Cloud resource rendering method and device based on artificial intelligence, electronic equipment and medium

Abstract

The embodiment of the invention discloses a cloud resource rendering method and device based on artificial intelligence, electronic equipment and a medium. According to the method, after a cloud resource rendering request sent by a terminal is received, the rendering type of the cloud resource is determined according to the rendering request, then a rendering strategy for rendering the cloud resource is determined according to the rendering type, and the rendering strategy is sent to a cloud end so that the cloud resource can be rendered in the cloud end by adopting the rendering strategy. And finally, the rendering data of the cloud resources transmitted by the cloud end can be received, and the rendering data is transmitted to the corresponding terminal, so that high-quality immersive experience is provided for the user. According to the method, the terminal burden can be effectively reduced, the rendering quality and speed can be improved, the cloud resource compatibility can be enhanced, and cloud resource sharing can be promoted.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to an artificial intelligence-based cloud resource rendering method, device, electronic device, and medium. Background Art

[0002] In the wave of educational informatization, teaching resources are no longer limited to traditional textbooks and blackboards. Instead, they are rapidly developing in a diversified and digital direction, with a constant emergence of resources in various forms, including text, images, audio, and video. At the same time, terminal devices are also becoming increasingly diverse. Different devices, such as computers, tablets, and mobile phones, have significant differences in performance, screen size, and operating systems. Therefore, ensuring the high-quality presentation of various teaching resources on different terminals has become a major challenge in the process of educational informatization. Summary of the Invention

[0003] This application provides an artificial intelligence-based cloud resource rendering method, device, electronic device and medium, which can effectively reduce the terminal burden, improve rendering quality and speed, enhance cloud resource compatibility, and also provide users with a high-quality immersive experience.

[0004] In a first aspect, the present application provides an artificial intelligence-based cloud resource rendering method, which is applied to a cloud resource pool, and the method includes: The receiving terminal sends a rendering request for cloud resources; Determine the rendering type for cloud resources based on the rendering request; Determine the rendering strategy for cloud resources based on the rendering type; Sending the rendering strategy to the cloud, so that the cloud resources are rendered using the rendering strategy in the cloud; Receives rendering data of cloud resources transmitted from the cloud and transmits the rendering data to the corresponding terminal.

[0005] In a second aspect, the present application further provides an artificial intelligence-based cloud resource rendering method, which is applied to the cloud, and the method includes: receiving a rendering strategy of cloud resources transmitted by the cloud resource pool; Based on the rendering strategy, determine the decomposition strategy for decomposing cloud resources; Decompose cloud resources according to the decomposition strategy to obtain multiple sub-cloud resources; Perform distributed processing on multiple sub-cloud resources to obtain rendering data for each sub-cloud resource; The rendering data of each sub-cloud resource is merged to obtain the rendering data of the cloud resource.

[0006] In a third aspect, the present application further provides an artificial intelligence-based cloud resource rendering device, which is applied to a cloud resource pool, and the device includes: A first receiving unit, configured to receive a rendering request for cloud resources sent by a terminal; A first determining unit, configured to determine a rendering type for cloud resources according to a rendering request; A second determining unit is configured to determine a rendering strategy for rendering the cloud resources according to the rendering type; A sending unit, configured to send the rendering strategy to the cloud, so that the cloud resources are rendered using the rendering strategy in the cloud; The second receiving unit receives rendering data of cloud resources transmitted from the cloud, and transmits the rendering data to a corresponding terminal.

[0007] In a fourth aspect, the present application further provides an artificial intelligence-based cloud resource rendering device, which is applied to the cloud, and the device includes: A third receiving unit is configured to receive a rendering strategy of cloud resources transmitted by the cloud resource pool; a third determining unit, configured to determine a decomposition strategy for decomposing cloud resources based on the rendering strategy; A decomposition unit is used to decompose cloud resources according to a decomposition strategy to obtain multiple sub-cloud resources; A first processing unit is configured to perform distributed processing on multiple sub-cloud resources to obtain rendering data for each sub-cloud resource; The second processing unit is configured to merge the rendering data of each sub-cloud resource to obtain the rendering data of the cloud resource.

[0008] In the fifth aspect, an embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the computer program, the artificial intelligence-based cloud resource rendering method provided in the first aspect above is implemented.

[0009] In a sixth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the artificial intelligence-based cloud resource rendering method provided in the first aspect above.

[0010] In a seventh aspect, an embodiment of the present application further provides a computer program product, including a computer program or instructions, which is executed by a processor to implement the artificial intelligence-based cloud resource rendering method provided in the first aspect.

[0011] The embodiments of the present invention provide an artificial intelligence-based cloud resource rendering method, device, electronic device, and medium. After receiving a rendering request for a cloud resource from a terminal, the method determines the rendering type of the cloud resource based on the rendering request, then determines a rendering strategy for rendering the cloud resource based on the rendering type, and sends the rendering strategy to the cloud so that the cloud resource is rendered using the rendering strategy in the cloud. Finally, the method receives the rendering data of the cloud resource transmitted from the cloud and transmits the rendering data to the corresponding terminal to provide the user with a high-quality immersive experience. This application can not only effectively reduce the burden on the terminal, improve rendering quality and speed, and enhance cloud resource compatibility, but also promote cloud resource sharing. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0013] Figure 1 This is an architecture diagram of the artificial intelligence-based cloud resource rendering method provided in an embodiment of the present application; Figure 2 A flowchart of a cloud resource rendering method based on artificial intelligence provided in an embodiment of the present application; Figure 3 A schematic block diagram of an artificial intelligence-based cloud resource rendering device provided in an embodiment of the present application; Figure 4 This is a schematic block diagram of an electronic device provided in this application. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] It will be understood that when used in this specification and the appended claims, the terms including and comprising indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0016] It should also be understood that the terms used in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0017] It should be further understood that the terms and / or used in the specification and appended claims refer to and include any and all possible combinations of one or more of the associated listed items.

[0018] In addition, in this application, unless otherwise clearly specified or limited in the embodiments, the terms "installation", "connection", "connection" and "fixation" appearing in the embodiments should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection. It can also be a mechanical connection, an electrical connection, etc.; of course, it can also be a direct connection, or an indirect connection through an intermediate medium, or it can be the internal communication of two elements, or the interaction between two elements. For those skilled in the art, the specific meanings of the above terms in this application can be understood based on the specific implementation.

[0019] See also Figure 1 , Figure 1 This is an architecture diagram of the artificial intelligence-based cloud resource rendering method provided in an embodiment of the present application. The artificial intelligence-based cloud resource rendering method provided in an embodiment of the present application can be applied to the management platform where the cloud resource pool 102 is located, and can also be applied to the cloud 101. The method is executed by application software installed in the cloud resource pool 102 or the cloud 101. At the same time, the cloud resource pool 102 is located between the cloud 101 and the user terminal 103. The cloud 101 is equipped with multiple cloud servers. The cloud resource pool 102 can be understood as an intermediate server between the cloud 101 and the user terminal 103.

[0020] It should be noted that the application scenarios described in the following embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Ordinary technicians in this field can know that with the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0021] The following is a detailed description of the artificial intelligence-based cloud resource rendering method provided by this application.

[0022] like Figure 2 As shown, the method includes the following steps S210~S250.

[0023] S210: A receiving terminal sends a rendering request for cloud resources; S220: Determine the rendering type for the cloud resources according to the rendering request; S230: Determine a rendering strategy for rendering cloud resources according to the rendering type; S240: Send the rendering strategy to the cloud, so that the cloud resources are rendered using the rendering strategy in the cloud; S250: Receive rendering data of cloud resources transmitted from the cloud, and transmit the rendering data to a corresponding terminal.

[0024] In this application, when a terminal sends a cloud resource rendering request, the user selects the required cloud resource on the terminal device. The system generates a standardized request instruction containing information such as the cloud resource ID and terminal type, and transmits it to the cloud resource pool management platform via a network protocol. After receiving the request, the cloud resource pool management platform parses and verifies it to ensure the integrity and legitimacy of the request information, preparing for the subsequent rendering process. The cloud resource can be a teaching cloud resource.

[0025] Specifically, cloud resources include image rendering, video rendering, and 3D model rendering. The rendering type should be determined based on the format of the cloud resource itself, the performance and display characteristics of the terminal device, and the actual needs of the teaching scenario. For example, high-definition video cloud resources can be rendered in high definition on high-performance computers. However, on mobile devices such as phones, adaptive adjustments must be made based on the screen size and resolution. Selecting the appropriate rendering type ensures optimal presentation of the cloud resource on the terminal.

[0026] When formulating cloud resource rendering strategies, this application considers that the complexity of cloud resources themselves, such as the large amount of data in high-definition videos and the high computing requirements of 3D models, will affect rendering time and cloud resource consumption. At the same time, the performance of terminal devices, such as the processing power of CPU and GPU, determines the efficiency and effect of rendering. In order for this application to determine the rendering type of cloud resources based on the rendering request, and determine the rendering strategy for rendering cloud resources based on the rendering type, the rendering strategy is then sent to the cloud so that the cloud resources can be rendered using the rendering strategy in the cloud.

[0027] After receiving the rendering strategy, the cloud server first loads the cloud resources into the cloud server's memory. Based on the strategy, the cloud server then invokes the corresponding rendering engine and algorithms to perform operations such as format conversion and image processing on the cloud resources. If the cloud resource is a 3D model, lighting calculations and texture mapping are performed; if it is a video, video encoding optimization is performed. Furthermore, during the rendering process, the cloud server utilizes powerful computing power and GPU acceleration technology to increase rendering speed. Distributed rendering technology is also used to distribute tasks to multiple servers for parallel processing, further improving efficiency.

[0028] In addition, after the cloud completes the rendering, it will encode the rendered data into a format suitable for network transmission. For example, video streaming data often uses encoding standards such as H.264 and H.265. There are two ways to transmit data to the terminal: one is to use real-time streaming protocols such as RTMP and HLS to send data in blocks in real time, and the terminal plays it while receiving it; the other is to first store the data in a cache server in the cloud, and then the terminal downloads it from the cache server as needed. In addition, this application can use content distribution network technology to cache data to the node closest to the terminal to reduce transmission delays.

[0029] In some embodiments, determining the rendering type for the cloud resource according to the rendering request includes: parsing the rendering request to obtain a parsing result of the rendering request; and determining the rendering type for the cloud resource according to the parsing result.

[0030] In this application, a rendering request is typically sent by a terminal device to a cloud resource pool. The rendering request includes key information related to the cloud resources. During the parsing of the rendering request, this application can extract key information related to the cloud resources to provide a basis for subsequent determination of the rendering type.

[0031] Specifically, during the process of parsing the rendering request, this application can receive the request and verify its integrity, parse the request parameters, and then perform standardization and normalization to obtain cloud resource attributes, terminal capabilities, user needs and network conditions, so as to determine the most suitable rendering type and achieve the goal of balancing rendering quality, efficiency and user experience.

[0032] During the process of receiving and verifying the integrity of requests, the cloud resource pool can verify the integrity of the request, check for missing required fields, and verify the correct protocol format, preventing invalid requests from entering subsequent processes. Rendering requests sent by terminals must use a standardized format that includes the cloud resource ID, terminal device information (such as model, operating system, screen resolution, CPU / GPU performance), user preferences (such as image quality requirements and interaction mode), and network environment parameters (such as bandwidth and latency).

[0033] At the same time, during the parsing of request parameters, cloud resource ID parsing, terminal capability parsing, user demand parsing, and network condition parsing can be performed. Cloud resource ID parsing can locate cloud resources in the cloud resource pool using unique identifiers, determining the cloud resource type (such as video, image, 3D model, VR / AR content) and format (MP4, PNG, OBJ, FBX, etc.). Terminal capability parsing can extract the terminal device's hardware parameters (such as CPU model, GPU performance, memory size) and software environment (operating system version, supported codecs) to determine the terminal's rendering capabilities. For example, low-performance devices may require simplified rendering, while high-performance devices may support high resolution or real-time interaction. User demand parsing can identify the user's explicit rendering preferences, such as high-definition priority, low-latency priority, or traffic-saving mode, as well as whether real-time interaction is required (such as online virtual experiments). Network condition parsing can determine whether to use streaming rendering (such as real-time transmission) or pre-rendering transmission (such as caching and downloading) based on network bandwidth and latency data.

[0034] In addition, during the standardization and normalization process, requests sent by different terminals can be standardized and converted. For example, the performance parameters of devices from different operating systems or manufacturers can be unified into comparable indicators to ensure consistency in subsequent decisions.

[0035] In some embodiments, a rendering strategy for rendering cloud resources is determined based on the rendering type, including: determining the cloud resource computing amount and rendering time required for the cloud resources based on the rendering type; and determining the rendering strategy for rendering cloud resources based on the cloud resource computing amount and rendering time.

[0036] In this application, the formulation of the rendering strategy can rely on the accurate assessment of the cloud resource computing requirements and rendering time. Different types of cloud resources (such as images, videos, 3D models) have large differences in the consumption of computing cloud resources (CPU, GPU, memory, storage) and time during the rendering process.

[0037] Specifically, in the process of determining the cloud resource computing amount and rendering time required for cloud resources based on the rendering type, this application needs to evaluate the cloud resource computing amount and make a rendering time prediction. In the process of evaluating the cloud resource computing amount, a cloud resource type and complexity analysis can be performed, and cloud resource preprocessing requirements can be included in the evaluation. At the same time, terminal performance adaptation needs to be considered. In particular, the cloud resource type and complexity analysis needs to consider image cloud resources, video cloud resources, 3D models and VR / AR cloud resources and interactive cloud resources. The cloud resource preprocessing requirements need to consider the cloud resource consumption in the preprocessing stage (such as CPU time and memory usage).

[0038] Furthermore, when predicting rendering time, this application can estimate the baseline time required to complete the rendering based on the type and complexity of cloud resources and the baseline performance of the cloud server (such as single-core rendering speed and GPU acceleration efficiency). Furthermore, if there are multiple concurrent cloud users, task queuing delays must be considered. The rendering time should include the waiting time from when the task enters the queue to when the rendering actually begins.

[0039] Specifically, when determining rendering strategies based on cloud resource computing capacity and rendering time, this application must comprehensively consider factors such as cloud resource availability, user demand priorities, and cost efficiency. Specifically, cloud resources with high real-time requirements (such as live online classrooms and urgent teaching tasks) can be prioritized for high-performance servers or GPU instances to shorten rendering times. Meanwhile, non-real-time cloud resources (such as after-class review videos) can be scheduled to idle time periods or low-priority queues to utilize the idle computing power of the cloud resource pool and reduce rendering costs.

[0040] This application can efficiently utilize cloud resources to complete cloud resource rendering by accurately evaluating the computing capacity and rendering time of cloud resources, and combining cloud resource scheduling, mode selection, dynamic optimization and other strategies, while meeting the user experience requirements of different terminals and scenarios.

[0041] In some embodiments, a rendering strategy for rendering cloud resources is determined based on the cloud resource computing amount and rendering time, including: determining the priority of rendering cloud resources according to the rendering type; adjusting the cloud resource computing amount and rendering time required for the cloud resources based on the priority to obtain the target cloud resource computing amount and target rendering time of the cloud resources; and determining the rendering strategy for rendering cloud resources according to the target cloud resource computing amount and target rendering time.

[0042] Specifically, this application adjusts the cloud resource computing amount and rendering time required for cloud resources by priority, obtains the target cloud resource computing amount and target rendering time of cloud resources, and determines the rendering strategy for rendering cloud resources based on the target cloud resource computing amount and target rendering time, to ensure that high-priority tasks are completed quickly, while also balancing cloud resource utilization.

[0043] In this application, priority division is the basis for cloud resource scheduling and policy formulation, and it needs to be dynamically evaluated based on multiple factors. Priority division can be determined based on the urgency of user needs. The urgency of user needs includes high priority, medium priority and low priority. High priority includes real-time interactive cloud resources (such as online virtual experiments), urgent teaching tasks (such as live courses), and high-priority requests explicitly marked by users. Medium priority includes regular cloud resources (such as recorded course videos) and planned cloud resource rendering tasks. Low priority includes non-real-time cloud resources (such as after-class exercise materials) and background pre-processing tasks.

[0044] Specifically, in the process of adjusting the cloud resource computing amount and rendering time required for cloud resources based on priority, this application can allocate more computing cloud resources (such as multi-core CPUs, high-performance GPUs) to high-priority cloud resources to shorten the computing time, and at the same time reduce the total rendering time by increasing rendering nodes (such as cloud server instances) and enabling real-time rendering technology (such as streaming); for low-priority cloud resources, it can simplify model details (such as LOD technology), reduce resolution, reduce special effects, reduce computing amount, and allow completion before the deadline to avoid occupying immediate cloud resources.

[0045] In some embodiments, based on the priority, the cloud resource computing amount and rendering time required for the cloud resource are adjusted to obtain the target cloud resource computing amount and target rendering time of the cloud resource, including: determining whether the priority is higher than a preset threshold; if higher than the threshold, adjusting the cloud resource computing amount and rendering time required for the cloud resource to obtain the target cloud resource computing amount and target rendering time of the cloud resource.

[0046] In this application, through priority threshold judgment and dynamic cloud resource adjustment, the cloud resource pool can flexibly respond to the rendering needs of high-priority cloud resources, ensuring that core tasks are completed within a limited time. At the same time, the adjustment process can balance cloud resource efficiency and user experience, and combine elastic computing and intelligent scheduling to achieve high efficiency and reliability of cloud resource rendering.

[0047] Specifically, priority thresholds are used to distinguish high-priority tasks from low-priority tasks, thereby triggering different cloud resource adjustment policies. Thresholds can be set based on pre-set rules or dynamically adjusted based on cloud resource pool load and cloud resource availability. Pre-set rules can include fixed priority thresholds (for example, a value range of 1-10 with a threshold of 7, where tasks with a priority ≥ 7 are considered high priority) based on cloud resource type (such as real-time interaction, live courses, and regular videos) and user needs.

[0048] In the process of dynamic adjustment based on the cloud resource pool load and cloud resource availability, when cloud resources are tight, the threshold can be increased (for example, from 7 to 8) to reduce the number of high-priority tasks and avoid cloud resource overload; when cloud resources are sufficient, the threshold can be lowered to allow more tasks to enjoy high-priority treatment.

[0049] In the cloud resource rendering method provided in the embodiment of the present invention, after the receiving terminal sends a rendering request for the cloud resource, the rendering type of the cloud resource is determined according to the rendering request, and then the rendering strategy for rendering the cloud resource is determined according to the rendering type, and the rendering strategy is sent to the cloud so that the cloud resource is rendered using the rendering strategy in the cloud. Finally, the rendering data of the cloud resource transmitted from the cloud can be received and the rendering data can be transmitted to the corresponding terminal to provide the user with a high-quality immersive learning experience. This application can not only effectively reduce the burden on the terminal, improve the rendering quality and speed, and enhance the compatibility of cloud resources, but also promote the sharing of educational cloud resources, providing strong support for the development of educational informatization.

[0050] In some embodiments, the present application also provides an artificial intelligence-based cloud resource rendering method, which is applied to the cloud, and the method includes: receiving a rendering strategy for cloud resources transmitted by a cloud resource pool; determining a decomposition strategy for decomposing cloud resources based on the rendering strategy; decomposing cloud resources according to the decomposition strategy to obtain multiple sub-cloud resources; performing distributed processing on multiple sub-cloud resources to obtain rendering data for each sub-cloud resource; and merging the rendering data of each sub-cloud resource to obtain rendering data for cloud resources.

[0051] In this application, cloud resources are decomposed into subtasks for parallel processing through cloud distributed technology. Combined with dynamic scheduling and optimization strategies, efficient and reliable rendering of cloud resources is achieved. At the same time, through the intelligent decomposition strategy and the elastic scheduling of the distributed system, it can effectively cope with scenarios with high cloud resource complexity and high rendering timeliness requirements, and promote the development of cloud resource rendering technology in educational informatization towards high performance and high availability. In addition, this application can also meet large-scale concurrency needs and reduce costs and improve user experience through technologies such as cloud resource reuse and intelligent scheduling.

[0052] The decomposition strategy can split the original cloud resources into multiple independent sub-cloud resource units. For example, a 3D model with 1 million faces can be split into 10 sub-models, each with 100,000 faces.

[0053] At the same time, this application combines the rendering data of each sub-cloud resource to obtain the rendering data of the cloud resource, and can perform data synchronization and alignment, splicing and combination, and post-processing and optimization. Among them, data synchronization and alignment can ensure that the timestamps, spatial positions (such as 3D model coordinates), or frame order of the rendering data of each sub-cloud resource are correctly aligned.

[0054] During the stitching and combination process, for 3D models, the rendering results of each sub-model can be stitched together according to the original structure, and the lighting and texture information can be merged; for videos, the sub-frames can be merged in frame order to generate a complete video stream; for images, the rendering results of each block can be stitched together into a complete high-resolution image.

[0055] During the post-processing and optimization process, color correction, frame rate adjustment, and special effects integration can be unified to ensure that the final rendering data meets the strategic requirements.

[0056] In addition, this application can store the merged complete rendering data in a cloud resource library, or directly stream it to the user terminal (such as VR equipment, online classroom platform) for real-time access or downloading by the user.

[0057] An embodiment of the present invention further provides an artificial intelligence-based cloud resource rendering device 300, which is used to execute any embodiment of the aforementioned artificial intelligence-based cloud resource rendering method.

[0058] Specifically, see Figure 3 , Figure 3 It is a schematic block diagram of an artificial intelligence-based cloud resource rendering device 300 provided in an embodiment of the present invention.

[0059] like Figure 3 As shown, the artificial intelligence-based cloud resource rendering device 300 provided in this application includes: a first receiving unit 310, a first determining unit 320, a second determining unit 330, a sending unit 340 and a second receiving unit 350.

[0060] The first receiving unit 310 is used to receive a rendering request for cloud resources sent by a terminal; the first determining unit 320 is used to determine the rendering type of the cloud resources based on the rendering request; the second determining unit 330 is used to determine the rendering strategy for rendering the cloud resources based on the rendering type; the sending unit 340 is used to send the rendering strategy to the cloud so that the cloud resources are rendered using the rendering strategy in the cloud; the second receiving unit 350 receives the rendering data of the cloud resources transmitted from the cloud and transmits the rendering data to the corresponding terminal.

[0061] An embodiment of the present invention also provides an artificial intelligence-based cloud resource rendering device, which is applied to the cloud. The device includes: a third receiving unit, a third determining unit, a decomposition unit, a first processing unit, and a second processing unit.

[0062] The third receiving unit is used to receive the rendering strategy of the cloud resources transmitted by the cloud resource pool; the third determining unit is used to determine the decomposition strategy for decomposing the cloud resources based on the rendering strategy; the decomposition unit is used to decompose the cloud resources according to the decomposition strategy to obtain multiple sub-cloud resources; the first processing unit is used to perform distributed processing on the multiple sub-cloud resources to obtain rendering data for each sub-cloud resource; the second processing unit is used to merge the rendering data of each sub-cloud resource to obtain rendering data for the cloud resource.

[0063] The AI-based cloud resource rendering device 300 provided in the embodiment of the present application can, after receiving a rendering request for a cloud resource from a terminal, determine the rendering type for the cloud resource based on the rendering request, then determine a rendering strategy for rendering the cloud resource based on the rendering type, and send the rendering strategy to the cloud so that the cloud resource can be rendered using the rendering strategy in the cloud. Finally, it can receive the rendering data of the cloud resource transmitted from the cloud and transmit the rendering data to the corresponding terminal to provide users with a high-quality immersive learning experience. This application can not only effectively reduce the burden on the terminal, improve the rendering quality and speed, and enhance the compatibility of cloud resources, but also promote the sharing of educational cloud resources, providing strong support for the development of educational informatization.

[0064] It should be noted that technical personnel in the relevant field can clearly understand that the specific implementation process of the above-mentioned artificial intelligence-based cloud resource rendering device and each unit can refer to the corresponding description in the aforementioned method embodiment. For the convenience and conciseness of the description, it will not be repeated here.

[0065] The above-mentioned cloud resource rendering device can be implemented in the form of a computer program. The computer program can be used in Figure 4 Runs on the electronic devices shown.

[0066] See also Figure 4 , Figure 4 is a schematic block diagram of an electronic device provided by an embodiment of the present invention.

[0067] See Figure 4 The device 400 includes a processor 402 , a memory, and a network interface 405 connected via a system bus 401 , wherein the memory may include a storage medium 403 and an internal memory 404 .

[0068] The storage medium 403 may store an operating system 4031 and a computer program 4032. When the computer program 4032 is executed, the processor 402 may execute an artificial intelligence-based cloud resource rendering method.

[0069] The processor 402 is used to provide computing and control capabilities to support the operation of the entire device 400.

[0070] The internal memory 404 provides an environment for the operation of the computer program 4032 in the non-volatile storage medium 403. When the computer program 4032 is executed by the processor 402, the processor 402 can execute the rendering method of the cloud resources.

[0071] The network interface 405 is used for network communication, such as providing data information transmission. Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the device 400 to which the solution of the present invention is applied. The specific device 400 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0072] Among them, the processor 402 is used to run the computer program 4032 stored in the memory to achieve the following functions: receiving a rendering request for cloud resources sent by the terminal; determining the rendering type of the cloud resources based on the rendering request; determining the rendering strategy for rendering the cloud resources based on the rendering type; sending the rendering strategy to the cloud so as to render the cloud resources in the cloud using the rendering strategy; receiving rendering data of the cloud resources transmitted from the cloud, and transmitting the rendering data to the corresponding terminal.

[0073] Among them, the processor 402 is used to run the computer program 4032 stored in the memory to achieve the following functions: receiving the rendering strategy of the cloud resources transmitted by the cloud resource pool; determining the decomposition strategy for decomposing the cloud resources based on the rendering strategy; decomposing the cloud resources according to the decomposition strategy to obtain multiple sub-cloud resources; performing distributed processing on the multiple sub-cloud resources to obtain rendering data of each sub-cloud resource; merging the rendering data of each sub-cloud resource to obtain rendering data of the cloud resource.

[0074] In some embodiments, when determining the rendering type for cloud resources in response to a rendering request, the processor 402 further specifically implements the following steps: parsing the rendering request to obtain a parsing result of the rendering request; and determining the rendering type for the cloud resources based on the parsing result.

[0075] In some embodiments, when the processor 402 implements the rendering strategy for rendering cloud resources based on the rendering type, it also specifically implements the following steps: determining the cloud resource computing amount and rendering time required for the cloud resources based on the rendering type; and determining the rendering strategy for rendering the cloud resources based on the cloud resource computing amount and rendering time.

[0076] In some embodiments, when the processor 402 implements the rendering strategy for rendering cloud resources based on the cloud resource computing amount and rendering time, it specifically implements the following steps: determining the priority for rendering cloud resources based on the rendering type; adjusting the cloud resource computing amount and rendering time required for the cloud resources based on the priority to obtain the target cloud resource computing amount and target rendering time of the cloud resources; determining the rendering strategy for rendering cloud resources based on the target cloud resource computing amount and target rendering time.

[0077] In some embodiments, when the processor 402 adjusts the cloud resource computing amount and rendering time required for the cloud resources based on the priority and obtains the target cloud resource computing amount and target rendering time of the cloud resources, it specifically implements the following steps: determining whether the priority is higher than a preset threshold; if it is higher than the threshold, adjusting the cloud resource computing amount and rendering time required for the cloud resources to obtain the target cloud resource computing amount and target rendering time of the cloud resources.

[0078] Those skilled in the art will understand that Figure 4 The embodiment of the device 400 shown in the figure does not constitute a limitation on the specific structure of the device 400. In other embodiments, the device 400 may include more or fewer components than shown, or combine certain components, or arrange the components differently. For example, in some embodiments, the device 400 may only include a memory and a processor 402. In such an embodiment, the structure and function of the memory and processor 402 are the same as those in the figure. Figure 4 The embodiments shown are consistent and will not be described again here.

[0079] It should be understood that in the embodiment of the present invention, the processor 402 may be a central processing unit (CPU), other general-purpose processors 402, digital signal processors 402 (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor 402 may be a microprocessor 402 or any conventional processor 402.

[0080] According to one aspect of the present application, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device implements the following steps: receiving a rendering request for a cloud resource sent by a terminal; determining a rendering type for the cloud resource based on the rendering request; determining a rendering strategy for rendering the cloud resource based on the rendering type; sending the rendering strategy to the cloud so that the cloud resource is rendered using the rendering strategy in the cloud; receiving rendering data of the cloud resource transmitted from the cloud, and transmitting the rendering data to a corresponding terminal.

[0081] According to one aspect of the present application, a computer program product or computer program is also provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the electronic device to implement the following steps: receiving a rendering strategy for cloud resources transmitted by a cloud resource pool; determining a decomposition strategy for decomposing the cloud resources based on the rendering strategy; decomposing the cloud resources according to the decomposition strategy to obtain multiple sub-cloud resources; performing distributed processing on the multiple sub-cloud resources to obtain rendering data for each sub-cloud resource; and merging the rendering data of each sub-cloud resource to obtain rendering data for the cloud resource.

[0082] Those skilled in the art will appreciate that all or part of the steps in the method of the above-described embodiment can be implemented by instructing the relevant hardware through a computer program. The computer program includes program instructions, which can be stored in a storage medium that is computer-readable. The program instructions are executed by at least one processor in the computer system to implement the steps in the method of the above-described embodiment.

[0083] In another embodiment of the present invention, a computer storage medium is provided. The storage medium can be a non-volatile computer-readable storage medium or a volatile storage medium. The storage medium stores a computer program 4032, wherein when the computer program 4032 is executed by the processor 402, the following steps are implemented: receiving a rendering request for a cloud resource sent by a terminal; determining a rendering type for the cloud resource based on the rendering request; determining a rendering strategy for rendering the cloud resource based on the rendering type; sending the rendering strategy to the cloud so that the cloud resource is rendered using the rendering strategy in the cloud; receiving rendering data of the cloud resource transmitted from the cloud, and transmitting the rendering data to the corresponding terminal.

[0084] In another embodiment of the present invention, a computer storage medium is provided. The storage medium may be a non-volatile computer-readable storage medium or a volatile storage medium. The storage medium stores a computer program 4032, wherein when executed by the processor 402, the computer program 4032 implements the following steps: receiving a rendering strategy for cloud resources transmitted by a cloud resource pool; determining a decomposition strategy for decomposing the cloud resources based on the rendering strategy; decomposing the cloud resources according to the decomposition strategy to obtain multiple sub-cloud resources; performing distributed processing on the multiple sub-cloud resources to obtain rendering data for each sub-cloud resource; and merging the rendering data of each sub-cloud resource to obtain rendering data for the cloud resource.

[0085] In some embodiments, when the processor executes program instructions to determine the rendering type for cloud resources based on the rendering request, it specifically implements the following steps: parsing the rendering request to obtain a parsing result of the rendering request; and determining the rendering type for the cloud resources based on the parsing result.

[0086] In some embodiments, when the processor executes program instructions to determine the rendering strategy for rendering cloud resources based on the rendering type, it also specifically implements the following steps: determining the cloud resource computing amount and rendering time required for the cloud resources based on the rendering type; and determining the rendering strategy for rendering the cloud resources based on the cloud resource computing amount and rendering time.

[0087] In some embodiments, when the processor executes program instructions to implement a rendering strategy for rendering cloud resources based on the cloud resource computing amount and rendering time, it specifically implements the following steps: determining the priority for rendering cloud resources based on the rendering type; adjusting the cloud resource computing amount and rendering time required for the cloud resources based on the priority to obtain the target cloud resource computing amount and target rendering time of the cloud resources; determining the rendering strategy for rendering cloud resources based on the target cloud resource computing amount and target rendering time.

[0088] In some embodiments, when the processor executes program instructions to adjust the cloud resource computing amount and rendering time required for the cloud resources based on priority, and obtains the target cloud resource computing amount and target rendering time of the cloud resources, it specifically implements the following steps: determining whether the priority is higher than a preset threshold; if it is higher than the threshold, adjusting the cloud resource computing amount and rendering time required for the cloud resources, and obtaining the target cloud resource computing amount and target rendering time of the cloud resources.

[0089] The storage medium may be any computer-readable storage medium that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk.

[0090] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and other division methods may be used in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not implemented.

[0092] The steps in the method of the embodiment of the present application can be adjusted in order, combined, and deleted according to actual needs. The units in the device of the embodiment of the present application can be combined, divided, and deleted according to actual needs. In addition, the functional units in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into a single unit.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling an electronic device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method provided in each embodiment of the present application.

[0094] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A cloud resource rendering method based on artificial intelligence, characterized in that: Applied to a cloud resource pool, the method includes: The receiving terminal sends a rendering request for cloud resources; Determining a rendering type for the cloud resource according to the rendering request; Determining a rendering strategy for rendering the cloud resources according to the rendering type; Sending the rendering strategy to the cloud, so that the cloud resource is rendered using the rendering strategy in the cloud; Receive the rendering data of the cloud resource transmitted by the cloud, and transmit the rendering data to the corresponding terminal.

2. The cloud resource rendering method based on artificial intelligence according to claim 1, characterized in that: The determining, according to the rendering request, a rendering type for the cloud resource includes: Parsing the rendering request to obtain a parsing result of the rendering request; Determine a rendering type for the cloud resource according to the parsing result.

3. The method for rendering cloud resources based on artificial intelligence according to claim 1, characterized in that: The determining, according to the rendering type, a rendering strategy for rendering the cloud resource includes: Determining the cloud resource computing capacity and rendering time required by the cloud resources according to the rendering type; A rendering strategy for rendering the cloud resources is determined based on the cloud resource computing amount and the rendering time.

4. The method for rendering cloud resources based on artificial intelligence according to claim 3, characterized in that: The determining, based on the cloud resource computing amount and the rendering time, a rendering strategy for rendering the cloud resource includes: Determining a priority for rendering the cloud resources according to the rendering type; Based on the priority, adjusting the cloud resource computing amount and rendering time required for the cloud resource to obtain a target cloud resource computing amount and a target rendering time for the cloud resource; A rendering strategy for rendering the cloud resources is determined according to the target cloud resource computing amount and the target rendering time.

5. The method for rendering cloud resources based on artificial intelligence according to claim 4, characterized in that: The adjusting the cloud resource computing amount and rendering time required for the cloud resource based on the priority to obtain the target cloud resource computing amount and target rendering time of the cloud resource includes: Determining whether the priority is higher than a preset threshold; If it is higher than the threshold, the cloud resource computing amount and rendering time required for the cloud resource are adjusted to obtain the target cloud resource computing amount and target rendering time of the cloud resource.

6. A cloud resource rendering method based on artificial intelligence, characterized in that: Applied to the cloud, the method includes: receiving a rendering strategy of the cloud resources transmitted by a cloud resource pool; Determining a decomposition strategy for decomposing the cloud resources based on the rendering strategy; Decomposing the cloud resources according to the decomposition strategy to obtain multiple sub-cloud resources; Performing distributed processing on the multiple sub-cloud resources to obtain rendering data for each of the sub-cloud resources; The rendering data of each of the sub-cloud resources is merged to obtain the rendering data of the cloud resource.

7. A cloud resource rendering method and device based on artificial intelligence, characterized in that: Applied to a cloud resource pool, the device includes: A first receiving unit, configured to receive a rendering request for cloud resources sent by a terminal; A first determining unit, configured to determine a rendering type for the cloud resource according to the rendering request; A second determining unit, configured to determine a rendering strategy for rendering the cloud resource according to the rendering type; A sending unit, configured to send the rendering strategy to a cloud, so that the cloud resource is rendered using the rendering strategy in the cloud; The second receiving unit receives the rendering data of the cloud resource transmitted from the cloud, and transmits the rendering data to a corresponding terminal.

8. A cloud resource rendering method and device based on artificial intelligence, characterized in that: Applied to the cloud, the device includes: A third receiving unit, configured to receive the rendering strategy of the cloud resources transmitted by the cloud resource pool; a third determining unit, configured to determine a decomposition strategy for decomposing the cloud resources based on the rendering strategy; a decomposition unit, configured to decompose the cloud resources according to the decomposition strategy to obtain a plurality of sub-cloud resources; A first processing unit is configured to perform distributed processing on the plurality of sub-cloud resources to obtain rendering data of each of the sub-cloud resources; The second processing unit is configured to merge the rendering data of each of the sub-cloud resources to obtain the rendering data of the cloud resource.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the artificial intelligence-based cloud resource rendering method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to execute the artificial intelligence-based cloud resource rendering method according to any one of claims 1 to 6.

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