Virtual reality operation integrated supervision method based on multi-dimensional data

Abstract

This invention discloses an integrated monitoring method for virtual reality operation based on multi-dimensional data, belonging to the field of intelligent data management. It addresses the technical problems in existing intelligent teaching solutions, such as the limited means of monitoring and evaluating teachers' teaching, and the inability to provide dynamic prompts and adjustment suggestions, leading to poor overall effectiveness in virtual reality operation management. By implementing modular monitoring and analysis of different teaching knowledge points from the teacher's perspective, it not only obtains information on the teacher's different teaching states but also effectively improves the accuracy of monitoring and analysis of different teaching knowledge points. Furthermore, by integrating various data related to students' problem-solving for different teaching knowledge points to obtain a learning monitoring value, and using this value to comprehensively evaluate the teacher's teaching effectiveness from the students' perspective, it provides timely and efficient information on students' mastery of different teaching knowledge points.

Description

Technical Field

[0001] This invention relates to the field of intelligent data management, specifically to an integrated monitoring method for virtual reality operation based on multidimensional data. Background Technology

[0002] Virtual reality teaching is a method of teaching and learning activities that utilizes virtual reality technology. Through specialized equipment, such as VR headsets and controllers, 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 shortcomings in implementation. They do not modularly monitor and analyze the teaching situation of different teaching knowledge points from the perspectives of both teachers and students, nor do they integrate the analysis results from different dimensions to evaluate and integrate the teaching situation of teachers on different teaching knowledge points and provide dynamic prompts and adjustment suggestions, resulting in poor overall effectiveness of virtual reality operation management. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated monitoring method for virtual reality operation based on multi-dimensional data, which addresses the technical problems in existing solutions where the means of monitoring and evaluating teachers' teaching in smart teaching are limited, and the inability to provide dynamic prompts and adjustment suggestions for teachers' teaching results in poor overall effectiveness of virtual reality operation management.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] An integrated regulatory approach for virtual reality operation based on multidimensional data includes:

[0007] The monitoring and data processing of teachers' teaching results in teaching supervision values.

[0008] The teaching of knowledge points by teachers is evaluated based on the teaching monitoring value. The teaching monitoring value is matched with the preset teaching monitoring range to obtain teaching monitoring data including positive teaching signals, first teaching abnormal signals and second teaching abnormal signals.

[0009] The student monitoring value is obtained by monitoring and verifying the teaching effectiveness of teachers from the student's perspective.

[0010] The teaching effectiveness of teachers' teaching knowledge points is verified and feedback is provided based on the learning monitoring value. The learning monitoring value is matched with the preset learning monitoring range to obtain teaching verification data including positive learning signal, first negative learning signal and second negative learning signal.

[0011] The evaluation results are obtained by integrating the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teaching process of teachers and evaluating the overall teaching situation.

[0012] Based on the evaluation results, the teaching plan is dynamically prompted to be implemented and dynamically displayed using VR technology.

[0013] Preferably, the teacher obtains the real-time teaching knowledge points and corresponding knowledge point weights; obtains the teaching duration corresponding to the knowledge point weights; extracts the values ​​of the marked knowledge point weights and teaching durations, and obtains the teaching supervision value corresponding to the teaching knowledge points through calculation.

[0014] Preferably, the teaching monitoring value is matched and analyzed with the teaching monitoring range to obtain the teaching correct signal, the first teaching abnormal signal or the second teaching abnormal signal, and the total number of normal teaching times, the total number of first teaching abnormal times or the total number of second teaching abnormal times for the teaching knowledge point are incremented by one according to the teaching correct signal, the first teaching abnormal signal or the second teaching abnormal signal respectively.

[0015] The teaching monitoring value, along with the corresponding positive teaching signal, the first negative teaching signal, and the second negative teaching signal, constitute the teaching monitoring data corresponding to the teaching knowledge point.

[0016] Preferably, the student's problem-solving performance during the learning process is monitored and statistically analyzed according to the student's student ID number.

[0017] The total number of practice questions completed by the statistical examinee during the learning process of the teaching knowledge points;

[0018] Obtain the difficulty level of 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 weight;

[0019] The first time a student answers a question correctly is recorded; and the second time a student answers a question incorrectly is recorded.

[0020] When students sequentially verify and provide feedback on the teaching effectiveness of teachers' teaching knowledge points, the numerical values ​​of the marked data are extracted and the corresponding student monitoring values ​​for the teaching knowledge points are obtained through calculation.

[0021] Preferably, the learning monitoring value is matched and analyzed with the learning monitoring range to obtain the learning positive signal, the first learning abnormal signal or the second learning abnormal signal, and the total number of normal teaching times, the total number of first learning abnormal times or the total number of second learning abnormal times for the teaching knowledge point are incremented by one according to the learning positive signal, the first learning abnormal signal or the second learning abnormal signal;

[0022] The student monitoring value, along with the corresponding positive student signal, the first negative student signal, and the second negative student signal, constitute the teaching verification data.

[0023] Preferably, 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 first teaching anomaly signals and second teaching anomaly signals obtained in the traversal results is counted and marked respectively;

[0024] In addition, the total number of occurrences of the first and second learning anomaly signals obtained from the statistical traversal results is counted and marked respectively; the values ​​of each marked data are extracted and combined to obtain the total teaching value corresponding to smart teaching.

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

[0026] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0027] This invention implements modular monitoring and analysis of different teaching knowledge points from the teacher's perspective. It can acquire different teaching states during the teaching process, integrate various data on student performance related to different knowledge points to obtain a learning monitoring value, and conduct an overall evaluation of the teacher's teaching effectiveness from the student's perspective based on this value. This allows for timely and efficient acquisition of students' mastery of different knowledge points. By integrating the teaching monitoring and verification data obtained from both the teacher and student perspectives, a comprehensive teaching value is obtained. Based on this comprehensive teaching value, the teacher's overall teaching process and methods are analyzed, evaluated, and categorized. This enables teachers to promptly and efficiently identify deficiencies in the teaching process, allowing for targeted adjustments to the teaching process and methods, thereby improving the overall effectiveness of intelligent teaching monitoring and management. Attached Figure Description

[0028] The invention will now be further described with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart of the integrated monitoring method for virtual reality operation based on multidimensional data according to the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] like Figure 1 As shown, this invention is an integrated monitoring method for virtual reality operation based on multi-dimensional data, comprising:

[0032] From the teacher's perspective, monitoring and data processing of teaching results in teaching supervision values; including:

[0033] The system obtains real-time teaching knowledge points and corresponding teaching durations from teachers, assigns different weights to different teaching knowledge points, matches the obtained teaching knowledge points with all pre-stored teaching knowledge points in the database to obtain the corresponding weights, and marks them 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 differentiated analysis and evaluation of different teaching knowledge points by teachers. The specific value of the knowledge point weight can be obtained by relying on the historical exam big data corresponding to the teaching knowledge point. The more history students fail the exam for the corresponding teaching knowledge point, the greater the weight of the corresponding knowledge point, and the more time teachers need to spend on key teaching.

[0035] Conversely, the more history students pass the exams for the corresponding knowledge points, the lower the weight of the corresponding knowledge points, and the more time teachers can spend teaching those knowledge points and teach them more simply.

[0036] The teaching time corresponding to the knowledge point weight is marked as JS; the unit of teaching time is minutes; when evaluating the teacher's teaching of the knowledge points in turn, the values ​​of the marked knowledge point weight and teaching time are extracted and combined, and the teaching supervision value JJ corresponding to the teaching knowledge point is obtained by calculation; the formula for calculating the teaching supervision value JJ is: In the formula, JS0 is the standard teaching time corresponding to the teaching knowledge point, which can be obtained based on the historical exam big data corresponding to the teaching knowledge point;

[0037] It should be noted that the teaching monitoring value is a numerical value used to comprehensively evaluate the teaching performance of teachers on different knowledge points during the teaching process. Different knowledge points have different levels of difficulty, so the time required for teachers to teach and explain them also varies. For example, difficult knowledge points require more than one class period to explain, while simple knowledge points can be explained in half a class period. If a difficult knowledge point is explained in half a class period and an easy knowledge point is explained in one class period, it is completely inconsistent with teaching requirements and teaching logic. The teaching monitoring value, on the other hand, is a numerical value used to evaluate the teacher's teaching of the knowledge points.

[0038] The teaching of knowledge points by teachers is evaluated based on the teaching monitoring value, and the teaching monitoring value is matched with the preset teaching monitoring scope; similarly, the teaching monitoring scope can be obtained based on the historical test big data corresponding to the teaching knowledge points, and the specific value can be determined based on the number of students who failed the test.

[0039] If the teaching supervision value falls within the teaching supervision range, the teaching of the corresponding teaching knowledge point is determined to be normal and a teaching correction signal is generated. Based on the teaching correction signal, the total number of times the teaching knowledge point is normal is incremented by one.

[0040] If the teaching monitoring value is less than the minimum value of the teaching monitoring range, the teaching of the corresponding teaching knowledge point is determined to be a type of teaching abnormality and a first teaching abnormality signal is generated. Based on the first teaching abnormality signal, the total number of first teaching abnormalities of the teaching knowledge point is incremented by one. A type of teaching abnormality can be that the teaching of the teaching knowledge point is too fast.

[0041] If the teaching monitoring value is greater than the maximum value of the teaching monitoring range, the teaching of the corresponding teaching knowledge point is determined to be a type II teaching abnormality and a second teaching abnormality signal is generated. The total number of second teaching abnormalities for the teaching knowledge point is incremented by one based on the second teaching abnormality signal. A type II teaching abnormality can be that the teaching of the teaching knowledge point is too slow.

[0042] The teaching monitoring values, along with the corresponding positive teaching signals, first negative teaching signals, and second negative teaching signals, constitute the teaching monitoring data for each teaching knowledge point and are uploaded to the cloud platform. After receiving the teaching monitoring data, the cloud platform generates feedback instructions and verifies and provides feedback on the teaching status of the corresponding teaching knowledge points from the student's perspective based on the feedback instructions.

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

[0044] The student monitoring score is obtained by monitoring and verifying the teaching effectiveness of teachers from the student's perspective; including:

[0045] Based on the student's student ID, the system monitors and statistically analyzes the student's problem-solving performance during the learning process of the teaching knowledge points.

[0046] The total number of questions completed by the statistical examinee during the learning of the teaching knowledge points is recorded and marked as ZT;

[0047] Obtain the difficulty level of 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 weight and mark it as TQ;

[0048] The exercise difficulty weighting table includes several different exercise difficulties and their corresponding weights. Each exercise difficulty has a pre-set weight, and the specific value of the weight can be obtained from historical big data on the exercise. The more errors a historical student made in answering a question, the greater the weight of that question; conversely, the more correct answers a historical student got, the smaller the weight of that question. The exercise difficulty includes, but is not limited to, easy, relatively easy, moderate, relatively difficult, and very difficult.

[0049] The system calculates the first time a user answers a question correctly and marks it as YS; and the second time a user answers a question incorrectly and marks it as ES; both the first and second time durations are in minutes.

[0050] When students sequentially verify and provide feedback on the teaching effectiveness of teachers' instructional knowledge points, the numerical values ​​of the marked data are extracted and integrated to calculate the corresponding student monitoring value XJ for each knowledge point. The formula for calculating the student monitoring value XJ is as follows: In the formula, XZ represents the total number of students, and ZTX represents the problem-solving coefficient corresponding to the teaching knowledge points learned by each student.

[0051] The formula for calculating the problem-solving coefficient ZTX is as follows:

[0052]

[0053] In the formula, g1 and g2 are preset proportional coefficients with 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 questions answered correctly, T2 is the total number of questions answered incorrectly, and T1 + T2 = ZT; α is the compensation coefficient corresponding to the teaching knowledge point, with a value range of (0, 6), and can take the value of 1.0237;

[0054] It should be noted that the problem-solving coefficient is a numerical value used to integrate and calculate the results of students' correct and incorrect problem-solving.

[0055] The learning monitoring value is a numerical value used to integrate various data on students' problem-solving performance related to the teaching knowledge points in order to make an overall evaluation of the teacher's teaching effectiveness.

[0056] The teaching effectiveness of teachers is verified and feedback is provided based on the learning monitoring value, and the learning monitoring value is matched with the preset learning monitoring scope; similarly, the learning monitoring scope can be obtained based on historical exam big data corresponding to the teaching knowledge points.

[0057] If the learning supervisor value falls within the learning supervisor's scope, the teaching effect of the corresponding teaching knowledge point is determined to be excellent and a positive learning signal is generated. Based on the positive learning signal, the total number of times the teaching knowledge point is taught normally is incremented 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 be a type of learning abnormality and a first learning abnormality signal is generated. Based on the first learning abnormality signal, the total number of first learning abnormalities of the teaching knowledge point is incremented by one. A type of learning abnormality can be that the number of people who have mastered the teaching knowledge point is small.

[0059] If the teaching monitoring value is greater than the maximum value of the teaching monitoring range, the teaching of the corresponding teaching knowledge point is determined to be a type II learning abnormality and a second learning abnormality signal is generated. Based on the second learning abnormality signal, the total number of second learning abnormalities for the teaching knowledge point is incremented by one. Type II learning abnormality can be a situation where there are many students who have not fully mastered the teaching knowledge point.

[0060] The learning monitoring value, along with the corresponding positive learning signal, the first negative learning signal, and the second negative learning signal, constitute the teaching verification data for the teaching knowledge points and are uploaded to the cloud platform.

[0061] In this embodiment of the invention, by integrating various data on students' problem-solving performance corresponding to different teaching knowledge points to obtain learning monitoring values, and by using these learning monitoring values ​​to comprehensively evaluate the teaching effectiveness of teachers from the students' perspective, it is possible to obtain students' mastery of different teaching knowledge points in a timely and efficient manner. This effectively improves the accuracy and diversity of teaching effectiveness analysis corresponding to teaching knowledge points, so as to provide reliable data support for subsequent adjustments to teachers' teaching methods.

[0062] The evaluation results are obtained by integrating teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teacher's teaching process; including:

[0063] Acquire and iterate through all teaching knowledge points in the teaching process of teachers, and count the total number of first and second teaching abnormal signals obtained in the traversal results. Mark the corresponding total number of first and second teaching abnormalities as YJ and EJ, respectively.

[0064] And the total number of first and second learning anomalies obtained from the statistical traversal results, and the corresponding total number of first and second learning anomalies are marked as YX and EX, respectively;

[0065] Extract the numerical values ​​of each marked data point and integrate them to calculate the total teaching value JZ corresponding to smart teaching. The formula for calculating the total teaching value JZ is as follows:

[0066] In the formula, f1, f2, f3, and f4 are preset different proportional coefficients, and 0 < f1 < f2 < 1. f1 can take the value of 0.364, f2 can take the value of 0.617, 2 < f3 < f4, f3 can take the value of 2.523, and f4 can take the value of 4.135; N is the total number of teaching knowledge points.

[0067] It should be noted that the overall teaching value is a numerical value used to comprehensively evaluate whether the teaching process and teaching methods in the teacher's smart teaching process are normal.

[0068] It is worth noting that the calculation by formula in this embodiment of the invention is only a means of data processing. In fact, the sample teaching data can be trained in advance using existing algorithm models to obtain sample models. The values ​​obtained by training the preprocessed monitoring data through the sample models are named teaching monitoring value, learning monitoring value and teaching total value, respectively.

[0069] When evaluating the overall teaching performance of teachers based on the teaching evaluation value, the teaching evaluation value is matched with a preset first teaching evaluation threshold and a second teaching evaluation threshold; wherein, the first teaching evaluation threshold is less than the second teaching evaluation threshold; the first teaching evaluation threshold and the second teaching evaluation threshold can be obtained based on historical exam big data;

[0070] If the teaching whole value is less than the first teaching whole value threshold, the teacher's teaching process is judged to be normal and the first whole value signal is generated.

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

[0072] If the teaching evaluation value is less than the second teaching evaluation threshold, the teacher's teaching method is determined to be normal and a third evaluation signal is generated.

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

[0074] If the teaching evaluation value is not less than the sum of the first teaching evaluation threshold and the second teaching evaluation threshold, then the teacher's teaching process and teaching method are determined to be abnormal and a fifth evaluation signal is generated.

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

[0076] In this embodiment of the invention, teaching monitoring data and teaching verification data obtained from monitoring and analysis of teachers and students are integrated to obtain a teaching evaluation value. Based on the teaching evaluation value, the overall teaching process and teaching methods of teachers are analyzed, evaluated and classified. This allows teachers to discover defects in the teaching process in a timely and efficient manner, so as to make targeted adjustments to the teaching process and teaching methods, thereby improving the overall effect of intelligent teaching monitoring and management.

[0077] Based on the evaluation results, the teaching plan will be dynamically prompted and implemented, and dynamically demonstrated using VR technology; including:

[0078] The evaluation results are iterated through, and the evaluation signals obtained from the iteration are adaptively and dynamically prompted to improve the teaching results and the implementation of the teaching plan.

[0079] If the evaluation results contain the first or third whole evaluation signal after traversal, a prompt indicating that the teaching process or teaching method is normal will be generated and displayed through VR technology.

[0080] If the evaluation results contain a second or fourth integrated evaluation signal after traversal, a prompt indicating an abnormal teaching process or teaching method will be generated, and a management prompt indicating that the teaching process or teaching method needs to be adjusted will be displayed through VR technology.

[0081] If the evaluation results contain the fifth evaluation signal after traversal, a prompt will be generated indicating that the teaching process and teaching method are abnormal. A management prompt will also be generated indicating that the teaching process and teaching method need to be adjusted, and this will be displayed through VR technology.

[0082] In this embodiment of the invention, by adaptively implementing corresponding anomaly prompts and management prompts for different evaluation signals in the evaluation results, teachers can make targeted adjustments and improvements to the problems existing in their teaching process, which can effectively improve the overall teaching effect of teachers and the overall effect of smart teaching management.

[0083] Furthermore, the formulas mentioned above are all dimensionless calculations, derived from software simulation using a large amount of collected data to approximate the real situation. The proportionality coefficients in the formulas and the preset thresholds in the analysis process are set by those skilled in the art based on the actual situation or obtained through large-scale data simulation. The magnitude of the proportionality coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The magnitude of the proportionality coefficient depends on the amount of sample data and the processing coefficients initially set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantified value, it is acceptable.

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

[0085] The modules described as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0086] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in the form of hardware plus software functional modules.

[0087] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for integrated monitoring of virtual reality operation based on multi-dimensional data, characterized in that: include: The monitoring and data processing of teachers' teaching results in a teaching supervision value. This involves obtaining real-time teaching knowledge points and corresponding teaching durations, assigning different weights to different teaching knowledge points, matching the obtained teaching knowledge points with all pre-stored teaching knowledge points in the database to obtain the corresponding weights, and marking them as ZQ. The teaching time corresponding to the knowledge point weight is marked as JS; the unit of teaching time is minutes; when evaluating the teacher's teaching of the knowledge points in turn, the values ​​of the marked knowledge point weight and teaching time are extracted and combined, and the teaching supervision value JJ corresponding to the teaching knowledge point is obtained by calculation; the formula for calculating the teaching supervision value JJ is: In the formula, JS0 represents the standard teaching duration corresponding to the teaching knowledge point. The teaching of knowledge points by teachers is evaluated based on the teaching monitoring value. The teaching monitoring value is matched with the preset teaching monitoring range to obtain teaching monitoring data including positive teaching signals, first teaching abnormal signals and second teaching abnormal signals. The student monitoring system monitors and verifies the teaching effectiveness of teachers to obtain student monitoring scores; specifically, it monitors and statistically analyzes students' problem-solving performance during the learning process based on their student ID numbers. The total number of questions completed by the statistical examinee during the learning of the teaching knowledge points is recorded and marked as ZT; Obtain the difficulty level of 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 weight and mark it as TQ; The system calculates the first time a user answers a question correctly and marks it as YS; and the second time a user answers a question incorrectly and marks it as ES. When students sequentially verify and provide feedback on the teaching effectiveness of teachers' instructional knowledge points, the numerical values ​​of the marked data are extracted and integrated to calculate the corresponding student monitoring value XJ for each knowledge point. The formula for calculating the student monitoring value XJ is as follows: In the formula, XZ represents the total number of students, and ZTX represents the problem-solving coefficient corresponding to the teaching knowledge points learned by each student. The formula for calculating the problem-solving coefficient ZTX is as follows: ; In the formula, g1 and g2 are preset different proportional coefficients and 1 < g1 < g2; T1 is the total number of questions answered correctly and T2 is the total number of questions answered incorrectly, and T1 + T2 = ZT; α is the compensation coefficient corresponding to the teaching knowledge point, and its value range is (0, 6). The teaching effectiveness of teachers' teaching knowledge points is verified and feedback is provided based on the learning monitoring value. The learning monitoring value is matched with the preset learning monitoring range to obtain teaching verification data including positive learning signal, first negative learning signal and second negative learning signal. The evaluation results are obtained by integrating the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teaching process of teachers and evaluating the overall teaching situation. Among them, the teaching monitoring data and teaching verification data corresponding to all teaching knowledge points in the teaching process are obtained and traversed. The total number of first teaching abnormality signals and second teaching abnormality signals obtained in the traversal results is counted, and the corresponding total number of first teaching abnormality signals and second teaching abnormality signals are marked as YJ and EJ, respectively. And the total number of first and second learning anomalies obtained from the statistical traversal results, and the corresponding total number of first and second learning anomalies are marked as YX and EX, respectively; Extract the numerical values ​​of each marked data point and integrate them to calculate the total teaching value JZ corresponding to smart teaching. The formula for calculating the total teaching value JZ is as follows: In the formula, f1, f2, f3, and f4 are preset different proportional coefficients, and 0 < f1 < f2 < 1, 2 < f3 < f4; N is the total number of teaching knowledge points. When evaluating the overall teaching performance of teachers based on the teaching evaluation value, the teaching evaluation value is matched with a preset first teaching evaluation threshold and a second teaching evaluation threshold; wherein, the first teaching evaluation threshold is less than the second teaching evaluation threshold. If the teaching whole value is less than the first teaching whole value threshold, the teacher's teaching process is judged to be normal and the first whole value signal is generated. If the teaching evaluation value is not less than the first teaching evaluation threshold, the teacher's teaching process is determined to be abnormal and a second evaluation signal is generated. If the teaching evaluation value is less than the second teaching evaluation threshold, the teacher's teaching method is determined to be normal and a third evaluation signal is generated. If the teaching evaluation value is not less than the second teaching evaluation threshold, the teacher's teaching method is determined to be abnormal and a fourth evaluation signal is generated. If the teaching evaluation value is not less than the sum of the first teaching evaluation threshold and the second teaching evaluation threshold, then the teacher's teaching process and teaching method are determined to be abnormal and a fifth evaluation signal is generated. The teaching whole value and the corresponding first whole value signal, second whole value signal, third whole value signal, fourth whole value signal and fifth whole value signal constitute the evaluation result and are uploaded to the database and cloud platform; Based on the evaluation results, the teaching plan will be dynamically prompted and implemented, and dynamically demonstrated using VR technology; including: The evaluation results are iterated through, and the evaluation signals obtained from the iteration are adaptively and dynamically prompted to improve the teaching results and the implementation of the teaching plan. If the evaluation results contain the first or third whole evaluation signal after traversal, a prompt indicating that the teaching process or teaching method is normal will be generated and displayed through VR technology. If the evaluation results contain a second or fourth integrated evaluation signal after traversal, a prompt indicating an abnormal teaching process or teaching method will be generated, and a management prompt indicating that the teaching process or teaching method needs to be adjusted will be displayed through VR technology. If the evaluation results contain the fifth evaluation signal after traversal, a prompt will be generated indicating that the teaching process and teaching method are abnormal. A management prompt will also be generated indicating that the teaching process and teaching method need to be adjusted, and this will be displayed through VR technology.

2. The integrated monitoring method for virtual reality operation based on multi-dimensional data according to claim 1, characterized in that, The teaching monitoring value is matched and analyzed with the teaching monitoring range to obtain the teaching correct signal, the first teaching abnormal signal or the second teaching abnormal signal. Based on the teaching correct signal, the first teaching abnormal signal or the second teaching abnormal signal, the total number of teaching normal times, the total number of teaching abnormal times or the second teaching abnormal times of the teaching knowledge point are increased by one respectively. The teaching monitoring value, along with the corresponding positive teaching signal, the first negative teaching signal, and the second negative teaching signal, constitute the teaching monitoring data corresponding to the teaching knowledge point.

3. The integrated monitoring method for virtual reality operation based on multi-dimensional data according to claim 1, characterized in that, The learning monitoring value is matched and analyzed with the learning monitoring range to obtain the learning positive signal, the first learning abnormal signal or the second learning abnormal signal. Based on the learning positive signal, the first learning abnormal signal or the second learning abnormal signal, the total number of normal teaching times, the total number of first learning abnormal times or the total number of second learning abnormal times for the teaching knowledge point are increased by one respectively. The student monitoring value, along with the corresponding positive student signal, the first negative student signal, and the second negative student signal, constitute the teaching verification data.

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