Practical training teaching plan adaptation method and device, terminal and medium

Abstract

The application provides a practical training teaching plan adaptation method and device, a terminal and a medium. The method comprises the following steps: obtaining student teaching videos, practical training simulation data and student question and answer data; obtaining a teaching resistance index according to the student teaching videos, the practical training simulation data and the student question and answer data; obtaining a key death weight according to the practical training simulation data and the student question and answer data; and adapting a practical training teaching plan according to the teaching resistance index and the key death weight. The method realizes reasonable allocation of the weight of key process details in the teaching plan, realizes an elastic adjustment mechanism of actual learning efficiency, and realizes self-evolution and precise adaptation of the practical training teaching plan by using a data-driven mode.

Description

Technical Field

[0001] This invention relates to the field of simulation technology, specifically to a method, device, terminal, and medium for adapting practical training lesson plans. Background Technology

[0002] Currently, practical training design commonly suffers from "static" and "experience-dependent" flaws. Traditional lesson plans often pre-determine fixed difficulty levels and content granularities based on teachers' subjective experience, failing to adapt to the real-time changes in cognitive load and operational proficiency among different classes and individual students during practical training. This easily leads to an imbalance in workload, with "overly difficult content causing cognitive overload" or "overly superficial content causing inefficient teaching." More seriously, the existing evaluation system suffers from a "knowledge-practice separation" blind spot, failing to effectively identify implicit high-risk knowledge points that "score highly in theoretical assessments but are prone to serious malfunctions in practical operations." This results in a lack of sufficient weight and demonstration resources for key process details in lesson plans. Furthermore, the allocation of teaching time usually adopts a pre-determined fixed class period approach, lacking a flexible adjustment mechanism based on actual learning efficiency, leading to insufficient time for key intensive stages rather than wasted resources on crucial stages. Summary of the Invention

[0003] In view of the shortcomings of existing technologies, this invention proposes a method, device, terminal and medium for adapting practical training lesson plans, aiming to achieve self-evolution and precise adaptation of lesson plans.

[0004] In a first aspect, embodiments of this application provide a method for adapting practical training lesson plans, the method comprising: Acquire student teaching videos, practical training simulation data, and student Q&A data; The teaching resistance index is obtained based on the student teaching videos, the practical training simulation data, and the student question and answer data. Based on the training simulation data and the student question-and-answer data, the critical lethal weights are obtained; The practical training lesson plan is adapted based on the teaching resistance index and the critical lethal weight.

[0005] Optionally, obtaining the teaching resistance index based on the student teaching videos, the practical training simulation data, and the student question-and-answer data includes: Based on the student teaching videos, the student's hand hovering time, time variance of the same action, and process standardization score were obtained. Based on the student question-and-answer data, the semantic ambiguity of the student questions and answers is obtained; Based on the training simulation data, the functional detection failure rate is obtained; The teaching resistance index is obtained based on the student's hand hovering time, the time variance of the same action, the process standardization score, the semantic ambiguity of the student's question and answer, and the function detection failure rate.

[0006] Optionally, the step of obtaining the teaching resistance index based on the student's hand hovering time, the time variance of the same action, the process standardization score, the semantic ambiguity of the student's question and answer, and the functional detection failure rate includes: ; in, The teaching resistance index; The time it takes for the student's hand to hover during the k-th training session; Let V be the time variance of the same action in the k-th training session; The score for process standardization in the k-th training session; The semantic ambiguity of student questions and answers in the k-th training session; Let be the functional test failure rate of the kth training session; These are the weighting coefficients for the first mode. The modal weighting coefficient is the first modality. These are the weighting coefficients for the third mode; The weighting coefficients for the fourth mode; This is the first adjustment constant; This is the second adjustment constant.

[0007] Optionally, obtaining the critical lethal weights based on the training simulation data and the student question-and-answer data includes: Based on the training simulation data, the correlation degree of operational faults is obtained; Based on the student question and answer data, the student's theoretical question and answer score is obtained; Based on the operational failure correlation degree and the student's theoretical question-and-answer score, the critical lethal weight is obtained.

[0008] Optionally, obtaining the critical lethal weight based on the operational failure correlation degree and the student's theoretical question-and-answer score includes: ; in, The critical lethal weight for the k-th training stage; Let the correlation of operational faults in the k-th training session be denoted as . Students are scored based on their answers to theoretical questions.

[0009] Optionally, adapting the practical training plan based on the teaching resistance index and the critical lethal weight includes: Based on the teaching resistance index, a standard for dynamically adjusting operational difficulty is obtained; Based on the teaching resistance index and the critical lethal weight, the teaching task planning time is obtained; The practical training lesson plan is adapted by dynamically adjusting the operational difficulty standards and the time allotted for teaching tasks.

[0010] Optionally, the step of planning the teaching task time based on the teaching resistance index and the critical lethal weight includes: ; in, Total course duration; Let be the teaching resistance index for the k-th practical training session; The critical lethal weight for the k-th training stage; Let j be the teaching resistance index for the j-th practical training session. Let be the critical lethal weight of the j-th training session.

[0011] Secondly, embodiments of this application provide a practical training lesson plan adapter, including: The data acquisition module is used to acquire student teaching videos, practical training simulation data, and student Q&A data; The teaching resistance determination module is used to obtain a teaching resistance index based on the student teaching videos, the practical training simulation data, and the student question and answer data. The lethal weight determination module is used to obtain the key lethal weights based on the training simulation data and the student question and answer data. The lesson plan adaptation module is used to adapt the practical training lesson plan according to the teaching resistance index and the critical lethal weight.

[0012] Thirdly, embodiments of this application provide a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the training lesson plan adaptation method as described in any one of the first aspects above.

[0013] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the training lesson plan adaptation method as described in any one of the first aspects above.

[0014] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the training lesson plan adaptation method described in any one of the first aspects.

[0015] In this embodiment, student teaching videos, practical training simulation data, and student Q&A data are acquired; a teaching resistance index is obtained based on the student teaching videos, practical training simulation data, and student Q&A data; a critical lethal weight is obtained based on the practical training simulation data and student Q&A data; and the practical training lesson plan is adapted based on the teaching resistance index and the critical lethal weight. This achieves a reasonable allocation of the weight of key process details in the lesson plan; it implements a flexible adjustment mechanism for actual learning efficiency; and it enables the practical training lesson plan to self-evolve and accurately adapt using a data-driven approach. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the practical training lesson plan adaptation method provided in this application. Figure 2 This is a schematic diagram of the structure of the training lesson plan adapter provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0019] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0020] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] Figure 1The diagram illustrates a first embodiment of the practical training lesson plan adaptation method provided in this application. It is provided as an example and not a limitation; this method can be applied to the aforementioned practical training lesson plan adaptation device. Figure 1 As shown, the method may include: S10: Acquire student teaching videos, practical training simulation data, and student Q&A data; To achieve self-evolution and precise adaptation of lesson plans and address the issue of unbalanced capacity in practical training, the practical training lesson plan adaptation device acquires student teaching videos, practical training simulation data, and student question-and-answer data. The student teaching videos are individual operation videos of students during practical training. The practical training simulation data consists of data acquired by the virtual simulation device during student training, representing the virtual simulation results. The student question-and-answer data comprises responses to voice questions posed by the practical training lesson plan adaptation device during practical training, and represents large-scale model question-and-answer data.

[0022] The practical training lesson plan adaptation device uses a visual inspection model (transformer or yolo) to detect the installation process and techniques, and uses scanning circuits and circuit simulation operation (controlling the virtual model) to realize the functional testing of the practical training project.

[0023] S20, based on the student teaching videos, the practical training simulation data, and the student question and answer data, the teaching resistance index is obtained; After acquiring student teaching videos, training simulation data, and student Q&A data, the training lesson plan adaptation device obtains a teaching resistance index based on these data.

[0024] As one implementation method, a teaching resistance index is obtained based on the student teaching videos, the practical training simulation data, and the student question-and-answer data, specifically including: A1. Based on the student teaching video, obtain the student's hand hovering time, time variance of the same action, and process standardization score; After acquiring the student's teaching video, the practical training lesson plan adapter device calculates the student's hand hovering time based on the video. Time variance of the same action and process standardization score .

[0025] Among them, the student's hand hovering time This refers to the time during which the hand (or tool-holding device) remains stationary during the operation detected by the visual detection model (by Transformer or YOLO) in the k-th training session. (Student hand hovering time) The basic load term is the student's hand hovering hesitation time extracted using the YOLO vision algorithm.

[0026] Among them, the time variance of the same action To detect the time variance of the same action within the k-th testing cycle (k-th training stage), for example, stripping thread, if the time taken for each stripping action is consistent across 5 repetitions, it indicates high stability. If one stripping action takes 20 seconds and another only 5 seconds, it indicates instability, leading to greater teaching resistance and requiring more resources to be allocated to this stage. Time variance of the same action. This represents the variance of student actions during the operation at this node. The system calculates this by visually tracking the consistency of student actions in repetitive operations (such as continuous wire stripping and screw tightening). The larger this value, the more unstable the student's skill performance, and the more non-linearly the subsequent consolidation time resources should increase. Therefore, it is used as a multiplier factor to amplify the base load.

[0027] Among them, the score for process standardization The score for process standardization based on computer vision recognition (range 0-1) is derived from the system's comprehensive evaluation of component layout deviation (Euclidean distance), wire horizontal and vertical straightness, and tool placement. A lower process score (i.e., a lower score indicates a lower standardization). The larger the resistance weight, the higher the risk of future rework or equipment damage, even if the student has successfully completed the circuit. Therefore, the resistance weight needs to be increased to allocate more error correction resources. A vision system is used to detect the installation process of the training project, including whether the wires are horizontal and vertical (the tilt threshold is set in the image processing model), and the dimensions of the component installation positions, assigning a score between 0 and 1.0.

[0028] A2. Based on the student question-and-answer data, the semantic ambiguity of the student questions and answers is obtained; After acquiring student question-and-answer data, the practical training lesson plan adaptation device calculates the semantic ambiguity of the student questions and answers based on this data. .

[0029] Among them, the semantic ambiguity of student question and answer This represents the semantic ambiguity of student question-and-answer responses based on a large model analysis. Specifically, it includes the student's hand hovering time. Semantic ambiguity in student Q&A This reflects the students' current cognitive and operational instantaneous resistance.

[0030] A3. Based on the training simulation data, the functional detection failure rate is obtained; After acquiring the training simulation data, the training lesson plan adaptation device calculates the functional detection failure rate based on the training simulation data. .

[0031] Among them, the functional test failure rate The result risk item refers to the failure rate of functional testing. Functional testing is achieved by scanning circuits and circuit simulation operation (control virtual model) in the equipment. It will determine how much of the wiring function has been completed through circuit scanning. The failure rate of virtual simulation or physical power-on test is retained, and the original exponential penalty mechanism is maintained to maintain high sensitivity to high-risk nodes.

[0032] A4. The teaching resistance index is obtained based on the student's hand hovering time, the time variance of the same action, the process standardization score, the semantic ambiguity of the student's question and answer, and the functional detection failure rate.

[0033] After obtaining the student's hand hovering time, the time variance of the same action, the process standardization score, the semantic ambiguity of student questions and answers, and the function detection failure rate, the training lesson plan adaptation device obtains the teaching resistance index based on the student's hand hovering time, the time variance of the same action, the process standardization score, the semantic ambiguity of student questions and answers, and the function detection failure rate.

[0034] As one implementation method, a teaching resistance index is obtained based on the student's hand hovering time, the variance of the time of the same action, the process standardization score, the semantic ambiguity of the student's question and answer, and the functional detection failure rate, including: ; Where k represents the kth training session; The teaching resistance index; The time it takes for the student's hand to hover during the k-th training session; Let V be the time variance of the same action in the k-th training session; The score for process standardization in the k-th training session; The semantic ambiguity of student questions and answers in the k-th training session; Let be the functional test failure rate of the kth training session; These are the weighting coefficients for the first mode. The modal weighting coefficient is the first modality. These are the weighting coefficients for the third mode; The weighting coefficients for the fourth mode; This is the first adjustment constant; This is the second adjustment constant.

[0035] The teaching resistance index not only quantifies "whether one can do it" and "whether one is right or wrong", but also incorporates "whether one is stable" and "whether one is proficient" into the resistance assessment, thereby providing more forward-looking data support for the equipment scheduling and personalized duration recommendations of subsequent courses.

[0036] To standardize and quantify the level of difficulty students encounter in mastering the same knowledge and skills within the same practical training sub-tasks, this application introduces a "comprehensive teaching resistance" model. The system calculates the teaching resistance index for each specific teaching node by collecting multimodal data from students in real time at those nodes. .

[0037] S30, Based on the training simulation data and the student question and answer data, obtain the key lethal weights; After acquiring the training simulation data and the student question-and-answer data, the training lesson plan adaptation device obtains the critical lethal weights based on the training simulation data and the student question-and-answer data.

[0038] As one implementation method, based on the training simulation data and the student question-and-answer data, the critical lethal weights are obtained, including: B1. Based on the training simulation data, the operational fault correlation degree is obtained; After acquiring the training simulation data, the training lesson plan adaptation device obtains the operational fault correlation degree based on the training simulation data. .

[0039] Among them, operational fault correlation No. Step operation ( The mutual information correlation between each training step and a serious virtual device malfunction (such as motor burnout). That is, the probability of a serious virtual device malfunction (such as motor burnout) in the k-th step operation.

[0040] B2. Based on the student question and answer data, obtain the student's theoretical question and answer score; After acquiring student question-and-answer data, the practical training lesson plan adaptation device calculates the students' theoretical question-and-answer scores based on that data. .

[0041] Among them, the students' theoretical question and answer scores The training lesson plan adapter device issues voice questions, and students score theoretical questions based on their answers to the voice questions.

[0042] B3. Based on the operational failure correlation degree and the student's theoretical question-and-answer score, the critical lethal weight is obtained.

[0043] After obtaining the operational fault correlation degree and the student's theoretical question and answer score, the practical training lesson plan adaptation device obtains the critical lethal weight based on the operational fault correlation degree and the student's theoretical question and answer score.

[0044] As one implementation method, the critical lethal weight is obtained based on the operational failure correlation degree and the student's theoretical question-and-answer score, including: ; in, The critical lethal weight for the k-th training stage; Let the correlation of operational faults in the k-th training session be denoted as . Students are scored based on their answers to theoretical questions.

[0045] in, This is a weighted term for cognitive confidence based on the Sigmoid function. When the training lesson plan adaptation device detects a node that has a high theoretical score but is strongly correlated with a serious fault, the calculated lethal weight will increase significantly. The system will then automatically mark this node as a "mandatory demonstration point" and activate the video lock mechanism in the lesson plan, forcing students to complete the safety demonstration before unlocking the device for further operation.

[0046] Building upon the precise quantification of teaching obstacles, this application further implements a key planning strategy based on implicit risk identification. To address the safety hazard of "high expectations but low skills," mutual information is calculated to identify "black swan" events (also known as critical lethal weights) that students believe they have mastered but which actually pose extremely high risks in practice. Based on this, the key points of the lesson plan are adjusted.

[0047] S40, adapt the practical training lesson plan according to the teaching resistance index and the critical lethal weight; The training lesson plan adaptation device obtains the teaching resistance index and the critical lethal weight, and then dynamically adjusts the operation difficulty standard and the teaching task planning time based on the teaching resistance index and the critical lethal weight.

[0048] In other words, for the calculated high-resistance nodes, this application employs a bidirectional path of "content splitting" and "threshold adjustment" for adaptive reconstruction to achieve load balancing. When teaching resistance... Excessively high and the main contribution comes from When (i.e., when cognition is ambiguous), the system determines that the information density of the current knowledge point is too high and automatically triggers a content splitting program to break down the original teaching nodes. The process is broken down into multiple sub-node sequences with sequential logical relationships, thereby reducing the cognitive load on individual nodes.

[0049] As one implementation method, when the resistance mainly stems from operational hesitation or trial and error, the system determines it as a lack of skill. In this case, the content structure is not changed, but the difficulty level of the virtual training operation is dynamically adjusted. (Dynamically adjust the operational difficulty standard), adapting the practical training lesson plan according to the aforementioned teaching resistance index and critical lethal weight, including: C1, based on the teaching resistance index, obtain the standard for dynamically adjusting the operational difficulty; After obtaining the teaching resistance index, the training lesson plan adaptation device obtains a dynamic adjustment standard for the operational difficulty based on the teaching resistance index. Based on the aforementioned teaching resistance index, a dynamic adjustment standard for operational difficulty is derived, specifically including: ; in, To dynamically adjust the operational difficulty standards; Standard operating difficulty; This is the teaching resistance index.

[0050] That is, as the resistance increases, the difficulty and workload of practical operation will be reduced in subsequent adjustments. For example, the precision of the seam operation in the fabrication of the trough will be relaxed first to provide students with a step-by-step practical operation standard. The standard will be gradually tightened after the students become more proficient.

[0051] C2, based on the teaching resistance index and the critical lethal weight, the teaching task planning time; After obtaining the teaching resistance index and the critical lethal weight, the training lesson plan adaptation device obtains the teaching task planning time based on the teaching resistance index and the critical lethal weight. As one implementation method, the teaching task planning time is obtained based on the teaching resistance index and the critical lethal weight, specifically including: ; in, Plan your time for teaching tasks; Total course duration; Let be the teaching resistance index for the k-th practical training session; The critical lethal weight for the k-th training stage; Let j be the teaching resistance index for the j-th practical training session. Let be the critical lethal weight of the j-th training session.

[0052] This application flexibly reallocates course time resources based on the teaching resistance and key weight of each node. To ensure that teaching time is used effectively, the system recalculates the recommended planning time for practical training sub-tasks based on the characteristics of each practical training task node. .

[0053] C3 adapts the practical training lesson plan by dynamically adjusting the operational difficulty standards and the teaching task planning time.

[0054] After obtaining the standards for dynamically adjusting the operational difficulty and the planned time for teaching tasks, the practical training lesson plan adaptation device adapts the practical training lesson plan by dynamically adjusting the standards for dynamically adjusting the operational difficulty and the planned time for teaching tasks.

[0055] This application presents a dynamic load balancing and adaptive reconstruction method for practical training lesson plans based on multimodal feedback. Utilizing a data-driven approach, it ensures that high-resistance, high-risk teaching nodes automatically receive more time and resources, while the time allocated to low-resistance, non-core nodes is correspondingly reduced. Through the coordinated adjustment of these four dimensions, this invention transforms practical training lesson plans from static text to dynamic intelligent agents, effectively addressing the core pain point of mismatch between teaching and learning, and providing a method for adjusting the time and resources of electrical engineering skills training classes.

[0056] In summary, this application obtains student teaching videos, practical training simulation data, and student Q&A data; derives a teaching resistance index based on these data; derives a critical lethal weight based on the same data; and adapts the practical training lesson plan based on the teaching resistance index and the critical lethal weight. This enables the lesson plan to self-evolve and precisely adapt. It solves the load imbalance phenomenon that leads to either "overly difficult teaching content causing cognitive overload" or "overly superficial content causing inefficient teaching." It achieves a reasonable allocation of the weight of key process details in the lesson plan; it implements a flexible adjustment mechanism for actual learning efficiency; and it enables the self-evolution and precise adaptation of the practical training lesson plan using a data-driven approach.

[0057] For those consistent with the above, please refer to Figure 2 , Figure 2 This application provides a schematic diagram of the structure of a training lesson plan adapter device. Figure 2 As shown, the device includes: The data acquisition module 201 is used to acquire student teaching videos, practical training simulation data, and student question and answer data; The teaching resistance determination module 202 is used to obtain a teaching resistance index based on the student teaching video, the practical training simulation data, and the student question and answer data. The lethal weight determination module 203 is used to obtain the key lethal weights based on the training simulation data and the student question and answer data. The lesson plan adaptation module 204 is used to adapt the practical training lesson plan according to the teaching resistance index and the critical lethal weight.

[0058] like Figure 3 As shown, this application embodiment also provides a terminal device 2, which includes: at least one processor 20, a memory 21, and a computer program 22 stored in the memory 21 and executable on the at least one processor. The processor 20 and the memory 21 are connected. When the processor 20 executes the computer program 22, it implements the steps in the robot motion planning method embodiment.

[0059] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the training lesson plan adaptation methods described in the above method embodiments.

[0060] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the practical training lesson plan adaptation methods described in the above method embodiments.

[0061] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a device / terminal equipment, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable storage media cannot be electrical carrier signals or telecommunication signals.

[0062] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0063] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0064] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0065] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

Claims

1. A method for adapting (assigning) practical training lesson plans, characterized in that, The method includes: Acquire student teaching videos, practical training simulation data, and student Q&A data; The teaching resistance index is obtained based on the student teaching videos, the practical training simulation data, and the student question and answer data. Based on the training simulation data and the student question-and-answer data, the critical lethal weights are obtained; The practical training lesson plan is adapted based on the teaching resistance index and the critical lethal weight.

2. The practical training lesson plan adaptation method according to claim 1, characterized in that, The process of obtaining the teaching resistance index based on the student teaching videos, the practical training simulation data, and the student question-and-answer data includes: Based on the student teaching videos, the student's hand hovering time, time variance of the same action, and process standardization score were obtained. Based on the student question-and-answer data, the semantic ambiguity of the student questions and answers is obtained; Based on the training simulation data, the functional detection failure rate is obtained; The teaching resistance index is obtained based on the student's hand hovering time, the time variance of the same action, the process standardization score, the semantic ambiguity of the student's question and answer, and the function detection failure rate.

3. The practical training lesson plan adaptation method according to claim 2, characterized in that, The teaching resistance index is obtained based on the student's hand hovering time, the variance of the same action time, the process standardization score, the semantic ambiguity of the student's question and answer, and the functional detection failure rate, including: ； in, The teaching resistance index; The time it takes for the student's hand to hover during the k-th training session; Let V be the time variance of the same action in the k-th training session; The score for process standardization in the k-th training session; The semantic ambiguity of student questions and answers in the k-th training session; Let be the functional test failure rate of the kth training session; These are the weighting coefficients for the first mode. The modal weighting coefficient is the first modality. These are the weighting coefficients for the third mode; The weighting coefficients for the fourth mode; This is the first adjustment constant; This is the second adjustment constant.

4. The practical training lesson plan adaptation method according to claim 1, characterized in that, The process of obtaining critical lethal weights based on the training simulation data and the student question-and-answer data includes: Based on the training simulation data, the correlation degree of operational faults is obtained; Based on the student question and answer data, the student's theoretical question and answer score is obtained; Based on the operational failure correlation degree and the student's theoretical question-and-answer score, the critical lethal weight is obtained.

5. The practical training lesson plan adaptation method according to claim 4, characterized in that, The critical lethal weights are obtained based on the operational failure correlation degree and the student's theoretical question-and-answer score, including: ； in, The critical lethal weight for the k-th training stage; Let the correlation of operational faults in the k-th training session be denoted as . Students are scored based on their answers to theoretical questions.

6. The practical training lesson plan adaptation method according to claim 1, characterized in that, The process of dynamically adjusting the operational difficulty standard and teaching task planning time based on the teaching resistance index and the critical lethal weight includes: Based on the teaching resistance index, a standard for dynamically adjusting operational difficulty is obtained; Based on the teaching resistance index and the critical lethal weight, the teaching task planning time is obtained; The practical training lesson plan is adapted by dynamically adjusting the operational difficulty standards and the time allotted for teaching tasks.

7. The practical training lesson plan adaptation method according to claim 6, characterized in that, The method of planning the teaching task time based on the teaching resistance index and the critical lethal weight includes: ； in, Total course duration; Let be the teaching resistance index for the k-th practical training session; The critical lethal weight for the k-th training stage; Let j be the teaching resistance index for the j-th practical training session. Let be the critical lethal weight of the j-th training session.

8. A practical training lesson plan adapter device, characterized in that, include: The data acquisition module is used to acquire student teaching videos, practical training simulation data, and student Q&A data; The teaching resistance determination module is used to obtain a teaching resistance index based on the student teaching videos, the practical training simulation data, and the student question and answer data. The lethal weight determination module is used to obtain the key lethal weights based on the training simulation data and the student question and answer data. The lesson plan adaptation module is used to adapt the practical training lesson plan according to the teaching resistance index and the critical lethal weight.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the practical training lesson plan adaptation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the training lesson plan adaptation method as described in any one of claims 1 to 7.

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