virtual simulation teaching scene where the target user's actual experimental operation is to be performed.

[0025] S102: Determine the degree of operational stagnation of the target user in the actual experimental operation based on the number of occurrences.

[0026] Specifically, one feasible implementation method is as follows: Substitute the occurrence count into the calculation formula for the operational stagnation degree. The calculation formula is: Operational Stagnation Degree = Occurrence Count × Weighting Coefficient + Base Stagnation Value. The weighting coefficient is preset to 0.3-0.5 (adjusted according to the complexity of the target experiment; the weighting coefficient is 0.5 for complex experiments and 0.3 for simple experiments). The base stagnation value is preset to 0.1 (corresponding to the base stagnation state when there are no extra movement paths). The value range of the operational stagnation degree is 0-1. The larger the value, the more severe the user's operational stagnation and the lower the proficiency of the actual experimental operation. During the calculation process, if the occurrence count is 0, then the operational stagnation degree = base stagnation value 0.1. If the occurrence count is ≥5 times, the weighting coefficient is automatically increased by 0.1 to ensure accurate judgment of severe stagnation.

[0027] S103: When the degree of operational stagnation exceeds the preset stagnation threshold, construct a target virtual auxiliary force field with directional traction characteristics between the current position and the target operational position.

[0028] Specifically, if the user's operational stagnation exceeds a preset threshold, it indicates significant user confusion and an inability to accurately locate the target operation position. In this case, generating a text pop-up to assist the user in locating the target operation position, as is the traditional approach, would likely disrupt the user's immersion in the virtual teaching scenario. Therefore, activating the virtual auxiliary force field generation module (embedded in Unity) is recommended. This is a force field rendering plugin for a 3D engine. It constructs a virtual auxiliary force field with directional traction characteristics between the current position and the target operation position. The specific parameters of this virtual auxiliary force field are as follows: the force field type is a linear directional force field; the traction direction is along a straight line from the current position to the target operation position; the traction force is set as the initial traction force, which can be set to 0.8-1.2N (virtual mechanical units, adjusted according to the virtual mass of the manipulated object, the greater the mass, the greater the traction force); the force field's range of action is the straight-line area between the current position and the target operation position; the force field is visualized as a light blue semi-transparent streamline (to facilitate users' intuitive perception of the traction direction); the core function of the force field is to provide directional traction from the current position to the target operation position, guiding the target user to operate the mouse, controlling the manipulated object to move towards the target operation position, and completing the actual experimental operation, while not interfering with the user's autonomous operation intention (the traction force can be actively canceled by the user, only playing an auxiliary guiding role).

[0029] In other embodiments, a feasible way to construct a target virtual auxiliary force field is as follows: based on historical experimental operation records cached in a database, multiple historical knowledge points that caused operational errors in the target experiment are obtained. In other words, operational errors in the target experiment are induced by a deviation in the mastery of historical knowledge points. For example, the target experiment is a chemical acid-base titration experiment, and the erroneous experimental operation is "adding liquid to a conical flask". The historical knowledge points that caused this operational error include, but are not limited to, experimental-related knowledge points such as "the nozzle should be tightly attached to the flask mouth" and "the correct holding angle of the conical flask". The historical experimental operation records include, but are not limited to, experimental operations that have resulted in errors in the target experiment within a historical period and the reasons for the errors (knowledge points related to the operational errors), with the historical period being within the past year.

[0030] The process involves counting the number of times a single historical knowledge point appears repeatedly across multiple historical knowledge points. If this number exceeds a preset threshold, it indicates that the historical knowledge point frequently causes operational errors. This historical knowledge point is then identified as a reference knowledge point associated with operational errors, meaning it is likely to cause errors in the target experiment. Next, based on the aforementioned historical experimental operation records, multiple historical experimental operations in the target experiment that resulted in operational errors due to a single reference knowledge point are obtained. Similarly, the number of times a single historical experimental operation appears repeatedly across multiple historical experimental operations is counted. If this number exceeds a preset threshold, the historical experimental operation is identified as a reference experimental operation corresponding to that single reference knowledge point, meaning it is likely to cause errors due to that single reference knowledge point. Each reference knowledge point corresponds to multiple reference experimental operations. That is, a poor grasp of the same knowledge point may lead to errors in multiple experimental operations within the target experiment. For example, a poor grasp of the knowledge point of reading rules may result in errors in experimental operations such as: "reading the initial liquid level of the burette," "reading the final liquid level of the burette," and "reading the liquid volume of the graduated cylinder," etc.

[0031] Furthermore, a first risk value is assessed for each reference knowledge point that could cause an operational error. This first risk value is the ratio of the number of times a single reference knowledge point recurs to the sum of the number of times all reference knowledge points recur, representing the likelihood of a single reference knowledge point causing an operational error in the target experiment. Next, a second risk value is assessed for each reference experimental operation corresponding to a single reference knowledge point. This second risk value is the ratio of the number of times a single reference experimental operation recurs to the sum of the number of times all reference experimental operations recur, representing the likelihood of a single reference knowledge point causing an error in the reference experimental operation in the target experiment. For example, in the target experiment, two reference knowledge points O and P are likely to cause operational errors. Reference knowledge point O recurs 65 times, and reference knowledge point P recurs 35 times. Following the calculation logic of the first risk value, the first risk value for reference knowledge point O can be determined as: 65 / (65+35) = 0.65; where reference knowledge point O corresponds to reference experimental operations O1 and O2, and reference knowledge point P corresponds to reference experimental operations P1 and P2, etc. The reference experimental operation O1 was repeated 25 times, and the reference experimental operation O2 was repeated 75 times. Therefore, according to the calculation logic of the second risk value mentioned above, the second risk value of the reference experimental operation O1 can be determined as: 25 / (25+75)=0.25.

[0032] Furthermore, based on the actual experimental operation that the target user is about to perform, the first risk value of a single reference knowledge point, and the second risk values ​​of each reference experimental operation corresponding to the single reference knowledge point, the initial traction force of the virtual auxiliary force field to be constructed on the manipulated object is determined. One feasible implementation method is as follows: if there is an actual experimental operation among the various reference experimental operations corresponding to a single reference knowledge point, then the reference knowledge point is determined as the actual knowledge point, that is, the knowledge point that may cause errors in the actual experimental operation in the target experiment, and there is at least one actual knowledge point. Multiplying the first risk value of a single actual knowledge point by the second risk value of the actual experimental operation corresponding to the single actual knowledge point yields the initial risk index corresponding to the single actual knowledge point, which characterizes the probability of errors in the actual experimental operation due to the single actual knowledge point in the target experiment.

[0033] The system retrieves the target user's total number of answers and the number of incorrect answers for specific knowledge points from pre-cached online quiz records in the database. These records include, but are not limited to, the knowledge points covered in the questions answered by different users during online practice within a preset time period (which can be the past six months). The ratio of incorrect answers to total answers is calculated; a higher ratio indicates a weaker grasp of the knowledge point. Then, using a preset mapping formula, the ratio is mapped to the interval [1, 2] to obtain the target user's weakness coefficient for that knowledge point. A higher weakness coefficient indicates a weaker grasp of the knowledge point. The mapping formula is: Weakness Coefficient S = 1 + P, where P represents the ratio. For example, if the ratio of the number of answers to the total number of answers is 0.3, then the weakness coefficient is 1 + 0.3 = 1.3.

[0034] Furthermore, the initial risk index is adjusted by multiplying the weakness coefficient by the initial risk index to obtain the final risk index. The larger the weakness coefficient, the more likely the target user is to make mistakes in actual experimental operations due to specific knowledge points. This allows for a more accurate assessment of the likelihood of the target user making mistakes in actual experimental operations due to specific knowledge points. Then, the final risk indices corresponding to each specific knowledge point are summed to obtain the comprehensive risk coefficient for the target user's mistakes in actual experimental operations during the target experiment.

[0035] Finally, based on the comprehensive risk index, the initial traction force on the manipulated object during the process from the current position to the target operation position is determined. The higher the comprehensive risk index, the greater the risk of error in the actual experimental operation. In the target virtual simulation teaching scenario, the less the target user can accurately move the manipulated object to the target operation position, the greater the corresponding initial traction force. This allows for more targeted assistance or guidance for the target user in the target experiment, improving the learning effect of the target user in experimental teaching. One feasible way to determine the initial traction force is to determine the initial traction force matching the comprehensive risk index through a preset traction force matching table. The traction force matching table includes different risk index ranges and their corresponding traction forces. For example, there are risk index ranges of 0-0.2 with a corresponding traction force of 0.8N; risk index ranges of 0.2-0.4 with a corresponding traction force of 0.9N; risk index ranges of 0.4-0.6 with a corresponding traction force of 1N, and so on. If the comprehensive risk index is 0.5, then the initial traction force is 1N. Furthermore, the initial traction force is used as a setting parameter for the virtual auxiliary force field, thereby constructing a target virtual auxiliary force field oriented from the current position to the target operating position.

[0036] In addition to directly determining the initial traction force based on the comprehensive risk index, other embodiments determine the associated experimental operations following the actual experimental operations in the target experiment according to the preset target experimental operation specifications. These specifications include the different actual operations involved in the target experiment and the order of these operations. If associated experimental operations exist among the various reference experimental operations corresponding to a single reference knowledge point, then that reference knowledge point is identified as an associated knowledge point—that is, a knowledge point that may cause errors in the associated experimental operations; at least one associated knowledge point exists. Next, the first risk value of a single associated knowledge point is multiplied by the second risk value of its corresponding associated experimental operation to obtain the initial risk coefficient for that single associated knowledge point, representing the likelihood of the single associated knowledge point causing errors in the associated experimental operations. Then, the mastery weakness coefficient of a single associated knowledge point is multiplied by the initial risk coefficient to obtain the final risk coefficient for that single associated knowledge point. The final risk coefficients for each associated knowledge point are then summed to obtain the comprehensive risk coefficient for the target user's errors in the associated experimental operations during the target experiment. If the overall risk coefficient is not greater than the preset threshold, it indicates that the target user is less likely to make mistakes in the associated experimental operation. However, if an actual experimental operation goes wrong, the associated experimental operation that the target user would instinctively execute correctly will be affected due to the correlation between the operation and its aftermath. In this case, the importance of correctly executing the actual experimental operation becomes apparent. When executing the actual experimental operation, the traction force from the current position to the target operation position needs to be stronger. Therefore, based on the overall risk coefficient, the smaller the overall risk coefficient, the larger the index correction factor, and the index correction factor should not be less than 1. A feasible way to determine the index correction factor is to normalize the overall risk coefficient K to the interval [0, 1] and use a reverse linear mapping to convert it into an index correction factor F, with the mapping relationship F = 2 − K. The smaller the overall risk coefficient, the larger the index correction factor, and the index correction factor is always not less than 1. When the overall risk coefficient is 0, the correction factor takes the maximum value of 2; when the overall risk coefficient is 1, the correction factor takes the minimum value of 1.

[0037] Furthermore, the index correction factor is multiplied by the comprehensive risk index to obtain a product. Finally, based on the above traction matching table, the traction force corresponding to the product is determined, thereby determining the initial traction force on the operated object during the process from the current position to the target operation position.

[0038] In one embodiment, before teaching the target experiment in the target virtual simulation teaching scenario, based on the aforementioned target experiment operation specifications, all target experiment operations involved in the target experiment are obtained. If a single actual target operation exists among the various reference experiment operations corresponding to a reference knowledge point, then that reference knowledge point is identified as an important knowledge point, i.e., a knowledge point that may cause errors in the target experiment operation, and there is at least one important knowledge point. Next, the first risk value of a single important knowledge point is multiplied by the second risk value of its corresponding target experiment operation to obtain the risk index of the important knowledge point causing errors in the target experiment operation, which represents the probability that a single target experiment operation will fail due to an important knowledge point. Then, the highest risk index is selected from the risk indices corresponding to each important knowledge point, and the important knowledge point corresponding to the highest risk index is identified as a knowledge point to be noted for a single target experiment operation, i.e., the knowledge point most likely to cause errors in the target experiment operation.

[0039] Finally, learning videos matching each knowledge point to be noted are selected from the pre-set knowledge point learning video library, and the corresponding learning videos for each knowledge point to be noted are sent to the target user's terminal for display in the order of operation of each target experiment. This reduces the risk of errors in the experimental operation when the target user enters the target virtual simulation teaching scenario and improves the overall effect of experimental teaching.

[0040] In another embodiment, from all experimental operations of the target experiment, at least one experimental operation that triggered the virtual auxiliary force field during the target user's execution is selected as a focus experimental operation. If the focus experimental operation exists among the reference experimental operations corresponding to a single reference knowledge point, then that reference knowledge point is identified as a focus knowledge point. If at least one focus knowledge point exists, then a first risk value of the focus knowledge point is multiplied by a second risk value of the focus experimental operation to obtain a risk value indicating that the focus knowledge point caused an error in the focus experimental operation. The higher the risk value, the more likely the error in the focus experimental operation is due to the focus knowledge point. If the risk value exceeds a preset threshold, it indicates a high probability that the focus experimental operation is due to the focus knowledge point. In this case, a specific exercise corresponding to the focus knowledge point is selected from a preset exercise question bank and sent to the target user's terminal. This allows for targeted reinforcement exercises of the corresponding weak knowledge points based on the target user's actual operation in the simulation environment after the target experiment ends. The exercise question bank includes exercise questions matching different knowledge points.

[0041] S104: Based on the target virtual auxiliary force field, perform traction processing on the manipulated object in the target virtual simulation teaching scenario from the current position to the target operation position.

[0042] Specifically, the system uses the operation log database on the server (a MySQL database storing all virtual experiment operation records and virtual auxiliary force field construction records of the target user) to obtain the cumulative number of times the target user has constructed virtual auxiliary force fields in historical experimental teaching scenarios (preset to be all similar virtual experimental teaching scenarios within the past semester). Then, it calls the preset first correction factor mapping rule and determines the first traction force correction factor based on this rule. The core logic of the first correction factor mapping rule is as follows: the more times the cumulative number of constructions, the more times the target user triggers the virtual auxiliary force field in experimental teaching, and the higher the possibility of dependence on the virtual auxiliary force field in the target experiment. Therefore, the smaller the first traction force correction factor is, and the first traction force correction factor is a positive number not greater than 1. The specific mapping rule is as follows: when N=0, the first traction force correction factor = 1.0; when 1≤N≤3, the first traction force correction factor = 0.8; when 4≤N≤6, the first traction force correction factor = 0.6; when N≥7, the first traction force correction factor = 0.5; if the cumulative number of constructions is within the above range, the correction factor is calculated by linear interpolation (e.g., when N=2, the correction factor = 0.9).

[0043] Next, the initial traction force of the target virtual auxiliary force field is multiplied by a first traction force correction factor to obtain the first traction force. The smaller the first traction force correction factor, the more the target user has experienced the assistance of the virtual auxiliary force field, and the greater the possibility of dependence on the virtual auxiliary force field. Correspondingly, the smaller the first traction force, the lower the risk of the target user's dependence on the virtual auxiliary force field, thus improving the quality and effectiveness of experimental teaching. At the same time, the initial traction force of the target virtual auxiliary force field is adjusted to the first traction force.

[0044] Based on the collision detection function of the Unity 3D engine, the 3D coordinates of the manipulated object are tracked in real time and compared with all preset non-target operation positions in the scene (all operable positions other than the target operation position involved in the actual experimental operation are preset with a 3D coordinate range). When the core coordinates of the manipulated object are detected to fall into the coordinate range of a certain non-target operation position (deviation ≤ 2 scene units), a timer is started. If the manipulated object remains in the non-target operation position, does not move to the target operation position, and is not actively operated by the user, the timer continues to accumulate. If the manipulated object leaves the non-target operation position (coordinate deviation > 2 scene units) or the user actively triggers an operation, the timer stops. The accumulated time of the timer at this time is the duration of the continuous stay in the non-target operation position (denoted as T, in seconds). If the manipulated object stays in different non-target operation positions multiple times during the traction process, the duration of the continuous stay in each non-target operation position is counted separately.

[0045] The system invokes a preset second correction factor mapping rule. Based on this rule, a second traction correction factor is determined. This rule is set based on the correlation between the duration of continuous dwell time and the degree of user confusion. The core logic is: the longer the continuous dwell time, the higher the degree of user confusion and the more difficult it is for the user to independently leave the non-target operation position. Therefore, the second traction correction factor is larger, and this correction factor is not less than 1. The specific mapping rules are as follows: when T < 1 second (no obvious dwell time, no user confusion), the second traction correction factor = 1.0; when 1 second ≤ T < 3 seconds (short dwell time, slight user confusion), the second traction correction factor = 1.2; when 3 seconds ≤ T < 5 seconds (moderate dwell time, moderate user confusion), the second traction correction factor = 1.5; when T ≥ 5 seconds (long dwell time, severe user confusion), the second traction correction factor = 1.8; if the continuous dwell time is within the above range, the correction factor is calculated using linear interpolation (e.g., when T = 2 seconds, the correction factor = 1.1).

[0046] Multiplying the first traction force by the second traction force correction factor yields the second traction force. This optimizes the traction force of the target virtual auxiliary force field from the perspective of the duration of continuous stay at non-target operating positions during the journey from the current position to the target operating position. A longer continuous stay corresponds to a larger traction force, resulting in better guidance for the target user. Finally, the first traction force of the target virtual auxiliary force field is adjusted to the second traction force, thus ensuring both the effectiveness of the assisted guidance and the cultivation of the user's independent operating ability.

[0047] S105: Detect whether the operated object has reached the target operation position from the current position. If the operated object has reached the target operation position from the current position, cancel the target virtual auxiliary force field.

[0048] Specifically, using the collision detection function based on the Unity 3D engine, with a detection accuracy of ±1 scene unit, the 3D coordinates of the manipulated object are detected in real time and compared with the 3D coordinates of the target operation position. The judgment criteria are: if the core coordinates of the manipulated object deviate from the coordinates of the target operation position by ≤2 scene units and the dwell time is ≥2 seconds, then the manipulated object is judged to have reached the target operation position from its current position. If the manipulated object is detected to have reached the target operation position, the target virtual auxiliary force field is canceled, the visual streamline of the force field disappears synchronously, the traction force stops acting, and a prompt message pops up on the scene interface (such as "The target operation position has been reached, please continue to perform the experimental operation"). If the manipulated object is detected not to have reached the target operation position, the traction process continues until the judgment criteria are met.

[0049] The implementation principle of the intelligent teaching method based on virtual simulation in this application embodiment is as follows: Based on the occurrence quantity, the user's operational stagnation level in the actual experimental operation is determined, quantifying the target user's operational proficiency and the degree to which they cannot accurately locate the target operation position, providing an objective and accurate basis for subsequent assisted guidance. When the operational stagnation level exceeds a preset stagnation threshold, it indicates that the user has experienced significant operational stagnation and difficulty in completing the operation independently. At this time, a target virtual auxiliary force field with directional traction characteristics is constructed between the current position and the target operation position, providing directional traction support without interfering with the user's willingness to operate independently, achieving precise adaptation between assisted guidance and the user's operational state. Then, based on this virtual auxiliary force field, directional traction processing is performed on the operated object in the scene, guiding the user to quickly locate the target operation position and smoothly advance the experimental operation. Finally, the position of the operated object is detected in real time. When it is confirmed that the object has reached the target operation position, the virtual auxiliary force field is immediately withdrawn to avoid excessive assistance leading to user dependence, balancing the effectiveness of assisted guidance with the cultivation of the user's independent operation ability. This solution, through a complete closed loop of "operation status capture—stagnation degree quantification—dynamic auxiliary construction—directional guidance—auxiliary cancellation," accurately adapts to the real-time operation status of target users in virtual simulation teaching, and provides differentiated and dynamic auxiliary guidance for different stagnation levels, thereby improving teaching effectiveness.

[0050] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0051] Please see Figure 2 This is a schematic diagram of the structure of a virtual simulation-based smart teaching system provided in an embodiment of this application. This virtual simulation-based smart teaching system can be implemented as all or part of a system through software, hardware, or a combination of both. The system includes an information acquisition module 11, a state evaluation module 12, a force field construction module 13, an operation assistance module 14, and an operation cancellation module 15.

[0052] The information acquisition module 11 is used to acquire the actual experimental operation to be performed by the target user in the target virtual simulation teaching scenario, and to acquire the number of occurrences of the operation movement path from the current position to the non-target operation position in the target virtual simulation teaching scenario. The non-target operation position is the operation position other than the target operation position involved in the actual experimental operation. The target virtual simulation teaching scenario is the virtual simulation teaching scenario of the experimental teaching of the target experiment. The status assessment module 12 is used to determine the degree of operational stagnation of the target user in the actual experimental operation based on the number of occurrences. The force field construction module 13 is used to construct a target virtual auxiliary force field with directional traction characteristics between the current position and the target operation position when the operation stagnation exceeds a preset stagnation threshold. The target virtual auxiliary force field is used to provide directional traction force from the current position to the target operation position. Operation assistance module 14 is used to perform traction processing on the manipulated object in the target virtual simulation teaching scene from the current position to the target operation position based on the target virtual auxiliary force field; The auxiliary cancellation module 15 is used to detect whether the operated object has reached the target operation position from the current position. If the operated object has reached the target operation position from the current position, the target virtual auxiliary force field is cancelled.

[0053] Optionally, the operation assistance module 14 is specifically used for: Obtain the cumulative number of times the target user has constructed a virtual auxiliary force field in historical experimental teaching; The first traction correction factor is determined based on the cumulative number of constructions. The more cumulative constructions, the smaller the first traction correction factor. The first traction correction factor is a positive number not greater than 1. Multiply the initial traction force of the target virtual auxiliary force field by the first traction force correction factor to obtain the first traction force of the target virtual auxiliary force field, and adjust the initial traction force to the first traction force; Statistically determine the duration of time the manipulated object remains at a non-target operation location during its journey from the current location to the target operation location; Based on the duration of continuous stay, the second traction force correction factor of the target virtual auxiliary force field is determined. The second traction force correction factor is not less than 1. The longer the duration of continuous stay, the larger the second traction force correction factor is. The first traction force is multiplied by the second traction force correction factor to obtain the second traction force, and the first traction force is adjusted to the second traction force.

[0054] Optional, force field construction module 13, specifically used for: Obtain multiple historical knowledge points that triggered operational errors in the target experiment, and filter multiple reference knowledge points related to the operational errors from these historical knowledge points; Obtain multiple historical experimental operations in the target experiment that resulted in operational errors due to reference knowledge points, and then filter multiple reference experimental operations that are prone to operational errors from these historical experimental operations; Assess the first risk value of operational errors caused by the reference knowledge points, and assess the second risk value of operational errors caused by each reference experimental operation; Based on the actual experimental operation, the first risk value, and each of the second risk values, determine the initial traction force on the object being operated on during the process from the current position to the target operation position; Based on the initial traction force, a target virtual auxiliary force field with directional traction characteristics is constructed.

[0055] Optional, force field construction module 13, specifically used for: When there is an actual experimental operation in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is identified as the actual knowledge point. Based on the first risk value of the actual knowledge points and the second risk value of the actual experimental operation, the initial risk index of the actual knowledge points causing errors in the actual experimental operation is determined. The initial risk index is adjusted based on the target users' weak grasp of the actual knowledge points to obtain the final risk index; The final risk indices corresponding to each actual knowledge point are summed to obtain the comprehensive risk index of errors in actual experimental operations. Based on the comprehensive risk index, the initial traction force on the manipulated object is determined during the process from the current position to the target operation position. The higher the comprehensive risk index, the greater the initial traction force.

[0056] Optional, force field construction module 13, specifically used for: In the target experiment, identify the associated experimental operations that follow the actual experimental operations, and when there are associated experimental operations in each of the reference experimental operations corresponding to the reference knowledge point, identify the reference knowledge point as the associated knowledge point. Based on the first risk value of the related knowledge points and the second risk value of the related experimental operations, the initial risk coefficient of the related knowledge points causing errors in the related experimental operations is determined. The initial risk coefficient is adjusted based on the target user's level of understanding of related knowledge points to obtain the final risk coefficient. The final risk coefficients corresponding to each related knowledge point are summed to obtain the comprehensive risk coefficient for errors in related experimental operations. If the overall risk coefficient is not greater than the preset coefficient threshold, then the index correction factor is determined based on the overall risk coefficient, and the initial traction force on the operated object during the process from the current position to the target operation position is determined based on the product of the index correction factor and the overall risk index. The smaller the overall risk coefficient, the larger the index correction factor, and the index correction factor is not less than 1.

[0057] Optional, such as Figure 3 As shown, the system also includes a knowledge learning module 16, which is specifically used for: When there is a single target experimental operation in each reference experimental operation corresponding to the reference knowledge point, the reference knowledge point is identified as an important knowledge point. Based on the first risk value of the important knowledge point and the second risk value of the target experimental operation, the risk index of the important knowledge point causing the target experimental operation to fail is determined. The target experimental operation is the single experimental operation covered in the target experiment. Select the highest risk index from the risk indices corresponding to each important knowledge point, and determine the important knowledge points corresponding to the highest risk index as the knowledge points to be noted in the target experimental operation. According to the order of operation of each target experiment, the corresponding learning videos of the knowledge points to be noted are displayed on the target user's terminal in sequence.

[0058] Optionally, the system also includes a practice module 17, specifically for: After the experimental teaching of the target experiment is completed, acquire at least one experimental operation that has triggered the virtual auxiliary force field and is of interest. When there is an experimental operation to be concerned in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is identified as the knowledge point to be concerned, and the risk value of the knowledge point to be concerned causing the experimental operation to be concerned to fail is determined based on the first risk value of the knowledge point to be concerned and the second risk value of the experimental operation to be concerned. If the risk value exceeds the preset threshold, then select the corresponding exclusive exercises for the knowledge points to be monitored from the preset question bank and send the exclusive exercises to the target user's terminal.

[0059] It should be noted that the above-described embodiment of a virtual simulation-based smart teaching system, when executing a virtual simulation-based smart teaching method, only illustrates the division of the functional modules described above. In practical applications, the functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the above-described embodiment of a virtual simulation-based smart teaching system and a virtual simulation-based smart teaching method embodiment belong to the same concept, and their implementation process is detailed in the method embodiment, which will not be repeated here.

[0060] This application also discloses a computer-readable storage medium, which stores a computer program, wherein when the computer program is executed by a processor, it implements a smart teaching method based on virtual simulation as described in the above embodiments.

[0061] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or system capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0062] The above-described intelligent teaching method based on virtual simulation is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the method.

[0063] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned intelligent teaching method based on virtual simulation.

[0064] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.

[0065] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0066] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0067] In this electronic device, a smart teaching method based on virtual simulation described in the above embodiment is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.

[0068] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A smart teaching method based on virtual simulation, characterized in that, The method includes: The actual experimental operation to be performed by the target user in the target virtual simulation teaching scenario is obtained, and the number of occurrences of the operation movement path of the target user from the current position to the non-target operation position in the target virtual simulation teaching scenario is obtained. The non-target operation position is the operation position other than the target operation position involved in the actual experimental operation. The target virtual simulation teaching scenario is the virtual simulation teaching scenario of the experimental teaching of the target experiment. Based on the number of occurrences, determine the degree of operational stagnation of the target user in relation to the actual experimental operation; When the degree of operational stagnation exceeds a preset stagnation threshold, a target virtual auxiliary force field with directional traction characteristics is constructed between the current position and the target operating position. The target virtual auxiliary force field is used to provide directional traction force from the current position to the target operating position. Based on the target virtual auxiliary force field, the manipulated object in the target virtual simulation teaching scenario is subjected to traction processing from the current position to the target operation position; Detect whether the operated object has reached the target operation position from the current position. If the operated object has reached the target operation position from the current position, then cancel the target virtual auxiliary force field.

2. The intelligent teaching method based on virtual simulation according to claim 1, characterized in that, The step of performing a traction process on the manipulated object in the target virtual simulation teaching scenario, based on the target virtual auxiliary force field, from the current position to the target operation position, specifically includes: Obtain the cumulative number of times the target user has constructed a virtual auxiliary force field in historical experimental teaching; A first traction correction factor is determined based on the cumulative number of constructions. The more cumulative constructions, the smaller the first traction correction factor. The first traction correction factor is a positive number not greater than 1. Multiply the initial traction force of the target virtual auxiliary force field by the first traction force correction factor to obtain the first traction force of the target virtual auxiliary force field, and adjust the initial traction force to the first traction force; The duration of continuous stay of the operated object at non-target operation positions during the process from the current position to the target operation position is calculated. Based on the duration of continuous stay, a second traction force correction factor for the target virtual auxiliary force field is determined. The second traction force correction factor is not less than 1. The longer the duration of continuous stay, the larger the second traction force correction factor becomes. The first traction force is multiplied by the second traction force correction factor to obtain the second traction force, and the first traction force is adjusted to the second traction force.

3. The intelligent teaching method based on virtual simulation according to claim 1, characterized in that, The construction of a target virtual auxiliary force field with directional traction characteristics between the current position and the target operating position specifically includes: Obtain multiple historical knowledge points that triggered operational errors in the target experiment, and filter multiple reference knowledge points associated with the operational errors from these historical knowledge points; Obtain multiple historical experimental operations in the target experiment that resulted in operational errors due to the reference knowledge points, and filter multiple reference experimental operations that are prone to operational errors from the multiple historical experimental operations; Assess a first risk value for operational errors caused by the reference knowledge points, and assess a second risk value for operational errors in each of the reference experimental operations; Based on the actual experimental operation, the first risk value, and each of the second risk values, determine the initial traction force on the object being operated on during the process from the current position to the target operation position; Based on the initial traction force, a target virtual auxiliary force field with directional traction characteristics is constructed.

4. The intelligent teaching method based on virtual simulation according to claim 3, characterized in that, The step of determining the initial traction force on the manipulated object during the process from the current position to the target operation position based on the actual experimental operation, the first risk value, and each of the second risk values ​​specifically includes: When the actual experimental operation exists in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is determined as the actual knowledge point. Based on the first risk value of the actual knowledge point and the second risk value of the actual experimental operation, an initial risk index is determined for the actual knowledge point to cause an error in the actual experimental operation. The initial risk index is adjusted based on the target user's weakness in mastering the actual knowledge points to obtain the final risk index. The final risk indices corresponding to each of the actual knowledge points are summed to obtain the comprehensive risk index of errors occurring in the actual experimental operation. Based on the comprehensive risk index, the initial traction force on the object being operated is determined during the process from the current position to the target operation position. The larger the comprehensive risk index, the larger the initial traction force.

5. The intelligent teaching method based on virtual simulation according to claim 4, characterized in that, The determination of the initial traction force on the manipulated object during the process from the current position to the target operation position based on the comprehensive risk index specifically includes: In the target experiment, the associated experimental operation that follows the actual experimental operation is obtained, and when the associated experimental operation exists in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is determined as the associated knowledge point. Based on the first risk value of the associated knowledge point and the second risk value of the associated experimental operation, determine the initial risk coefficient of the associated knowledge point causing the associated experimental operation to fail; Based on the target user's weakness in mastering the related knowledge points, the initial risk coefficient is corrected to obtain the final risk coefficient; The final risk coefficients corresponding to each of the aforementioned related knowledge points are summed to obtain the comprehensive risk coefficient for errors in the related experimental operations. If the comprehensive risk coefficient is not greater than a preset coefficient threshold, then an index correction factor is determined based on the comprehensive risk coefficient, and the initial traction force on the operated object during the process from the current position to the target operation position is determined based on the product of the index correction factor and the comprehensive risk index. The smaller the comprehensive risk coefficient, the larger the index correction factor, and the index correction factor is not less than 1.

6. The intelligent teaching method based on virtual simulation according to claim 3, characterized in that, Before obtaining the actual experimental operations to be performed by the target user in the target virtual simulation teaching scenario, the method further includes: When there is a single target experimental operation in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is identified as an important knowledge point, and based on the first risk value of the important knowledge point and the second risk value of the target experimental operation, the risk index of the important knowledge point causing the target experimental operation to fail is determined, and the target experimental operation is a single experimental operation covered in the target experiment; Select the highest risk index from the risk indices corresponding to each of the important knowledge points, and determine the important knowledge point corresponding to the highest risk index as the knowledge point to be noted in the target experimental operation. According to the order of operation of each of the target experimental operations, the corresponding learning videos of the knowledge points to be noted are displayed in turn on the terminal of the target user.

7. The intelligent teaching method based on virtual simulation according to claim 3, characterized in that, The method further includes: After the experimental teaching of the target experiment is completed, at least one experimental operation of interest that has triggered the virtual auxiliary force field is obtained; When the experimental operation to be concerned exists in each of the reference experimental operations corresponding to the reference knowledge point, the reference knowledge point is identified as the knowledge point to be concerned, and based on the first risk value of the knowledge point to be concerned and the second risk value of the experimental operation to be concerned, the risk value of the knowledge point to be concerned causing the experimental operation to be concerned to fail is determined. If the risk value exceeds a preset threshold, then the specific exercises corresponding to the knowledge points to be monitored are selected from the preset question bank, and the specific exercises are sent to the target user's terminal.

8. A smart teaching system based on virtual simulation, characterized in that, include: The information acquisition module (11) is used to acquire the actual experimental operation to be performed by the target user in the target virtual simulation teaching scenario, and to acquire the number of occurrences of the operation movement path of the target user from the current position to the non-target operation position in the target virtual simulation teaching scenario. The non-target operation position is the operation position other than the target operation position involved in the actual experimental operation. The target virtual simulation teaching scenario is the virtual simulation teaching scenario of the experimental teaching of the target experiment. The status assessment module (12) is used to determine the degree of operational stagnation of the target user in relation to the actual experimental operation based on the number of occurrences. The force field construction module (13) is used to construct a target virtual auxiliary force field with directional traction characteristics between the current position and the target operation position when the operation stagnation exceeds a preset stagnation threshold. The target virtual auxiliary force field is used to provide directional traction force from the current position to the target operation position. The operation assistance module (14) is used to perform traction processing on the object being operated on in the target virtual simulation teaching scenario from the current position to the target operation position based on the target virtual auxiliary force field; The auxiliary cancellation module (15) is used to detect whether the operated object has reached the target operation position from the current position. If the operated object has reached the target operation position from the current position, the target virtual auxiliary force field is cancelled.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method of any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it implements the method of any one of claims 1-7.

Images