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

Abstract

The invention discloses a virtual reality operation integrated supervision method based on multi-dimensional data, and belongs to the field of intelligent data management. The method is used for solving the technical problem that the overall effect of virtual reality operation management is poor due to the fact that teaching monitoring and evaluation means of teachers are single in the intelligent teaching process and dynamic prompt and adjustment suggestions cannot be given to teaching of the teachers in an existing scheme. Different teaching knowledge points in the teaching process are modularly monitored and analyzed from the aspect of teachers, so that different teaching states in the teaching process of the teachers can be obtained, and the monitoring and analysis accuracy of the different teaching knowledge points in the teaching process can be effectively improved; various data of students in the aspect of question answering corresponding to different teaching knowledge points are integrated to obtain the school supervision value, and the teaching effect of teachers is integrally evaluated from the aspect of the students according to the school supervision value, so that the mastering conditions of the students on the different teaching knowledge points can be timely and efficiently obtained.

Description

Technical Field

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

[0002] Virtual reality teaching is a method of teaching and learning activities using virtual reality technology. Through specialized equipment 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] The existing virtual reality teaching management program has certain defects in its implementation. It does not conduct modular monitoring and analysis of the teaching situation of different teaching knowledge points from the perspectives of teachers and students, and does not integrate the analysis results of different dimensions to evaluate and integrate the teaching situation of teachers' different teaching knowledge points and provide dynamic prompts and adjustment 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 virtual reality operation supervision method based on multidimensional data, which is used to solve the technical problems that the existing solutions have a single means of teaching monitoring and evaluation for teachers in the process of smart teaching, and cannot provide dynamic prompts and adjustment suggestions for teachers' teaching, resulting in poor overall effect of virtual reality operation management.

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

[0006] Virtual reality operation integrated supervision method based on multi-dimensional data, including:

[0007] From the teacher's perspective, monitor the teacher's teaching and process the data to obtain the teaching supervision value;

[0008] 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;

[0009] Monitor the teaching effect of teachers from the students' perspective and verify the data to obtain the learning supervision value;

[0010] 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;

[0011] 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;

[0012] Based on the evaluation results, dynamic prompts are given for the implementation of the teaching plan adaptively and dynamically displayed through VR technology.

[0013] Preferably, the teacher's real-time teaching knowledge points and the corresponding knowledge point weights are obtained; the teaching time corresponding to the knowledge point weights is obtained; the values ​​of the marked knowledge point weights and teaching time are extracted and the teaching supervision values ​​corresponding to the teaching knowledge points are obtained by calculation.

[0014] Preferably, the teaching supervision value is matched and analyzed with the teaching supervision range to obtain a teaching correction signal, a first teaching abnormality signal or a second teaching abnormality signal, and the total number of normal teaching times of the teaching knowledge point is increased by one, the total number of first teaching abnormalities is increased by one or the total number of second teaching abnormalities is increased by one according to the teaching correction signal, the first teaching abnormality signal or the second teaching abnormality signal;

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

[0016] Preferably, the students' performance in learning the teaching knowledge points is monitored and data is collected according to their student ID numbers;

[0017] The total number of questions that statisticians answer while learning the teaching knowledge points;

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

[0019] The first time duration corresponding to when the statistician answered the question correctly; and the second time duration corresponding to when the statistician answered the question incorrectly;

[0020] When the students verify and give feedback on the teaching effects of the teacher's teaching knowledge points in turn, the values ​​of the marked data are extracted and the corresponding learning supervision values ​​of the teaching knowledge points are obtained through calculation.

[0021] Preferably, the learning supervision value is matched and analyzed with the learning supervision range to obtain a learning positive signal, a first learning abnormality signal or a second learning abnormality signal, and the total number of normal teaching times of the teaching knowledge point is increased by one, the total number of first learning abnormalities is increased by one or the total number of second learning abnormalities is increased by one according to the learning positive signal, the first learning abnormality signal or the second learning abnormality signal;

[0022] The learning supervision value and the corresponding learning approval signal, first learning exception signal and second learning exception 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 occurrences of the first teaching abnormal signal and the second teaching abnormal signal obtained in the traversal result is counted and marked respectively;

[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] From the teacher's perspective, monitor the teacher's teaching and process the data to obtain the teaching supervision value; including:

[0033] Obtain the teacher's real-time teaching knowledge points and the corresponding teaching time, set different teaching knowledge points to correspond to different knowledge point weights, match the obtained teaching knowledge points with all the pre-stored teaching knowledge points in the database to obtain the corresponding knowledge point weights and mark them as ZQ;

[0034] Among them, the knowledge point weight is used to digitally represent text-based teaching knowledge points of different difficulty levels, which can provide effective data support for the differentiated analysis and evaluation of teachers' different teaching knowledge points; the specific value of the knowledge point weight can be obtained by relying on the big data of history exams corresponding to the teaching knowledge points. The more unqualified students' history exam scores corresponding to the teaching knowledge points, the greater the corresponding knowledge point weight, and the more time teachers need to focus on key teaching;

[0035] On the contrary, the more history students pass the exam for the teaching knowledge point, the smaller the weight of the corresponding knowledge point, and the teacher can reduce the teaching time of the knowledge point and conduct simple teaching;

[0036] 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, 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 monitoring value is a numerical value used to evaluate the teaching situation of different teaching knowledge points during the teacher's teaching process; different teaching knowledge points have different levels of difficulty, so the time teachers need to spend on teaching and explaining is also different. For example, difficult teaching knowledge points require more than one class period to explain, while simple teaching knowledge points can be explained in half a class period; when difficult teaching knowledge points take half a class period to explain, and easy teaching knowledge points take one class period to explain, it is completely inconsistent with teaching requirements and teaching logic; and the teaching monitoring value is a numerical value used to evaluate the teaching of teachers' teaching knowledge points;

[0038] Evaluate 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 who failed the examination;

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

[0040] If the teaching monitoring value is less than the minimum value of the teaching monitoring range, it is determined that the teaching of the corresponding teaching knowledge point belongs to a type of teaching abnormality and a first teaching abnormality signal is generated. According to the first teaching abnormality signal, the total number of first teaching abnormalities of the teaching knowledge point is increased by one; a type of teaching abnormality may 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, it is determined that the teaching of the corresponding teaching knowledge point belongs to the second type of teaching abnormality and a second teaching abnormality signal is generated. According to the second teaching abnormality signal, the total number of the second teaching abnormality of the teaching knowledge point is increased by one; the second type of teaching abnormality may be that the teaching of the teaching knowledge point is too slow;

[0042] The teaching monitoring value and the corresponding teaching correction signal, the first teaching error signal and the second teaching error 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 verifies and feedbacks the teaching situation of the corresponding teaching knowledge point from the students' perspective based on the feedback instruction.

[0043] In the embodiment of the present 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'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 smart teaching.

[0044] Monitor the teaching effect of teachers from the students' perspective and verify the data to obtain the learning supervision value; including:

[0045] Monitor and compile statistics of students' performance in learning teaching knowledge points according to their student ID numbers;

[0046] The total number of questions that the statisticians completed while learning the teaching knowledge points is marked as ZT;

[0047] 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;

[0048] The question difficulty weight table includes a number of different question difficulties and corresponding question weights. Different question difficulties are preset with a corresponding question weight. The specific value of the question weight can be obtained by relying on the big data of history questions corresponding to the question. The more mistakes the history students make in the question, the greater the corresponding question weight; conversely, the more correct answers the history students make in the question, the smaller the corresponding question weight. The question difficulty includes but is not limited to simple, relatively simple, moderate, relatively difficult and very difficult.

[0049] The first time duration when the statistician answered the question correctly is marked as YS; and the second time duration when the statistician answered the question incorrectly is marked as ES; the units of the first time duration and the second time duration are both minutes;

[0050] 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;

[0051] Among them, the calculation formula of the test coefficient ZTX is:

[0052]

[0053] Wherein, g1 and g2 are preset different proportional coefficients and 1<g1<g2, g1 can be 1.567, g2 can be 3.216; 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, the value range is (0, 6), and the value can be 1.0237;

[0054] It should be noted that the test coefficient is a value used to integrate the correct and incorrect test results of students;

[0055] The learning monitoring value is a numerical value used to integrate various data on students' questions corresponding to the teaching knowledge points to make an overall evaluation of the teacher's teaching effect;

[0056] Verify and feedback the teaching effect of the teacher's teaching knowledge points based on the learning supervision value, and match the learning supervision value with the preset learning supervision range; similarly, the learning supervision range can be obtained based on the historical examination big data corresponding to the teaching knowledge points;

[0057] If the learning supervision value is within the learning supervision range, the teaching effect of the corresponding teaching knowledge point is judged to be excellent and a learning correction signal is generated. According to the learning correction signal, the total number of normal teaching times of the teaching knowledge point is increased 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 value is a numerical value used to evaluate whether the teaching process and teaching methods of teachers in the process of smart teaching are normal;

[0068] It is worth noting that the calculation by formula in the embodiment of the present invention is only a means of data processing. In fact, the sample teaching data can also be trained in advance by an existing algorithm model to obtain a sample model, and the values ​​obtained by training the pre-processed monitoring data by the sample model are named as teaching supervision value, learning supervision value and teaching integral value respectively;

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

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

[0071] If the teaching adjustment value is not less than the first teaching adjustment threshold, the teacher's teaching process is judged to be abnormal and a second adjustment signal is generated; the abnormal teaching process includes but is not limited to the teaching explanation speed of the teaching knowledge point being too fast or too slow;

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

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

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

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

[0076] In an embodiment of the present invention, the teaching monitoring data and teaching verification data obtained through monitoring and analysis of teachers and students are integrated to obtain a teaching overall value, and the teacher's overall teaching process and teaching methods are analyzed, evaluated and classified based on the teaching overall value. This allows teachers to promptly and efficiently discover defects in the teaching process so that they can make targeted adjustments to the teaching process and teaching methods, thereby improving the overall effect of smart teaching monitoring and management.

[0077] According to the evaluation results, dynamic prompts are given to the implementation of the teaching plan and dynamic display is performed through VR technology; including:

[0078] The evaluation results are traversed, and according to the obtained evaluation signals, dynamic prompts are given to the students on the teaching results and the implementation of the teaching plan;

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

[0080] If the evaluation results contain the second adjustment signal or the fourth adjustment signal after traversal, a prompt is generated that the teaching process is abnormal or the teaching method is abnormal, and a management prompt is given that the teaching process or the teaching method needs to be adjusted and displayed through VR technology;

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

[0082] In the embodiment of the present invention, by adaptively implementing corresponding abnormal 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 effect of teachers' teaching and the overall effect of smart teaching management;

[0083] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The proportional coefficient in the formula and the various preset thresholds in the analysis process are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data; the size of the proportional coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the proportional coefficient depends on the amount of sample data and the preliminary setting of the corresponding processing coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0084] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the above-described embodiments of the invention are only 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 on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0087] It is obvious to a person 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 solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution 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: Wherein, 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: Wherein, f1, f2, f3, 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 situation of the teacher according to the teaching adjustment value, the teaching adjustment value is matched with the preset first teaching adjustment threshold and the second teaching adjustment threshold; If the teaching adjustment value is less than the first teaching adjustment threshold, it is determined that the teacher's teaching process is normal and a first adjustment signal is generated; If the teaching adjustment value is not less than the first teaching adjustment threshold, it is determined that the teacher's teaching process is abnormal and a second adjustment signal is generated; If the teaching adjustment value is less than the second teaching adjustment threshold, it is determined that the teacher's teaching method is normal and a third adjustment signal is generated; If the teaching adjustment value is not less than the second teaching adjustment threshold, it is determined that the teacher's teaching method is abnormal and a fourth adjustment signal is generated; If the teaching adjustment value is not less than the cumulative value of the first teaching adjustment threshold and the second teaching adjustment threshold, it is determined that the teacher's teaching process and teaching method are abnormal and a fifth adjustment signal is generated; The teaching adjustment value and the corresponding first adjustment signal, second adjustment signal, third adjustment signal, fourth adjustment signal and fifth adjustment signal constitute the evaluation result and are uploaded to the database and the cloud platform; According to the evaluation results, dynamic prompts are given to the implementation of the teaching plan and dynamic display is performed through VR technology; including: The evaluation results are traversed, and according to the obtained evaluation signals, dynamic prompts are given to the students on the teaching results and the implementation of the teaching plan; If the evaluation results contain the first adjustment signal or the third adjustment signal after traversal, a prompt indicating that the teaching process is normal or the teaching method is normal is generated and displayed through VR technology; If the evaluation results contain the second adjustment signal or the fourth adjustment signal after traversal, a prompt is generated that the teaching process is abnormal or the teaching method is abnormal, and a management prompt is given that the teaching process or the teaching method needs to be adjusted and displayed through VR technology; If the evaluation results contain the fifth adjustment signal after traversal, prompts are generated indicating that the teaching process and teaching method are abnormal, and management prompts are generated indicating that the teaching process and teaching method need to be adjusted and displayed through VR technology.

2. The virtual reality operation integrated supervision method based on multidimensional data according to claim 1 is characterized in that: 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 teaching monitoring value and the corresponding teaching correction signal, the first teaching error signal and the second teaching error signal constitute the teaching monitoring data corresponding to the teaching knowledge point.

3. The virtual reality operation integrated supervision method based on multidimensional data according to claim 1 is characterized in that: Match and analyze the learning supervision value with the learning supervision range to obtain a learning correction signal, a first learning error signal, or a second learning error signal, and increase the total number of normal teaching times of the teaching knowledge point by one, the total number of first learning errors by one, or the total number of second learning errors by one according to the learning correction signal, the first learning error signal, or the second learning error signal; The learning supervision value and the corresponding learning approval signal, first learning exception signal and second learning exception signal constitute the teaching verification data.

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