Energy industry metaverse ultra-large grid cloud rendering model loading system and method

Abstract

The present invention relates to the field of cloud rendering technology, and specifically to an energy industry metaverse ultra-large grid cloud rendering model loading system and method, comprising a client, an application layer front-end service, an application layer back-end service, a platform layer service and a platform basic support service layer, wherein the application layer front-end service comprises a streaming rendering interface, a model interaction OpenAPI and a data panel interface, the application layer back-end service comprises an application data service, a scene data service and a Gis service, the platform layer service comprises a pairing service and a cloud rendering streaming service, and the platform basic support service layer comprises a high simulation engine pixel streaming service resource cluster and a database service, and the present invention can enable the cloud rendering streaming service to return to the portal level when the client requests to switch the digital twin scene, uninstall the current digital twin scene sub-level, and simultaneously mount the new scene under the portal level and enter the new sub-level, so as to realize the hot switching of the digital twin scene, thereby greatly reducing the time consumption.

Description

Technical Field

[0001] The present invention relates to the field of cloud rendering technology, and in particular to a system and method for loading a large number of grid cloud rendering models of an energy industry metaverse. Background Art

[0002] The Industrial Metaverse refers to the next generation of fully immersive three-dimensional Internet, an integration of multiple technical directions such as digital twins, the Internet of Things, the Industrial Internet, and XR. Cloud rendering refers to rendering a 3D program on a remote server. The user terminal accesses resources through Web software or directly clicks a "cloud rendering" button in the local 3D program and uses high-speed Internet access. Instructions are issued from the user terminal, and the server performs the corresponding rendering tasks according to the instructions, and the rendering result screen is transmitted back to the user terminal for display. This technology research and development mainly solves the service termination and automatic locking of the scene loading process encountered when medium and large complex energy infrastructure creates high-simulation scenes and achieves instant and high-precision presentation through cloud rendering technology. However, the existing technology has similar problems such as high cost, high bit error rate, and slow response speed. Generally, cloud rendering streaming service and digital twin model are bound, that is, only one digital twin model can be loaded in each running life cycle of the program. If cloud rendering service of another digital twin model is required, it is necessary to kill the currently running cloud rendering streaming service process, and then run another cloud rendering service process bound to the corresponding digital twin model. In this process, a lot of time will be consumed in stopping and starting the service process. Therefore, the cloud rendering platform is required to solve this problem by binding the cloud rendering service to the portal level. Therefore, a system and method for loading a large number of grid cloud rendering models of the energy industry metaverse are invented.

[0003] In the prior art, there are the following problems:

[0004] The existing cloud rendering streaming service and digital twin model are bound, which means that only one digital twin model can be loaded in each running life cycle of the program. If you need to provide cloud rendering service for another digital twin model, you need to kill the currently running cloud rendering streaming service process, and then run another cloud rendering service process bound to the corresponding digital twin model. Therefore, this process will consume a lot of time in stopping and starting the service process. Summary of the invention

[0005] The purpose of the present invention is to provide a system and method for loading a large amount of grid cloud rendering models of the energy industry metaverse to solve the problems raised in the above-mentioned background technology.

[0006] The technical solution of the present invention is: an energy industry metaverse ultra-large grid cloud rendering model loading system, including a client, an application layer front-end service, an application layer back-end service, a platform layer service and a platform basic support service layer, wherein the application layer front-end service includes a streaming rendering interface, a model interaction OpenAPI and a data panel interface, the application layer back-end service includes an application data service, a scene data service and a Gis service, the platform layer service includes a pairing service and a cloud rendering streaming service, and the platform basic support service layer includes a high simulation engine pixel streaming service resource cluster and a database service.

[0007] Preferably, the method for dynamic hot loading of a large number of grid cloud rendering models of the energy industry metaverse comprises the following steps:

[0008] Step S1: The client sends a command request to the application layer front end, and the streaming rendering page, model interaction OpenAPI and data panel in the application layer front end service realize code module separation;

[0009] Step S2: Pairing the streaming rendering page, model interaction OpenAPI, and data panel implementation code. The server is responsible for monitoring the availability of the cloud rendering service container in the resource pool, and returns the connection URL of the available container to the client when the client applies to connect to the cloud rendering service;

[0010] Step S3: The client then initiates a pairing connection request to the container according to the received URL;

[0011] Step S4: In the backend service, the application data service, the scene data service, and the GIS service continue to relay their respective direct connections through signaling and the Web server;

[0012] Step S5: The cloud rendering streaming service process is started, and a special model called "portal level" is loaded;

[0013] Step S6: The portal level loads the corresponding specific digital twin scene model into the cloud rendering streaming service according to the digital twin scene ID requested by the client when establishing the service connection, and mounts it under the portal level as a sub-level and enters the sub-level. When the client requests to switch the digital twin scene, the cloud rendering streaming service will return to the portal level, uninstall the current digital twin scene sub-level, and mount the new scene under the portal level and enter the new sub-level.

[0014] The present invention provides a system and method for loading a large amount of grid cloud rendering models of the energy industry metaverse through improvements, which has the following improvements and advantages compared with the prior art:

[0015] Compared with the existing energy industry metaverse ultra-large grid cloud rendering model loading system and method, the cloud rendering platform of the present invention solves this problem by binding the cloud rendering service to the portal level. When the cloud rendering streaming service process is started, it will not bind to any specific digital twin model, but load a special model called the "portal level". The portal level itself does not carry any specific scene, but runs as an empty main level in the cloud rendering streaming service, and according to the digital twin scene ID requested by the client when establishing a service connection, the corresponding specific digital twin scene model is loaded into the cloud rendering streaming service, and mounted under the portal level as a sub-level and enters the sub-level. When the client requests to switch the digital twin scene, the cloud rendering streaming service will return to the portal level, uninstall the current digital twin scene sub-level, and mount the new scene under the portal level and enter the new sub-level. In this way, hot switching of digital twin scenes can be achieved, thereby greatly reducing time consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further explained below in conjunction with the accompanying drawings and Examples:

[0017] Figure 1 It is the overall logical architecture diagram of the present invention;

[0018] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in detail below, and the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] The present invention provides a system and method for loading a large amount of grid cloud rendering models of the energy industry metaverse through improvement. The technical solution of the present invention is:

[0021] Embodiment 1:

[0022] like Figure 1As shown, a system for loading models of ultra-large grid cloud rendering in the energy industry metaverse includes a client, an application layer front-end service, an application layer back-end service, a platform layer service and a platform basic support service layer. The application layer front-end service includes a streaming rendering interface, a model interaction OpenAPI and a data panel interface. The application layer back-end service includes an application data service, a scene data service and a Gis service. The platform layer service includes a pairing service and a cloud rendering streaming service. The platform basic support service layer includes a high simulation engine pixel streaming service resource cluster and a database service.

[0023] The method for dynamic hot loading of a large number of grid cloud rendering models of the energy industry metaverse includes the following steps:

[0024] Step S1: The client sends a command request to the front end of the application layer. The streaming rendering page, model interaction OpenAPI, and data panel in the front end service of the application layer realize code module separation, reducing resource consumption during dynamic level loading;

[0025] Step S2: Push streaming rendering page, model interaction OpenAPI and data panel implementation code The pairing server is responsible for monitoring the availability status of the cloud rendering service container in the resource pool, and returns the connection URL of the available container to the client when the client applies to connect to the cloud rendering service. The pairing server can perform dynamic allocation and load balancing;

[0026] Step S3: The client initiates a pairing connection request to the container according to the received URL, and the client receives feedback and sends an instruction request;

[0027] Step S4: In the backend service, the application data service, the scene data service, and the GIS service continue to relay their respective direct connections through signaling and the Web server to reduce channel resource loss;

[0028] Step S5: The cloud rendering streaming service process is started, and a special model called the "portal level" is loaded. When the process is started, it will not be bound to any specific digital twin model;

[0029] Step S6: The portal level loads the corresponding specific digital twin scene model into the cloud rendering streaming service according to the digital twin scene ID requested by the client when establishing the service connection, and mounts it under the portal level as a sub-level and enters the sub-level. When the client requests to switch the digital twin scene, the cloud rendering streaming service will return to the portal level, uninstall the current digital twin scene sub-level, and mount the new scene under the portal level and enter the new sub-level. The number of model grids in the level is approximately replaced by the parent object, and the file data is first divided into multiple sub-data, and each sub-data is loaded at different file startup stages, thereby reducing memory resource usage, improving running speed, and solving the problem of service termination and automatic locking of scene loading process.

[0030] Working principle: Step S1: The client sends a command request to the front-end of the application layer. The streaming rendering page, model interaction OpenAPI, and data panel in the front-end service of the application layer realize the separation of code modules, reducing the resource consumption when the level is dynamically loaded;

[0031] Step S2: Push streaming rendering page, model interaction OpenAPI and data panel implementation code The pairing server is responsible for monitoring the availability status of the cloud rendering service container in the resource pool, and returns the connection URL of the available container to the client when the client applies to connect to the cloud rendering service. The pairing server can perform dynamic allocation and load balancing;

[0032] Step S3: The client initiates a pairing connection request to the container according to the received URL, and the client receives feedback and sends an instruction request;

[0033] Step S4: In the backend service, the application data service, the scene data service, and the GIS service continue to relay their respective direct connections through signaling and the Web server to reduce channel resource loss;

[0034] Step S5: The cloud rendering streaming service process is started, and a special model called the "portal level" is loaded. When the process is started, it will not be bound to any specific digital twin model;

[0035] Step S6: The portal level loads the corresponding specific digital twin scene model into the cloud rendering streaming service according to the digital twin scene ID requested by the client when establishing the service connection, and mounts it under the portal level as a sub-level and enters the sub-level. When the client requests to switch the digital twin scene, the cloud rendering streaming service will return to the portal level, uninstall the current digital twin scene sub-level, and mount the new scene under the portal level and enter the new sub-level. The number of model grids in the level is approximately replaced by the parent object, and the file data is first divided into multiple sub-data, and each sub-data is loaded at different file startup stages, thereby reducing memory resource usage, improving running speed, and solving the problem of service termination and automatic locking of scene loading process.

[0036] The above description enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The energy industry metaverse ultra-large grid cloud rendering model loading system includes a client, an application layer front-end service, an application layer back-end service, a platform layer service, and a platform basic support service layer, and is characterized by: The application layer front-end service includes a streaming rendering interface, a model interaction OpenAPI and a data panel interface; the application layer back-end service includes an application data service, a scene data service and a Gis service; the platform layer service includes a pairing service and a cloud rendering streaming service; the platform basic support service layer includes a high-simulation engine pixel streaming service resource cluster and a database service; The method for performing dynamic hot loading using the energy industry metaverse ultra-large grid cloud rendering model loading system comprises the following steps: Step S1: The client sends a command request to the front end of the application layer. The streaming rendering interface, model interaction OpenAPI, and data panel interface in the front end service of the application layer realize code module separation, thereby reducing resource consumption during dynamic level loading. Step S2: The pairing service is responsible for monitoring the availability status of the cloud rendering service container in the resource pool, and returns the connection URL of the available container to the client when the client applies to connect to the cloud rendering service. The pairing service can perform dynamic allocation and load balancing; Step S3: the client initiates a pairing connection request to the container according to the received URL, and the client receives a feedback and sends an instruction request; Step S4: In the backend service, the application data service, the scene data service, and the GIS service continue to relay their respective direct connections through signaling and the Web server to reduce channel resource loss; Step S5: The cloud rendering streaming service process is started, and a special model called "portal level" is loaded. When the process is started, no specific digital twin model is bound; Step S6: The portal level loads the corresponding specific digital twin scene model into the cloud rendering streaming service according to the digital twin scene ID requested by the client when establishing the service connection, mounts it under the portal level as a sub-level, and enters the sub-level; Step S7: When the client requests to switch the digital twin scene, the cloud rendering streaming service will return to the portal level, uninstall the current digital twin scene sub-level, and mount the new scene under the portal level and enter the new sub-level.

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