Offline immersive large space system based on VR

Abstract

The invention discloses an offline immersive large-space system based on VR, and relates to the field of VR. Comprising a data comprehensive control module, the data comprehensive control module comprises a data input module, an immersive interaction scene conversion module, an image output module and a storage module, and the sensing type action acquisition module comprises a sensor worn on the body of a user and a sensor arranged in a large space; the system comprises a data comprehensive control module, a sensing type action obtaining module and a multi-sensory interaction module, the multi-sensory interaction module is composed of a plurality of interaction modules worn on the body of a user and installed in a large space, and the intelligent management module is used for managing data of the data comprehensive control module, the sensing type action obtaining module and the multi-sensory interaction module and controlling the data comprehensive control module, the sensing type action obtaining module and the multi-sensory interaction module at the same time to achieve the intelligent operation function. According to the method, the spatial position of the user can be determined at the same time through three means of radar distance measurement, laser distance measurement and image state acquisition, and then a comprehensive evaluation form is adopted for the three means, so that the position accuracy is improved, and the use experience is improved.

Description

Technical Field

[0001] The present invention relates to the field of VR, and particularly to an offline immersive large-space system based on VR. Background Art

[0002] VR technology, namely virtual reality technology (abbreviated as VR), is a technology that uses a computer to simulate and generate a virtual world in a three-dimensional space, providing users with simulations of senses such as vision, hearing, and touch, making users feel as if they are on the scene. This technology creates an immersive environment, enabling users to interact and experience in this virtual world.

[0003] After retrieval, a patent with the Chinese patent publication number CN118521740B discloses an immersive interactive intelligent central control screen based on VR technology, including: an interactive space information acquisition subsystem for acquiring the interactive space information selected by a target interactive party; an immersive interactive scene construction subsystem for constructing an immersive interactive scene based on VR technology according to the interactive space information; an interactive action acquisition subsystem for acquiring the interactive actions of the target interactive party in the immersive interactive scene; an interactive feedback determination subsystem for determining interactive feedback according to the interactive actions; and an intelligent interaction subsystem for performing intelligent interaction according to the interactive feedback.

[0004] The above patent has the following deficiencies: Since VR immersive experience requires corresponding environmental changes according to the virtual VR real scene, it is necessary to determine the spatial position of the user. The above patent cannot perform precise positioning, and particularly, the inability to perform precise positioning in a large space will seriously affect the experience.

[0005] Therefore, the present invention proposes an offline immersive large-space system based on VR. Summary of the Invention

[0006] The purpose of the present invention is to solve the deficiencies existing in the prior art, and propose an offline immersive large-space system based on VR.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions:

[0008] An offline immersive large-space system based on VR, including:

[0009] A data comprehensive control module, the data comprehensive control module includes a data input module, an immersive interactive scene conversion module, an image output module, and a storage module;

[0010] A sensing action acquisition module, which includes sensors worn on the user's body and sensors arranged in the large space;

[0011] A multi-sensory interaction module, which consists of multiple interaction modules worn on the user's body and installed in a large space;

[0012] An intelligent management module, which is used to manage and control the data of the data comprehensive control module, the sensing action acquisition module, and the multi-sensory interaction module, and realize the intelligent operation function.

[0013] Preferably: The data input module is used for external devices to input data into the system, and the input data types include system basic parameter data and VR scene data.

[0014] Preferably: The immersive interaction scene conversion module is used to receive the VR scene data of the data input module, and then process the data and convert it into a VR real scene image.

[0015] Preferably: The image output module is used to receive the VR real scene image converted by the immersive interaction scene conversion module and display it to the user through VR devices.

[0016] Preferably: The storage module is used to store system basic parameter data, system usage record data, and VR real scene image data, and it stores the VR real scene image data using a distributed classification storage architecture.

[0017] Preferably: In the sensing action acquisition module, it is used to obtain the body movement information of the user. The sensors worn on the user's body include multiple displacement sensors worn on the user's torso and limbs, and the sensors installed in the large space include multiple matrix radar ranging units, matrix laser ranging units, and ranging units based on image processing.

[0018] Preferably: In the multi-sensory interaction module, it is used to convert the environmental change information of the VR real scene image into the corresponding environmental information in the large space, which specifically includes environmental temperature information, environmental humidity information, environmental space displacement information, and human body movement interaction information.

[0019] Preferably: The logic of the distributed classification storage architecture of the storage module is:

[0020] S1: Obtain the playback times M and the total playback time span T of each independent VR real scene image according to the past usage records;

[0021] S2: According to the formula Calculate the popularity of each independent VR real scene image;

[0022] S3: Divide the storage module into multiple different storage units, each storage unit corresponds to an interval of popularity, and each storage unit contains multiple storage spaces of different classifications;

[0023] S4: Divide the VR live-action images into storage units of matching intervals. After all the VR live-action images are divided, then classify and divide the VR live-action images of each storage unit. Finally, store them in different storage spaces according to the classification results.

[0024] Preferably: In the step S4, the classification is performed by using the method of cluster analysis. The cluster analysis is to divide the data into K clusters based on K-means, and each cluster is represented by a centroid, specifically as follows: Where J is the objective function of K-means, r ik is an indicator variable. If the data point x i belongs to cluster k, it is 1, otherwise it is 0. μ k is the centroid of cluster k, and the centroid type of cluster k is the VR live-action image name type or the VR live-action image file size.

[0025] Preferably: For the sensing type action acquisition module, the method for obtaining the user's spatial position data through the matrix type radar ranging unit, the matrix type laser ranging unit, and the ranging unit based on image processing includes the following steps:

[0026] A1: Input the three-dimensional terrain map under the entire large space and establish a three-dimensional space model;

[0027] A2: Obtain the spatial position change amounts (Δx1, Δy1, Δz1) of the user through the radar ranging unit, obtain the spatial position change amounts (Δx2, Δy2, Δz2) of the user through the matrix type laser ranging unit, and obtain the spatial position (x3, y3, z3) of the user before movement and the spatial position (x'3, y'3, z'3) after movement calculated through the ranging unit based on image processing;

[0028] A3: Then calculate the spatial position change amounts (Δx3, Δy3, Δz3) obtained by the ranging unit based on image processing, where

[0029] A4: Finally, calculate the state change amounts (Δx, Δy, Δz) of the final decision according to the formula

[0030] A5: Then determine the current spatial position of the user based on the initial state and in combination with the state change amounts.

[0031] The beneficial effects of the present invention are as follows:

[0032] 1. The present invention can simultaneously determine the spatial position of the user through three means of radar ranging, laser ranging, and image state acquisition. Then, for the three means, a comprehensive evaluation form is adopted, thereby increasing the accuracy of the position and enhancing the user experience. Brief Description of the Drawings

[0033] Figure 1 The figure is an architecture diagram of the offline immersive large - space system based on VR proposed by the present invention. Detailed Description of the Preferred Embodiments

[0034] The technical solution of the present invention will be further described in detail below in conjunction with the specific embodiments.

[0035] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", "connection", and "setting" should be understood in a broad sense. For example, it can be fixedly connected and set, or detachably connected and set, or integrally connected and set. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0036] Embodiment 1:

[0037] The offline immersive large - space system based on VR includes:

[0038] A data comprehensive control module, which includes a data input module, an immersive interaction scene conversion module, an image output module, and a storage module;

[0039] A sensing - type action acquisition module, which includes sensors worn on the user's body and sensors arranged in the large space;

[0040] A multi - sensory interaction module, which is composed of multiple interaction modules worn on the user's body and installed in the large space;

[0041] An intelligent management module, which is used to manage and control the data of the data comprehensive control module, the sensing - type action acquisition module, and the multi - sensory interaction module, and realize the intelligent operation function.

[0042] The data input module is used for external devices to input data into the system, and the input data types include system basic parameter data and VR scene data.

[0043] The immersive interaction scene conversion module is used to receive the VR scene data of the data input module, and then process the data and convert it into VR real - scene images.

[0044] The image output module is used to receive the VR real - scene images converted by the immersive interaction scene conversion module and display them to the user through VR devices.

[0045] The storage module is used to store the basic system parameter data, system usage record data, and VR real-scene image data, and it stores the VR real-scene image data using a distributed classification storage architecture.

[0046] In the sensing motion acquisition module, it is used to obtain the body motion information of the user. The sensors worn on the user's body include multiple displacement sensors worn on the user's torso and limbs, and the sensors set in the large space include multiple matrix radar ranging units, matrix laser ranging units, and range measurement units based on image processing.

[0047] In the multi-sensory interaction module, it is used to convert the environmental change information of the VR real-scene image into the corresponding environmental information in the large space, which specifically includes environmental temperature information, environmental humidity information, environmental space displacement information, and human motion interaction information.

[0048] The human motion interaction information is obtained through tactile gloves and vibrating vests worn on the human body.

[0049] Embodiment 2:

[0050] The offline immersive large-space system based on VR includes:

[0051] A data comprehensive control module, which includes a data input module, an immersive interaction scene conversion module, an image output module, and a storage module;

[0052] A sensing motion acquisition module, which includes sensors worn on the user's body and sensors set in the large space;

[0053] A multi-sensory interaction module, which is composed of multiple interaction modules worn on the user's body and installed in the large space;

[0054] An intelligent management module, which is used to manage and control the data of the data comprehensive control module, the sensing motion acquisition module, and the multi-sensory interaction module to achieve the intelligent operation function.

[0055] The data input module is used for external devices to input data into the system, and the input data types include basic system parameter data and VR scene data.

[0056] The immersive interaction scene conversion module is used to receive the VR scene data of the data input module, and then process the data and convert it into a VR real-scene image.

[0057] The image output module is used to receive the VR real-scene image converted by the immersive interaction scene conversion module and display it to the user through VR devices.

[0058] The storage module is used to store the basic system parameter data, system usage record data, and VR real-scene image data, and it stores the VR real-scene image data using a distributed classification storage architecture.

[0059] In the sensing motion acquisition module, it is used to obtain the body motion information of the user. Among them, the sensors worn on the user's body include multiple displacement sensors worn on the user's torso and limbs, and the sensors arranged in a large space include multiple matrix radar ranging units, matrix laser ranging units, and range measurement units based on image processing.

[0060] In the multi-sensory interaction module, it is used to convert the environmental change information of the VR real-scene image into the corresponding environmental information in the large space, which specifically includes environmental temperature information, environmental humidity information, environmental space displacement information, and human motion interaction information.

[0061] The human motion interaction information is obtained through tactile gloves and vibrating vests worn on the human body.

[0062] The distributed classification storage architecture logic of the storage module is as follows:

[0063] S1: Obtain the playback times M and the total playback time span T of each independent VR real-scene image according to the past usage records;

[0064] S2: According to the formula Calculate the popularity of each independent VR real-scene image;

[0065] S3: Divide the storage module into multiple different storage units, each storage unit corresponding to a popularity interval, and each storage unit contains multiple storage spaces of different classifications;

[0066] S4: Divide the VR real-scene images into the storage units that match the intervals. After all the VR real-scene images are divided, then classify and divide the VR real-scene images in each storage unit, and finally store them in different storage spaces according to the classification and division results.

[0067] In the S4 step, the classification is performed using the clustering analysis method. Clustering analysis is to divide the data into K clusters based on K-means, and each cluster is represented by a centroid. Specifically as follows: Where J is the objective function of K-means, r ik is an indicator variable. If the data point x i belongs to cluster k, it is 1, otherwise it is 0, and μ k is the centroid of cluster k.

[0068] In the S4 step, the centroid type of cluster k is the VR real-scene image name type.

[0069] Example 3:

[0070] The VR-based offline immersive large-space system includes:

[0071] A data comprehensive control module, which includes a data input module, an immersive interaction scene conversion module, an image output module, and a storage module;

[0072] A sensing motion acquisition module, which includes sensors worn on the user's body and sensors installed in the large space;

[0073] A multi-sensory interaction module, which consists of multiple interaction modules worn on the user's body and installed in the large space;

[0074] An intelligent management module, which is used to manage and control the data of the data comprehensive control module, the sensing motion acquisition module, and the multi-sensory interaction module to achieve the intelligent operation function.

[0075] The data input module is used for external devices to input data into the system, and the input data types include system basic parameter data and VR scene data.

[0076] The immersive interaction scene conversion module is used to receive the VR scene data of the data input module, and then process the data and convert it into VR real-scene images.

[0077] The image output module is used to receive the VR real-scene images converted by the immersive interaction scene conversion module and display them to the user through VR devices.

[0078] The storage module is used to store system basic parameter data, system usage record data, and VR real-scene image data, and it stores the VR real-scene image data using a distributed classification storage architecture.

[0079] In the sensing motion acquisition module, it is used to obtain the body motion information of the user. The sensors worn on the user's body include multiple displacement sensors worn on the user's torso and limbs, and the sensors installed in the large space include multiple matrix radar ranging units, matrix laser ranging units, and image processing-based ranging units.

[0080] In the multi-sensory interaction module, it is used to convert the environmental change information of the VR real-scene image into the corresponding environmental information in the large space, which specifically includes environmental temperature information, environmental humidity information, environmental space displacement information, and human motion interaction information.

[0081] The human motion interaction information is obtained through tactile gloves and vibrating vests worn on the human body.

[0082] The distributed classification storage architecture logic of the storage module is as follows:

[0083] S1: Obtain the playback times M and the total playback time span T of each independent VR live-action image according to past usage records;

[0084] S2: According to the formula Calculate the popularity of each independent VR live-action image;

[0085] S3: Divide the storage module into multiple different storage units. Each storage unit corresponds to an interval of popularity, and each storage unit contains multiple storage spaces of different classifications;

[0086] S4: Divide the VR live-action images into storage units that match the intervals. After all VR live-action images are divided, then classify and divide the VR live-action images in each storage unit. Finally, according to the classification result, store them in different storage spaces.

[0087] In the step S4, the classification is performed by using the method of cluster analysis. Cluster analysis is to divide the data into K clusters based on K-means, and each cluster is represented by a centroid. Specifically as follows: Where J is the objective function of K-means, r ik is an indicator variable. If the data point x i belongs to cluster k, it is 1, otherwise it is 0, and μ k is the centroid of cluster k.

[0088] In the step S4, the centroid type of cluster k is the file size of the VR live-action image.

[0089] Embodiment 4:

[0090] The VR-based offline immersive large-space system includes:

[0091] A data comprehensive control module, which includes a data input module, an immersive interaction scene conversion module, an image output module, and a storage module;

[0092] A sensing action acquisition module, which includes sensors worn on the user's body and sensors arranged in the large space;

[0093] A multi-sensory interaction module, which is composed of multiple interaction modules worn on the user's body and installed in the large space;

[0094] An intelligent management module, which is used to manage and control the data of the data comprehensive control module, the sensing action acquisition module, and the multi-sensory interaction module, and realize the intelligent operation function.

[0095] The data input module is used for external devices to input data into the system, and the input data types include system basic parameter data and VR scene data.

[0096] The immersive interaction scene conversion module is used to receive the VR scene data of the data input module, and then process the data into VR real scene images.

[0097] The image output module is used to receive the VR real scene images converted by the immersive interaction scene conversion module and display them to the user through VR devices.

[0098] The storage module is used to store system basic parameter data, system usage record data, and VR real scene image data, and it stores the VR real scene image data using a distributed classification storage architecture.

[0099] In the sensing action acquisition module, it is used to obtain the body movement information of the user. The sensors worn on the user's body include multiple displacement sensors worn on the user's torso and limbs, and the sensors set in the large space include multiple matrix radar ranging units, matrix laser ranging units, and ranging units based on image processing.

[0100] In the multi-sensory interaction module, it is used to convert the environmental change information of the VR real scene image into the corresponding environmental information in the large space, which specifically includes environmental temperature information, environmental humidity information, environmental space displacement information, and human body movement interaction information.

[0101] The human body movement interaction information is obtained through tactile gloves and vibrating vests worn on the human body.

[0102] The distributed classification storage architecture logic of the storage module is as follows:

[0103] S1: Obtain the playback times M and the total playback time span T of each independent VR real scene image according to the past usage records;

[0104] S2: According to the formula Calculate the popularity of each independent VR real scene image;

[0105] S3: Divide the storage module into multiple different storage units, each storage unit corresponding to a popularity interval, and each storage unit contains multiple different classification storage spaces;

[0106] S4: Divide the VR real scene images into storage units that match the intervals. After all the VR real scene images are divided, then classify and divide the VR real scene images in each storage unit, and finally store them in different storage spaces according to the classification and division results.

[0107] In the step S4, clustering analysis is used for classification. Clustering analysis is to divide data into K clusters based on K-means, and each cluster is represented by a centroid, which is specifically as follows: where J is the objective function of K-means, and r ik is an indicator variable. If the data point x i belongs to cluster k, it is 1; otherwise, it is 0. μ k is the centroid of cluster k.

[0108] In the step S4, the centroid type of cluster k is the VR real-scene image name type or the VR real-scene image file size.

[0109] For the sensing action acquisition module, the method for obtaining the user's spatial position data through the matrix radar ranging unit, the matrix laser ranging unit, and the ranging unit based on image processing includes the following steps:

[0110] A1: Input the three-dimensional terrain map in the entire large space and establish a three-dimensional space model;

[0111] A2: Obtain the user's spatial position change amounts (Δx1, Δy1, Δz1) through the radar ranging unit, the user's spatial position change amounts (Δx2, Δy2, Δz2) through the matrix laser ranging unit, and the user's pre-movement spatial position (x3, y3, z3) and post-movement spatial position (x′3, y′3, z′3) calculated and obtained through the ranging unit based on image processing;

[0112] A3: Then calculate the spatial position change amounts (Δx3, Δy3, Δz3) obtained by the ranging unit based on image processing, where

[0113] A4: Finally, calculate the state change amounts (Δx, Δy, Δz) of the final decision according to the formula ;

[0114] A5: Then, based on the initial state and combined with the state change amounts, determine the user's current spatial position.

[0115] As mentioned above, it is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. Offline immersive large space system based on VR, characterized by: include: A data integrated control module, the data integrated control module includes a data input module, an immersive interactive scene conversion module, an image output module and a storage module; A sensor-based motion acquisition module, which includes a sensor worn on the user's body and a sensor set in a large space; Multi-sensory interactive modules, which consist of multiple interactive modules worn on the user's body and installed in a large space; The intelligent management module is used to manage and control the data of the data integrated control module, the sensor-type motion acquisition module, and the multi-sensory interaction module to realize intelligent operation functions.

2. The offline immersive large space system based on VR according to claim 1, characterized in that: The data input module is used for external devices to input data into the system, and the input data types include system basic parameter data and VR scene data.

3. The offline immersive large space system based on VR according to claim 1, characterized in that: The immersive interactive scene conversion module is used to receive the VR scene data from the data input module, and then process the data and convert it into VR real-scene images.

4. The offline immersive large space system based on VR according to claim 1, characterized in that: The image output module is used to receive the VR real scene image converted by the immersive interactive scene conversion module and display it to the user through the VR device.

5. The offline immersive large space system based on VR according to claim 1, characterized in that: The storage module is used to store system basic parameter data, system usage record data and VR real-scene image data, and it uses a distributed classification storage architecture to store the VR real-scene image data.

6. The offline immersive large space system based on VR according to claim 1, characterized in that: The sensor-based motion acquisition module is used to acquire the user's body movement information, wherein the sensors worn on the user's body include multiple displacement sensors worn on the user's torso and limbs, and the sensors arranged in a large space include multiple matrix radar ranging units, matrix laser ranging units, and ranging units based on image processing.

7. The offline immersive large space system based on VR according to claim 1, characterized in that: In the multi-sensory interaction module, it is used to convert the environmental change information of the VR real-scene image into the corresponding large-space environmental information, which specifically includes environmental temperature information, environmental humidity information, environmental space displacement information and human body movement interaction information.

8. The offline immersive large space system based on VR according to claim 5, characterized in that: The distributed classification storage architecture logic of the storage module is: S1: Obtain the number of times each independent VR real scene image is played M and the total playing time span T according to the previous usage records; S2: According to the formula Calculate the popularity of each independent VR real-life image; S3: Divide the storage module into a plurality of different storage units, each storage unit corresponds to a popularity interval, and each storage unit includes a plurality of storage spaces of different categories; S4: Divide the VR real scene images into storage units of matching intervals until all the VR real scene images are divided, then classify the VR real scene images in each storage unit, and finally store them in different storage spaces according to the classification results.

9. The offline immersive large space system based on VR according to claim 8, characterized in that: In the step S4, the classification is performed by cluster analysis, and the cluster analysis is to divide the data into K clusters based on K-means, and each cluster is represented by a centroid, as follows: Where J is the objective function of K-means, r ik is an indicator variable, if the data point x i If it belongs to cluster k, it is 1, otherwise it is 0, μ k It is the centroid of cluster k. The centroid type of cluster k is the VR real scene image name type or the VR real scene image file size.

10. The offline immersive large space system based on VR according to claim 1, characterized in that: The method of the sensor action acquisition module for acquiring the user's spatial position data through a matrix radar ranging unit, a matrix laser ranging unit and a ranging unit based on image processing comprises the following steps: A1: Input the 3D terrain map of the entire large space and build a 3D space model; A2: The user's spatial position change (Δx1, Δy1, Δz1) is obtained by the radar ranging unit, the spatial position change (Δx2, Δy2, Δz2) is obtained by the matrix laser ranging unit, and the spatial position of the user before the movement (x3, y3, z3) and after the movement (x′3, y′3, z′3) are obtained by the ranging unit based on image processing; A3: Then calculate the spatial position change (Δx3, Δy3, Δz3) obtained by the ranging unit based on image processing, where A4: Finally, according to the formula Calculate the state change (Δx, Δy, Δz) of the final decision; A5: Then determine the user's current spatial position based on the initial state and the state change.

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