CN113672092B - VR live-action teaching model big data teaching knowledge mining method and system - Google Patents

Abstract

The invention discloses a method and a system for mining teaching knowledge of VR real scene teaching model big data, comprising: capturing the teaching knowledge big data related to the teaching model and processing the big data by shooting multi-angle and multi-part images outside and inside the teaching model , obtain the teaching knowledge big data set of teaching model images; carry out teaching knowledge mining point annotations on the external and internal multi-angle and multiple parts of the teaching model; distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, The knowledge mining point of the teaching model VR real scene knowledge mining point annotation map is linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data link teaching model is obtained; through the VR annotation big data link teaching model, the teaching knowledge is automatically carried out in the teaching process at the same time. Big data new knowledge points mining and model real-world teaching knowledge growth, continue to mine new teaching knowledge points.

Description

A VR real scene teaching model big data teaching knowledge mining method and system

technical field

The invention relates to the technical field of VR real-scene teaching data mining, and more particularly, the invention relates to a method and system for mining big data teaching knowledge of a VR real-scene teaching model.

Background technique

The existing teaching model technology has not completely solved the problem of teaching knowledge mining in the big data of the VR real-world teaching model, including the following problems: how to obtain the teaching knowledge big data, how to mark the teaching knowledge points outside and inside the teaching model, and how to identify the VR real-life knowledge of the teaching model. How to automatically increase teaching knowledge and discover new teaching knowledge points while carrying out all-round high-definition display of teaching models are technical problems that need to be solved; therefore, it is necessary to propose a VR real-world teaching A model big data teaching knowledge mining method and system are provided to at least partially solve the problems existing in the prior art.

SUMMARY OF THE INVENTION

A series of concepts in simplified form have been introduced in the Summary section, which are described in further detail in the Detailed Description section. The Summary of the Invention section of the present invention is not intended to attempt to limit the key features and essential technical features of the claimed technical solution, nor is it intended to attempt to determine the protection scope of the claimed technical solution.

In order to at least partially solve the above problems, the present invention provides a method for mining big data teaching knowledge of VR real scene teaching model, including:

S100 , capturing the teaching knowledge big data related to the teaching model and processing the big data by shooting multi-angle and multi-part images outside and inside the teaching model to obtain a big data set of teaching knowledge of the teaching model image;

S200, seamless image splicing is performed on the multi-angle and multi-part images outside and inside the teaching model, and teaching knowledge mining points are marked on the outside and inside of the teaching model from multiple angles and multiple parts, so as to obtain a VR real scene knowledge mining point labeling map of the teaching model;

S300: Distributing the teaching knowledge big data set of the teaching model image to the teaching model VR real scene knowledge mining point annotation map, and linking the knowledge mining point of the teaching model VR real scene knowledge mining point annotation map with the teaching knowledge big data related to the teaching model to obtain the VR annotation Big data link teaching model;

S400. Mark the big data to link the teaching model through VR, and display the teaching model in high-definition in all directions during the teaching process. At the same time, it automatically conducts the mining of new knowledge points of teaching knowledge big data and the growth of teaching knowledge in the real scene of the model, automatically records the content of teaching knowledge growth, and continuously explores new knowledge points. teaching knowledge points.

Preferably, S100 includes:

S101, shooting multi-angle and multi-part images outside and inside the teaching model through a high-definition camera, to obtain a multi-angle image set inside and outside the teaching model;

S102. Capture the teaching knowledge big data related to the teaching model through the multi-angle image sets inside and outside the teaching model, and obtain the teaching knowledge big data related to the teaching model;

S103. Perform automatic multi-cycle data screening on the big data of teaching knowledge related to the teaching model, including: screening of the teaching scope of the teaching model teaching knowledge, screening of the indirect correlation of the teaching knowledge of the teaching model, and screening of the direct correlation of the teaching knowledge of the teaching model;

S104. Perform automatic multi-dimensional data sorting on the teaching knowledge big data related to the teaching model; the multi-dimensional data sorting includes: first-dimensional data sorting, second-dimensional data sorting, and third-dimensional data sorting; the first-dimensional data sorting includes: teaching model Organize teaching knowledge; organize teaching model teaching knowledge syllabus; organize teaching model teaching knowledge content; the second dimension data organization includes: teaching model testing knowledge organized; teaching model testing knowledge syllabus; teaching model testing knowledge content ; The third dimension data collation includes: the teaching model expands the knowledge to organize; the teaching model expands the knowledge syllabus; the teaching model expands the knowledge content.

S105 , by performing multi-cycle data automatic screening and multi-dimensional sorting on the teaching knowledge big data related to the teaching model, and adding the multi-angle image sets inside and outside the teaching model to obtain the teaching model image teaching knowledge big data set.

Preferably, S200 includes:

S201. Perform image recognition on the external and internal multi-angle and multi-part images of the teaching model respectively according to the shooting sequence, and according to the image recognition data, according to the external and internal multi-angles of the teaching model, identify the positional relationship of the multi-part images, and create a three-dimensional space for the teaching model Coordinate System;

S202, distributing the multi-part images into the three-dimensional space coordinate system of the teaching model, and scaling the image coincidence and edge in the same coordinate system according to the coordinate size and ratio of the three-dimensional space coordinate system of the teaching model, to obtain a multi-part zoomed image in the same coordinate system;

S203, performing de-coincidence and smooth splicing on the zoomed images of multiple parts in the same coordinate system, and performing seamless image splicing on the multi-angle and multi-part images outside and inside the teaching model, so as to obtain a seamless splicing image of multiple parts in the teaching model in the same coordinate system;

S204 , selecting a teaching knowledge mining point in which images of multiple parts of the teaching model are seamlessly spliced with the same coordinate system, and labeling the teaching knowledge mining points on the outside and inside of the teaching model from multiple angles and multiple parts to obtain a VR real scene knowledge mining point annotation map of the teaching model.

Preferably, S300 includes:

S300 includes:

S301, establishing a plurality of data distribution modules for the knowledge mining points of the VR real scene knowledge mining point annotation map of the teaching model;

S302. Compare and match the teaching knowledge point keywords of the data distribution module and the keywords of the teaching model image teaching knowledge big data set, and distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, to obtain Teaching model VR real scene knowledge mining point data distribution module;

S303 , establishing a network link between the VR real scene knowledge mining point data distribution module of the teaching model and the teaching knowledge big data, and linking the knowledge mining points of the teaching model VR real scene knowledge mining point labeling map to the teaching knowledge big data related to the teaching model, and obtaining the VR labeling big data Data link teaching model.

Preferably, S400 includes:

S401. Convert the text and voice information of the high-definition VR real scene annotation points of the teaching model corresponding to the multi-part teaching knowledge points of the teaching model to each other, and match the images of the video data of the teaching knowledge points with the logic process information of the teaching knowledge points;

S402 , display the teaching model in an all-round high-definition during the teaching process, and display the text and voice information of the teaching model when selecting an annotation point, and simultaneously display the logic process of the teaching knowledge point when clicking on the video data of the teaching knowledge point;

S403. Automatically perform teaching knowledge big data mining new knowledge points and model real-world teaching knowledge growth at the same time as teaching, automatically record the content of teaching knowledge growth, and continuously mine new teaching knowledge points; mining new knowledge points and existing teaching knowledge points through the teaching part of the teaching model Knowledge points, calculate the growth rate of real-life teaching knowledge points of the model; according to the growth rate value of the real-life teaching knowledge points of the calculation model, analyze and determine whether the growth rate of the real-life teaching knowledge points of the model is greater than the set teaching knowledge growth rate, so as to determine the mining of new teaching knowledge Whether the point meets the knowledge growth rate of the teaching requirements; if the growth rate value of the model real-life teaching knowledge point is less than the set teaching knowledge growth rate value, the total number of cycles of teaching knowledge mining will be increased, new mining teaching parts will be added, and new mining teaching parts will be increased. New knowledge points.

A VR real scene teaching model big data teaching knowledge mining system, comprising:

The teaching model image knowledge big data subsystem is used to capture the teaching knowledge big data related to the teaching model and process the big data by taking multi-angle and multi-part images outside and inside the teaching model to obtain the teaching model image teaching knowledge big data set ;

The teaching model VR real scene knowledge mining point labeling sub-system performs seamless image splicing of the external and internal multi-angle and multi-part images of the teaching model, and performs teaching knowledge mining point labeling on the external and internal multi-angle and multi-part images to obtain the teaching model. VR reality knowledge mining point annotation map;

The teaching model VR annotation big data link subsystem is used to distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, and the knowledge mining point link teaching model of the teaching model VR real scene knowledge mining point annotation map Relevant teaching knowledge big data, get VR annotation big data link teaching model;

The sub-system of continuous mining of teaching knowledge points is used to mark the big data link teaching model through VR, and display the teaching model in all directions in high-definition during the teaching process. The content of teaching knowledge is increased, and new teaching knowledge points are continuously explored.

Preferably, the teaching model image knowledge big data subsystem includes:

The multi-angle image subsystem of the teaching model captures multi-angle and multi-part images outside and inside the teaching model through high-definition cameras, and obtains a multi-dimensional image set inside and outside the teaching model;

The teaching model knowledge capture big data subsystem captures the teaching knowledge big data related to the teaching model through the multi-angle image sets inside and outside the teaching model, and obtains the teaching knowledge big data related to the teaching model;

The teaching knowledge big data loop screening subsystem performs automatic multi-loop data screening on the teaching knowledge big data related to the teaching model, including: teaching model teaching knowledge teaching scope screening, teaching model teaching knowledge indirect correlation screening and teaching model teaching knowledge directly related Sexual screening;

The teaching knowledge multi-dimensional collation subsystem automatically collates the multi-dimensional data of the teaching knowledge big data related to the teaching model; the multi-dimensional data collation includes: the first dimension data collation, the second dimension data collation, the third dimension data collation; the first dimension data collation; Data collation includes: orderly collation of teaching model teaching knowledge; collation of teaching model teaching knowledge syllabus; collation of teaching model teaching knowledge content; second dimension data collation includes: orderly collation of teaching model testing knowledge; collation of teaching model testing knowledge syllabus; The teaching model tests the knowledge content arrangement; the third dimension data arrangement includes: the teaching model expands the knowledge orderly arrangement; the teaching model expands the knowledge syllabus arrangement; the teaching model expands the knowledge content arrangement.

The teaching model image knowledge data set subsystem automatically filters and multi-dimensionally organizes the multi-cycle data of the teaching knowledge big data related to the teaching model, and adds the multi-angle image sets inside and outside the teaching model to obtain the teaching model image teaching knowledge big data set.

Preferably, the teaching model VR real scene knowledge mining point labeling sub-system includes:

The three-dimensional space coordinate system subsystem of the teaching model is used for image recognition of the multi-angle and multi-part images outside and inside the teaching model according to the shooting sequence. The position relationship of the teaching model is created, and the three-dimensional space coordinate system of the teaching model is created;

The same coordinate system distribution scaling subsystem is used to distribute the images of multiple parts into the three-dimensional space coordinate system of the teaching model, and perform the same coordinate system scaling on the coincidence and edges of the images according to the coordinate size and proportion of the three-dimensional space coordinate system of the teaching model to obtain the multi-part image. Scale the image in the same coordinate system;

The coordinate system seamless splicing subsystem is used to de-coincide and smoothly splicing the zoomed images of multiple parts in the same coordinate system, and perform seamless image splicing of the multi-angle and multi-part images outside and inside the teaching model, and obtain the teaching model with multiple parts in the same coordinate system. seamless stitching of images;

The teaching knowledge mining point labeling subsystem is used to select the teaching knowledge mining points where the images of the teaching model are seamlessly spliced with the same coordinate system. Annotated map of real-world knowledge mining points.

Preferably, the teaching model VR labeling big data link subsystem includes:

The teaching model knowledge mining point distribution subsystem is used to establish a plurality of data distribution modules for the knowledge mining points of the teaching model VR real scene knowledge mining point label map;

The teaching model knowledge mining point labeling subsystem is used to compare and match the teaching knowledge point keywords of the data distribution module and the keywords of the teaching model image teaching knowledge big data set, and distribute the teaching model image teaching knowledge big data set to the teaching model. VR real scene knowledge mining point annotation map, get the teaching model VR real scene knowledge mining point data distribution module;

The teaching model VR annotation big data link subsystem is used to establish a network link between the teaching model VR real scene knowledge mining point data distribution module and the teaching knowledge big data, and the knowledge mining point link of the teaching model VR real scene knowledge mining point annotation map is related to the teaching model. The teaching knowledge big data is obtained, and the VR labeling big data link teaching model is obtained.

Preferably, the continuous mining sub-system of teaching knowledge points includes:

The logic matching subsystem of the teaching knowledge point information is used to convert the text and voice information of the teaching model high-definition VR real scene annotation points corresponding to the teaching knowledge points of the teaching model to each other, and to convert the video data of the teaching knowledge points and the logic process of the teaching knowledge points. information to match;

The real scene display subsystem of teaching model knowledge information is used to display the teaching model in all directions in high-definition during the teaching process, and display the text and voice information of the teaching model when selecting the marked point. logical process;

The teaching model knowledge mining and updating subsystem is used to automatically conduct teaching knowledge big data mining new knowledge points and model real-world teaching knowledge growth at the same time as teaching, automatically record the content of teaching knowledge growth, and continuously mine new teaching knowledge points; through the teaching model teaching part Excavate new knowledge points and existing knowledge points, and calculate the growth rate of model real-life teaching knowledge points; according to the calculation model's real-scene teaching knowledge point growth rate, analyze and determine whether the model's real-scene teaching knowledge point growth rate is greater than the set teaching knowledge growth rate. value to determine whether the mining of new teaching knowledge points meets the knowledge growth rate of teaching requirements; if the growth rate value of model real-life teaching knowledge points is less than the set value of teaching knowledge growth rate, the total number of cycles of teaching knowledge mining will be increased, and new mining teaching will be increased. Parts, increase the new knowledge points for newly excavated teaching parts.

Compared with the prior art, the present invention at least includes the following beneficial effects:

By taking multi-angle and multi-part images of the outside and inside of the teaching model, capturing the teaching knowledge big data related to the teaching model and processing the big data, a large data set of teaching knowledge of the teaching model image is obtained; Part images are seamlessly spliced, and the multi-angle and multi-part external and internal parts of the teaching model are labeled with teaching knowledge mining points to obtain the VR real-life knowledge mining point annotation map of the teaching model; the teaching knowledge big data set of the teaching model images is distributed to the teaching model. The VR real scene knowledge mining point annotation map, the knowledge mining point of the teaching model VR real scene knowledge mining point annotation map is linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data link teaching model is obtained; through the VR annotation big data link teaching model, In the teaching process, the teaching model is displayed in all directions in high-definition, and at the same time, the mining of new knowledge points of teaching knowledge big data and the growth of real teaching knowledge of the model are automatically performed, and the content of teaching knowledge growth is automatically recorded, and new teaching knowledge points are continuously mined; through multi-angle and multi-part images Capture teaching knowledge big data with knowledge points, and use big data processing technology to process the teaching knowledge big data of multi-angle and multi-part images; using smooth image splicing can realize seamless image splicing of multi-angle and multi-part images. Select teaching knowledge points from multiple angles and parts outside and inside the teaching model, and mark the teaching knowledge mining points; distribute the data in the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining points according to the corresponding teaching parts. In the teaching process, the teaching model and knowledge points can be displayed in an all-round high-definition through the VR real scene of the teaching model, and automatically according to the teaching content and teaching progress. Mining of new knowledge points of teaching knowledge big data, continuous improvement of teaching content of teaching model and continuous exploration of new knowledge points.

For the method and system for mining big data teaching knowledge of VR real-world teaching model according to the present invention, other advantages, objectives and features of the present invention will be partly reflected by the following description, and partly will be based on the research and practice of the present invention. understood by those skilled in the art.

Description of drawings

The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the attached image:

Fig. 1 is a kind of VR real scene teaching model big data teaching knowledge mining method step diagram according to the present invention;

Fig. 2 is a kind of VR real scene teaching model big data teaching knowledge mining system diagram according to the present invention;

FIG. 3 is a diagram of an embodiment of a method and system for mining big data teaching knowledge of a VR real-world teaching model according to the present invention.

Detailed ways

The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, so that those skilled in the art can implement the invention with reference to the description. As shown in Figures 1-3, the present invention provides a big data teaching knowledge mining method for VR real scene teaching model, including:

S100 , capturing the teaching knowledge big data related to the teaching model and processing the big data by shooting multi-angle and multi-part images outside and inside the teaching model to obtain a big data set of teaching knowledge of the teaching model image;

S200, seamless image splicing is performed on the multi-angle and multi-part images outside and inside the teaching model, and teaching knowledge mining points are marked on the outside and inside of the teaching model from multiple angles and multiple parts, so as to obtain a VR real scene knowledge mining point labeling map of the teaching model;

S300: Distributing the teaching knowledge big data set of the teaching model image to the teaching model VR real scene knowledge mining point annotation map, and linking the knowledge mining point of the teaching model VR real scene knowledge mining point annotation map with the teaching knowledge big data related to the teaching model to obtain the VR annotation Big data link teaching model;

S400. Mark the big data to link the teaching model through VR, and display the teaching model in high-definition in all directions during the teaching process. At the same time, it automatically conducts the mining of new knowledge points of teaching knowledge big data and the growth of teaching knowledge in the real scene of the model, automatically records the content of teaching knowledge growth, and continuously explores new knowledge points. teaching knowledge points.

The working principle of the above technical solution is as follows: by taking multi-angle and multi-part images outside and inside the teaching model, grabbing the teaching knowledge big data related to the teaching model and processing the big data to obtain the teaching model image teaching knowledge big data set; The multi-angle and multi-part images outside and inside the model are seamlessly spliced, and the multi-angle and multi-part outside and inside of the teaching model are marked with teaching knowledge mining points to obtain the VR real scene knowledge mining point annotation map of the teaching model; The knowledge big data set is distributed to the teaching model VR real scene knowledge mining point annotation map, and the knowledge mining point of the teaching model VR real scene knowledge mining point annotation map is linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data link teaching model is obtained; VR annotation big data links the teaching model, displays the teaching model in full HD in the teaching process, and automatically conducts the mining of new knowledge points of teaching knowledge big data and the growth of teaching knowledge in the real scene of the model, automatically records the content of teaching knowledge growth, and continuously excavates new teaching knowledge Grasp the big data of teaching knowledge through multi-angle and multi-part images and knowledge points, and use big data processing technology to process the data of multi-angle and multi-part image teaching knowledge big data; use smooth image splicing to image multi-angle and multi-part images Seamless splicing, selecting teaching knowledge points from multiple angles and parts outside and inside the teaching model, and labeling the teaching knowledge mining points; distribute the data in the teaching model image teaching knowledge big data set to the teaching model VR real scene according to the corresponding teaching parts Annotated map of knowledge mining points; during the teaching process, the teaching model and knowledge points are displayed in an all-round high-definition through the VR real scene of the teaching model, and at the same time, the teaching knowledge big data and new knowledge points are automatically mined according to the teaching content and teaching progress.

The beneficial effects of the above technical solutions are as follows: by shooting multi-angle and multi-part images outside and inside the teaching model, grabbing the teaching knowledge big data related to the teaching model and processing the big data to obtain the teaching model image teaching knowledge big data set; The multi-angle and multi-part images outside and inside the model are seamlessly spliced, and the multi-angle and multi-part outside and inside of the teaching model are marked with teaching knowledge mining points to obtain the VR real scene knowledge mining point annotation map of the teaching model; The knowledge big data set is distributed to the teaching model VR real scene knowledge mining point annotation map, and the knowledge mining point of the teaching model VR real scene knowledge mining point annotation map is linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data link teaching model is obtained; VR annotation big data links the teaching model, displays the teaching model in full HD in the teaching process, and automatically conducts the mining of new knowledge points of teaching knowledge big data and the growth of teaching knowledge in the real scene of the model, automatically records the content of teaching knowledge growth, and continuously excavates new teaching knowledge Grasp the big data of teaching knowledge through multi-angle and multi-part images and knowledge points, and use big data processing technology to process the data of multi-angle and multi-part image teaching knowledge big data; using smooth image stitching can realize multi-angle and multi-part images. For seamless image splicing, you can select teaching knowledge points from multiple angles and parts inside and outside the teaching model, and mark the teaching knowledge mining points; distribute the data in the teaching knowledge big data set of the teaching model images according to the corresponding teaching parts. The teaching model VR real scene knowledge mining point annotation map can establish a comprehensive teaching model and the VR real scene knowledge mining point annotation corresponding to the teaching part; in the teaching process, the teaching model and knowledge points can be displayed in an all-round high-definition through the VR real scene of the teaching model, and at the same time According to the teaching content and teaching progress, it automatically conducts the mining of new knowledge points of teaching knowledge big data, and continuously improves the teaching content of the teaching model and constantly explores new knowledge points.

In one embodiment, S100 includes:

S101, shooting multi-angle and multi-part images outside and inside the teaching model through a high-definition camera, to obtain a multi-angle image set inside and outside the teaching model;

S102. Capture the teaching knowledge big data related to the teaching model through the multi-angle image sets inside and outside the teaching model, and obtain the teaching knowledge big data related to the teaching model;

S103. Perform automatic multi-cycle data screening on the big data of teaching knowledge related to the teaching model, including: screening of the teaching scope of the teaching model teaching knowledge, screening of the indirect correlation of the teaching knowledge of the teaching model, and screening of the direct correlation of the teaching knowledge of the teaching model;

S104. Perform automatic multi-dimensional data sorting on the teaching knowledge big data related to the teaching model; the multi-dimensional data sorting includes: first-dimensional data sorting, second-dimensional data sorting, and third-dimensional data sorting; the first-dimensional data sorting includes: teaching model Organize teaching knowledge; organize teaching model teaching knowledge syllabus; organize teaching model teaching knowledge content; the second dimension data organization includes: teaching model testing knowledge organized; teaching model testing knowledge syllabus; teaching model testing knowledge content ; The third dimension data collation includes: the teaching model expands the knowledge to organize; the teaching model expands the knowledge syllabus; the teaching model expands the knowledge content.

S105 , by performing multi-cycle data automatic screening and multi-dimensional sorting on the teaching knowledge big data related to the teaching model, and adding the multi-angle image sets inside and outside the teaching model to obtain the teaching model image teaching knowledge big data set.

The working principle of the above technical solution is as follows: the multi-angle and multi-part images outside and inside the teaching model are captured by a high-definition camera, and a multi-angle image set inside and outside the teaching model is obtained; through the multi-angle image set inside and outside the teaching model, the teaching knowledge related to the teaching model is captured. Big data, get the big data of teaching knowledge related to the teaching model; perform automatic multi-cycle data screening on the big data of teaching knowledge related to the teaching model, including: the teaching scope screening of the teaching model teaching knowledge, the indirect correlation screening of the teaching model teaching knowledge and the teaching model teaching Screening of direct correlation of knowledge; automatic multi-dimensional data sorting for teaching knowledge big data related to teaching models; multi-dimensional data sorting includes: first-dimensional data sorting, second-dimensional data sorting, and third-dimensional data sorting; first-dimensional data sorting Including: orderly arrangement of teaching model teaching knowledge; arrangement of teaching model teaching knowledge syllabus; arrangement of teaching model teaching knowledge content; second dimension data arrangement includes: orderly arrangement of teaching model testing knowledge; arrangement of teaching model testing knowledge syllabus; teaching model Test knowledge content sorting; third-dimensional data sorting includes: teaching model expansion knowledge organization; teaching model expansion knowledge syllabus sorting; teaching model expansion knowledge content sorting; automatic multi-cycle data selection through the teaching model related teaching knowledge big data And multi-dimensional sorting, adding multi-angle image sets inside and outside the teaching model, to obtain a large data set of teaching model image teaching knowledge.

The beneficial effects of the above technical solutions are as follows: the multi-angle and multi-part images outside and inside the teaching model are captured by high-definition cameras to obtain a multi-angle image set inside and outside the teaching model; through the multi-angle image set inside and outside the teaching model, the teaching knowledge related to the teaching model is captured Big data, get the big data of teaching knowledge related to the teaching model; perform automatic multi-cycle data screening on the big data of teaching knowledge related to the teaching model, including: the teaching scope screening of the teaching model teaching knowledge, the indirect correlation screening of the teaching model teaching knowledge and the teaching model teaching Screening of direct correlation of knowledge; automatic multi-dimensional data sorting for teaching knowledge big data related to teaching models; multi-dimensional data sorting includes: first-dimensional data sorting, second-dimensional data sorting, and third-dimensional data sorting; first-dimensional data sorting Including: orderly arrangement of teaching model teaching knowledge; arrangement of teaching model teaching knowledge syllabus; arrangement of teaching model teaching knowledge content; second dimension data arrangement includes: orderly arrangement of teaching model testing knowledge; arrangement of teaching model testing knowledge syllabus; teaching model Test knowledge content sorting; third-dimensional data sorting includes: teaching model expansion knowledge organization; teaching model expansion knowledge syllabus sorting; teaching model expansion knowledge content sorting; automatic multi-cycle data selection through the teaching model related teaching knowledge big data And multi-dimensional sorting, adding multi-angle image sets inside and outside the teaching model, to obtain a large data set of teaching model image teaching knowledge; teaching knowledge related to the teaching model The big data can enrich the teaching knowledge content, and the automatic screening of multi-cycle data can obtain the teaching content and Progress-related knowledge data; automatic multi-dimensional data sorting can make teaching knowledge clearer.

In one embodiment, S200 includes:

S201. Perform image recognition on the external and internal multi-angle and multi-part images of the teaching model respectively according to the shooting sequence, and according to the image recognition data, according to the external and internal multi-angles of the teaching model, identify the positional relationship of the multi-part images, and create a three-dimensional space for the teaching model Coordinate System;

S202, distributing the multi-part images into the three-dimensional space coordinate system of the teaching model, and scaling the image coincidence and edge in the same coordinate system according to the coordinate size and ratio of the three-dimensional space coordinate system of the teaching model, to obtain a multi-part zoomed image in the same coordinate system;

S203, performing de-coincidence and smooth splicing on the zoomed images of multiple parts in the same coordinate system, and performing seamless image splicing on the multi-angle and multi-part images outside and inside the teaching model, so as to obtain a seamless splicing image of multiple parts in the teaching model in the same coordinate system;

S204 , selecting a teaching knowledge mining point in which images of multiple parts of the teaching model are seamlessly spliced with the same coordinate system, and labeling the teaching knowledge mining points on the outside and inside of the teaching model from multiple angles and multiple parts to obtain a VR real scene knowledge mining point annotation map of the teaching model.

The working principle of the above technical solution is as follows: image recognition is performed on the multi-angle and multi-part images outside and inside the teaching model according to the shooting sequence, and the positional relationship of the multi-part images is recognized according to the image recognition data and according to the multi-angle outside and inside the teaching model. , create a three-dimensional space coordinate system of the teaching model; distribute the images of multiple parts into the three-dimensional space coordinate system of the teaching model, and scale the image coincidence and edge according to the coordinate size and proportion of the three-dimensional space coordinate system of the teaching model to obtain the same coordinate system for multiple parts. Coordinate system zoomed images; de-coincident and smooth splicing of multi-part zoomed images of the same coordinate system, seamless image splicing of multi-angle and multi-part images outside and inside of the teaching model, to obtain a seamless splicing image of multiple parts of the teaching model in the same coordinate system; Select the teaching knowledge mining points where the multi-parts of the teaching model are seamlessly spliced with the same coordinate system, and mark the teaching knowledge mining points on the outside and inside of the teaching model from multiple angles and multiple parts, and obtain the teaching model VR real scene knowledge mining point annotation map.

The beneficial effects of the above technical solutions are: image recognition is performed on the multi-angle and multi-part images outside and inside the teaching model respectively according to the shooting sequence, and the positional relationship of the multi-part images is recognized according to the image recognition data and according to the multi-angle outside and inside the teaching model. , create a three-dimensional space coordinate system of the teaching model; distribute the images of multiple parts into the three-dimensional space coordinate system of the teaching model, and scale the image coincidence and edge according to the coordinate size and proportion of the three-dimensional space coordinate system of the teaching model to obtain the same coordinate system for multiple parts. Coordinate system zoomed images; de-coincident and smooth splicing of multi-part zoomed images of the same coordinate system, seamless image splicing of multi-angle and multi-part images outside and inside of the teaching model, to obtain a seamless splicing image of multiple parts of the teaching model in the same coordinate system; Select the teaching knowledge mining points of the multi-part and the same coordinate system seamlessly spliced images of the teaching model, and label the teaching knowledge mining points from multiple angles and parts outside and inside the teaching model, and obtain the teaching model VR real scene knowledge mining point annotation map; According to the image recognition The three-dimensional space coordinate system of the teaching model can be created according to the positional relationship of the images of multiple parts from the outside and inside of the teaching model, which can make the three-dimensional space coordinate system of the teaching model more suitable for the actual three-dimensional space position of the teaching model; the coordinates of multiple parts of the teaching model are the same. The system can make the spatial and positional relationships interconnected, and is conducive to the point labeling of teaching knowledge mining.

In one embodiment, S300 includes:

S301, establishing a plurality of data distribution modules for the knowledge mining points of the VR real scene knowledge mining point annotation map of the teaching model;

S302. Compare and match the teaching knowledge point keywords of the data distribution module and the keywords of the teaching model image teaching knowledge big data set, and distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, to obtain Teaching model VR real scene knowledge mining point data distribution module;

S303 , establishing a network link between the VR real scene knowledge mining point data distribution module of the teaching model and the teaching knowledge big data, and linking the knowledge mining points of the teaching model VR real scene knowledge mining point labeling map to the teaching knowledge big data related to the teaching model, and obtaining the VR labeling big data Data link teaching model.

The working principle of the above technical solution is as follows: establishing a plurality of data distribution modules from the knowledge mining points of the VR real scene knowledge mining point annotation map of the teaching model; Compare and match the words, distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, and obtain the teaching model VR real scene knowledge mining point data distribution module; distribute the teaching model VR real scene knowledge mining point data distribution module and The teaching knowledge big data establishes a network link, and the knowledge mining point of the teaching model VR real scene knowledge mining point annotation map is linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data link teaching model is obtained.

The beneficial effects of the above technical solutions are: establishing a plurality of data distribution modules from the knowledge mining points of the VR real scene knowledge mining point annotation map of the teaching model; the teaching knowledge point keywords of the data distribution module and the teaching model image teaching knowledge big data set. Compare and match the words, distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, and obtain the teaching model VR real scene knowledge mining point data distribution module; distribute the teaching model VR real scene knowledge mining point data distribution module and The teaching knowledge big data establishes a network link, and the knowledge mining points of the teaching model VR real scene knowledge mining point annotation map are linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data is linked to the teaching model; the teaching model VR real scene knowledge mining point is annotated The knowledge mining point of the graph establishes multiple data distribution modules, and the scattered graphs and points can be distributed modularly; the teaching knowledge point keywords of the data distribution module and the keywords of the teaching model image teaching knowledge big data set are compared and matched, which can be Make matches more accurate by keywords.

In one embodiment, S400 includes:

S401. Convert the text and voice information of the high-definition VR real scene annotation points of the teaching model corresponding to the multi-part teaching knowledge points of the teaching model to each other, and match the images of the video data of the teaching knowledge points with the logic process information of the teaching knowledge points;

S402 , display the teaching model in an all-round high-definition during the teaching process, and display the text and voice information of the teaching model when selecting an annotation point, and simultaneously display the logic process of the teaching knowledge point when clicking on the video data of the teaching knowledge point;

S403. Automatically perform teaching knowledge big data mining new knowledge points and model real-world teaching knowledge growth at the same time as teaching, automatically record the content of teaching knowledge growth, and continuously mine new teaching knowledge points; mining new knowledge points and existing teaching knowledge points through the teaching part of the teaching model Knowledge points, calculate the growth rate of real-life teaching knowledge points of the model; according to the growth rate value of the real-life teaching knowledge points of the calculation model, analyze and determine whether the growth rate of the real-life teaching knowledge points of the model is greater than the set teaching knowledge growth rate, so as to determine the mining of new teaching knowledge Whether the point meets the knowledge growth rate of the teaching requirements; if the growth rate value of the model real-life teaching knowledge point is less than the set teaching knowledge growth rate value, the total number of cycles of teaching knowledge mining will be increased, new mining teaching parts will be added, and new mining teaching parts will be increased. New knowledge points.

The working principle of the above technical solution is as follows: the text and voice information of the high-definition VR real scene annotation points of the teaching model corresponding to the teaching knowledge points of the teaching model are converted into each other, and the images of the video materials of the teaching knowledge points and the logic process information of the teaching knowledge points are matched. ; In the teaching process, the teaching model is displayed in all directions in high-definition, and the text and voice information of the teaching model is displayed when selecting the marked point. When the video data of the teaching knowledge point is clicked, the logic process of the teaching knowledge point is displayed at the same time; the teaching knowledge is automatically displayed at the same time as teaching. Big data mining new knowledge points and model real-world teaching knowledge growth, automatically recording the content of teaching knowledge growth, and continuously mining new teaching knowledge points;

Through the mining of new knowledge points and existing knowledge points in the teaching part of the teaching model, the growth rate of knowledge points in real-life teaching of the model is calculated. The calculation formula is as follows:

为教学知识挖掘总循环轮数，i为

的连续正整数；根据计算模型实景教学知识点增长率值，分析判定模型实景教学知识点增长率值是否大于设定的教学知识增长率值，以确定挖掘新教学知识点是否满足教学要求的知识增长速度；模型实景教学知识点增长率值小于设定的教学知识增长率值，则增加教学知识挖掘总循环轮数，增加新挖掘教学部位，增加新挖掘教学部位的新知识点数。Among them, Z _wd is the growth rate of knowledge points in model real-world teaching, P(I _wdi ) is the number of teaching parts of the existing teaching model, Q(D _sli ) is the number of existing knowledge points in the teaching parts of the existing teaching model, P(I _{nwdi )} ) is the number of general positions after the teaching model newly excavated the teaching position, Q(D _nsli ) is the number of knowledge points after the new knowledge points of the new teaching position excavated by the teaching model,

The total number of rounds of mining for teaching knowledge, i is

is a continuous positive integer; according to the growth rate value of the real-life teaching knowledge point of the calculation model, analyze and determine whether the model's real-life teaching knowledge point growth rate value is greater than the set teaching knowledge growth rate value, so as to determine whether mining new teaching knowledge points meets the knowledge required for teaching Growth rate; if the growth rate value of model real-life teaching knowledge points is less than the set value of teaching knowledge growth rate, the total number of rounds of teaching knowledge mining will be increased, new mining teaching parts will be added, and new knowledge points of new mining teaching parts will be added.

The beneficial effects of the above technical solutions are as follows: the text and voice information of the high-definition VR real scene annotation points of the teaching model corresponding to the teaching knowledge points of the teaching model are transformed into each other, and the images of the video materials of the teaching knowledge points and the logic process information of the teaching knowledge points are matched. ; In the teaching process, the teaching model is displayed in all directions in high-definition, and the text and voice information of the teaching model is displayed when selecting the marked point. When the video data of the teaching knowledge point is clicked, the logic process of the teaching knowledge point is displayed at the same time; the teaching knowledge is automatically displayed at the same time as teaching. Big data mines new knowledge points and model real-life teaching knowledge growth, automatically records the content of teaching knowledge growth, and continuously mines new teaching knowledge points; through the mining of new knowledge points and existing knowledge points in the teaching part of the teaching model, the model real-life teaching knowledge points are calculated. Growth rate; according to the growth rate value of the real-life teaching knowledge point of the calculation model, analyze and determine whether the model's real-life teaching knowledge point growth rate value is greater than the set teaching knowledge growth rate value, so as to determine whether the mining new teaching knowledge point meets the knowledge growth rate of the teaching requirements. ; If the growth rate value of model real-life teaching knowledge points is less than the set value of teaching knowledge growth rate, increase the total number of rounds of teaching knowledge mining, increase new mining teaching parts, increase the number of new knowledge points in new mining teaching parts, and increase teaching knowledge growth rate To meet the teaching requirements, continue to increase teaching knowledge points and improve teaching efficiency.

A VR real scene teaching model big data teaching knowledge mining system, comprising:

The teaching model image knowledge big data subsystem is used to capture the teaching knowledge big data related to the teaching model and process the big data by taking multi-angle and multi-part images outside and inside the teaching model to obtain the teaching model image teaching knowledge big data set ;

The teaching model VR real scene knowledge mining point labeling sub-system performs seamless image splicing of the external and internal multi-angle and multi-part images of the teaching model, and performs teaching knowledge mining point labeling on the external and internal multi-angle and multi-part images to obtain the teaching model. VR reality knowledge mining point annotation map;

The teaching model VR annotation big data link subsystem is used to distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, and the knowledge mining point link teaching model of the teaching model VR real scene knowledge mining point annotation map Relevant teaching knowledge big data, get VR annotation big data link teaching model;

The sub-system of continuous mining of teaching knowledge points is used to mark the big data link teaching model through VR, and display the teaching model in all directions in high-definition during the teaching process. The content of teaching knowledge is increased, and new teaching knowledge points are continuously explored.

The working principle of the above technical solution is as follows:

The teaching model image knowledge big data subsystem is used to capture the teaching knowledge big data related to the teaching model and process the big data by taking multi-angle and multi-part images outside and inside the teaching model to obtain the teaching model image teaching knowledge big data set ;

The teaching model VR real scene knowledge mining point labeling sub-system performs seamless image splicing of the external and internal multi-angle and multi-part images of the teaching model, and performs teaching knowledge mining point labeling on the external and internal multi-angle and multi-part images to obtain the teaching model. VR reality knowledge mining point annotation map;

The teaching model VR annotation big data link subsystem is used to distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, and the knowledge mining point link teaching model of the teaching model VR real scene knowledge mining point annotation map Relevant teaching knowledge big data, get VR annotation big data link teaching model;

The sub-system of continuous mining of teaching knowledge points is used to mark the big data link teaching model through VR, and display the teaching model in all directions in high-definition during the teaching process. The content of teaching knowledge is increased, and new teaching knowledge points are continuously explored.

The beneficial effects of the above technical solutions are as follows: a teaching model image knowledge big data subsystem is used to capture the teaching knowledge big data related to the teaching model and process the big data by shooting multi-angle and multi-part images outside and inside the teaching model to obtain Teaching model image teaching knowledge big data set; teaching model VR real scene knowledge mining point labeling sub-system, seamless image splicing of multi-angle and multi-part images outside and inside the teaching model, and multi-angle and multi-part images outside and inside the teaching model. The teaching knowledge mining point annotation is obtained, and the teaching model VR real scene knowledge mining point annotation map is obtained; the teaching model VR annotation big data link subsystem is used to distribute the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map. The knowledge mining point of the teaching model VR real scene knowledge mining point annotation map is linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data link teaching model is obtained; Model, in the teaching process, the teaching model is displayed in an all-round high-definition, and at the same time, it automatically conducts the mining of new knowledge points of teaching knowledge big data and the growth of real teaching knowledge of the model, automatically records the content of teaching knowledge growth, and continuously explores new teaching knowledge points; Part images and knowledge points capture the big data of teaching knowledge, and use big data processing technology to process the big data of teaching knowledge of multi-angle and multi-part images; using smooth image splicing can realize seamless image splicing of multi-angle and multi-part images, The teaching knowledge points can be selected from multiple angles and parts outside and inside the teaching model, and the teaching knowledge mining points can be marked; Point annotation map, you can establish a comprehensive teaching model and the VR real scene knowledge mining point annotation corresponding to the teaching part; you can display the teaching model and knowledge points in an all-round high-definition through the VR real scene of the teaching model during the teaching process, and at the same time according to the teaching content and teaching progress. Automatically mine new knowledge points of teaching knowledge big data, continuously improve the teaching content of the teaching model and constantly explore new knowledge points.

In one embodiment, the teaching model image knowledge big data subsystem includes:

The multi-angle image subsystem of the teaching model captures multi-angle and multi-part images outside and inside the teaching model through high-definition cameras, and obtains a multi-dimensional image set inside and outside the teaching model;

The teaching model knowledge capture big data subsystem captures the teaching knowledge big data related to the teaching model through the multi-angle image sets inside and outside the teaching model, and obtains the teaching knowledge big data related to the teaching model;

The teaching knowledge big data loop screening subsystem performs automatic multi-loop data screening on the teaching knowledge big data related to the teaching model, including: teaching model teaching knowledge teaching scope screening, teaching model teaching knowledge indirect correlation screening and teaching model teaching knowledge directly related Sexual screening;

The teaching knowledge multi-dimensional collation subsystem automatically collates the multi-dimensional data of the teaching knowledge big data related to the teaching model; the multi-dimensional data collation includes: the first dimension data collation, the second dimension data collation, the third dimension data collation; the first dimension data collation; Data collation includes: orderly collation of teaching model teaching knowledge; collation of teaching model teaching knowledge syllabus; collation of teaching model teaching knowledge content; second dimension data collation includes: orderly collation of teaching model testing knowledge; collation of teaching model testing knowledge syllabus; The teaching model tests the knowledge content arrangement; the third dimension data arrangement includes: the teaching model expands the knowledge orderly arrangement; the teaching model expands the knowledge syllabus arrangement; the teaching model expands the knowledge content arrangement.

The teaching model image knowledge data set subsystem automatically filters and multi-dimensionally organizes the multi-cycle data of the teaching knowledge big data related to the teaching model, and adds the multi-angle image sets inside and outside the teaching model to obtain the teaching model image teaching knowledge big data set.

The working principle of the above technical solution is: the teaching model image knowledge big data subsystem includes:

The multi-angle image subsystem of the teaching model captures multi-angle and multi-part images outside and inside the teaching model through high-definition cameras, and obtains a multi-dimensional image set inside and outside the teaching model;

The teaching model knowledge capture big data subsystem captures the teaching knowledge big data related to the teaching model through the multi-angle image sets inside and outside the teaching model, and obtains the teaching knowledge big data related to the teaching model;

The teaching knowledge big data loop screening subsystem performs automatic multi-loop data screening on the teaching knowledge big data related to the teaching model, including: teaching model teaching knowledge teaching scope screening, teaching model teaching knowledge indirect correlation screening and teaching model teaching knowledge directly related Sexual screening;

The teaching knowledge multi-dimensional collation subsystem automatically collates the multi-dimensional data of the teaching knowledge big data related to the teaching model; the multi-dimensional data collation includes: the first dimension data collation, the second dimension data collation, the third dimension data collation; the first dimension data collation; Data collation includes: orderly collation of teaching model teaching knowledge; collation of teaching model teaching knowledge syllabus; collation of teaching model teaching knowledge content; second dimension data collation includes: orderly collation of teaching model testing knowledge; collation of teaching model testing knowledge syllabus; Teaching model test knowledge content sorting; third-dimensional data sorting includes: teaching model expansion knowledge organization; teaching model expansion knowledge syllabus sorting; teaching model expansion knowledge content sorting;

The teaching model image knowledge data set subsystem, through the automatic multi-cycle data screening and multi-dimensional sorting of the teaching knowledge big data related to the teaching model, and adding the multi-angle image sets inside and outside the teaching model to obtain the teaching model image teaching knowledge big data set;

Grasp the big data of teaching knowledge through multi-angle and multi-part images and knowledge points, and use big data processing technology to process the big data of teaching knowledge of multi-angle and multi-part images; use image smooth splicing to seamlessly image multi-angle and multi-part images Splicing, selecting teaching knowledge points from multiple angles and parts inside and outside the teaching model, and marking the teaching knowledge mining points; Distributing the data in the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining according to the corresponding teaching parts Point annotation map; in the teaching process, the teaching model and knowledge points are displayed in full HD through the VR real scene of the teaching model, and at the same time, the teaching knowledge big data and new knowledge points are automatically mined according to the teaching content and teaching progress.

The beneficial effects of the above technical solutions are: the multi-angle image subsystem of the teaching model captures multi-angle and multi-part images outside and inside the teaching model through high-definition cameras to obtain a multi-dimensional image set inside and outside the teaching model; the teaching model knowledge captures big data subsystem , through the multi-angle image sets inside and outside the teaching model, capture the teaching knowledge big data related to the teaching model, and obtain the teaching knowledge big data related to the teaching model; Data automatic screening, including: teaching model teaching knowledge teaching scope screening, teaching model teaching knowledge indirect correlation screening and teaching model teaching knowledge direct correlation screening; teaching knowledge multi-dimensional sorting subsystem, which conducts big data on teaching knowledge related to teaching model. Automatic sorting of multi-dimensional data; multi-dimensional data sorting includes: first-dimensional data sorting, second-dimensional data sorting, and third-dimensional data sorting; first-dimensional data sorting includes: teaching model teaching knowledge orderly sorting; teaching model teaching knowledge teaching Syllabus sorting; teaching model teaching knowledge content sorting; second dimension data sorting includes: teaching model testing knowledge orderly sorting; teaching model testing knowledge syllabus sorting; teaching model testing knowledge content sorting; third dimension data sorting includes: teaching model expansion The knowledge is organized; the teaching model expands the knowledge syllabus; the teaching model expands the knowledge content. The teaching model image knowledge data set subsystem, through the automatic multi-cycle data selection and multi-dimensional sorting of the teaching knowledge big data related to the teaching model, and adding the multi-angle image sets inside and outside the teaching model to obtain the teaching model image teaching knowledge big data set; The big data of teaching knowledge related to the teaching model is automatically screened and multi-dimensionally sorted by multi-cycle data, and the multi-angle image sets inside and outside the teaching model are added to obtain the big data set of teaching knowledge of teaching model images; the big data of teaching knowledge related to the teaching model can make the teaching knowledge content More abundant, automatic screening of multi-cycle data can obtain knowledge data related to teaching content and progress; automatic sorting of multi-dimensional data can make teaching knowledge clearer.

In one embodiment, the teaching model VR real scene knowledge mining point labeling subsystem includes:

The three-dimensional space coordinate system subsystem of the teaching model is used for image recognition of the multi-angle and multi-part images outside and inside the teaching model according to the shooting sequence. The position relationship of the teaching model is created, and the three-dimensional space coordinate system of the teaching model is created;

The same coordinate system distribution scaling subsystem is used to distribute the images of multiple parts into the three-dimensional space coordinate system of the teaching model, and perform the same coordinate system scaling on the coincidence and edges of the images according to the coordinate size and proportion of the three-dimensional space coordinate system of the teaching model to obtain the multi-part image. Scale the image in the same coordinate system;

The coordinate system seamless splicing subsystem is used to de-coincide and smoothly splicing the zoomed images of multiple parts in the same coordinate system, and perform seamless image splicing of the multi-angle and multi-part images outside and inside the teaching model, and obtain the teaching model with multiple parts in the same coordinate system. seamless stitching of images;

The teaching knowledge mining point labeling subsystem is used to select the teaching knowledge mining points where the images of the teaching model are seamlessly spliced with the same coordinate system. Annotated map of real-world knowledge mining points.

The working principle of the above technical solution is: the teaching model VR real scene knowledge mining point labeling sub-system includes:

The three-dimensional space coordinate system subsystem of the teaching model is used for image recognition of the multi-angle and multi-part images outside and inside the teaching model according to the shooting sequence. The position relationship of the teaching model is created, and the three-dimensional space coordinate system of the teaching model is created;

The same coordinate system distribution scaling subsystem is used to distribute the images of multiple parts into the three-dimensional space coordinate system of the teaching model, and perform the same coordinate system scaling on the coincidence and edges of the images according to the coordinate size and proportion of the three-dimensional space coordinate system of the teaching model to obtain the multi-part image. Scale the image in the same coordinate system;

The coordinate system seamless splicing subsystem is used to de-coincide and smoothly splicing the zoomed images of multiple parts in the same coordinate system, and perform seamless image splicing of the multi-angle and multi-part images outside and inside the teaching model, and obtain the teaching model with multiple parts in the same coordinate system. seamless stitching of images;

The teaching knowledge mining point labeling subsystem is used to select the teaching knowledge mining points where the images of the teaching model are seamlessly spliced with the same coordinate system. Annotated map of real-world knowledge mining points.

The beneficial effects of the above technical solutions are: the three-dimensional space coordinate system subsystem of the teaching model is used for image recognition of the multi-angle and multi-part images outside and inside the teaching model respectively according to the shooting sequence, according to the image recognition data, according to the outside and inside of the teaching model. The multi-angle, identify the positional relationship of the multi-part images, and create the three-dimensional space coordinate system of the teaching model; the same coordinate system distribution zoom subsystem is used to distribute the multi-part images into the three-dimensional space coordinate system of the teaching model, and the image coincidence and edge are according to the coordinate system. The coordinate size and ratio of the three-dimensional space coordinate system of the teaching model are scaled with the same coordinate system, and the scaled image of the same coordinate system of multiple parts is obtained; Perform seamless image splicing of multi-angle and multi-part images outside and inside the teaching model, and obtain a seamless splicing image of multiple parts of the teaching model with the same coordinate system; the teaching knowledge mining point labeling subsystem is used to select the teaching model. The teaching knowledge mining points of the spliced images are stitched, and the teaching knowledge mining points are marked on the outside and inside of the teaching model from multiple angles and parts, and the teaching model VR real scene knowledge mining point annotation map is obtained; according to the image recognition data, according to the external and internal teaching model. Multi-angle recognition of the positional relationship of multi-part images to create a three-dimensional space coordinate system of the teaching model, which can make the three-dimensional space coordinate system of the teaching model more suitable for the actual three-dimensional space position of the teaching model; the same coordinate system of multiple parts of the teaching model can make the space and position relationship interconnected , and is conducive to teaching knowledge mining point annotation.

In one embodiment, the teaching model VR annotation big data link subsystem includes:

The teaching model knowledge mining point distribution subsystem is used to establish a plurality of data distribution modules for the knowledge mining points of the teaching model VR real scene knowledge mining point label map;

The teaching model knowledge mining point labeling subsystem is used to compare and match the teaching knowledge point keywords of the data distribution module and the keywords of the teaching model image teaching knowledge big data set, and distribute the teaching model image teaching knowledge big data set to the teaching model. VR reality knowledge mining point annotation map, get the teaching model VR reality knowledge mining point data distribution module;

The teaching model VR annotation big data link subsystem is used to establish a network link between the teaching model VR real scene knowledge mining point data distribution module and the teaching knowledge big data, and the knowledge mining point link of the teaching model VR real scene knowledge mining point annotation map is related to the teaching model. The teaching knowledge big data is obtained, and the VR labeling big data link teaching model is obtained.

The working principle of the above technical solution is: the teaching model VR labeling big data link subsystem includes:

The teaching model knowledge mining point distribution subsystem is used to establish a plurality of data distribution modules for the knowledge mining points of the teaching model VR real scene knowledge mining point label map;

The teaching model knowledge mining point labeling subsystem is used to compare and match the teaching knowledge point keywords of the data distribution module and the keywords of the teaching model image teaching knowledge big data set, and distribute the teaching model image teaching knowledge big data set to the teaching model. VR reality knowledge mining point annotation map, get the teaching model VR reality knowledge mining point data distribution module;

The teaching model VR annotation big data link subsystem is used to establish a network link between the teaching model VR real scene knowledge mining point data distribution module and the teaching knowledge big data, and the knowledge mining point link of the teaching model VR real scene knowledge mining point annotation map is related to the teaching model. The teaching knowledge big data is obtained, and the VR labeling big data link teaching model is obtained.

The beneficial effects of the above technical solutions are as follows: the teaching model knowledge mining point distribution subsystem is used to establish a plurality of data distribution modules for the knowledge mining points of the teaching model VR real scene knowledge mining point annotation map; the teaching model knowledge mining point annotation subsystem uses It compares and matches the teaching knowledge point keywords of the data distribution module and the keywords of the teaching model image teaching knowledge big data set, distributes the teaching model image teaching knowledge big data set to the teaching model VR real scene knowledge mining point annotation map, and obtains teaching Model VR real scene knowledge mining point data distribution module; teaching model VR labeling big data link subsystem, used to establish network link between the teaching model VR real scene knowledge mining point data distribution module and teaching knowledge big data, and the teaching model VR real scene knowledge mining point The knowledge mining points of the annotation map are linked to the teaching knowledge big data related to the teaching model, and the VR annotation big data is linked to the teaching model. The graphs and points are distributed modularly; the teaching knowledge point keywords of the data distribution module are compared and matched with the keywords of the teaching model image teaching knowledge large data set, and the matching relationship can be more accurate through the keywords.

In one embodiment, the teaching knowledge point continuous mining sub-system includes:

The logic matching subsystem of the teaching knowledge point information is used to convert the text and voice information of the teaching model high-definition VR real scene annotation points corresponding to the teaching knowledge points of the teaching model to each other, and to convert the video data of the teaching knowledge points and the logic process of the teaching knowledge points. information to match;

The real scene display subsystem of teaching model knowledge information is used to display the teaching model in all directions in high-definition during the teaching process, and display the text and voice information of the teaching model when selecting the marked point. logical process;

The teaching model knowledge mining and updating subsystem is used to automatically conduct teaching knowledge big data mining new knowledge points and model real-world teaching knowledge growth at the same time as teaching, automatically record the content of teaching knowledge growth, and continuously mine new teaching knowledge points; through the teaching model teaching part Excavate new knowledge points and existing knowledge points, and calculate the growth rate of model real-life teaching knowledge points; according to the calculation model's real-scene teaching knowledge point growth rate, analyze and determine whether the model's real-scene teaching knowledge point growth rate is greater than the set teaching knowledge growth rate. value to determine whether the mining of new teaching knowledge points meets the knowledge growth rate of teaching requirements; if the growth rate value of model real-life teaching knowledge points is less than the set value of teaching knowledge growth rate, the total number of cycles of teaching knowledge mining will be increased, and new mining teaching will be increased. Parts, increase the new knowledge points for newly excavated teaching parts.

The working principle of the above technical solution is: the continuous mining sub-system of teaching knowledge points includes:

The logic matching subsystem of the teaching knowledge point information is used to convert the text and voice information of the teaching model high-definition VR real scene annotation points corresponding to the teaching knowledge points of the teaching model to each other, and to convert the video data of the teaching knowledge points and the logic process of the teaching knowledge points. information to match;

The real scene display subsystem of teaching model knowledge information is used to display the teaching model in all directions in high-definition during the teaching process, and display the text and voice information of the teaching model when selecting the marked point. logical process;

The teaching model knowledge mining and updating subsystem is used to automatically mine teaching knowledge big data new knowledge points and model real-world teaching knowledge growth at the same time as teaching, automatically record the content of teaching knowledge growth, and continuously mine new teaching knowledge points;

Through the mining of new knowledge points and existing knowledge points in the teaching part of the teaching model, the growth rate of knowledge points in real-life teaching of the model is calculated. The calculation formula is as follows:

为教学知识挖掘总循环轮数，i为

的连续正整数；根据计算模型实景教学知识点增长率值，分析判定模型实景教学知识点增长率值是否大于设定的教学知识增长率值，以确定挖掘新教学知识点是否满足教学要求的知识增长速度；模型实景教学知识点增长率值小于设定的教学知识增长率值，则增加教学知识挖掘总循环轮数，增加新挖掘教学部位，增加新挖掘教学部位的新知识点数。Among them, Z _wd is the growth rate of knowledge points in model real-world teaching, P(I _wdi ) is the number of teaching parts of the existing teaching model, Q(D _sli ) is the number of existing knowledge points in the teaching parts of the existing teaching model, P(I _{nwdi )} ) is the number of general positions after the teaching model newly excavated the teaching position, Q(D _nsli ) is the number of knowledge points after the new knowledge points of the new teaching position excavated by the teaching model,

The total number of rounds of mining for teaching knowledge, i is

is a continuous positive integer; according to the growth rate value of the real-life teaching knowledge point of the calculation model, analyze and determine whether the model's real-life teaching knowledge point growth rate value is greater than the set teaching knowledge growth rate value, so as to determine whether mining new teaching knowledge points meets the knowledge required for teaching Growth rate; if the growth rate value of model real-life teaching knowledge points is less than the set value of teaching knowledge growth rate, the total number of rounds of teaching knowledge mining will be increased, new mining teaching parts will be added, and new knowledge points of new mining teaching parts will be added.

The beneficial effects of the above technical solutions are: a logical matching subsystem for teaching knowledge point information, which is used to mutually transform the text and voice information of the teaching model high-definition VR real scene annotation points corresponding to the teaching knowledge points in multiple parts of the teaching model, and convert the video data of the teaching knowledge points to each other. The image of the teaching knowledge point is matched with the logical process information of the teaching knowledge point; the real scene display subsystem of the teaching model knowledge information is used to display the teaching model in an all-round high-definition during the teaching process, and display the text and voice information of the teaching model when selecting the marked point. When the video materials of knowledge points are displayed, the logic process of teaching knowledge points is displayed at the same time; the knowledge mining and updating subsystem of teaching model is used to automatically perform teaching knowledge big data mining new knowledge points and model real-life teaching knowledge growth at the same time as teaching, and automatically record teaching knowledge growth. content, and continuously excavate new teaching knowledge points; through the mining of new knowledge points and existing knowledge points in the teaching part of the teaching model, calculate the growth rate of the model’s real-life teaching knowledge points; according to the calculation model’s real-scene teaching knowledge point growth rate value, analyze and determine the model’s real scene Whether the growth rate of teaching knowledge points is greater than the set growth rate of teaching knowledge, to determine whether the new teaching knowledge points can meet the knowledge growth rate of teaching requirements; the growth rate of model real teaching knowledge points is less than the set growth rate of teaching knowledge , then increase the total number of rounds of teaching knowledge mining, increase the number of new mining teaching parts, increase the number of new knowledge points in the new mining teaching parts, increase the growth rate of teaching knowledge to meet the teaching requirements, continue to increase teaching knowledge points and improve teaching efficiency.

Although the embodiment of the present invention has been disclosed as above, it is not limited to the application listed in the description and the embodiment, and it can be applied to various fields suitable for the present invention. For those skilled in the art, it can be easily Therefore, the invention is not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the appended claims and the scope of equivalents.

Claims (8)

1. A VR live-action teaching model big data teaching knowledge mining method is characterized by comprising the following steps:

s100, capturing teaching knowledge big data related to a teaching model and carrying out big data processing by shooting multi-angle multi-position images outside and inside the teaching model to obtain a teaching knowledge big data set of the teaching model image;

s200, seamlessly splicing images of the multi-angle multi-position images outside and inside the teaching model, and marking teaching knowledge mining points of the multi-angle multi-position images outside and inside the teaching model to obtain a VR real scene knowledge mining point marking image of the teaching model;

s300, distributing the teaching model image teaching knowledge big data set to a teaching model VR real knowledge mining point label graph, and linking the knowledge mining points of the teaching model VR real knowledge mining point label graph with teaching model related teaching knowledge big data to obtain a VR labeled big data link teaching model;

s400, marking big data through VR to link a teaching model, carrying out omnibearing high-definition display on the teaching model in the teaching process, simultaneously automatically mining new knowledge points of the big data of teaching knowledge and increasing teaching knowledge of model scenes, automatically recording the content of the increased teaching knowledge, and continuously mining new teaching knowledge points;

s100 comprises:

s101, shooting multi-angle multi-position images outside and inside a teaching model through high-definition shooting to obtain an inside and outside multi-angle image set of the teaching model;

s102, capturing large teaching knowledge data related to a teaching model through an internal and external multi-angle image set of the teaching model to obtain the large teaching knowledge data related to the teaching model;

s103, multi-cycle data automatic screening is carried out on the big data of the teaching knowledge related to the teaching model, and the method comprises the following steps: screening teaching range of teaching model teaching knowledge, screening indirect relevance of teaching model teaching knowledge and screening direct relevance of teaching model teaching knowledge;

s104, carrying out multi-dimensional data automatic arrangement on the big data of the teaching knowledge related to the teaching model; the multidimensional data sorting comprises the following steps: sorting first dimension data, sorting second dimension data and sorting third dimension data; the first dimension data sorting comprises: organizing teaching knowledge of the teaching model; organizing teaching knowledge and teaching principles of the teaching model; organizing teaching knowledge contents of the teaching model; the second dimension data sorting comprises: testing knowledge and organizing a teaching model; the teaching model tests knowledge and teaching order; the teaching model tests the content of knowledge and arranges; the third dimension data sorting comprises the following steps: the teaching model expands knowledge and arranges the knowledge; organizing teaching models with extended knowledge; arranging the extended knowledge content of the teaching model;

and S105, performing multi-cycle data automatic screening and multi-dimensional sorting on the teaching model related teaching knowledge big data, and adding the multi-angle image sets inside and outside the teaching model to obtain a teaching model image teaching knowledge big data set.

2. The VR live-action teaching model big data teaching knowledge mining method of claim 1, wherein S200 comprises:

s201, respectively carrying out image recognition on multi-angle multi-position images outside and inside the teaching model according to a shooting sequence, recognizing the position relation of the multi-position images according to image recognition data and multi-angles outside and inside the teaching model, and creating a three-dimensional space coordinate system of the teaching model;

s202, distributing the multi-position images into a three-dimensional space coordinate system of the teaching model, and carrying out same coordinate system scaling on image superposition and edges according to the coordinate size and the proportion of the three-dimensional space coordinate system of the teaching model to obtain multi-position same coordinate system scaled images;

s203, de-overlapping and smoothly splicing the multi-position co-coordinate system zoomed images, and carrying out image seamless splicing on multi-angle multi-position images outside and inside the teaching model to obtain a multi-position co-coordinate system seamless spliced image of the teaching model;

s204, selecting teaching knowledge mining points of a plurality of parts of the teaching model, seamlessly splicing the images in the same coordinate system, and labeling the teaching knowledge mining points on the outer part and the inner part of the teaching model in a multi-angle and multi-part mode to obtain a VR real scene knowledge mining point labeling image of the teaching model.

3. The VR live-action teaching model big data teaching knowledge mining method of claim 1, wherein S300 comprises:

s301, establishing a plurality of data distribution modules for knowledge mining points of a teaching model VR live-action knowledge mining point label graph;

s302, comparing and matching keywords of the teaching knowledge points of the data distribution module with keywords of a large teaching model image teaching knowledge data set, distributing the large teaching model image teaching knowledge data set to a teaching model VR live-action knowledge mining point label graph, and obtaining a data distribution module of the teaching model VR live-action knowledge mining points;

s303, establishing network link between the teaching model VR live-action knowledge mining point data distribution module and teaching knowledge big data, and linking the knowledge mining points of the teaching model VR live-action knowledge mining point labeled graph with the teaching knowledge big data related to the teaching model to obtain a VR labeled big data link teaching model.

4. The method for mining the VR live-action teaching model big data teaching knowledge as claimed in claim 1, wherein S400 includes:

s401, mutually converting the characters and voice information of the teaching model multi-part teaching knowledge points corresponding to the teaching model high-definition VR live-action annotation points, and matching the images of the teaching knowledge point video data with the teaching knowledge point logic process information;

s402, performing omnibearing high-definition display on the teaching model in the teaching process, displaying character and voice information of the teaching model when a marking point is selected, and simultaneously displaying the logic process of a teaching knowledge point when video data of the teaching knowledge point is clicked;

s403, automatically mining new knowledge points and model live-action teaching knowledge growth by using the big teaching knowledge data during teaching, automatically recording the growth content of the teaching knowledge, and continuously mining new teaching knowledge points; calculating the growth rate of the model live-action teaching knowledge points by mining new knowledge points and existing knowledge points of the teaching model teaching part; analyzing and judging whether the growth rate value of the model live-action teaching knowledge points is larger than a set teaching knowledge growth rate value according to the growth rate value of the model live-action teaching knowledge points so as to determine whether the knowledge growth rate of the new teaching knowledge points meeting the teaching requirements is mined; and if the growth rate value of the teaching knowledge points of the model live-action teaching is smaller than the set growth rate value of the teaching knowledge, increasing the total cycle number of teaching knowledge mining, increasing the newly mined teaching part and increasing the number of the new knowledge points of the newly mined teaching part.

5. The utility model provides a VR outdoor scene teaching model big data teaching knowledge mining system which characterized in that includes:

the system comprises a teaching model image knowledge big data subsystem, a teaching model image big data subsystem and a big data processing subsystem, wherein the teaching model image knowledge big data subsystem is used for capturing teaching knowledge big data related to a teaching model and carrying out big data processing by shooting multi-angle multi-position images outside and inside the teaching model to obtain a teaching model image teaching knowledge big data set;

the teaching model VR live-action knowledge mining point marking subsystem carries out image seamless splicing on multi-angle multi-position images outside and inside the teaching model, carries out teaching knowledge mining point marking on the multi-angle multi-position images outside and inside the teaching model, and obtains a teaching model VR live-action knowledge mining point marking image;

the teaching model VR big data link subsystem is used for distributing a teaching model image teaching knowledge big data set to a teaching model VR real knowledge mining point label graph, and linking the teaching knowledge big data related to the teaching model to the knowledge mining points of the teaching model VR real knowledge mining point label graph to obtain a VR big data link teaching model;

the teaching knowledge point continuous mining subsystem is used for marking big data through VR to link a teaching model, performing omnibearing high-definition display on the teaching model in the teaching process, automatically mining new knowledge points of the big data of the teaching knowledge and increasing model live-action teaching knowledge, automatically recording the content of the increased teaching knowledge, and continuously mining the new teaching knowledge points;

the teaching model image knowledge big data subsystem comprises:

the teaching model multi-angle image subsystem shoots multi-angle multi-position images outside and inside the teaching model through high-definition camera shooting to obtain an inside and outside multi-dimensional image set of the teaching model;

the teaching model knowledge capturing big data subsystem captures teaching knowledge big data related to a teaching model through a multi-angle image set inside and outside the teaching model to obtain the teaching knowledge big data related to the teaching model;

teaching knowledge big data cycle screening subsystem carries out multicycle data autofilter to the relevant teaching knowledge big data of teaching model, includes: screening teaching range of teaching model teaching knowledge, screening indirect relevance of teaching model teaching knowledge and screening direct relevance of teaching model teaching knowledge;

the teaching knowledge multidimensional sorting subsystem is used for automatically sorting multidimensional data of the big teaching knowledge data related to the teaching model; the multidimensional data sorting comprises the following steps: sorting first dimension data, sorting second dimension data and sorting third dimension data; the first dimension data sorting comprises: organizing teaching knowledge of the teaching model; organizing teaching knowledge and teaching principles of the teaching model; organizing teaching knowledge contents of the teaching model; the second dimension data sorting comprises: testing knowledge organization and arrangement of the teaching model; teaching model testing knowledge and teaching principle arrangement; the teaching model tests the content of knowledge and arranges; the third dimension data sorting comprises the following steps: the teaching model expands knowledge and arranges the knowledge; organizing teaching models with extended knowledge; arranging the extended knowledge content of the teaching model;

the teaching model image knowledge data set subsystem performs multi-cycle data automatic screening and multi-dimensional arrangement on teaching model related teaching knowledge big data, and adds an internal and external multi-angle image set of a teaching model to obtain a teaching model image teaching knowledge big data set.

6. The VR live-action teaching model big data teaching knowledge mining system of claim 5, wherein the teaching model VR live-action knowledge mining point annotation subsystem comprises:

the teaching model three-dimensional space coordinate system subsystem is used for respectively carrying out image recognition on multi-angle multi-position images outside and inside the teaching model according to a shooting sequence, recognizing the position relation of the multi-position images according to image recognition data and multi-angles outside and inside the teaching model, and creating a teaching model three-dimensional space coordinate system;

the same coordinate system distribution scaling subsystem is used for distributing the multi-position images into a teaching model three-dimensional space coordinate system, and carrying out same coordinate system scaling on image superposition and edges according to the coordinate size and proportion of the teaching model three-dimensional space coordinate system to obtain multi-position same coordinate system scaling images;

the coordinate system seamless splicing subsystem is used for carrying out de-registration smooth splicing on the zoomed images of the plurality of parts in the same coordinate system, carrying out image seamless splicing on the multi-angle multi-part images outside and inside the teaching model and obtaining a multi-part same coordinate system seamless spliced image of the teaching model;

and the teaching knowledge mining point labeling subsystem is used for selecting teaching knowledge mining points of a plurality of parts of the teaching model seamlessly spliced image with the same coordinate system, and labeling the teaching knowledge mining points on the outer part and the inner part of the teaching model in a multi-angle and multi-part manner to obtain a VR live-action knowledge mining point labeling image of the teaching model.

7. The VR real teaching model big data teaching knowledge mining system of claim 5, wherein the teaching model VR labeling big data link subsystem includes:

the teaching model knowledge mining point distribution subsystem is used for establishing a plurality of data distribution modules for the knowledge mining points of the real scene knowledge mining point label graph of the teaching model VR;

the teaching model knowledge mining point annotation subsystem is used for comparing and matching the keywords of the teaching knowledge points of the data distribution module with the keywords of the teaching model image teaching knowledge big data set, distributing the teaching model image teaching knowledge big data set to a teaching model VR live-action knowledge mining point annotation graph, and obtaining a teaching model VR live-action knowledge mining point data distribution module;

and the teaching model VR marking big data link subsystem is used for establishing network link between the teaching model VR live-action knowledge mining point data distribution module and the teaching knowledge big data, and linking the knowledge mining points of the teaching model VR live-action knowledge mining point marking graph with the teaching knowledge big data related to the teaching model to obtain a VR marking big data link teaching model.

8. The VR real teaching model big data teaching knowledge mining system of claim 5, wherein the teaching knowledge point continuous mining subsystem comprises:

the teaching knowledge point information logic matching subsystem is used for mutually converting the character and voice information of the teaching model multi-part teaching knowledge points corresponding to the teaching model high-definition VR live-action annotation points and matching the image of the teaching knowledge point video data with the teaching knowledge point logic process information;

the teaching model knowledge information live-action display subsystem is used for carrying out omnibearing high-definition display on a teaching model in the teaching process, displaying character and voice information of the teaching model when a marking point is selected, and simultaneously displaying a logic process of a teaching knowledge point when video data of the teaching knowledge point is clicked;

the teaching model knowledge mining and updating subsystem is used for automatically mining new knowledge points and model live-action teaching knowledge growth by using the big teaching knowledge data during teaching, automatically recording the growth content of the teaching knowledge and continuously mining new teaching knowledge points; calculating the growth rate of the model real scene teaching knowledge points by mining new knowledge points and existing knowledge points of the teaching model teaching part; analyzing and judging whether the growth rate value of the model live-action teaching knowledge points is larger than a set teaching knowledge growth rate value according to the growth rate value of the model live-action teaching knowledge points so as to determine whether the knowledge growth rate of the new teaching knowledge points meeting the teaching requirements is mined; and if the growth rate value of the model live-action teaching knowledge points is smaller than the set growth rate value of the teaching knowledge, increasing the total cycle number of teaching knowledge mining, increasing the newly mined teaching part and increasing the number of the new knowledge points of the newly mined teaching part.

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