Virtual Reality Operation Integrated Supervision Method Based on Multidimensional Data

Abstract

The present invention discloses a virtual reality operation integrated supervision method based on multi-dimensional data, belonging to the field of intelligent data management; it is used to solve the technical problems in the existing solutions that the teaching monitoring and evaluation means for teachers in the intelligent teaching process are single, and the teaching of teachers cannot be dynamically prompted and adjusted, resulting in poor overall effect of virtual reality operation management; by implementing modular monitoring and analysis of different teaching knowledge points in the teaching process from the teacher's aspect, not only can different teaching states in the teacher's teaching process be obtained, but also the accuracy of monitoring and analysis of different teaching knowledge points in the teaching process can be effectively improved; by integrating various data on the students' answering questions corresponding to different teaching knowledge points to obtain a supervision value, and overall evaluating the teaching effect of the teacher from the student's aspect according to the supervision value, the mastery of different teaching knowledge points by the students can be obtained in a timely and efficient manner.

Description

Technical Field

[0001] The present invention relates to the field of intelligent data management, and particularly to an integrated supervision method for virtual reality operation based on multi-dimensional data. Background Art

[0002] Virtual reality teaching is a method of using virtual reality technology for teaching and learning activities. Through specialized devices such as VR head-mounted displays and handles, students can immerse themselves in a virtual three-dimensional environment, interact with virtual objects, and experience various learning scenarios and situations.

[0003] Existing virtual reality teaching management solutions have certain defects in implementation. They do not modularly monitor and analyze the teaching situations of different teaching knowledge points from the aspects of teachers and students, and integrate the analysis results of different dimensions to evaluate and integrate the teaching situations of teachers' different teaching knowledge points, and dynamically prompt and adjust suggestions, resulting in poor overall effect of virtual reality operation management. Summary of the Invention

[0004] The purpose of the present invention is to provide an integrated supervision method for virtual reality operation based on multi-dimensional data, which is used to solve the technical problems that in the existing solutions, the teaching monitoring and evaluation means for teachers in the intelligent teaching process are single, and it is impossible to dynamically prompt and adjust suggestions for teachers' teaching, resulting in poor overall effect of virtual reality operation management.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] An integrated supervision method for virtual reality operation based on multi-dimensional data includes:

[0007] Monitoring the teaching of teachers from the teacher aspect and processing data to obtain a teaching supervision value;

[0008] Evaluating the teaching of teachers' teaching knowledge points according to the teaching supervision value, and matching the teaching supervision value with a preset teaching supervision range to obtain teaching monitoring data including a teaching correction signal, a first teaching difference signal, and a second teaching difference signal;

[0009] Monitoring the teaching effect of teachers from the student aspect and verifying data to obtain a learning supervision value;

[0010] Verifying and feeding back the teaching effect of teachers' teaching knowledge points according to the learning supervision value, and matching the learning supervision value with a preset learning supervision range to obtain teaching verification data including a learning correction signal, a first learning difference signal, and a second learning difference signal;

[0011] Integrating the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teaching process of teachers to evaluate the overall teaching situation and obtain an evaluation result;

[0012] Dynamically prompt the implementation of its teaching plan adaptively according to the evaluation results and dynamically display it through VR technology.

[0013] Preferably, obtain the teacher's real-time teaching knowledge points and the corresponding knowledge point weights; obtain the teaching duration corresponding to the knowledge point weights; extract the marked numerical values of the knowledge point weights and teaching durations and calculate to obtain the teaching supervision value corresponding to the teaching knowledge points.

[0014] Preferably, perform matching analysis on the teaching supervision value and the teaching supervision range to obtain a correction signal, a first teaching anomaly signal, or a second teaching anomaly signal, and respectively increment the total number of normal teaching times of the teaching knowledge points, the total number of first teaching anomalies, or the total number of second teaching anomalies by one according to the correction signal, the first teaching anomaly signal, or the second teaching anomaly signal;

[0015] The teaching supervision value and the corresponding correction signal, first teaching anomaly signal, and second teaching anomaly signal constitute the teaching monitoring data corresponding to the teaching knowledge points.

[0016] Preferably, monitor and statistically analyze the students' problem-solving situations during the learning of teaching knowledge points in sequence according to the students' student numbers;

[0017] Statistically analyze the total number of problems solved by the students during the learning of teaching knowledge points;

[0018] Obtain the difficulty levels of the exercises for different teaching knowledge points and match them with the pre-stored exercise difficulty weight table in the database to obtain the corresponding exercise weights;

[0019] Statistically analyze the first problem-solving duration corresponding to the students' correct problem-solving; and statistically analyze the second problem-solving duration corresponding to the students' incorrect problem-solving;

[0020] When verifying and feeding back the teaching effects of the teacher's teaching knowledge points from the students' perspective in sequence, extract the marked numerical values of the various data and calculate to obtain the learning supervision value corresponding to the teaching knowledge points.

[0021] Preferably, perform matching analysis on the learning supervision value and the learning supervision range to obtain a correction signal, a first learning anomaly signal, or a second learning anomaly signal, and respectively increment the total number of normal teaching times of the teaching knowledge points, the total number of first learning anomalies, or the total number of second learning anomalies by one according to the correction signal, the first learning anomaly signal, or the second learning anomaly signal;

[0022] The learning supervision value and the corresponding correction signal, first learning anomaly signal, and second learning anomaly signal constitute the teaching verification data.

[0023] Preferably, obtain the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points during the teacher's teaching process and traverse them, statistically analyze and respectively mark the total number of occurrences of the first teaching anomaly signal and the second teaching anomaly signal obtained in the traversal results;

[0024] Also, the total number of occurrences of the first learning anomaly signal and the second learning anomaly signal obtained in the traversal result is counted and marked respectively; the numerical values of the marked data are extracted and integrated in parallel to obtain the teaching integral value corresponding to the smart teaching.

[0025] Preferably, when evaluating the overall teaching situation of the teacher based on the teaching adjustment value, the teaching adjustment value is matched with the preset first teaching adjustment threshold and the second teaching adjustment threshold to obtain an evaluation result consisting of the first adjustment signal, the second adjustment signal, the third adjustment signal, the fourth adjustment signal, and the fifth adjustment signal.

[0026] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0027] The present invention implements modular monitoring and analysis of different teaching knowledge points in the teaching process from the teacher's perspective, so as to obtain different teaching states in the teacher's teaching process, integrate various data on students' question-solving corresponding to different teaching knowledge points to obtain learning monitoring values, and make an overall evaluation of the teacher's teaching effect from the students' perspective based on the learning monitoring values, so as to timely and efficiently obtain the students' mastery of different teaching knowledge points, integrate the teaching monitoring data and teaching verification data obtained through monitoring and analysis from the teacher and students' perspectives to obtain a teaching overall value, and analyze, evaluate and classify the teacher's overall teaching process and teaching methods based on the teaching overall value, so as to enable the teacher to timely and efficiently discover the defects in the teaching process, so as to make targeted adjustments to the teaching process and teaching methods, thereby improving the overall effect of smart teaching monitoring and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below in conjunction with the accompanying drawings.

[0029] Figure 1 The present invention is a flowchart of the integrated supervision method for virtual reality operation based on multi-dimensional data. DETAILED DESCRIPTION

[0030] 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.

[0031] like Figure 1 As shown, the present invention is a virtual reality operation integrated supervision method based on multi-dimensional data, comprising:

[0032] Monitor the teaching of teachers from the teacher's side and process the data to obtain the teaching supervision value; including:

[0033] Obtain the real-time teaching knowledge points of the teacher and the corresponding teaching duration. Set that each different teaching knowledge point corresponds to a different knowledge point weight. Match the obtained teaching knowledge points with all the pre-stored teaching knowledge points in the database to obtain the corresponding knowledge point weight and mark it as ZQ;

[0034] Among them, the knowledge point weight is used to digitally represent text-based teaching knowledge points of different difficulties, which can provide effective data support for the differential analysis and evaluation of different teaching knowledge points of teachers; the specific value of the knowledge point weight can be obtained by relying on the historical examination big data corresponding to the teaching knowledge points. The more unqualified historical student examination results corresponding to the teaching knowledge points, the greater the corresponding knowledge point weight, and the more time the teacher needs to focus on key teaching;

[0035] On the contrary, the more qualified historical student examination results corresponding to the teaching knowledge points, the smaller the corresponding knowledge point weight, and the teacher can reduce the teaching time of this knowledge point for simple teaching;

[0036] And mark the teaching duration corresponding to the knowledge point weight as JS; the unit of the teaching duration is minutes. When evaluating the teaching of the teacher's teaching knowledge points in turn, extract the marked values of the knowledge point weight and the teaching duration and combine and integrate them. Calculate to obtain the teaching supervision value JJ corresponding to the teaching knowledge point; the calculation formula of the teaching supervision value JJ is: In the formula, JS0 is the standard teaching duration corresponding to the teaching knowledge point, which can be obtained based on the historical examination big data corresponding to the teaching knowledge point;

[0037] It should be noted that the teaching supervision value is a value used to comprehensively evaluate the teaching situation of different teaching knowledge points in the teacher's teaching process; the difficulty levels of different teaching knowledge points are different, so the time required for the teacher to spend on teaching and explaining is also different. For example, difficult teaching knowledge points require more than one class period for explanation, and simple teaching knowledge points can be explained in half a class period; when difficult teaching knowledge points are explained in half a class period and easy teaching knowledge points are explained in one class period, it completely does not meet the teaching requirements and teaching logic; and the teaching supervision value is a value used to evaluate the teaching of the teacher's teaching knowledge points;

[0038] Evaluate the teaching of the teacher's teaching knowledge points according to the teaching supervision value, and match the teaching supervision value with the preset teaching supervision range; similarly, the teaching supervision range can be obtained based on the historical examination big data corresponding to the teaching knowledge points, and the specific value can be determined according to the number of students corresponding to unqualified examinations;

[0039] If the teaching supervisor value belongs to the teaching supervisor range, it is determined that the teaching of the corresponding teaching knowledge point is normal and a correction signal is generated. According to the correction signal, the total number of normal teaching times of the teaching knowledge point is incremented by one;

[0040] If the teaching supervisor value is less than the minimum value of the teaching supervisor range, it is determined that the teaching of the corresponding teaching knowledge point belongs to a type-I teaching anomaly and a first teaching anomaly signal is generated. According to the first teaching anomaly signal, the total number of first teaching anomalies of the teaching knowledge point is incremented by one; The type-I teaching anomaly can be that the teaching of the teaching knowledge point is too fast;

[0041] If the teaching supervisor value is greater than the maximum value of the teaching supervisor range, it is determined that the teaching of the corresponding teaching knowledge point belongs to a type-II teaching anomaly and a second teaching anomaly signal is generated. According to the second teaching anomaly signal, the total number of second teaching anomalies of the teaching knowledge point is incremented by one; The type-II teaching anomaly can be that the teaching of the teaching knowledge point is too slow;

[0042] The teaching supervisor value and the corresponding correction signal, first teaching anomaly signal, and second teaching anomaly signal constitute the teaching monitoring data corresponding to the teaching knowledge point and are uploaded to the cloud platform. After receiving the teaching monitoring data, the cloud platform generates a feedback instruction and, according to the feedback instruction, verifies and gives feedback on the teaching situation of the corresponding teaching knowledge point from the perspective of the students.

[0043] In the embodiments of the present invention, by implementing modular monitoring and analysis of different teaching knowledge points in the teaching process from the perspective of the teacher, not only can different teaching states in the teacher's teaching process be obtained, but also the accuracy of monitoring and analysis of different teaching knowledge points in the teaching process can be effectively improved, which can provide reliable data support for the overall state analysis and management of subsequent intelligent teaching.

[0044] Monitoring the teaching effect of the teacher from the perspective of the students and verifying the data to obtain the student supervisor value, including:

[0045] Monitoring and statistically analyzing the students' question-solving situations during the learning process of the teaching knowledge points in sequence according to the student numbers of the students;

[0046] Counting the total number of questions the students answered during the learning process of the teaching knowledge point and marking it as ZT;

[0047] Obtaining the difficulty levels of the exercises for different teaching knowledge points and matching them with the pre-stored exercise difficulty weight table in the database to obtain the corresponding exercise weights and marking them as TQ;

[0048] Among them, the exercise difficulty weight table contains several different exercise difficulties and corresponding exercise weights. A corresponding exercise weight is preset for different exercise difficulties. The specific value of the exercise weight can be obtained based on the historical big data of the exercises done by students. The more historical mistakes students make in the corresponding exercises, the greater the corresponding exercise weight; conversely, the more correct historical scores students get in the corresponding exercises, the smaller the corresponding exercise weight. The exercise difficulty includes but is not limited to simple, relatively simple, moderate, relatively difficult, and very difficult;

[0049] Statistically record the first exercise duration corresponding to when a student answers an exercise correctly and mark it as YS; and statistically record the second exercise duration corresponding to when a student answers an exercise incorrectly and mark it as ES. The units of the first exercise duration and the second exercise duration are both minutes;

[0050] When verifying and feeding back the teaching effect of the teaching knowledge points of the teacher from the students' aspect in turn, extract the numerical values of the marked data items and combine and integrate them. Obtain the supervisor value XJ corresponding to the teaching knowledge point through calculation; The calculation formula for the supervisor value XJ is: In the formula, XZ is the total number of students, and ZTX is the exercise coefficient corresponding to the teaching knowledge points learned by each student;

[0051] Among them, the calculation formula for the exercise coefficient ZTX is:

[0052]

[0053] In the formula, g1 and g2 are preset different proportionality coefficients and 1 < g1 < g2. g1 can take the value of 1.567, and g2 can take the value of 3.216; T1 is the total number of correctly answered exercises, T2 is the total number of incorrectly answered exercises, and T1 + T2 = ZT; α is the compensation coefficient corresponding to the teaching knowledge point, and the value range is (0, 6), and it can take the value of 1.0237;

[0054] It should be noted that the exercise coefficient is a numerical value used to integrate and calculate the exercise results of students' correct and incorrect answers;

[0055] The supervisor value is a numerical value used to integrate various data of students' exercises corresponding to the teaching knowledge point to comprehensively evaluate the teaching effect of the teacher;

[0056] Verify and feedback the teaching effect of the teaching knowledge points of the teacher according to the supervisor value, and match the supervisor value with the preset supervisor range; Similarly, the supervisor range can be obtained based on the historical exam big data corresponding to the teaching knowledge point;

[0057] If the supervisor value belongs to the supervisor range, it is determined that the teaching effect of the corresponding teaching knowledge point is excellent and a learning positive signal is generated. According to the learning positive signal, increase the total number of normal teaching times of the teaching knowledge point by one;

[0058] If the teaching monitoring value is less than the minimum value of the teaching monitoring range, the teaching effect of the corresponding teaching knowledge point is determined to belong to a type of learning anomaly and a first learning anomaly signal is generated. According to the first learning anomaly signal, the total number of first learning anomalies of the teaching knowledge point is increased by one; a type of learning anomaly can be that the number of people who master the teaching knowledge point is small;

[0059] If the teaching monitoring value is greater than the maximum value of the teaching monitoring range, it is determined that the teaching of the corresponding teaching knowledge point belongs to the second type of learning anomaly and a second learning anomaly signal is generated. According to the second learning anomaly signal, the total number of second learning anomalies of the teaching knowledge point is increased by one; the second type of learning anomaly can be that there are many people who do not fully master the teaching knowledge point;

[0060] The learning supervision value and the corresponding learning approval signal, first learning exception signal and second learning exception signal constitute the teaching verification data corresponding to the teaching knowledge points and are uploaded to the cloud platform.

[0061] In an embodiment of the present invention, various data on students' test-taking corresponding to different teaching knowledge points are integrated to obtain learning monitoring values, and an overall evaluation of the teacher's teaching effectiveness from the students' perspective is performed based on the learning monitoring values. This allows students' mastery of different teaching knowledge points to be obtained in a timely and efficient manner, effectively improving the accuracy and diversity of the teaching effectiveness analysis corresponding to the teaching knowledge points, so as to provide reliable data support for subsequent adjustments in the teacher's teaching methods.

[0062] Integrate the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teacher's teaching process to evaluate the overall situation of his teaching and obtain the evaluation results; including:

[0063] Obtain the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teacher's teaching process and traverse them, count the total number of first teaching anomaly signals and second teaching anomaly signals obtained in the traversal results, and mark the corresponding total number of first teaching anomalies and second teaching anomalies as YJ and EJ respectively;

[0064] And the total number of occurrences of the first learning abnormal signal and the second learning abnormal signal obtained in the traversal result is counted, and the corresponding total number of first learning abnormalities and the total number of second learning abnormalities are marked as YX and EX respectively;

[0065] The values of the marked data are extracted and integrated in parallel, and the teaching integral value JZ corresponding to the smart teaching is obtained by calculation; the calculation formula of the teaching integral value JZ is:

[0066] Where, f1, f2, f3, and f4 are preset different proportional coefficients, and 0<f1<f2<1, f1 can be taken as 0.364, f2 can be taken as 0.617, 2<f3<f4, f3 can be taken as 2.523, and f4 can be taken as 4.135; N is the total number of teaching knowledge points;

[0067] It should be noted that the teaching overall evaluation value is a value used to overall evaluate whether the teaching process and teaching methods in the teacher's intelligent teaching process are carried out normally;

[0068] It is worth noting that in the embodiments of the present invention, calculating through formulas is only a means of data processing. In fact, sample teaching data can also be pre-trained through existing algorithm models to obtain a sample model, and the values obtained by training the preprocessed monitoring data through the sample model are respectively named teaching supervision value, learning supervision value, and teaching overall evaluation value;

[0069] When evaluating the overall situation of the teacher's teaching according to the teaching overall evaluation value, the teaching overall evaluation value is matched with a preset first teaching overall evaluation threshold and a second teaching overall evaluation threshold; among them, the first teaching overall evaluation threshold is less than the second teaching overall evaluation threshold; the first teaching overall evaluation threshold and the second teaching overall evaluation threshold can be obtained based on historical examination big data;

[0070] If the teaching overall evaluation value is less than the first teaching overall evaluation threshold, it is determined that the teacher's teaching process is normal and a first overall evaluation signal is generated;

[0071] If the teaching overall evaluation value is not less than the first teaching overall evaluation threshold, it is determined that the teacher's teaching process is abnormal and a second overall evaluation signal is generated; the abnormal teaching process includes but is not limited to the teaching explanation speed of teaching knowledge points being too fast or too slow;

[0072] If the teaching overall evaluation value is less than the second teaching overall evaluation threshold, it is determined that the teacher's teaching method is normal and a third overall evaluation signal is generated;

[0073] If the teaching overall evaluation value is not less than the second teaching overall evaluation threshold, it is determined that the teacher's teaching method is abnormal and a fourth overall evaluation signal is generated; the abnormal teaching method includes but is not limited to the teaching explanation content of teaching knowledge points being incomplete and difficult to understand;

[0074] If the teaching overall evaluation value is not less than the cumulative value of the first teaching overall evaluation threshold and the second teaching overall evaluation threshold, it is determined that both the teacher's teaching process and teaching method are abnormal and a fifth overall evaluation signal is generated;

[0075] The teaching overall evaluation value and the corresponding first overall evaluation signal, second overall evaluation signal, third overall evaluation signal, fourth overall evaluation signal, and fifth overall evaluation signal constitute an evaluation result and are uploaded to the database and the cloud platform;

[0076] In the embodiments of the present invention, by integrating the teaching monitoring data and teaching verification data obtained through monitoring and analysis on the teacher side and the student side to obtain a teaching integration value, and analyzing, evaluating, and classifying the overall teaching process and teaching methods of the teacher based on the teaching integration value, teachers can timely and efficiently discover the deficiencies in the teaching process, so as to adjust the teaching process and teaching methods targeted, thereby improving the overall effect of intelligent teaching monitoring management.

[0077] Dynamically prompt the implementation of its teaching plan adaptively according to the evaluation results and display it dynamically through VR technology; including:

[0078] Traverse the evaluation results, and dynamically prompt the class results and the implementation of the teaching plan adaptively according to the integration signals obtained by the traversal;

[0079] If the evaluation results after traversal include the first integration signal or the third integration signal, generate a prompt that the teaching process is normal or the teaching method is normal and display it through VR technology;

[0080] If the evaluation results after traversal include the second integration signal or the fourth integration signal, generate a prompt that the teaching process is abnormal or the teaching method is abnormal, and prompt a management prompt that the teaching process needs to be adjusted or the teaching method needs to be adjusted and display it through VR technology;

[0081] If the evaluation results after traversal include the fifth integration signal, generate a prompt that the teaching process is abnormal and the teaching method is abnormal, and generate a management prompt that the teaching process needs to be adjusted and the teaching method needs to be adjusted and display it through VR technology.

[0082] In the embodiments of the present invention, by adaptively implementing corresponding abnormal prompts and management prompts through different integration signals in the evaluation results, teachers can adjust and improve the problems existing in their teaching process targeted, which can effectively improve the overall effect of teachers' teaching and the overall effect of intelligent teaching management;

[0083] In addition, the formulas involved above are all calculated by removing the dimension and taking their numerical values. It is a formula obtained by software simulation of collecting a large amount of data to be closest to the real situation. The proportionality coefficient in the formula and each preset threshold in the analysis process are set by those skilled in the art according to the actual situation or obtained by simulating a large amount of data; the size of the proportionality coefficient is a specific value obtained by quantifying each parameter for subsequent comparison. Regarding the size of the proportionality coefficient, it depends on the amount of sample data and the processing coefficients initially set by those skilled in the art for each group of sample data; as long as it does not affect the proportional relationship between the parameters and the quantified values.

[0084] In several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the described embodiments of the invention are merely illustrative. For example, the division of modules is only a logical function division, and there may be other division methods in actual implementation.

[0085] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional module in various embodiments of the present invention can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above integrated modules can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0087] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A virtual reality operation integrated supervision method based on multi-dimensional data, characterized in that, include: From the teacher's perspective, the teacher's teaching is monitored and data is processed to obtain the teaching monitoring value; among them, the teacher's real-time teaching knowledge points and the corresponding teaching time are obtained, different teaching knowledge points are set to correspond to different knowledge point weights, and the obtained teaching knowledge points are matched with all the teaching knowledge points pre-stored in the database to obtain the corresponding knowledge point weights and mark them as ZQ; And the teaching time corresponding to the knowledge point weight is marked as JS; the unit of teaching time is minutes; when evaluating the teaching of the teacher's teaching knowledge points in turn, the values of the marked knowledge point weight and teaching time are extracted and integrated in parallel, and the teaching supervision value JJ corresponding to the teaching knowledge point is obtained by calculation; the calculation formula of the teaching supervision value JJ is: ; In the formula, JS0 is the standard teaching time corresponding to the teaching knowledge point; Evaluate the teacher's teaching of the teaching knowledge points according to the teaching monitoring value, match the teaching monitoring value with the preset teaching monitoring range to obtain teaching monitoring data including the teaching correction signal, the first teaching abnormality signal and the second teaching abnormality signal; The teaching effect of teachers is monitored from the students' perspective and data is verified to obtain the learning monitoring value; among them, the students' performance in learning teaching knowledge points is monitored and data is counted according to their student ID numbers; The total number of questions that the statisticians completed while learning the teaching knowledge points is marked as ZT; Obtain the difficulty of exercises of different teaching knowledge points and match them with the exercise difficulty weight table pre-stored in the database to obtain the corresponding exercise weight and mark it as TQ; The first time duration corresponding to the correct answer of the statistician is marked as YS; and the second time duration corresponding to the incorrect answer of the statistician is marked as ES; When the students verify and give feedback on the teaching effect of the teacher's teaching knowledge points in turn, the values of the marked data are extracted and integrated in parallel, and the learning supervision value XJ corresponding to the teaching knowledge point is obtained by calculation; the calculation formula of the learning supervision value XJ is: ; In the formula, XZ is the total number of students, and ZTX is the coefficient of the questions corresponding to the teaching knowledge points learned by each student; Among them, the calculation formula of the test coefficient ZTX is: ； In the formula, g1 and g2 are preset different proportional coefficients and 1<g1<g2; T1 is the total number of correct questions, T2 is the total number of wrong questions, and T1+T2=ZT; α is the compensation coefficient corresponding to the teaching knowledge point, and the value range is (0, 6); Verify and feedback the teaching effect of the teacher's teaching knowledge points according to the learning supervision value, match the learning supervision value with the preset learning supervision range to obtain teaching verification data including the learning positive signal, the first learning abnormal signal and the second learning abnormal signal; Integrate the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teacher's teaching process to evaluate the overall situation of his teaching and obtain the evaluation results; Among them, the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teacher's teaching process are obtained and traversed, and the total number of occurrences of the first teaching abnormality signal and the second teaching abnormality signal obtained in the traversal result is counted, and the corresponding total number of first teaching abnormalities and the total number of second teaching abnormalities are marked as YJ and EJ respectively; And the total number of occurrences of the first learning abnormal signal and the second learning abnormal signal obtained in the traversal result is counted, and the corresponding total number of first learning abnormalities and the total number of second learning abnormalities are marked as YX and EX respectively; The values of the marked data are extracted and integrated in parallel, and the teaching integral value JZ corresponding to the smart teaching is obtained by calculation; the calculation formula of the teaching integral value JZ is: ; where f1, f2, f3, and f4 are preset different proportionality coefficients, and 0 < f1 < f2 < 1, 2 < f3 < f4; N is the total number of teaching knowledge points; Based on the evaluation results, dynamic prompts are given for the implementation of the teaching plan adaptively and dynamically displayed through VR technology.

2. The virtual reality operation integrated supervision method based on multi-dimensional data according to claim 1, wherein, Matching and analyzing the teaching monitoring value with the teaching monitoring range, obtaining a teaching correction signal, a first teaching abnormality signal or a second teaching abnormality signal, and respectively adding one to the total number of normal teaching times of the teaching knowledge point, one to the total number of first teaching abnormalities or one to the total number of second teaching abnormalities according to the teaching correction signal, the first teaching abnormality signal or the second teaching abnormality signal; The supervisor value and the corresponding correction signal, first difference signal, and second difference signal constitute the teaching monitoring data corresponding to the teaching knowledge points.

3. The virtual reality operation integrated supervision method based on multi-dimensional data according to claim 1, wherein, Match and analyze the supervisor value with the supervisor range to obtain a correction signal, a first difference signal, or a second difference signal, and respectively increment the total number of normal teaching times of the teaching knowledge point by one, the total number of first learning anomalies by one, or the total number of second learning anomalies by one according to the correction signal, the first difference signal, or the second difference signal; The supervisor value and the corresponding correction signal, first difference signal, and second difference signal constitute the teaching verification data.

4. The virtual reality operation integrated supervision method based on multi-dimensional data according to claim 1, characterized in that When evaluating the overall situation of the teacher's teaching based on the teaching evaluation value, match the teaching evaluation value with the preset first teaching evaluation threshold and second teaching evaluation threshold to obtain an evaluation result composed of a first evaluation signal, a second evaluation signal, a third evaluation signal, a fourth evaluation signal, and a fifth evaluation signal.

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