Mobile equipment digital twin model rendering optimization method and system, terminal and medium

Abstract

The invention relates to the technical field of rendering, in particular to a mobile device digital twin model rendering optimization method and system, a terminal and a medium, and the method comprises the following steps: distributing an image rendering task to a cloud rendering system or an edge node based on the size of a rendering load; when the image rendering task is allocated to the cloud rendering system, the cloud rendering system splits the image rendering task into a plurality of sub-tasks and allocates each sub-task to different computing nodes, and a cloud rendering engine on the computing nodes executes the sub-tasks and outputs a first rendering result; the cloud rendering system collects the first rendering result, integrates the first rendering result and outputs the first rendering result to the mobile device; when the image rendering task is distributed to the edge node, the rendering engine on the edge node executes the image rendering task and outputs a second rendering result, and after the image rendering task is completed, the edge node outputs the second rendering result to the mobile device. The method has the strong computing power of a cloud rendering system and the low-delay characteristic of edge computing at the same time.

Description

Technical Field

[0001] The present invention relates to the field of rendering technology, and particularly to a method, system, terminal and medium for optimizing the rendering of digital twin models on mobile devices. Background Art

[0002] With the application of digital twin technology in multiple fields, the scale and complexity of digital twin models have gradually increased. Especially in the fields of intelligent manufacturing, smart city, and smart port, real-time monitoring and dynamic interaction have become the core requirements of the application. However, digital twin models often contain a large amount of dynamic data and complex three-dimensional scene rendering, usually requiring high computing power to ensure a smooth user experience and real-time interactivity. For high-performance computing platforms, rendering processing can achieve high rendering efficiency through technologies such as multi-threading and GPU acceleration. However, on mobile devices, especially smartphones and tablets, the hardware resources and computing power are relatively limited, facing great challenges.

[0003] On mobile devices, the main problems faced by digital twin rendering include the following aspects: (1) Limited computing resources: Mobile devices usually come with low-power CPUs and integrated GPUs, and the memory and storage space are also relatively limited. The large amount of real-time data calculation and complex three-dimensional rendering in digital twin scenarios often exceed the processing capabilities of these devices. (2) Battery life issue: High-intensity graphics rendering consumes a large amount of power. Especially in dynamically changing scenarios, continuous rendering processing will cause the battery to drain too quickly, affecting the battery life of the device. (3) Rendering delay and stuttering phenomena: In complex digital twin scenarios, due to limited computing power, rendering delay and stuttering may occur, greatly affecting the user experience. Especially in real-time monitoring, simulation, and interaction scenarios, the delay will reduce the responsiveness and accuracy of the application.

[0004] Based on the above problems, the industry has proposed some optimization methods, such as model simplification, view-distance-based culling, edge computing rendering distribution, etc. However, although model simplification can reduce the rendering calculation amount, it often sacrifices details and realism, affecting the fidelity of the rendering effect; although the view-distance-based culling technology can effectively reduce the objects that do not need to be rendered, this method relies on the static analysis of the scene, and for dynamically changing digital twin scenarios, the effect is not ideal; although edge computing rendering distribution can transfer the rendering task to a high-performance server, this requires a stable network connection. Summary of the Invention

[0005] To solve the technical problems that the existing rendering optimization methods for mobile device digital twin models cannot solve the problems of poor rendering quality, low rendering real-time performance, and low rendering smoothness under limited computing resources, on the one hand, the present invention provides a rendering optimization method for mobile device digital twin models, and on the other hand, it also provides a rendering optimization system for mobile device digital twin models, a terminal, and a medium.

[0006] To achieve the above object, the technical solution adopted by a rendering optimization method for mobile device digital twin models in the present invention is as follows: A rendering optimization method for mobile device digital twin models includes the following steps: Obtain the image rendering task of the mobile device and calculate the rendering load of the image rendering task; Allocate the image rendering task to the cloud rendering system or the edge node based on the size of the rendering load; When the image rendering task is allocated to the cloud rendering system, the cloud rendering system splits the image rendering task into multiple subtasks and allocates each subtask to different computing nodes. After receiving the subtask, the cloud rendering engine on the computing node extracts data from the cached data for rendering to execute the subtask and outputs the first rendering result. After all subtasks are completed, the cloud rendering system collects the first rendering results and integrates them. The cloud rendering system outputs the integrated first rendering result to the mobile device; When the image rendering task is allocated to the edge node, after receiving the image rendering task, the rendering engine on the edge node extracts data from the cached data for rendering to execute the image rendering task and outputs the second rendering result. After completing the image rendering task, the edge node outputs the second rendering result to the mobile device.

[0007] The present application invents a distributed rendering optimization method based on cloud rendering and edge computing. It allocates the image rendering task to the cloud rendering system or the edge node based on the size of the rendering load, optimizes the computing resource allocation, coordinates the cloud rendering system and the edge node to complete the user's request, reduces the computing burden of the mobile device, and at the same time has the powerful computing ability of the cloud rendering system and the low-latency characteristics of edge computing. It can not only significantly improve the rendering quality, rendering speed, and rendering real-time performance of the mobile device for processing large-scale and high-complexity image rendering tasks, but also improve the rendering response speed, improve the rendering smoothness, and improve the user interaction experience.

[0008] As a preferred implementation manner of the rendering optimization method for mobile device digital twin models, the allocating the image rendering task to the cloud rendering system or the edge node based on the size of the rendering load includes: Allocate the image rendering task with a rendering load exceeding the preset threshold to the cloud rendering system, and allocate the image rendering task with a rendering load not exceeding the preset threshold to the edge node.

[0009] As a preferred implementation of the rendering optimization method for the digital twin model of a mobile device, the steps for the cloud rendering system to output the integrated first rendering result to the mobile device are as follows: Obtain the current network data transmission rate, and determine whether it is necessary to compress the integrated first rendering result based on the magnitude of the current network data transmission rate. If it is necessary to compress the integrated first rendering result, the cloud rendering system selects the current required compression rate based on the magnitude of the current network data transmission rate, compresses the integrated first rendering result at the current required compression rate, and outputs the compressed first rendering result to the mobile device; then, based on the magnitude of the current network data transmission rate, repeatedly determine whether it is necessary to compress the integrated first rendering result until it is no longer necessary to compress the integrated first rendering result, and then the cloud rendering system outputs the integrated first rendering result to the mobile device. If it is not necessary to compress the integrated first rendering result, the cloud rendering system outputs the integrated first rendering result to the mobile device.

[0010] As a preferred implementation of the rendering optimization method for the digital twin model of a mobile device, after repeatedly determining whether it is necessary to compress the integrated first rendering result based on the magnitude of the current network data transmission rate, the rendering optimization method for the digital twin model of a mobile device further includes: When the result of determining whether it is necessary to compress the integrated first rendering result based on the magnitude of the current network data transmission rate is that it is necessary to compress the integrated first rendering result, calculate the change value of the current network data transmission rate, determine whether to reduce the compression rate based on the magnitude of the change value of the current network data transmission rate. If so, select the new required compression rate after reducing the compression rate based on the magnitude of the growth rate of the current network data transmission rate. The cloud rendering system compresses the integrated first rendering result at the new required compression rate and outputs the compressed first rendering result to the mobile device.

[0011] As a preferred implementation of the rendering optimization method for the digital twin model of a mobile device, the cache data is distributed among multiple cache levels, including the L1 cache level set on the terminal device; the L2 cache level set on the edge node; and the L3 cache level set on the cloud server.

[0012] As a preferred implementation of the rendering optimization method for the digital twin model of a mobile device, the relationship of the access frequencies of the cache data in the L1 cache level, the L2 cache level, and the L3 cache level is: L1 cache level > L2 cache level > L3 cache level.

[0013] As a preferred implementation of the method for optimizing the rendering of the digital twin model of a mobile device, after the cloud rendering system splits the image rendering task into multiple subtasks and assigns each subtask to a different computing node, the cloud rendering engines on the computing nodes simultaneously execute their respective subtasks.

[0014] In a second aspect, the present application discloses a system for optimizing the rendering of a digital twin model of a mobile device, which adopts the following technical solution: A task acquisition and allocation module, configured to acquire an image rendering task of a mobile device, calculate the rendering load of the image rendering task; and allocate the image rendering task to a cloud rendering system or an edge node based on the size of the rendering load. A cloud rendering module, configured to when the image rendering task is allocated to the cloud rendering system, the cloud rendering system splits the image rendering task into multiple subtasks and assigns each subtask to a different computing node. After receiving the subtask, the cloud rendering engine on the computing node extracts data from the cached data for rendering to execute the subtask and outputs a first rendering result. After all subtasks are completed, the cloud rendering system collects the first rendering results and integrates them, and the cloud rendering system outputs the integrated first rendering result to the mobile device. An edge rendering module, configured to when the image rendering task is allocated to the edge node, the rendering engine on the edge node extracts data from the cached data for rendering to execute the image rendering task and outputs a second rendering result. After completing the image rendering task, the edge node outputs the second rendering result to the mobile device.

[0015] In a third aspect, the present application discloses a terminal, which adopts the following technical solution: A terminal includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the method for optimizing the rendering of the digital twin model of a mobile device as described above are implemented.

[0016] In a fourth aspect, the present application discloses a medium, which adopts the following technical solution: A computer program is stored on the medium. When the computer program is executed by a processor, the steps of the method for optimizing the rendering of the digital twin model of a mobile device as described above are implemented.

[0017] The beneficial effects of the present invention include: The present application invents a distributed rendering optimization method based on cloud rendering and edge computing, which allocates image rendering tasks to the cloud rendering system or edge nodes according to the size of the rendering load, optimizes the allocation of computing resources, coordinates the cloud rendering system and edge nodes to complete the user's request, reduces the computing burden of mobile devices, and at the same time has the powerful computing power of the cloud rendering system and the low-latency characteristics of edge computing. It can not only significantly improve the rendering quality, rendering speed and rendering real-time performance of processing large-scale and high-complexity image rendering tasks, but also improve the rendering response speed, rendering fluency and user interaction experience.

[0018] When the network data transmission rate is small, the cloud rendering system automatically adapts to an appropriate compression rate according to the network data transmission rate, enabling users to quickly obtain a preliminary display of the image without waiting for the complete image to be loaded. When the network data transmission rate is fast, it gradually transmits the rendering results with a low compression rate and high resolution to meet the user's visual requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the present invention, the accompanying drawings required in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic flowchart of the rendering optimization method for the digital twin model of a mobile device in the specific embodiment of the present invention; Figure 2 It is a schematic block diagram of the rendering optimization system for the digital twin model of a mobile device in the specific embodiment of the present invention. SPECIFIC EMBODIMENTS

[0021] On mobile devices, the main problems faced by digital twin rendering include the following aspects: (1) Limited computing resources: Mobile devices are usually equipped with low-power CPUs and integrated GPUs, and the memory and storage space are also relatively limited. A large amount of real-time data calculation and complex 3D rendering in digital twin scenarios often exceed the processing capabilities of these devices. (2) Battery life issues: High-intensity graphics rendering consumes a large amount of power. Especially in dynamically changing scenarios, continuous rendering processing will cause the battery to be consumed too quickly, affecting the battery life of the device. (3) Rendering delay and stuttering phenomena: In complex digital twin scenarios, due to limited computing power, rendering delay and stuttering may occur, greatly affecting the user experience. Especially in real-time monitoring, simulation and interaction scenarios, the delay will reduce the responsiveness and accuracy of the application.

[0022] Based on the above problems, the industry has proposed some optimization methods, such as model simplification, view-based culling, edge computing rendering distribution, etc. However, although model simplification can reduce the amount of rendering calculations, it often sacrifices details and realism, affecting the realism of the rendering effect; although view-based culling technology can effectively reduce objects that do not need to be rendered, this method relies on static analysis of the scene, and the effect is not ideal for dynamically changing digital twin scenes; although edge computing rendering distribution can transfer rendering tasks to high-performance servers, this requires a stable network connection.

[0023] How to make the rendering quality of digital twin models on mobile devices high, the rendering real-time high, and the rendering smoothness high when the computing resources of mobile devices are limited has become a technical problem that needs to be solved urgently.

[0024] Based on this, this embodiment proposes a mobile device digital twin model rendering optimization method.

[0025] The following is a detailed description of the mobile device digital twin model rendering optimization method involved in this application. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to provide a thorough understanding of the embodiments of this application. However, it should be clear to those skilled in the art that this application can also be implemented in other embodiments without these specific details.

[0026] The phrase "one embodiment or some embodiments" described in the present application means that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Thus, the phrases "in one embodiment, in some embodiments, in some other embodiments, in some other embodiments" etc. where differences appear in the present application do not necessarily refer to the same embodiment, but mean one or more but not all embodiments, unless otherwise specifically emphasized in other ways.

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Reference Figure 1 , this application proposes a mobile device digital twin model rendering optimization method, comprising the following steps: S1. Obtain an image rendering task of a mobile device and calculate a rendering load of the image rendering task; S2, allocating image rendering tasks to cloud rendering systems or edge nodes based on the size of the rendering load; S3. When the image rendering task is assigned to the cloud rendering system, the cloud rendering system splits the image rendering task into multiple subtasks and assigns each subtask to a different computing node. After receiving the subtask, the cloud rendering engine on the computing node extracts data from the cached data for rendering to execute the subtask and outputs the first rendering result. After all subtasks are completed, the cloud rendering system collects and integrates the first rendering results, and then outputs the integrated first rendering result to the mobile device. When the image rendering task is assigned to the edge node, the rendering engine on the edge node extracts data from the cached data for rendering to execute the image rendering task and outputs the second rendering result. After completing the image rendering task, the edge node outputs the second rendering result to the mobile device.

[0029] This embodiment invents a distributed rendering optimization method based on cloud rendering and edge computing. The image rendering task is assigned to the cloud rendering system or the edge node based on the size of the rendering load, optimizing the allocation of computing resources, and collaborating with the cloud rendering system and the edge node to complete the user's request. At the same time, it has the powerful computing ability of the cloud rendering system and the low-latency characteristics of edge computing, which can not only significantly improve the rendering quality, rendering speed, and rendering real-time performance of processing large-scale and high-complexity image rendering tasks, but also improve the rendering response speed, enhance the rendering fluency, and improve the user interaction experience.

[0030] The cloud rendering system splits the image rendering task into multiple subtasks according to the complexity and computing requirements of the image rendering task and assigns each subtask to a different computing node. Each subtask will be independently executed on different computing nodes, reducing the computing pressure on a single node, thereby improving the computing efficiency of the overall system.

[0031] In some embodiments, when the cloud rendering system assigns subtasks to different computing nodes, it automatically performs balanced allocation according to the real-time load conditions of the computing nodes to ensure that the resources of all computing nodes are reasonably allocated. By dynamically adjusting the subtask assignment and the use of computing resources, it can flexibly handle image renderings of different scales and complexities, optimizing the computing resources.

[0032] In step S1, after receiving the subtask, the cloud rendering engine on the computing node extracts data from the cached data for rendering. In step S3, after receiving the image rendering task, the rendering engine on the edge node extracts data from the cached data for rendering. Among them, the data extracted from the cached data are images or frame data directly related to the image rendering task (or subtask), as well as other necessary rendering parameters and status information, which are the data required to complete the image rendering task (or subtask).

[0033] Assigning the image rendering task to the cloud rendering system or the edge node based on the size of the rendering load described in step S2 includes: Assigning the image rendering tasks with rendering loads exceeding a preset threshold to the cloud rendering system, and assigning the image rendering tasks with rendering loads not exceeding the preset threshold to the edge nodes.

[0034] The edge nodes can share the burden of the cloud rendering system. Moreover, the edge nodes are closer to the users. They can process the image rendering tasks locally, quickly respond to the users' rendering requests, and reduce the frequency of data communication with the cloud. In this way, the interaction latency can be significantly reduced and the interaction experience can be improved. The specific manifestations are as follows: Reducing network latency: The physical location of the edge nodes is close to the users, enabling them to quickly respond to the users' rendering requests, especially in scenarios that require instant feedback. Compared with the long-distance data transmission of the cloud, edge computing significantly reduces network latency through local processing. For example, in augmented reality (AR) and virtual reality (VR) applications, the users' interaction response time directly affects the experience quality. The edge computing nodes can immediately adjust the rendered content according to the users' actions to achieve smoother real-time interaction.

[0035] Local caching and data preprocessing: Edge nodes are usually equipped with local caches, which can pre-store and process some frequently used rendering content or scene data of the users. Through the caching strategy, the edge nodes can reduce the number of repeated requests and quickly extract data from the local cache for rendering. In this way, even under high-load conditions, the system can still maintain a low-latency response. For example, when the user accesses a certain scene or location again, the edge node can directly read the data from the cache without re-requesting the cloud, improving the rendering efficiency and response speed.

[0036] Specifically, the edge nodes can be used for the following image rendering tasks: Dynamic perspective adjustment: When the user changes the perspective, the edge node will adjust the rendered image at this perspective according to the user's operation. For example, in virtual reality applications, the changes in the user's head or eye movements require real-time adjustment of the perspective image. This process can be completed at the edge node, thus avoiding all rendering data being transmitted to the cloud for processing. The edge node ensures the smoothness of the perspective switching process by quickly responding to the changes in the user's operations.

[0037] Local area rendering update: When the user's perspective changes, the edge node only needs to update the rendered content in the local area within the field of view. For example, when the user quickly moves from one angle to another, the edge node only needs to process the image data of the changed part without re-rendering the entire scene. This local rendering update mechanism can significantly reduce the computational burden and improve the efficiency.

[0038] Simple Lighting Calculation: The edge computing node is also responsible for processing some lighting calculations and texture detail adjustments. Under an efficient lighting model, certain local lighting changes can be completed at the edge node without sending all rendering requests to the cloud. For example, when the user changes the viewing angle or the ambient light source changes, the edge node can adjust the lighting effect in real time without relying on remote cloud computing.

[0039] In some embodiments, the edge node adopts an incremental rendering update method and a regional rendering algorithm when completing the image rendering task.

[0040] Incremental Rendering Update Method: The edge node adopts an incremental rendering update method to ensure that only the content within the user's field of view is updated. This method can effectively reduce the amount of data transferred during rendering. For example, when the user moves in a virtual environment, the edge node only renders the local area within the user's current field of view, and the parts not within the field of view do not need to be updated, thus reducing unnecessary data transfer.

[0041] Regional Rendering Algorithm: During user interaction, the edge computing node adopts a regional rendering algorithm to only render and update the areas visible or about to be visible to the user. This rendering method based on field-of-view prediction can reduce the computational load and avoid unnecessary global rendering operations. By predicting the user's movement path through the algorithm, the edge node can preprocess the areas about to be displayed, reducing latency and stuttering during interaction.

[0042] The steps for the cloud rendering system in step S3 to output the integrated first rendering result to the mobile device include the following steps: S31. Obtain the current network data transmission rate, and determine whether it is necessary to compress the integrated first rendering result based on the magnitude of the current network data transmission rate; S32. If it is necessary to compress the integrated first rendering result, the cloud rendering system selects the current required compression ratio based on the magnitude of the current network data transmission rate, compresses the integrated first rendering result at the current required compression ratio, and outputs the compressed first rendering result to the mobile device; then repeatedly determine whether it is necessary to compress the integrated first rendering result based on the magnitude of the current network data transmission rate until it is no longer necessary to compress the integrated first rendering result, and then the cloud rendering system outputs the integrated first rendering result to the mobile device; S33. If it is not necessary to compress the integrated first rendering result, the cloud rendering system outputs the integrated first rendering result to the mobile device.

[0043] Among them, in step S31, when determining whether to compress the integrated first rendering result based on the magnitude of the current network data transmission rate, if the value of the current network data transmission rate exceeds the preset value, it indicates that the current network is good, and there is no need to compress the integrated first rendering result; if the value of the current network data transmission rate does not exceed the preset value, it indicates that the current network is poor, and it is necessary to compress the integrated first rendering result.

[0044] In step S32, when selecting the current required compression rate based on the magnitude of the current network data transmission rate, different magnitudes of the current network data transmission rate in different intervals correspond to different current required compression rates, and the appropriate current required compression rate can be selected according to the interval where the magnitude of the current network data transmission rate is located.

[0045] Furthermore, after repeatedly determining whether to compress the integrated first rendering result based on the magnitude of the current network data transmission rate in step S32, the rendering optimization method for the mobile device digital twin model further includes: When the result of determining whether to compress the integrated first rendering result based on the magnitude of the current network data transmission rate is that it is necessary to compress the integrated first rendering result, calculate the change value of the current network data transmission rate, and determine whether to reduce the compression rate based on the magnitude of the change value of the current network data transmission rate. If so, select the new required compression rate after the compression rate is reduced based on the magnitude of the current network data transmission rate, and the cloud rendering system compresses the integrated first rendering result at the new required compression rate and outputs the compressed first rendering result to the mobile device.

[0046] Among them, when determining whether to reduce the compression rate based on the magnitude of the change value of the current network data transmission rate, if the change value of the current network data transmission rate exceeds the preset change value, it indicates that the network has recovered well, and the compression rate can be reduced; if the change value of the current network data transmission rate does not exceed the preset change value, it indicates that the network has recovered poorly, and the compression rate remains unchanged.

[0047] Thus, when the network data transmission rate is small, the cloud rendering system automatically adapts an appropriate compression rate according to the network data transmission rate, enabling users to quickly obtain a preliminary display of the image without waiting for the complete image to load, and when the network data transmission rate is fast, gradually transmit the rendering result with a low compression rate and high resolution to meet the user's visual requirements.

[0048] In some embodiments, the cached data is distributed in multiple cache levels, including the L1 cache layer set on the terminal device; the L2 cache layer set on the edge node; and the L3 cache layer set on the cloud server.

[0049] Further, the relationship of the access frequencies of the cached data in the L1 cache layer, L2 cache layer, and L3 cache layer is: L1 cache layer > L2 cache layer > L3 cache layer. Thus, the response speed and processing capacity can be effectively improved.

[0050] Specifically, the cached content of the L1 cache layer is: the user's recent operation data, rendering results frequently accessed in the short term, or intermediate calculation results. The cached content of the L2 cache layer is: dynamic perspective adjustment, intermediate results of image rendering tasks, user behavior records, scene configurations, etc. The cached content of the L3 cache layer is: global user data, global scene information, static rendering resources stored in the cloud, image resources, user profile files, etc.

[0051] After the cloud rendering system splits an image rendering task into multiple subtasks and assigns each subtask to a different computing node, the cloud rendering engines on the computing nodes simultaneously execute their respective subtasks. Specifically, the effective parallelization of subtasks can be achieved through the MapReduce model. Especially when processing ultra-high-resolution images, parallel processing can significantly shorten the rendering time. For example, when rendering a large 3D scene, each computing node is responsible for processing a part of the image, and finally the cloud rendering system merges the rendering results into a complete image. This parallel processing method is very efficient in dealing with large-scale data sets and ensures the rapid generation of high-quality images.

[0052] As Figure 2 shown, the following is an embodiment of a mobile device digital twin model rendering optimization system provided by the present disclosure. A mobile device digital twin model rendering optimization system and the mobile device digital twin model rendering optimization method of the above embodiments belong to the same inventive concept. For the details not described in detail in the embodiment of the mobile device digital twin model rendering optimization system, reference can be made to the embodiment of the above mobile device digital twin model rendering optimization method.

[0053] A mobile device digital twin model rendering optimization system includes a task acquisition and assignment module, configured to acquire an image rendering task of a mobile device, calculate the rendering load of the image rendering task; and allocate the image rendering task to a cloud rendering system or an edge node based on the size of the rendering load; a cloud rendering module, configured to when the image rendering task is allocated to the cloud rendering system, the cloud rendering system splits the image rendering task into multiple subtasks and assigns each subtask to a different computing node. After receiving the subtask, the cloud rendering engine on the computing node extracts data from the cached data for rendering to execute the subtask and outputs a first rendering result. After all subtasks are completed, the cloud rendering system collects the first rendering results and integrates them, and the cloud rendering system outputs the integrated first rendering result to the mobile device; An edge rendering module, configured to, when an image rendering task is assigned to an edge node, after the rendering engine on the edge node receives the image rendering task, extract data from cached data for rendering to execute the image rendering task and output a second rendering result, and after completing the image rendering task, the edge node outputs the second rendering result to the mobile device.

[0054] The implementation of the system in this embodiment includes the integration and implementation of programming languages, development frameworks, distributed storage, and data analysis technologies. After completing the system development, multi-level tests are conducted to verify the functionality, stability, and compatibility of the system to ensure its reliability and efficiency in actual applications.

[0055] An embodiment of this application also proposes a terminal, including a memory, a processor, a communication unit, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the steps of a method for optimizing the rendering of a mobile device digital twin model; the memory, the processor, and the communication unit communicate through one or more buses.

[0056] The processor may include one or more processing units. For example, the processor may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0057] Among them, the processor may be the nerve center and command center of the terminal. The controller may generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching instructions and executing instructions.

[0058] The memory is used to store the execution instructions of the processor. The memory can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disc. When the execution instructions in the memory are executed by the processor, the terminal can execute some or all of the steps in the embodiments of the above-mentioned method for optimizing the rendering of the mobile device digital twin model.

[0059] The wireless communication function of the electronic device can be implemented by an antenna, a wireless communication module, a modulation and demodulation processor, a baseband processor, etc.

[0060] The wireless communication module can provide solutions for wireless communication including wireless local area network, Bluetooth, global navigation satellite system, frequency modulation, near field communication technology, infrared technology, etc. applied to the electronic device.

[0061] This embodiment also proposes a storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of a method for optimizing the rendering of a mobile device digital twin model.

[0062] Among them, a method for optimizing the rendering of a mobile device digital twin model includes: Obtain an image rendering task of the mobile device and calculate the rendering load of the image rendering task; Allocate the image rendering task to the cloud rendering system or the edge node based on the size of the rendering load; When the image rendering task is allocated to the cloud rendering system, the cloud rendering system splits the image rendering task into multiple subtasks and allocates each subtask to different computing nodes. After receiving the subtask, the cloud rendering engine on the computing node extracts data from the cached data for rendering to execute the subtask and outputs the first rendering result. After all subtasks are completed, the cloud rendering system collects the first rendering results and integrates them. The cloud rendering system outputs the integrated first rendering result to the mobile device; When the image rendering task is allocated to the edge node, the rendering engine on the edge node extracts data from the cached data for rendering to execute the image rendering task and outputs the second rendering result. After completing the image rendering task, the edge node outputs the second rendering result to the mobile device.

[0063] The storage medium of the present disclosure may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0064] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A mobile device digital twin model rendering optimization method, characterized in that: The following steps are involved: Obtain image rendering tasks of the mobile device and calculate the rendering load of the image rendering tasks; Distribute image rendering tasks to cloud rendering systems or edge nodes based on the size of the rendering load; When an image rendering task is assigned to the cloud rendering system, the cloud rendering system splits the image rendering task into multiple subtasks and assigns each subtask to a different computing node. After receiving the subtask, the cloud rendering engine on the computing node extracts data from cache data for rendering to execute the subtask and output a first rendering result. After all subtasks are completed, the cloud rendering system collects and integrates the first rendering results, and the cloud rendering system outputs the integrated first rendering results to the mobile device. When an image rendering task is assigned to an edge node, the rendering engine on the edge node extracts data from the cache data for rendering after receiving the image rendering task to execute the image rendering task and output a second rendering result. After completing the image rendering task, the edge node outputs the second rendering result to the mobile device.

2. A mobile device digital twin model rendering optimization method according to claim 1, characterized in that: The method of allocating the image rendering task to the cloud rendering system or edge node based on the size of the rendering load includes: The image rendering tasks whose rendering load exceeds the preset threshold are allocated to the cloud rendering system, and the image rendering tasks whose rendering load does not exceed the preset threshold are allocated to the edge nodes.

3. The mobile device digital twin model rendering optimization method according to claim 1 is characterized in that: The cloud rendering system outputs the integrated first rendering result to the mobile device including the following steps: Obtaining the current network data transmission rate, and determining whether the integrated first rendering result needs to be compressed based on the current network data transmission rate; If the integrated first rendering result needs to be compressed, the cloud rendering system selects the currently required compression rate based on the current network data transmission rate, compresses the integrated first rendering result at the currently required compression rate, and outputs the compressed first rendering result to the mobile device; then, based on the current network data transmission rate, it is repeatedly determined whether the integrated first rendering result needs to be compressed, until the integrated first rendering result does not need to be compressed, and the cloud rendering system outputs the integrated first rendering result to the mobile device; If the integrated first rendering result does not need to be compressed, the cloud rendering system outputs the integrated first rendering result to the mobile device.

4. The mobile device digital twin model rendering optimization method according to claim 3 is characterized in that: After repeatedly determining whether it is necessary to compress the integrated first rendering result based on the current network data transmission rate, the method further includes: When the result of determining whether to compress the integrated first rendering result based on the current network data transmission rate is that the integrated first rendering result needs to be compressed, the change value of the current network data transmission rate is calculated, and based on the change value of the current network data transmission rate, it is determined whether to reduce the compression rate; if so, a new required compression rate after the compression rate is reduced is selected based on the current network data transmission rate, and the cloud rendering system compresses the integrated first rendering result with the new required compression rate, and outputs the compressed first rendering result to the mobile device.

5. The mobile device digital twin model rendering optimization method according to claim 1, characterized in that: The cache data is distributed in multiple cache levels, including an L1 cache layer set on a terminal device; an L2 cache layer set on an edge node; and an L3 cache layer set on a cloud server.

6. A mobile device digital twin model rendering optimization method according to claim 5, characterized in that: The relationship between the access frequencies of cache data in the L1 cache layer, the L2 cache layer, and the L3 cache layer is: L1 cache layer>L2 cache layer>L3 cache layer.

7. The mobile device digital twin model rendering optimization method according to claim 1, characterized in that: After the cloud rendering system splits the image rendering task into multiple subtasks and assigns each subtask to a different computing node, the cloud rendering engines on the computing nodes execute their respective subtasks simultaneously.

8. A mobile device digital twin model rendering optimization system, characterized in that: include: The task acquisition and allocation module is configured to acquire image rendering tasks of mobile devices, calculate the rendering load of the image rendering tasks, and allocate the image rendering tasks to the cloud rendering system or edge nodes based on the size of the rendering load; The cloud rendering module is configured such that when an image rendering task is assigned to the cloud rendering system, the cloud rendering system splits the image rendering task into multiple subtasks and assigns each subtask to a different computing node, after receiving the subtask, the cloud rendering engine on the computing node extracts data from cache data for rendering to execute the subtask and output a first rendering result, after all subtasks are completed, the cloud rendering system collects and integrates the first rendering results, and the cloud rendering system outputs the integrated first rendering results to the mobile device; The edge rendering module is configured so that when an image rendering task is assigned to an edge node, the rendering engine on the edge node extracts data from cache data for rendering after receiving the image rendering task to execute the image rendering task and output a second rendering result. After completing the image rendering task, the edge node outputs the second rendering result to the mobile device.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the mobile device digital twin model rendering optimization method as described in any one of claims 1-7 are implemented.

10. A medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the mobile device digital twin model rendering optimization method as described in any one of claims 1 to 7 are implemented.

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