Distributed rendering method and system for three-dimensional scene model

Abstract

The invention provides a distributed rendering method and system for a three-dimensional scene model. The method comprises the steps that a to-be-rendered file, a to-be-rendered task, rendering parameters and corresponding rendering task price insurance data of a distributed rendering user are acquired, a to-be-rendered resource list is generated according to the to-be-rendered file, to-be-rendered asset data are constructed and fragmented, to-be-rendered asset fragmented data are acquired and then stored, and the to-be-rendered asset fragmented data are stored; the method comprises the following steps: performing task splitting on a to-be-rendered task to obtain a to-be-rendered micro-task, generating a micro-task smart contract, performing node bidding by a distributed computing node according to the micro-task smart contract to obtain a preset number of bid-winning distributed nodes, and performing micro-task rendering according to the micro-task smart contract. Generating rendering image data, and performing result verification through the block chain; according to the method, the to-be-rendered asset data, the rendering task container mirror image and the micro-task smart contract are constructed, and block chain decentralized task scheduling and task settlement are performed, so that distributed rendering of the three-dimensional scene model is realized.

Description

Technical Field

[0001] This application relates to the field of distributed computing technology, and more specifically, to a distributed rendering method and system for three-dimensional scene models. Background Technology

[0002] Traditional distributed rendering of 3D scene models mainly relies on a central server to collect data, which has performance bottlenecks, high data latency, and limited rendering efficiency. Distributed computing nodes require pre-installed secure rendering engines and plugins, which can easily lead to inconsistent rendering results. Rendering task allocation depends on a centralized scheduling engine, which is prone to single-node failures. Rendering task allocation and verification rely on manual intervention or centralized platform management, which can easily lead to unreasonable scheduling. Therefore, there is an urgent need for a distributed rendering method for 3D scene models that improves rendering efficiency, rendering consistency, and settlement efficiency.

[0003] Effective technical solutions are urgently needed to address the above problems. Summary of the Invention

[0004] The purpose of this application is to provide a distributed rendering method and system for 3D scene models. It can realize distributed rendering of 3D scene models by constructing the asset data to be rendered, the rendering task container image and the micro-task smart contract, as well as the decentralized task scheduling and task settlement of the blockchain.

[0005] Firstly, this application provides a distributed rendering method for 3D scene models, including the following steps: Obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user; process the files to be rendered to generate a list of resources to be rendered. The renderable asset data is constructed based on the renderable resource list, and the data is split into fragments to obtain renderable asset fragment data, which is then stored in a preset distributed storage node. The task to be rendered is split into micro-tasks to be rendered, and a smart contract for the micro-tasks is generated. Distributed computing nodes bid for nodes according to the micro-task smart contract and obtain a preset number of winning distributed nodes. The winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the results through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification results.

[0006] Optionally, in the distributed rendering method for a 3D scene model described in this application, the step of obtaining the file to be rendered, the task to be rendered, the rendering parameters, and the corresponding rendering task price data of the distributed rendering user, and processing the file to be rendered to generate a list of resources to be rendered, includes: The client module obtains the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user. The rendering parameters include resolution, frame range, and sampling rate, and the rendering task price guarantee data includes the rendering task bid amount and the rendering task warranty deposit. The file to be rendered is processed by the intelligent data asset package module based on the resolution, frame range, and sampling rate to generate a list of resources to be rendered.

[0007] Optionally, in the distributed rendering method for a 3D scene model described in this application, the step of constructing the asset data to be rendered based on the list of resources to be rendered, performing fragmentation processing to obtain fragmented asset data to be rendered, and storing it to a preset distributed storage node includes: Construct the asset data to be rendered based on the list of resources to be rendered; The asset data to be rendered is hashed to obtain the asset data ID. The asset data to be rendered is segmented according to preset rules to obtain segmented asset data to be rendered, and then stored in preset distributed storage nodes.

[0008] Optionally, the distributed rendering method for the 3D scene model described in this application further includes: The rendering task configuration file is obtained by performing matching processing based on the task to be rendered. The rendering task container image is obtained by modifying the rendering task configuration file in conjunction with the preset container image template. The containerized rendering microservice module performs a hash calculation based on the rendering task container image to obtain the rendering task container image ID.

[0009] Optionally, in the distributed rendering method for a 3D scene model described in this application, the step of splitting the task to be rendered into micro-tasks to be rendered and generating micro-task smart contracts includes: The task to be rendered is split into micro-tasks according to a preset splitting logic to obtain micro-tasks to be rendered, and an ID number is assigned to obtain the micro-task ID to be rendered. Obtain the time limit data of the task to be rendered; A microtask smart contract is generated based on the microtask ID to be rendered, the asset data ID to be rendered, the rendering task container image ID, the rendering parameters, the bidding amount of the rendering task, the quality assurance deposit of the rendering task, and the time limit data of the task to be rendered.

[0010] Optionally, in the distributed rendering method for the 3D scene model described in this application, the distributed computing nodes bid for nodes according to the microtask smart contract to obtain a preset number of winning distributed nodes, including: Obtain rendering capability assessment data for distributed computing nodes, including GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity; The GPU feature data, video memory data, computing power value, real-time network bandwidth and available storage capacity are input into a preset node rendering capability evaluation model for processing to obtain real-time rendering capability evaluation parameters. Based on the real-time rendering capability evaluation parameters and the micro-task smart contract, a matching analysis is performed to obtain the micro-tasks to be rendered. Distributed computing nodes bid for nodes based on the quality assurance deposit of the rendering task, obtain a preset number of winning distributed nodes, and allocate the micro-tasks to be rendered to any of the winning distributed nodes through the blockchain scheduling module.

[0011] Optionally, in the distributed rendering method for the 3D scene model described in this application, the winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the results through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification results, including: The winning distributed node downloads the corresponding asset fragment data to be rendered and the rendering task container image based on the asset data ID and the rendering task container image ID. The fragmented data of the asset to be rendered is spliced ​​together and its integrity is verified. If the verification fails, an early warning response will be output; If the verification is successful, the rendering task container image is loaded to perform micro-task rendering and generate rendering image data. The rendered image data is verified through a blockchain scheduling module. The validity of the distributed rendering is determined based on the verification result, including whether the distributed rendering is valid or invalid.

[0012] Optionally, in the distributed rendering method for the 3D scene model described in this application, the step of verifying the results through a blockchain scheduling module based on the rendered image data, and determining the valid state of distributed rendering based on the verification results, including whether distributed rendering is valid or invalid, includes: Obtain the first thumbnail hash value and the first rendering metadata of the rendered image data; The pre-defined number of qualified distributed nodes are assigned to parallel distributed nodes for redundant rendering, and the second thumbnail hash value and second rendering metadata are obtained. Perform a consistency check between the first thumbnail hash value and the first rendering metadata and the second thumbnail hash value and the second rendering metadata; If the consistency check passes, the distributed rendering is determined to be valid, and the bid amount for the rendering task is settled to the winning distributed node. The rendering task aggregation module is then used to stitch the images together to obtain the rendering task frame. If the consistency check fails, the distributed rendering validity status is determined to be distributed rendering invalid, and an early warning response is activated.

[0013] Secondly, this application provides a distributed rendering system for 3D scene models, the system comprising: The client module is used to obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price data of the distributed rendering user, as well as to receive the rendering task frames; The intelligent data asset package module is used to construct the asset data to be rendered, perform hash calculations, and handle sharding. The containerized rendering microservice module is used to build container images for rendering tasks; The blockchain scheduling module is used for splitting tasks to be rendered, distributing node bidding, adapting the allocation of micro-tasks to be rendered, determining the effective status of distributed rendering, and settling rendering tasks. The rendering task aggregation module is used to aggregate the rendering image blocks from each winning distributed node.

[0014] Optionally, in the distributed rendering system for the 3D scene model described in the application, the system further includes: a memory and a processor, wherein the memory includes a program for a distributed rendering method of the 3D scene model, and when the program for the distributed rendering method of the 3D scene model is executed by the processor, it implements the following steps: Obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user; process the files to be rendered to generate a list of resources to be rendered. The renderable asset data is constructed based on the renderable resource list, and the data is split into fragments to obtain renderable asset fragment data, which is then stored in a preset distributed storage node. The task to be rendered is split into micro-tasks to be rendered, and a smart contract for the micro-tasks is generated. Distributed computing nodes bid for nodes according to the micro-task smart contract and obtain a preset number of winning distributed nodes. The winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the results through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification results.

[0015] As can be seen from the above, the distributed rendering method and system for 3D scene models provided in this application realizes the distributed rendering of 3D scene models by constructing the asset data to be rendered, the rendering task container image and the micro-task smart contract, as well as the blockchain-based decentralized task scheduling and task settlement.

[0016] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing embodiments of this application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a distributed rendering method for a 3D scene model provided in an embodiment of this application; Figure 2 A high-level flowchart of the distributed rendering method for a 3D scene model provided in the embodiments of this application; Figure 3 This is a system block diagram of a distributed rendering system for a 3D scene model provided in an embodiment of this application. Detailed Implementation

[0019] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flowchart of a distributed rendering method for a 3D scene model according to some embodiments of this application. This distributed rendering method for 3D scene models is used in terminal devices, such as computers and mobile terminals. The distributed rendering method for 3D scene models includes the following steps: S11. Obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user. Process the files to be rendered to generate a list of resources to be rendered. S12. Construct the asset data to be rendered based on the list of resources to be rendered, perform fragmentation processing, obtain the fragmented asset data to be rendered, and store it in a preset distributed storage node. S13. The task to be rendered is split into micro-tasks to be rendered, and a micro-task smart contract is generated. S14. Distributed computing nodes bid for nodes according to the micro-task smart contract and obtain a preset number of winning distributed nodes. S15. The winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the result through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification result.

[0022] It should be noted that, in order to achieve distributed rendering of 3D scene models, firstly, the asset data to be rendered is constructed based on the file to be rendered; secondly, the rendering task container image is obtained based on the container image template; then, the rendering task is split and uploaded to the blockchain; simultaneously, distributed nodes bid and allocate tasks for real-time rendering based on their own capabilities; finally, the rendering results are verified and the rendering settlement is performed, realizing P2P data flow, containerized computing, and blockchain scheduling, thereby forming a distributed rendering paradigm where computation is static and data flows.

[0023] According to an embodiment of the present invention, the step of obtaining the file to be rendered, the task to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user, and processing the file to be rendered to generate a list of resources to be rendered includes: The client module obtains the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user. The rendering parameters include resolution, frame range, and sampling rate, and the rendering task price guarantee data includes the rendering task bid amount and the rendering task warranty deposit. The file to be rendered is processed by the intelligent data asset package module based on the resolution, frame range, and sampling rate to generate a list of resources to be rendered.

[0024] It should be noted that users upload the file to be rendered, the rendering task, rendering parameters, and the corresponding rendering task price guarantee data through the client module. The bid amount and quality guarantee amount for the rendering task are set by the user to increase the participation of distributed nodes and ensure timely and accurate real-time rendering. The file to be rendered is parsed into structured data, and then redundant resources are filtered out in conjunction with the rendering parameters to form a standardized list of resources to be rendered, including resource identifiers, types, paths, and associated rendering parameters. An example of the list of resources to be rendered is shown in Table 1. Table 1

[0025] To achieve automated and non-redundant resource inventory management.

[0026] According to an embodiment of the present invention, the step of constructing asset data to be rendered based on the list of resources to be rendered, performing fragmentation processing to obtain asset fragment data to be rendered, and storing it to a preset distributed storage node includes: Construct the asset data to be rendered based on the list of resources to be rendered; The asset data to be rendered is hashed to obtain the asset data ID. The asset data to be rendered is segmented according to preset rules to obtain segmented asset data to be rendered, and then stored in preset distributed storage nodes.

[0027] It should be noted that, based on the list of resources to be rendered, a standardized directory structure is created according to the relative storage path. All resources in the list of resources to be rendered are copied into a unified asset package container according to the directory structure to construct the asset data to be rendered. Then, a unique asset data ID to be rendered is determined through hash calculation. Since the asset data to be rendered is too large to be transmitted directly and efficiently, it is split into fixed-size fragments to facilitate transmission via P2P. After that, it is transmitted via P2P and stored to distributed nodes for easy download from the nearest node.

[0028] According to an embodiment of the present invention, it further includes: The rendering task configuration file is obtained by performing matching processing based on the task to be rendered. The rendering task container image is obtained by modifying the rendering task configuration file in conjunction with the preset container image template. The containerized rendering microservice module performs a hash calculation based on the rendering task container image to obtain the rendering task container image ID.

[0029] It should be noted that, in order to overcome the problem of poor compatibility between different nodes in traditional technologies, this embodiment first matches the required processing software and plugin versions according to the needs of the rendering task, and forms an executable configuration file. Then, based on the standard preset container image template, a unique rendering task container image is generated for each task to be rendered. Finally, a unique rendering task container image ID is generated through hash calculation, split by image layer and distributed through a P2P network for download and use by distributed rendering nodes, ensuring a consistent rendering environment.

[0030] According to an embodiment of the present invention, the step of splitting the task to be rendered into micro-tasks to be rendered and generating micro-task smart contracts includes: The task to be rendered is split into micro-tasks according to a preset splitting logic to obtain micro-tasks to be rendered, and an ID number is assigned to obtain the micro-task ID to be rendered. Obtain the time limit data of the task to be rendered; A microtask smart contract is generated based on the microtask ID to be rendered, the asset data ID to be rendered, the rendering task container image ID, the rendering parameters, the bidding amount of the rendering task, the quality assurance deposit of the rendering task, and the time limit data of the task to be rendered.

[0031] It should be noted that the blockchain scheduling module receives complete rendering tasks submitted by users through the user-end module. Then, it performs a two-level split based on image frames and image blocks, breaking down the complete rendering task into rendering micro-tasks and assigning each micro-task a unique ID. Next, a micro-task smart contract is generated for each micro-task, including the micro-task ID, the rendering asset data ID, the rendering task container image ID, rendering parameters, the rendering task bid amount, the rendering task warranty, and the rendering task time limit data. Finally, the contract is submitted to the blockchain network for easy querying by distributed nodes.

[0032] According to an embodiment of the present invention, the distributed computing nodes bid for nodes based on the microtask smart contract to obtain a preset number of winning distributed nodes, including: Obtain rendering capability assessment data for distributed computing nodes, including GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity; The GPU feature data, video memory data, computing power value, real-time network bandwidth and available storage capacity are input into a preset node rendering capability evaluation model for processing to obtain real-time rendering capability evaluation parameters. Based on the real-time rendering capability evaluation parameters and the micro-task smart contract, a matching analysis is performed to obtain the micro-tasks to be rendered. Distributed computing nodes bid for nodes based on the quality assurance deposit of the rendering task, obtain a preset number of winning distributed nodes, and allocate the micro-tasks to be rendered to any of the winning distributed nodes through the blockchain scheduling module.

[0033] It should be noted that, firstly, the rendering capability of distributed nodes is evaluated based on GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity. GPU feature data refers to the identifier representing the GPU model, which is represented by different values. After determining the rendering capability, matching analysis is performed based on the generated micro-task smart contract to obtain suitable micro-tasks to be rendered. A micro-task to be rendered may have multiple qualified distributed computing nodes. One node is randomly selected to be assigned to the micro-task to be rendered, and it is determined as the winning distributed node. This avoids the insufficient adaptation of traditional technology centers and scheduling. The preset node rendering capability evaluation model is obtained by training with a large amount of historical sample GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity, as well as the corresponding real-time rendering capability evaluation parameters.

[0034] According to an embodiment of the present invention, the winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the result through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification result, including: The winning distributed node downloads the corresponding asset fragment data to be rendered and the rendering task container image based on the asset data ID and the rendering task container image ID. The fragmented data of the asset to be rendered is spliced ​​together and its integrity is verified. If the verification fails, an early warning response will be output; If the verification is successful, the rendering task container image is loaded to perform micro-task rendering and generate rendering image data. The rendered image data is verified through a blockchain scheduling module. The validity of the distributed rendering is determined based on the verification result, including whether the distributed rendering is valid or invalid.

[0035] It should be noted that the winning distributed node queries the storage node through the P2P network based on the asset data ID to be rendered and the rendering task container image ID in the micro-task smart contract to obtain the corresponding asset fragment data to be rendered and the rendering task container image. After the asset fragment data to be rendered is downloaded, it is automatically stitched together and its integrity is verified. If the verification passes, it is further rendered in real time. After the winning distributed node completes the rendering, it generates the rendered image data, i.e., the rendered image block. Finally, the validity of the rendering result is verified through the blockchain scheduling module.

[0036] According to an embodiment of the present invention, the step of verifying the results of the rendered image data through a blockchain scheduling module, and determining the valid state of distributed rendering based on the verification results, including whether the distributed rendering is valid or invalid, includes: Obtain the first thumbnail hash value and the first rendering metadata of the rendered image data; The pre-defined number of qualified distributed nodes are assigned to parallel distributed nodes for redundant rendering, and the second thumbnail hash value and second rendering metadata are obtained. Perform a consistency check between the first thumbnail hash value and the first rendering metadata and the second thumbnail hash value and the second rendering metadata; If the consistency check passes, the distributed rendering is determined to be valid, and the bid amount for the rendering task is settled to the winning distributed node. The rendering task aggregation module is then used to stitch the images together to obtain the rendering task frame. If the consistency check fails, the distributed rendering validity status is determined to be distributed rendering invalid, and an early warning response is activated.

[0037] It should be noted that after the winning distributed node completes real-time rendering, it applies for rendering validity verification. The blockchain scheduling module extracts a preset proportion of adapted micro-tasks to be rendered and redistributes them to other distributed computing nodes for redundant rendering. The hash values ​​and rendering metadata of the two renderings are compared. If the comparison is successful, the distributed rendering is deemed valid, and task settlement and image stitching are performed to complete the rendering task. Otherwise, it is invalid. The rendering metadata, such as rendering time and GPU utilization, are determined and adjusted by those skilled in the art based on the rendering task.

[0038] Please refer to Figure 2 , Figure 2 This is a high-level flowchart of a distributed rendering method for a 3D scene model in some embodiments of this application.

[0039] Please refer to Figure 3 , Figure 3 This is a system block diagram of a distributed rendering method for a 3D scene model according to some embodiments of this application. In a second aspect, the present invention also discloses a distributed rendering system 3 for a 3D scene model, comprising: Client module 31 is used to obtain the file to be rendered, the task to be rendered, the rendering parameters and the corresponding rendering task price data of the distributed rendering user, and to receive the rendering task frame. The intelligent data asset package module 32 is used to construct the asset data to be rendered, perform hash calculations, and handle sharding. Containerized rendering microservice module 33 is used to build container images for rendering tasks; The blockchain scheduling module 34 is used for splitting the tasks to be rendered, distributing node bidding, adapting the allocation of micro-tasks to be rendered, determining the effective status of distributed rendering, and settling rendering tasks. The rendering task aggregation module 35 is used to aggregate the rendering image blocks of each winning distributed node.

[0040] According to an embodiment of the present invention, the distributed rendering system for the three-dimensional scene model includes a memory and a processor. The memory includes a distributed rendering method program for the three-dimensional scene model. When the distributed rendering method program for the three-dimensional scene model is executed by the processor, it performs the following steps: Obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user; process the files to be rendered to generate a list of resources to be rendered. The renderable asset data is constructed based on the renderable resource list, and the data is split into fragments to obtain renderable asset fragment data, which is then stored in a preset distributed storage node. The task to be rendered is split into micro-tasks to be rendered, and a smart contract for the micro-tasks is generated. Distributed computing nodes bid for nodes according to the micro-task smart contract and obtain a preset number of winning distributed nodes. The winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the results through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification results.

[0041] It should be noted that, in order to achieve distributed rendering of 3D scene models, firstly, the asset data to be rendered is constructed based on the file to be rendered; secondly, the rendering task container image is obtained based on the container image template; then, the rendering task is split and uploaded to the blockchain; simultaneously, distributed nodes bid and allocate tasks for real-time rendering based on their own capabilities; finally, the rendering results are verified and the rendering settlement is performed, realizing P2P data flow, containerized computing, and blockchain scheduling, thereby forming a distributed rendering paradigm where computation is static and data flows.

[0042] According to an embodiment of the present invention, the step of obtaining the file to be rendered, the task to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user, and processing the file to be rendered to generate a list of resources to be rendered includes: The client module obtains the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user. The rendering parameters include resolution, frame range, and sampling rate, and the rendering task price guarantee data includes the rendering task bid amount and the rendering task warranty deposit. The file to be rendered is processed by the intelligent data asset package module based on the resolution, frame range, and sampling rate to generate a list of resources to be rendered.

[0043] It should be noted that users upload the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data through the client module. The bid amount and quality guarantee amount for the rendering tasks are set by the user to improve the participation of distributed nodes and ensure timely and accurate real-time rendering. The files to be rendered are parsed into structured data, and then redundant resources are filtered out in combination with the rendering parameters to form a standardized list of resources to be rendered, including resource identifiers, types, paths, and associated rendering parameters. An example of the list of resources to be rendered is shown in Table 1, which realizes automated and non-redundant resource list organization.

[0044] According to an embodiment of the present invention, the step of constructing asset data to be rendered based on the list of resources to be rendered, performing fragmentation processing to obtain asset fragment data to be rendered, and storing it to a preset distributed storage node includes: Construct the asset data to be rendered based on the list of resources to be rendered; The asset data to be rendered is hashed to obtain the asset data ID. The asset data to be rendered is segmented according to preset rules to obtain segmented asset data to be rendered, and then stored in preset distributed storage nodes.

[0045] It should be noted that, based on the list of resources to be rendered, a standardized directory structure is created according to the relative storage path. All resources in the list of resources to be rendered are copied into a unified asset package container according to the directory structure to construct the asset data to be rendered. Then, a unique asset data ID to be rendered is determined through hash calculation. Since the asset data to be rendered is too large to be transmitted directly and efficiently, it is split into fixed-size fragments to facilitate transmission via P2P. After that, it is transmitted via P2P and stored to distributed nodes for easy download from the nearest node.

[0046] According to an embodiment of the present invention, it further includes: The rendering task configuration file is obtained by performing matching processing based on the task to be rendered. The rendering task container image is obtained by modifying the rendering task configuration file in conjunction with the preset container image template. The containerized rendering microservice module performs a hash calculation based on the rendering task container image to obtain the rendering task container image ID.

[0047] It should be noted that, in order to overcome the problem of poor compatibility between different nodes in traditional technologies, this embodiment first matches the required processing software and plugin versions according to the needs of the rendering task, and forms an executable configuration file. Then, based on the standard preset container image template, a unique rendering task container image is generated for each task to be rendered. Finally, a unique rendering task container image ID is generated through hash calculation, split by image layer and distributed through a P2P network for download and use by distributed rendering nodes, ensuring a consistent rendering environment.

[0048] According to an embodiment of the present invention, the step of splitting the task to be rendered into micro-tasks to be rendered and generating micro-task smart contracts includes: The task to be rendered is split into micro-tasks according to a preset splitting logic to obtain micro-tasks to be rendered, and an ID number is assigned to obtain the micro-task ID to be rendered. Obtain the time limit data of the task to be rendered; A microtask smart contract is generated based on the microtask ID to be rendered, the asset data ID to be rendered, the rendering task container image ID, the rendering parameters, the bidding amount of the rendering task, the quality assurance deposit of the rendering task, and the time limit data of the task to be rendered.

[0049] It should be noted that the blockchain scheduling module receives complete rendering tasks submitted by users through the user-end module. Then, it performs a two-level split based on image frames and image blocks, breaking down the complete rendering task into rendering micro-tasks and assigning each micro-task a unique ID. Next, a micro-task smart contract is generated for each micro-task, including the micro-task ID, the rendering asset data ID, the rendering task container image ID, rendering parameters, the rendering task bid amount, the rendering task warranty, and the rendering task time limit data. Finally, the contract is submitted to the blockchain network for easy querying by distributed nodes.

[0050] According to an embodiment of the present invention, the distributed computing nodes bid for nodes based on the microtask smart contract to obtain a preset number of winning distributed nodes, including: Obtain rendering capability assessment data for distributed computing nodes, including GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity; The GPU feature data, video memory data, computing power value, real-time network bandwidth and available storage capacity are input into a preset node rendering capability evaluation model for processing to obtain real-time rendering capability evaluation parameters. Based on the real-time rendering capability evaluation parameters and the micro-task smart contract, a matching analysis is performed to obtain the micro-tasks to be rendered. Distributed computing nodes bid for nodes based on the quality assurance deposit of the rendering task, obtain a preset number of winning distributed nodes, and allocate the micro-tasks to be rendered to any of the winning distributed nodes through the blockchain scheduling module.

[0051] It should be noted that, firstly, the rendering capability of distributed nodes is evaluated based on GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity. GPU feature data refers to the identifier representing the GPU model, which is represented by different values. After determining the rendering capability, matching analysis is performed based on the generated micro-task smart contract to obtain suitable micro-tasks to be rendered. A micro-task to be rendered may have multiple qualified distributed computing nodes. One node is randomly selected to be assigned to the micro-task to be rendered, and it is determined as the winning distributed node. This avoids the insufficient adaptation of traditional technology centers and scheduling. The preset node rendering capability evaluation model is obtained by training with a large amount of historical sample GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity, as well as the corresponding real-time rendering capability evaluation parameters.

[0052] According to an embodiment of the present invention, the winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the result through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification result, including: The winning distributed node downloads the corresponding asset fragment data to be rendered and the rendering task container image based on the asset data ID and the rendering task container image ID. The fragmented data of the asset to be rendered is spliced ​​together and its integrity is verified. If the verification fails, an early warning response will be output; If the verification is successful, the rendering task container image is loaded to perform micro-task rendering and generate rendering image data. The rendered image data is verified through a blockchain scheduling module. The validity of the distributed rendering is determined based on the verification result, including whether the distributed rendering is valid or invalid.

[0053] It should be noted that the winning distributed node queries the storage node through the P2P network based on the asset data ID to be rendered and the rendering task container image ID in the micro-task smart contract to obtain the corresponding asset fragment data to be rendered and the rendering task container image. After the asset fragment data to be rendered is downloaded, it is automatically stitched together and its integrity is verified. If the verification passes, it is further rendered in real time. After the winning distributed node completes the rendering, it generates the rendered image data, i.e., the rendered image block. Finally, the validity of the rendering result is verified through the blockchain scheduling module.

[0054] According to an embodiment of the present invention, the step of verifying the results of the rendered image data through a blockchain scheduling module, and determining the valid state of distributed rendering based on the verification results, including whether the distributed rendering is valid or invalid, includes: Obtain the first thumbnail hash value and the first rendering metadata of the rendered image data; The pre-defined number of qualified distributed nodes are assigned to parallel distributed nodes for redundant rendering, and the second thumbnail hash value and second rendering metadata are obtained. Perform a consistency check between the first thumbnail hash value and the first rendering metadata and the second thumbnail hash value and the second rendering metadata; If the consistency check passes, the distributed rendering is determined to be valid, and the bid amount for the rendering task is settled to the winning distributed node. The rendering task aggregation module is then used to stitch the images together to obtain the rendering task frame. If the consistency check fails, the distributed rendering validity status is determined to be distributed rendering invalid, and an early warning response is activated.

[0055] It should be noted that after the winning distributed node completes real-time rendering, it applies for rendering validity verification. The blockchain scheduling module extracts a preset proportion of adapted micro-tasks to be rendered and redistributes them to other distributed computing nodes for redundant rendering. The hash values ​​and rendering metadata of the two renderings are compared. If the comparison is successful, the distributed rendering is deemed valid, and task settlement and image stitching are performed to complete the rendering task. Otherwise, it is invalid. The rendering metadata, such as rendering time and GPU utilization, are determined and adjusted by those skilled in the art based on the rendering task.

[0056] The distributed rendering method and system for 3D scene models disclosed in this invention realizes distributed rendering of 3D scene models by constructing data of assets to be rendered, rendering task container images and micro-task smart contracts, as well as blockchain-based decentralized task scheduling and task settlement.

[0057] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0058] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0059] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0060] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0061] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A distributed rendering method for 3D scene models, characterized in that, Includes the following steps: Obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user; process the files to be rendered to generate a list of resources to be rendered. The renderable asset data is constructed based on the renderable resource list, and the data is split into fragments to obtain renderable asset fragment data, which is then stored in a preset distributed storage node. The task to be rendered is split into micro-tasks to be rendered, and a smart contract for the micro-tasks is generated. Distributed computing nodes bid for nodes according to the micro-task smart contract and obtain a preset number of winning distributed nodes. The winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the results through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification results.

2. The distributed rendering method for a three-dimensional scene model according to claim 1, characterized in that, The process involves obtaining the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data from the distributed rendering user; processing the files to be rendered; and generating a list of resources to be rendered, including: The client module obtains the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user. The rendering parameters include resolution, frame range, and sampling rate, and the rendering task price guarantee data includes the rendering task bid amount and the rendering task warranty deposit. The file to be rendered is processed by the intelligent data asset package module based on the resolution, frame range, and sampling rate to generate a list of resources to be rendered.

3. The distributed rendering method for a three-dimensional scene model according to claim 2, characterized in that, The step of constructing the asset data to be rendered based on the list of resources to be rendered, performing fragmentation processing to obtain fragmented asset data to be rendered, and storing it to a preset distributed storage node includes: Construct the asset data to be rendered based on the list of resources to be rendered; The asset data to be rendered is hashed to obtain the asset data ID. The asset data to be rendered is segmented according to preset rules to obtain segmented asset data to be rendered, and then stored in preset distributed storage nodes.

4. The distributed rendering method for a three-dimensional scene model according to claim 1, characterized in that, Also includes: The rendering task configuration file is obtained by performing matching processing based on the task to be rendered. The rendering task container image is obtained by modifying the rendering task configuration file in conjunction with the preset container image template. The containerized rendering microservice module performs a hash calculation based on the rendering task container image to obtain the rendering task container image ID.

5. The distributed rendering method for a three-dimensional scene model according to claim 1, characterized in that, The step of splitting the task to be rendered into micro-tasks and generating micro-task smart contracts includes: The task to be rendered is split into micro-tasks according to a preset splitting logic to obtain micro-tasks to be rendered, and an ID number is assigned to obtain the micro-task ID to be rendered. Obtain the time limit data of the task to be rendered; A microtask smart contract is generated based on the microtask ID to be rendered, the asset data ID to be rendered, the rendering task container image ID, the rendering parameters, the bidding amount of the rendering task, the quality assurance deposit of the rendering task, and the time limit data of the task to be rendered.

6. The distributed rendering method for a three-dimensional scene model according to claim 5, characterized in that, The distributed computing nodes bid for nodes according to the microtask smart contract, and obtain a preset number of winning distributed nodes, including: Obtain rendering capability assessment data for distributed computing nodes, including GPU feature data, video memory data, computing power, real-time network bandwidth, and available storage capacity; The GPU feature data, video memory data, computing power value, real-time network bandwidth and available storage capacity are input into a preset node rendering capability evaluation model for processing to obtain real-time rendering capability evaluation parameters. Based on the real-time rendering capability evaluation parameters and the micro-task smart contract, a matching analysis is performed to obtain the micro-tasks to be rendered. Distributed computing nodes bid for nodes based on the quality assurance deposit of the rendering task, obtain a preset number of winning distributed nodes, and allocate the micro-tasks to be rendered to any of the winning distributed nodes through the blockchain scheduling module.

7. The distributed rendering method for a three-dimensional scene model according to claim 6, characterized in that, The winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the results through the blockchain based on the rendered image data, and determines the valid state of the distributed rendering based on the verification results, including: The winning distributed node downloads the corresponding asset fragment data to be rendered and the rendering task container image based on the asset data ID and the rendering task container image ID. The fragmented data of the asset to be rendered is spliced ​​together and its integrity is verified. If the verification fails, an early warning response will be output; If the verification is successful, the rendering task container image is loaded to perform micro-task rendering and generate rendering image data. The rendered image data is verified through a blockchain scheduling module. The validity of the distributed rendering is determined based on the verification result, including whether the distributed rendering is valid or invalid.

8. The distributed rendering method for a three-dimensional scene model according to claim 7, characterized in that, The process involves verifying the rendered image data using a blockchain scheduling module, and determining the valid state of the distributed rendering based on the verification result, including whether the distributed rendering is valid or invalid. Obtain the first thumbnail hash value and the first rendering metadata of the rendered image data; The pre-defined number of qualified distributed nodes are assigned to parallel distributed nodes for redundant rendering, and the second thumbnail hash value and second rendering metadata are obtained. Perform a consistency check between the first thumbnail hash value and the first rendering metadata and the second thumbnail hash value and the second rendering metadata; If the consistency check passes, the distributed rendering is determined to be valid, and the bid amount for the rendering task is settled to the winning distributed node. The rendering task aggregation module is then used to stitch the images together to obtain the rendering task frame. If the consistency check fails, the distributed rendering validity status is determined to be distributed rendering invalid, and an early warning response is activated.

9. A distributed rendering system for a three-dimensional scene model, wherein the distributed rendering system for the three-dimensional scene model implements the distributed rendering method for the three-dimensional scene model as described in claims 1-8, characterized in that, include: The client module is used to obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price data of the distributed rendering user, as well as to receive the rendering task frames; The intelligent data asset package module is used to construct the asset data to be rendered, perform hash calculations, and handle sharding. The containerized rendering microservice module is used to build container images for rendering tasks; The blockchain scheduling module is used for splitting tasks to be rendered, distributing node bidding, adapting the allocation of micro-tasks to be rendered, determining the effective status of distributed rendering, and settling rendering tasks. The rendering task aggregation module is used to aggregate the rendering image blocks from each winning distributed node.

10. A distributed rendering system for three-dimensional scene models, characterized in that, It also includes a memory and a processor. The memory contains a program for a distributed rendering method of a 3D scene model. When the program for the distributed rendering method of the 3D scene model is executed by the processor, it performs the following steps: Obtain the files to be rendered, the tasks to be rendered, the rendering parameters, and the corresponding rendering task price guarantee data of the distributed rendering user; process the files to be rendered to generate a list of resources to be rendered. The renderable asset data is constructed based on the renderable resource list, and the data is split into fragments to obtain renderable asset fragment data, which is then stored in a preset distributed storage node. The task to be rendered is split into micro-tasks to be rendered, and a smart contract for the micro-tasks is generated. Distributed computing nodes bid for nodes according to the micro-task smart contract and obtain a preset number of winning distributed nodes. The winning distributed node performs micro-task rendering according to the micro-task smart contract, generates rendered image data, verifies the results through the blockchain based on the rendered image data, and determines the valid state of distributed rendering based on the verification results.

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