A simulation teaching training data management system and method based on knowledge graph

Abstract

The present invention discloses a simulation teaching and training data management system and method based on knowledge graph, which relates to the technical field of teaching and training data analysis. The system comprises: a teaching and training analysis module, a knowledge graph construction module, and a learning recommendation module; the teaching and training analysis module obtains task parameters in all teaching and training tasks and standard operation instruction parameters corresponding to the current teaching and training task content, and weights the standard operation instructions in each teaching and training task content, and is also used to evaluate the scores of trainees' teaching and training tasks; the knowledge graph construction module is used to construct a standard knowledge graph for teaching and training tasks, a knowledge graph for simulation teaching and training tasks, and a knowledge graph for actual training; the learning recommendation module is used to periodically evaluate the results of trainees' simulation teaching and training tasks and actual teaching and training tasks, and send recommended learning information to trainees based on the evaluation results.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching and training data analysis, and specifically to a simulation teaching and training data management system and method based on knowledge graph. Background Art

[0002] Simulation teaching and training refers to the construction of simulation scenarios through virtual reality, augmented reality and other means, allowing students to operate, train and learn in a simulated environment, which can effectively avoid safety risks in the real environment and improve the efficiency of students' teaching and training. However, only relying on the data of simulation teaching and training for learning outcome analysis lacks comprehensive testing of students' actual operations and theoretical knowledge. The final learning evaluation results are also unconvincing and cannot truly reflect the students' actual learning status and provide students with accurate personalized learning plans. Summary of the invention

[0003] The purpose of the present invention is to provide a simulation teaching training data management system and method based on knowledge graph to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for managing simulation teaching training data based on knowledge graph, comprising the following steps:

[0006] Step S100: Obtain the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content, and weight the operation instructions in each teaching and training task content to construct a standard knowledge graph of teaching and training tasks; wherein, the task parameters refer to the task parameters of the trainees in each teaching and training task; the operation instructions refer to the operation instructions taken by the trainees in each teaching and training task;

[0007] Step S200: Obtain the task parameters of the student's simulation teaching and training task and the student's operation instructions, evaluate the scores of each simulation teaching and training task, and construct a knowledge graph of the simulation teaching and training task;

[0008] Step S300: Obtain the task parameters and operation instructions of the actual teaching and training tasks of the students, evaluate the scores of each actual teaching and training task, and construct the actual training knowledge graph using the method in step S200;

[0009] Step S400: Setting a teaching and training task data sampling period, collecting task parameters and student operation instructions in the student simulation teaching and training tasks and actual teaching and training tasks within the period, constructing a knowledge graph of the simulation teaching and training tasks and an actual training knowledge graph for each period, calculating the edge deviation and comprehensive deviation of the knowledge graph of the simulation teaching and training tasks and the actual training knowledge graph for each period, and making a judgment and evaluation on the actual training operation of the student;

[0010] Step S500: Send recommended learning information to students based on the evaluation results of their teaching and training tasks.

[0011] In the above technical solution, step S100 includes the following contents:

[0012] Obtain the task parameters in the teaching and training task, synchronously obtain the standard operation instruction parameters corresponding to the current teaching and training task content to construct a standard operation instruction set, and mark each standard operation instruction parameter with a standard weight;

[0013] The task parameters and standard operation instructions are used as nodes of the teaching and training task standard knowledge graph, and the lines between the nodes corresponding to the task parameters and the nodes corresponding to the standard operation instructions are used as edges of the teaching and training task standard knowledge graph to construct a teaching and training task standard knowledge graph, and calculate the weights of each edge of the teaching and training task standard knowledge graph according to the formula:

[0014] ;

[0015] in, is the edge weight between the task parameter e and the operation instruction o in the standard knowledge graph of the teaching and training task, is the number of teaching and training tasks in which task parameters e and operation instructions o appear simultaneously, is the number of teaching and training tasks with one or more task parameters e and operation instructions o, t is the teaching and training task number, is the standard weight of operation instruction o in teaching training task t;

[0016] A standard knowledge graph of teaching and training tasks is constructed based on the standard operating instructions of each teaching and training task. This provides data reference when analyzing students' simulation teaching and training tasks and actual teaching and training task results to ensure the scientificity and credibility of the final evaluation results. At the same time, the relationship between each task parameter and the operating instructions is analyzed in detail to provide a data basis for subsequent learning recommendations.

[0017] In the above technical solution, step S200 includes the following contents:

[0018] Obtain the task parameters of the students' simulation teaching training tasks and the students' operation instructions;

[0019] Then, each simulation teaching and training task is evaluated by a percentage system. The higher the score, the better the evaluation result of the simulation teaching and training task.

[0020] Then, the task parameters and operation instructions are used as nodes of the simulation teaching and training task knowledge graph, and the lines between the nodes corresponding to the task parameters and the nodes corresponding to the operation instructions are used as edges of the simulation teaching and training task knowledge graph to construct a simulation teaching and training task knowledge graph, and calculate the weights of each edge of the simulation teaching and training task knowledge graph, according to the formula:

[0021] ;

[0022] in, is the edge weight between the task parameter e and the operation instruction o in the simulation teaching training task knowledge graph, is the number of simulation teaching training tasks in which task parameters e and operation instructions o appear simultaneously, is the number of simulation teaching and training tasks with one or more task parameters e and operation instructions o, t is the simulation teaching and training task number, is the evaluation score of the simulation teaching training task t, is the standard weight of operation instruction o in simulation teaching training task t; if the standard operation instruction set of simulation teaching training task t does not include operation instruction o, then The value is 0;

[0023] Set the repeated data sampling threshold , is a positive integer. If the trainee has repeated training on the same simulation teaching training task, the time point closest to the current time is selected. The weighted average of the selected task evaluation scores is used as the evaluation score of the simulation teaching training task for the trainees' repeated training; if the number of times the same task is completed is less than , then all the same task data are selected for calculation;

[0024] The weight v of each task evaluation score is:

[0025] ;

[0026] in, is the time interval between the task completion time and the current time point, x is the number of the same simulation teaching training task, The time interval between the task completion time point and the current time point of the simulation teaching training task x;

[0027] Detailed analysis of students' learning outcomes after completing teaching and training tasks will reflect students' mastery of the operating instructions corresponding to the task parameters in each teaching and training task, ensuring the accuracy of subsequent teaching recommendations.

[0028] In the above technical solution, step S400 is divided into the following steps:

[0029] Step S401: Setting a teaching and training task data sampling period, collecting task parameters of students' simulated teaching and training tasks and actual teaching and training tasks and students' operation instructions within the period;

[0030] Step S402: Use the method in step S200 and step S300 to construct a knowledge graph of simulation teaching training tasks and an actual training knowledge graph for each cycle;

[0031] Step S403: Calculate the edge deviation and comprehensive deviation of the knowledge graph of each period simulation teaching and training task and the actual training knowledge graph relative to the standard knowledge graph of the teaching and training task;

[0032] The edge deviation calculation formula is as follows:

[0033] ;

[0034] in, is the edge deviation between nodes corresponding to task parameter e and operation instruction o, is the edge weight of the task parameter e and the operation instruction o in the standard knowledge graph of the teaching and training task, is the edge weight of the task parameter e and the operation instruction o in the current knowledge graph;

[0035] Then according to the formula Calculate the comprehensive deviation between knowledge graphs;

[0036] Step S404: Obtain the comprehensive deviation of the knowledge graph of each period simulation teaching and training task and the actual training knowledge graph relative to the standard knowledge graph of the teaching and training task and , and evaluate the teaching and training task results of the trainees during the current teaching and training task data sampling period;

[0037] like , determine that the user's actual teaching and training task results are better than the simulation teaching and training task results, and increase the simulation teaching and training task volume in subsequent training; if , determine that the user's simulation teaching and training task results are better than the actual teaching and training task results, and increase the actual teaching and training task volume in subsequent training; where k is the teaching and training task result evaluation coefficient;

[0038] By analyzing the deviation of students' learning outcomes in simulation teaching training and actual teaching training relative to the operational standards, the students' learning effectiveness in different teaching and training methods is evaluated, and then the training volume of students in different teaching and training methods is balanced to ensure that students' theoretical knowledge and practical operation knowledge levels are improved simultaneously.

[0039] In the above technical solution, step S500 includes the following contents:

[0040] Obtain the evaluation results of the students' teaching and training task achievements within the current teaching and training task data sampling period, extract the knowledge graph corresponding to the teaching and training task methods with poor training achievements, calculate the sum of the edge deviation values ​​between the nodes corresponding to each teaching and training task, including each task parameter and operation instruction, in the knowledge graph, and use the calculation result as the recommended learning priority value of each historical teaching and training task. A teaching and training task with a larger recommended learning priority value has a higher recommended learning priority, and then send recommended learning information to the students. The recommended learning information includes the teaching and training task methods with poor training achievements and the teaching and training tasks sorted from high to low according to the recommended learning priority; wherein the teaching and training task methods include simulation teaching and training tasks and actual teaching and training tasks.

[0041] A simulation teaching and training data management system based on knowledge graph using the simulation teaching and training data management method based on knowledge graph described in the above technical solution, the system includes: a teaching and training analysis module, a knowledge graph construction module, and a learning recommendation module;

[0042] The teaching and training analysis module obtains the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content, and weights the standard operation instructions in each teaching and training task content. It is also used to evaluate the scores of the students' simulation teaching and training tasks and actual teaching and training tasks; the knowledge graph construction module is used to construct standard knowledge graphs for teaching and training tasks, simulation teaching and training task knowledge graphs, and actual training knowledge graphs; the learning recommendation module is used to evaluate the students' simulation teaching and training tasks and actual teaching and training task results within each teaching and training task data sampling period, and send recommended learning information to the students based on the evaluation results.

[0043] In the above technical solution, the teaching and training analysis module includes: a task parameter and operation instruction marking unit, a standard operation instruction weight marking unit, and a student teaching and training task evaluation unit;

[0044] The task parameter and operation instruction labeling unit is used to label the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content; the standard operation instruction weight labeling unit is used to weight the standard operation instructions in each teaching and training task content; the student teaching and training task evaluation unit is used to score and evaluate the student's simulation teaching and training tasks and actual teaching and training tasks.

[0045] In the above technical solution, the knowledge graph construction module includes: a teaching and training task standard knowledge graph construction unit, a simulation teaching and training task knowledge graph construction unit, and an actual training knowledge graph construction unit;

[0046] The teaching and training task standard knowledge graph construction unit is used to construct the teaching and training task standard knowledge graph; the simulation teaching and training task knowledge graph construction unit is used to construct the simulation teaching and training task knowledge graph; the actual training knowledge graph construction unit is used to construct the actual training knowledge graph construction unit.

[0047] In the above technical solution, the learning recommendation module includes: a learning outcome evaluation unit, a recommended learning priority analysis unit, and a learning recommendation unit;

[0048] The deviation analysis unit is used to calculate the edge deviation and comprehensive deviation of the simulation teaching and training task knowledge graph and the actual training knowledge graph of each period relative to the standard knowledge graph of the teaching and training tasks; the learning outcome evaluation unit evaluates the students' simulation teaching and training tasks and actual teaching and training task results within each teaching and training task data sampling period; the recommended learning priority analysis unit is used to calculate the recommended learning priority value of each teaching and training task; the learning recommendation unit sends recommended learning information to the students based on the evaluation results and the recommended learning priority values ​​of each teaching and training task.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] The present invention constructs a standard knowledge graph for teaching and training tasks, a knowledge graph for simulation teaching and training tasks, and a knowledge graph for actual training, and analyzes in detail the correlation between task parameters and operating instructions in each teaching and training task, and then conducts a comprehensive analysis of the students' teaching and training tasks and the students' actual completion results, so as to accurately reflect the students' learning and mastery level of each knowledge point in the teaching and training content under different teaching and training methods; the present invention also calculates and analyzes the deviation of each knowledge graph relative to the standard knowledge graph for teaching and training tasks, comprehensively evaluates the students' actual learning level, and provides a scientific data basis for the students' learning recommendations; in addition, the present invention also accurately provides matching teaching and training methods and teaching and training tasks based on the calculation results of the deviation between knowledge graphs, and provides students with accurate personalized learning recommendation functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of a method for managing simulation teaching training data based on knowledge graph in the present invention;

[0052] Figure 2 This is an organizational chart of a simulation teaching and training data management system based on knowledge graph in the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example: See Figure 1-Figure 2 , the present invention provides a technical solution,

[0055] like Figure 1 As shown, the present invention provides a method for managing simulation teaching training data based on knowledge graph, comprising the following steps:

[0056] Step S100: Obtain the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content, and weight the operation instructions in each teaching and training task content to construct a standard knowledge graph of teaching and training tasks; wherein, the task parameters refer to the task parameters of the trainees in each teaching and training task; the operation instructions refer to the operation instructions taken by the trainees in each teaching and training task;

[0057] Step S200: Obtain the task parameters of the student's simulation teaching and training task and the student's operation instructions, evaluate the scores of each simulation teaching and training task, and construct a knowledge graph of the simulation teaching and training task;

[0058] Step S300: Obtain the task parameters and operation instructions of the actual teaching and training tasks of the students, evaluate the scores of each actual teaching and training task, and construct the actual training knowledge graph using the method in step S200;

[0059] Step S400: Setting a teaching and training task data sampling period, collecting task parameters and student operation instructions in the student simulation teaching and training tasks and actual teaching and training tasks within the period, constructing a knowledge graph of the simulation teaching and training tasks and an actual training knowledge graph for each period, calculating the edge deviation and comprehensive deviation of the knowledge graph of the simulation teaching and training tasks and the actual training knowledge graph for each period, and making a judgment and evaluation on the actual training operation of the student;

[0060] Step S500: Send recommended learning information to students based on the evaluation results of their teaching and training tasks.

[0061] Step S100 includes the following contents:

[0062] Obtain the task parameters in the teaching and training task, and simultaneously obtain the standard operation instruction parameters corresponding to the current teaching and training task content to construct a standard operation instruction set, and perform standard weight labeling on each standard operation instruction parameter; wherein, the task parameters refer to the task parameters of the trainees in each teaching and training task; the operation instructions refer to the operation instructions taken by the trainees in each teaching and training task;

[0063] In the specific implementation, all teaching and training task information is obtained, the task parameters in each task and the standard operation of each teaching and training task are analyzed, each operation is refined and divided into a single operation instruction, and unified into an operation instruction set for all teaching and training tasks;

[0064] The task parameters and standard operation instructions are used as nodes of the teaching and training task standard knowledge graph, and the lines between the nodes corresponding to the task parameters and the nodes corresponding to the standard operation instructions are used as edges of the teaching and training task standard knowledge graph to construct a teaching and training task standard knowledge graph, and calculate the weights of each edge of the teaching and training task standard knowledge graph according to the formula:

[0065] ;

[0066] in, is the edge weight between the task parameter e and the operation instruction o in the standard knowledge graph of the teaching and training task, is the number of teaching and training tasks in which task parameters e and operation instructions o appear simultaneously, is the number of teaching and training tasks with one or more task parameters e and operation instructions o, t is the teaching and training task number, is the standard weight of operation instruction o in teaching training task t;

[0067] In the specific implementation, each task parameter is connected with the operation instruction as the edge of the knowledge graph, and the nodes at both ends of each edge are different, and then the data of each teaching and training task are obtained to construct the standard knowledge graph of the teaching and training task; wherein, the standard weight of the operation instruction o in the training task t refers to the importance measurement value of the instruction o in the teaching and training task t;

[0068] For the above calculation formula, the following calculation example is given: Assuming that there are 5 teaching and training tasks that contain at least one of the task parameter e and the operation instruction o, and 3 of them contain both the task parameter e and the operation instruction o, and the standard weights are 0.8, 0.9, and 0.7 respectively. It can be calculated that the edge weight of the task parameter e and the operation instruction o in the standard knowledge graph of the teaching and training tasks is 0.48.

[0069] Step S200 includes the following contents:

[0070] Obtain the task parameters of the students' simulation teaching training tasks and the students' operation instructions;

[0071] Then, each simulation teaching and training task is evaluated by a percentage system. The higher the score, the better the evaluation result of the simulation teaching and training task.

[0072] Then, the task parameters and operation instructions are used as nodes of the simulation teaching and training task knowledge graph, and the lines between the nodes corresponding to the task parameters and the nodes corresponding to the operation instructions are used as edges of the simulation teaching and training task knowledge graph to construct a simulation teaching and training task knowledge graph, and calculate the weights of each edge of the simulation teaching and training task knowledge graph, according to the formula:

[0073] ;

[0074] in, is the edge weight between the task parameter e and the operation instruction o in the simulation teaching training task knowledge graph, is the number of simulation teaching training tasks in which task parameters e and operation instructions o appear simultaneously, is the number of simulation teaching and training tasks with one or more task parameters e and operation instructions o, t is the simulation teaching and training task number, is the evaluation score of the simulation teaching training task t, is the standard weight of operation instruction o in simulation teaching training task t; if the standard operation instruction set of simulation teaching training task t does not include operation instruction o, then The value is 0;

[0075] For the above calculation formula, the following calculation example is given: Continuing with the data in the above calculation example, assuming that the students complete three of the teaching tasks and the evaluation scores are 90, 85, and 95 respectively, the edge weight between the task parameter e and the operation instruction o in the simulation teaching training task knowledge graph is calculated to be 0.43;

[0076] Set the repeated data sampling threshold , is a positive integer. If the trainee has repeated training on the same simulation teaching training task, the time point closest to the current time is selected. The weighted average of the selected task evaluation scores is used as the evaluation score of the simulation teaching training task for the trainees' repeated training; if the number of times the same task is completed is less than , then all the same task data are selected for calculation;

[0077] The weight v of each task evaluation score is:

[0078] ;

[0079] in, is the time interval between the task completion time and the current time point, x is the number of the same simulation teaching training task, The time interval between the task completion time point and the current time point of the simulation teaching training task x;

[0080] In the specific implementation, according to common sense, the closer the completion time point is to the current time point, the more credible the task evaluation result is. Therefore, the time interval parameter is introduced to perform multi-score weighted averaging, and the evaluation from the perspective of data results is more interpretable and scientific.

[0081] Step S400 is divided into the following steps:

[0082] Step S401: Setting a teaching and training task data sampling period, collecting task parameters of students' simulated teaching and training tasks and actual teaching and training tasks and students' operation instructions within the period;

[0083] Step S402: Use the method in step S200 and step S300 to construct a knowledge graph of simulation teaching training tasks and an actual training knowledge graph for each cycle;

[0084] Step S403: Calculate the edge deviation and comprehensive deviation of the knowledge graph of each period simulation teaching and training task and the actual training knowledge graph relative to the standard knowledge graph of the teaching and training task;

[0085] The edge deviation calculation formula is as follows:

[0086] ;

[0087] in, is the edge deviation between nodes corresponding to task parameter e and operation instruction o, is the edge weight of the task parameter e and the operation instruction o in the standard knowledge graph of the teaching and training task, is the edge weight of the task parameter e and the operation instruction o in the current knowledge graph;

[0088] Then according to the formula Calculate the comprehensive deviation between knowledge graphs;

[0089] Step S404: Obtain the comprehensive deviation of the knowledge graph of each period simulation teaching and training task and the actual training knowledge graph relative to the standard knowledge graph of the teaching and training task and , and evaluate the teaching and training task results of the trainees during the current teaching and training task data sampling period;

[0090] like , determine that the user's actual teaching and training task results are better than the simulation teaching and training task results, and increase the simulation teaching and training task volume in subsequent training; if , determine that the user's simulation teaching and training task results are better than the actual teaching and training task results, and increase the actual teaching and training task volume in subsequent training; where k is the teaching and training task result evaluation coefficient;

[0091] In the specific implementation, students consolidate and deepen their theoretical knowledge through simulation teaching training, and then conduct practical operation training to complete the entire process of teaching training tasks. Among them, simulation teaching training lacks the psychological interference of the actual environment and reflects more of the students' theoretical knowledge, while practical operation reflects the students' actual operation level. Therefore, by comparing the teaching and training results of the two, the actual learning tendency of the students can be reflected, and more accurate teaching recommendations can be provided to the students.

[0092] When setting the evaluation coefficient of teaching and training task results, if k=1, it means that the actual operation teaching of teaching and training and the simulation theory teaching are of the same importance, and the larger the k value is, the higher the importance of simulation theory teaching is.

[0093] Step S500 includes the following contents:

[0094] Obtain the evaluation results of the students' teaching and training task achievements within the current teaching and training task data sampling period, extract the knowledge graph corresponding to the teaching and training task methods with poor training achievements, calculate the sum of the edge deviation values ​​between the nodes corresponding to each teaching and training task, including each task parameter and operation instruction, in the knowledge graph, and use the calculation result as the recommended learning priority value of each historical teaching and training task. A teaching and training task with a larger recommended learning priority value has a higher recommended learning priority, and then send recommended learning information to the students. The recommended learning information includes the teaching and training task methods with poor training achievements and the teaching and training tasks sorted from high to low according to the recommended learning priority; wherein the teaching and training task methods include simulation teaching and training tasks and actual teaching and training tasks.

[0095] like Figure 2As shown, the present invention also provides a simulation teaching and training data management system based on knowledge graph, the system includes: a teaching and training analysis module, a knowledge graph construction module, and a learning recommendation module;

[0096] The teaching and training analysis module obtains the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content, and weights the standard operation instructions in each teaching and training task content. It is also used to evaluate the scores of the students' simulation teaching and training tasks and actual teaching and training tasks; the knowledge graph construction module is used to construct standard knowledge graphs for teaching and training tasks, simulation teaching and training task knowledge graphs, and actual training knowledge graphs; the learning recommendation module is used to evaluate the students' simulation teaching and training tasks and actual teaching and training task results within each teaching and training task data sampling period, and send recommended learning information to the students based on the evaluation results.

[0097] The teaching and training analysis module includes: task parameter and operation instruction marking unit, standard operation instruction weight marking unit, and student teaching and training task evaluation unit;

[0098] The task parameter and operation instruction labeling unit is used to label the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content; the standard operation instruction weight labeling unit is used to weight the standard operation instructions in each teaching and training task content; the student teaching and training task evaluation unit is used to score and evaluate the student's simulation teaching and training tasks and actual teaching and training tasks.

[0099] The knowledge graph construction module includes: teaching and training task standard knowledge graph construction unit, simulation teaching and training task knowledge graph construction unit, and actual training knowledge graph construction unit;

[0100] The teaching and training task standard knowledge graph construction unit is used to construct the teaching and training task standard knowledge graph; the simulation teaching and training task knowledge graph construction unit is used to construct the simulation teaching and training task knowledge graph; the actual training knowledge graph construction unit is used to construct the actual training knowledge graph construction unit.

[0101] The learning recommendation module includes: learning outcome assessment unit, recommended learning priority analysis unit, and learning recommendation unit;

[0102] The deviation analysis unit is used to calculate the edge deviation and comprehensive deviation of the simulation teaching and training task knowledge graph and the actual training knowledge graph of each period relative to the standard knowledge graph of the teaching and training tasks; the learning outcome evaluation unit evaluates the students' simulation teaching and training tasks and actual teaching and training task results within each teaching and training task data sampling period; the recommended learning priority analysis unit is used to calculate the recommended learning priority value of each teaching and training task; the learning recommendation unit sends recommended learning information to the students based on the evaluation results and the recommended learning priority values ​​of each teaching and training task.

[0103] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for managing simulation teaching training data based on knowledge graph, characterized in that , the method comprises the following steps: Step S100: Obtain the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content, and weight the operation instructions in each teaching and training task content to construct a standard knowledge graph of teaching and training tasks; wherein, the task parameters refer to the task parameters of the trainees in each teaching and training task; the operation instructions refer to the operation instructions taken by the trainees in each teaching and training task; Step S200: Obtain the task parameters of the student's simulation teaching and training task and the student's operation instructions, evaluate the scores of each simulation teaching and training task, and construct a knowledge graph of the simulation teaching and training task; Step S300: Obtain the task parameters and operation instructions of the actual teaching and training tasks of the students, evaluate the scores of each actual teaching and training task, and construct the actual training knowledge graph using the method in step S200; Step S400: Setting a teaching and training task data sampling period, collecting task parameters and student operation instructions in the student simulation teaching and training tasks and actual teaching and training tasks within the period, constructing a knowledge graph of the simulation teaching and training tasks and an actual training knowledge graph for each period, calculating the edge deviation and comprehensive deviation of the knowledge graph of the simulation teaching and training tasks and the actual training knowledge graph for each period, and judging and evaluating the results of the student teaching and training tasks; Step S500: Send recommended learning information to students based on the evaluation results of their teaching and training tasks.

2. A method for managing simulation teaching training data based on knowledge graph according to claim 1, characterized in that: The step S100 includes the following contents: Obtain the task parameters in the teaching and training task, synchronously obtain the standard operation instruction parameters corresponding to the current teaching and training task content to construct a standard operation instruction set, and mark each standard operation instruction parameter with a standard weight; The task parameters and standard operation instructions are used as nodes of the teaching and training task standard knowledge graph, and the lines between the nodes corresponding to the task parameters and the nodes corresponding to the standard operation instructions are used as edges of the teaching and training task standard knowledge graph to construct a teaching and training task standard knowledge graph, and calculate the weights of each edge of the teaching and training task standard knowledge graph according to the formula: ； in, is the edge weight between the task parameter e and the operation instruction o in the standard knowledge graph of the teaching and training task, is the number of teaching and training tasks in which task parameters e and operation instructions o appear simultaneously, is the number of teaching and training tasks with one or more task parameters e and operation instructions o, t is the teaching and training task number, is the standard weight of operation instruction o in the teaching training task t.

3. A method for managing simulation teaching training data based on knowledge graph according to claim 1, characterized in that: The step S200 includes the following contents: Obtain the task parameters of the students' simulation teaching training tasks and the students' operation instructions; Then, each simulation teaching and training task is evaluated by a percentage system. The higher the score, the better the evaluation result of the simulation teaching and training task. Then, the task parameters and operation instructions are used as nodes of the simulation teaching and training task knowledge graph, and the lines between the nodes corresponding to the task parameters and the nodes corresponding to the operation instructions are used as edges of the simulation teaching and training task knowledge graph to construct a simulation teaching and training task knowledge graph, and calculate the weights of each edge of the simulation teaching and training task knowledge graph, according to the formula: ； in, is the edge weight between the task parameter e and the operation instruction o in the simulation teaching training task knowledge graph, is the number of simulation teaching training tasks in which task parameters e and operation instructions o appear simultaneously, is the number of simulation teaching and training tasks with one or more task parameters e and operation instructions o, t is the simulation teaching and training task number, is the evaluation score of the simulation teaching training task t, is the standard weight of operation instruction o in simulation teaching training task t; if the standard operation instruction set of simulation teaching training task t does not include operation instruction o, then The value is 0; Set the repeated data sampling threshold , is a positive integer. If the trainee has repeated training on the same simulation teaching training task, the time point closest to the current time is selected. The weighted average of the selected task evaluation scores is used as the evaluation score of the simulation teaching training task for the trainees' repeated training; if the number of times the same task is completed is less than , then all the same task data are selected for calculation; The weight v of each task evaluation score is: ； in, is the time interval between the task completion time and the current time point, x is the number of the same simulation teaching training task, It is the time interval between the task completion time point and the current time point of the simulation teaching training task x.

4. A method for managing simulation teaching training data based on knowledge graph according to claim 1, characterized in that: The step S400 is divided into the following steps: Step S401: Setting a teaching and training task data sampling period, collecting task parameters of students' simulated teaching and training tasks and actual teaching and training tasks and students' operation instructions within the period; Step S402: Use the method in step S200 and step S300 to construct a knowledge graph of simulation teaching training tasks and an actual training knowledge graph for each cycle; Step S403: Calculate the edge deviation and comprehensive deviation of the knowledge graph of each period simulation teaching and training task and the actual training knowledge graph relative to the standard knowledge graph of the teaching and training task; The edge deviation calculation formula is as follows: ； in, is the edge deviation between nodes corresponding to task parameter e and operation instruction o, is the edge weight of the task parameter e and the operation instruction o in the standard knowledge graph of the teaching and training task, is the edge weight of the task parameter e and the operation instruction o in the current knowledge graph; Then according to the formula Calculate the comprehensive deviation between knowledge graphs; Step S404: Obtain the comprehensive deviation of the knowledge graph of each period simulation teaching and training task and the actual training knowledge graph relative to the standard knowledge graph of the teaching and training task and , and evaluate the teaching and training task results of the trainees during the current teaching and training task data sampling period; like , determine that the user's actual teaching and training task results are better than the simulation teaching and training task results, and increase the simulation teaching and training task volume in subsequent training; if , determine that the user's simulation teaching and training task results are better than the actual teaching and training task results, and increase the actual teaching and training task volume in subsequent training; where k is the teaching and training task result evaluation coefficient.

5. A method for managing simulation teaching training data based on knowledge graph according to claim 4, characterized in that: The step S500 includes the following contents: Obtain the evaluation results of the students' teaching and training task achievements within the current teaching and training task data sampling period, extract the knowledge graph corresponding to the teaching and training task methods with poor training achievements, calculate the sum of the edge deviation values ​​between the nodes corresponding to each teaching and training task, including each task parameter and operation instruction, in the knowledge graph, and use the calculation result as the recommended learning priority value of each historical teaching and training task. A teaching and training task with a larger recommended learning priority value has a higher recommended learning priority, and then send recommended learning information to the students. The recommended learning information includes the teaching and training task methods with poor training achievements and the teaching and training tasks sorted from high to low according to the recommended learning priority; wherein the teaching and training task methods include simulation teaching and training tasks and actual teaching and training tasks.

6. A simulation teaching training data management system based on knowledge graph using a simulation teaching training data management method based on knowledge graph according to any one of claims 1 to 5, the system comprising: Teaching and training analysis module, knowledge graph construction module, and learning recommendation module; The teaching and training analysis module obtains the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content, and weights the standard operation instructions in each teaching and training task content, and is also used to evaluate the scores of each simulation teaching and training task and actual teaching and training task of the trainees; the knowledge graph construction module is used to construct a standard knowledge graph for teaching and training tasks, a knowledge graph for simulation teaching and training tasks, and an actual training knowledge graph; The learning recommendation module is used to evaluate the students' simulated teaching and training tasks and actual teaching and training task results within each teaching and training task data sampling period, and send recommended learning information to the students based on the evaluation results.

7. A simulation teaching training data management system based on knowledge graph according to claim 6, characterized in that: The teaching and training analysis module includes: a task parameter and operation instruction marking unit, a standard operation instruction weight marking unit, and a student teaching and training task evaluation unit; The task parameter and operation instruction labeling unit is used to label the task parameters in all teaching and training tasks and the standard operation instruction parameters corresponding to the current teaching and training task content; the standard operation instruction weight labeling unit is used to weight the standard operation instructions in each teaching and training task content; the student teaching and training task evaluation unit is used to score and evaluate the student's simulation teaching and training tasks and actual teaching and training tasks.

8. The simulation teaching training data management system based on knowledge graph according to claim 6 is characterized in that: The knowledge graph construction module includes: a teaching and training task standard knowledge graph construction unit, a simulation teaching and training task knowledge graph construction unit, and an actual training knowledge graph construction unit; The teaching and training task standard knowledge graph construction unit is used to construct the teaching and training task standard knowledge graph; the simulation teaching and training task knowledge graph construction unit is used to construct the simulation teaching and training task knowledge graph; the actual training knowledge graph construction unit is used to construct the actual training knowledge graph construction unit.

9. The simulation teaching training data management system based on knowledge graph according to claim 6 is characterized in that: The learning recommendation module includes: a deviation analysis unit, a learning achievement evaluation unit, a recommended learning priority analysis unit, and a learning recommendation unit; The deviation analysis unit is used to calculate the edge deviation and comprehensive deviation of the simulation teaching and training task knowledge graph and the actual training knowledge graph of each period relative to the standard knowledge graph of the teaching and training tasks; the learning outcome evaluation unit evaluates the students' simulation teaching and training tasks and actual teaching and training task results within each teaching and training task data sampling period; the recommended learning priority analysis unit is used to calculate the recommended learning priority value of each teaching and training task; the learning recommendation unit sends recommended learning information to the students based on the evaluation results and the recommended learning priority values ​​of each teaching and training task.

Images