Digital twin system for double-engine rendering

Abstract

The invention discloses a dual-engine rendering digital twin system. The dual-engine rendering digital twin system comprises a cloud service platform, a server and a client, wherein the server carries a cloud engine, and the client carries a local engine; the cloud engine and the local engine have rendering capability; the client is used for responding to a rendering request initiated by a user and transmitting the rendering request to the cloud service platform; the cloud service platform is used for determining a plurality of to-be-rendered tasks according to the rendering request; when a plurality of to-be-rendered tasks are determined, obtaining first real-time performance of a client and second real-time performance of a server; and based on the first real-time performance, the second real-time performance and the plurality of to-be-rendered tasks, allocating the to-be-rendered tasks to a client and a server, so that the client and the server perform data rendering according to the allocated to-be-rendered tasks. According to the invention, the rendering efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and in particular to a dual-engine rendered digital twin system. Background Art

[0002] With the development of digital technology, digital twin technology has received widespread attention in many fields. The existing digital twin system uses a single rendering engine to concentrate all rendering tasks on the server side. The client obtains the rendering results from the server through network communication and displays them. However, this rendering method concentrates the rendering tasks on the server side, which requires the server side to process a large amount of rendering data, and has high hardware requirements on the server side. In addition, the way the server side undertakes all rendering tasks is prone to resource waste and load imbalance. Summary of the invention

[0003] An embodiment of the present invention provides a dual-engine rendering digital twin system, which can effectively solve the problem of existing digital twin systems that all rendering tasks are concentrated on the server side for processing, and improve rendering efficiency through collaborative rendering between the client and the server.

[0004] An embodiment of the present invention provides a dual-engine rendering digital twin system, comprising: a cloud service platform, a server, and a client; wherein the server is equipped with a cloud engine, and the client is equipped with a local engine; the cloud engine and the local engine have rendering capabilities;

[0005] The client is used to respond to a rendering request initiated by a user and transmit the rendering request to a cloud service platform;

[0006] The cloud service platform is used to determine a number of tasks to be rendered according to the rendering request; when determining the number of tasks to be rendered, obtain the first real-time performance of the client and the second real-time performance of the server; based on the first real-time performance, the second real-time performance and the number of tasks to be rendered, allocate each task to be rendered to the client and the server, so that the client and the server render data according to the allocated tasks to be rendered.

[0007] Furthermore, when determining a plurality of tasks to be rendered, obtaining a first real-time performance of the client and a second real-time performance of the server includes:

[0008] When determining a number of tasks to be rendered, obtaining a first computing capability, a first memory resource, and a first network bandwidth of the client at the current moment;

[0009] Determine a first real-time performance according to a first computing capability, a first memory resource, and a first network bandwidth;

[0010] Obtaining a second computing capability, a second memory resource, and a second network bandwidth of the server at the current moment;

[0011] The second real-time performance is determined according to the second computing capability, the second memory resource, and the second network bandwidth.

[0012] Furthermore, it also includes: a modeling module;

[0013] The modeling module is used to respond to the user's modeling operation and obtain the component information of each component and the device information of each component used to construct the BIM model to be rendered;

[0014] Establish a BIM model to be rendered based on the component information of each component and the device information of each component;

[0015] The BIM model to be rendered is uploaded to the cloud service platform.

[0016] Furthermore, allocating each task to be rendered to the client and the server based on the first real-time performance, the second real-time performance and the plurality of tasks to be rendered includes:

[0017] Detecting the rendering complexity of each task to be rendered, and determining the rendering complexity of each task to be rendered;

[0018] Allocate the rendering tasks whose rendering complexity is greater than a preset threshold to the server;

[0019] Each of the remaining unallocated tasks to be rendered is allocated according to the first real-time performance and the second real-time performance.

[0020] Furthermore, the detecting the rendering complexity of each task to be rendered to determine the rendering complexity of each task to be rendered includes:

[0021] For each task to be rendered, obtain the device information of each component to be rendered and the component information of each component to be rendered in the current task to be rendered;

[0022] Determine the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered according to the device information of each component to be rendered, and determine the rendering complexity of each component to be rendered according to the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered;

[0023] Determine the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered according to the component information of each component to be rendered, and determine the rendering complexity of each component to be rendered according to the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered;

[0024] The rendering complexity of the current task to be rendered is determined according to the rendering complexity of each component to be rendered and the rendering complexity of each member to be rendered.

[0025] Furthermore, after allocating the to-be-rendered tasks with rendering complexity greater than a preset threshold to the server, the method further includes:

[0026] For each of the remaining unassigned tasks to be rendered, determine the task type of the task to be rendered; wherein the task type includes global rendering and local rendering;

[0027] The allocating each of the remaining unallocated tasks to be rendered according to the first real-time performance and the second real-time performance includes:

[0028] Each of the remaining unallocated tasks to be rendered is allocated according to the task type, the first real-time performance, and the second real-time performance.

[0029] Furthermore, allocating each of the remaining unallocated tasks to be rendered according to the task type, the first real-time performance, and the second real-time performance includes:

[0030] For each of the remaining unassigned tasks to be rendered, when the task type is global rendering and the second real-time performance is greater than the first real-time performance, assigning the task to be rendered to the server;

[0031] When the task type is local rendering and the first real-time performance is greater than the second real-time performance, allocating the task to be rendered to the client;

[0032] When the task type is global rendering and the first real-time performance is greater than the second real-time performance, or when the task type is local rendering and the second real-time performance is greater than the first real-time performance, calculating the performance difference according to the first real-time performance and the second real-time performance;

[0033] When the performance difference is less than a preset difference, the to-be-rendered tasks whose task type is global rendering are allocated to the server, and the to-be-rendered tasks whose task type is local rendering are allocated to the client;

[0034] When the performance threshold is greater than a preset difference, comparing the number of tasks to be rendered on the client and the number of tasks to be rendered on the server;

[0035] If the number of tasks to be rendered on the client is greater than the number of tasks to be rendered on the server, the tasks to be rendered are assigned to the server;

[0036] If the number of tasks to be rendered on the client is less than the number of tasks to be rendered on the server, the tasks to be rendered are allocated to the client.

[0037] The following beneficial effects are achieved by implementing the present invention:

[0038] The present invention provides a dual-engine rendering digital twin system, including: a cloud service platform, a server and a client; wherein the server is equipped with a cloud engine, and the client is equipped with a local engine; by installing engines on both the client and the server, both the client and the server have rendering capabilities. The client responds to the rendering request initiated by the user, transmits the rendering request to the cloud server, and the cloud server determines the task to be rendered according to the rendering request, and obtains the first real-time performance of the client and the second real-time performance of the server when determining the task to be rendered. Based on the first real-time performance, the second real-time performance and a number of tasks to be rendered, each task to be rendered is assigned to the client and the server, thereby assigning multiple rendering tasks to the client and the server, so that the client and the server perform collaborative rendering, which solves the problem of the existing digital twin system concentrating all rendering tasks on the server side for processing, and improves rendering efficiency through collaborative rendering of the client and the server. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a structural schematic diagram of a dual-engine rendering digital twin system provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0040] 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.

[0041] like Figure 1 As shown, a dual-engine rendering digital twin system provided by an embodiment of the present invention includes: a cloud service platform 101, a server 102 and a client 103; wherein the server 102 is equipped with a cloud engine, and the client 103 is equipped with a local engine; the cloud engine and the local engine have rendering capabilities;

[0042] The client 103 is used to respond to a rendering request initiated by a user and transmit the rendering request to a cloud service platform;

[0043] The cloud service platform 101 is used to determine a number of tasks to be rendered according to the rendering request; when determining the number of tasks to be rendered, obtain the first real-time performance of the client 103 and the second real-time performance of the server 102; based on the first real-time performance, the second real-time performance and the number of tasks to be rendered, allocate each task to be rendered to the client 103 and the server 102, so that the client 103 and the server 102 perform data rendering according to the allocated tasks to be rendered.

[0044] In a preferred embodiment, it also includes: a modeling module; the modeling module is used to respond to user modeling operations, obtain component information of each component and device information of each component used to construct the BIM model to be rendered; establish the BIM model to be rendered according to the component information of each component and the device information of each component; upload the BIM model to be rendered to the cloud service platform 101.

[0045] Specifically, in the digital twin system rendered by dual engines, it mainly includes a modeling module, a client 103 equipped with a local engine, a server 102 equipped with a cloud engine, and a cloud service platform 101. The modeling module is used to respond to user modeling operations, obtain component information of each component used to construct the BIM model to be rendered and device information of each component, mainly including architectural, structural, electromechanical and other components and components, such as: walls, columns, beams, plates, doors, windows, pipes, equipment, etc. The BIM model to be rendered can be established through the spatial position, geometric shape, material properties and mutual correlation of each component and each component of these BIM models to be rendered uploaded by users. Preferably, the BIM model to be rendered can also be uploaded to the cloud service platform 101 after being established locally through the BIM modeling software.

[0046] Preferably, after the BIM model to be rendered is constructed, the BIM model to be rendered is lightweighted to reduce bandwidth requests during transmission of the BIM model to be rendered and to reduce memory usage of the BIM model to be rendered.

[0047] When a user initiates a rendering request in the client 103, the user rendering request is responded to and the rendering request is transmitted to the cloud service platform 101. The cloud service platform 101 determines a number of tasks to be rendered according to the rendering request, and allocates the tasks to be rendered to the client 103 and the server 102 for rendering. The tasks to be rendered are dynamically allocated according to the real-time performance of the server 102 and the client 103, the network status, and the complexity and rendering requirements of the tasks to be rendered.

[0048] In a preferred embodiment, when determining a number of tasks to be rendered, obtaining the first real-time performance of the client 103 and the second real-time performance of the server 102 includes: when determining a number of tasks to be rendered, obtaining the first computing power, first memory resources and first network bandwidth of the client 103 at the current moment; determining the first real-time performance based on the first computing power, first memory resources and first network bandwidth; obtaining the second computing power, second memory resources and second network bandwidth of the server 102 at the current moment; determining the second real-time performance based on the second computing power, second memory resources and second network bandwidth.

[0049] Specifically, when determining a number of rendering tasks, the cloud service platform 101 obtains the core performance indicators such as the first computing power, the first memory resource and the first network bandwidth of the client 103 at the current moment, which characterize the real-time performance and network status; and obtains the core performance indicators such as the second computing power, the second memory resource and the second network bandwidth of the server 102 at the current moment, which characterize the real-time performance and network status. Preferably, the detection of performance and network status can be obtained through a network test monitoring tool, which is automatically run regularly to collect core performance indicators such as CPU usage, CPU load, and memory occupancy of the server 102 and the client 103 in real time. These data can be accurately distributed through a dedicated API, thereby ensuring that the cloud service platform 101 can understand the performance of the server 102 and the client 103 in real time. In addition, in order to ensure the efficiency and stability of data transmission, the network test monitoring tool also needs to measure key network indicators such as network delay, bandwidth and packet loss rate between the server 102 and the cloud service platform 101, and between the client 103 and the cloud service platform 101 in real time. In addition, the server 102 will also perform ping command tests regularly to fully grasp the network status.

[0050] In a preferred embodiment, the method allocates each task to be rendered to the client 103 and the server 102 based on the first real-time performance, the second real-time performance and a number of tasks to be rendered, including: detecting the rendering complexity of each task to be rendered to determine the rendering complexity of each task to be rendered; allocating the tasks to be rendered whose rendering complexity is greater than a preset threshold to the server 102; and allocating each task to be rendered among the remaining unallocated tasks to be rendered according to the first real-time performance and the second real-time performance.

[0051] In a preferred embodiment, the detection of the rendering complexity of each task to be rendered and the determination of the rendering complexity of each task to be rendered include: for each task to be rendered, obtaining the device information of each component to be rendered and the component information of the component to be rendered in the current task to be rendered; determining the number of faces, material complexity and number of vertices required to be rendered when rendering each component to be rendered according to the device information of each component to be rendered, and determining the rendering complexity of each component to be rendered according to the number of faces, material complexity and number of vertices required to be rendered when rendering each component to be rendered; determining the number of faces, material complexity and number of vertices required to be rendered when rendering each component to be rendered according to the component information of each component to be rendered, and determining the rendering complexity of each component to be rendered according to the number of faces, material complexity and number of vertices required to be rendered when rendering each component to be rendered; determining the rendering complexity of the current task to be rendered according to the rendering complexity of each component to be rendered and the rendering complexity of each component to be rendered.

[0052] Specifically, after determining a number of tasks to be rendered, the rendering complexity of each task to be rendered is detected. For each task to be rendered, the device information of each component to be rendered and the component information of the component to be rendered in the current task to be rendered are obtained. According to the device information of each component to be rendered, the number of faces, material complexity and number of vertices required to be rendered when rendering each component to be rendered are determined, and then the rendering complexity of each component to be rendered is determined; according to the component information of each component to be rendered, the number of faces, material complexity and number of vertices required to be rendered when rendering each component to be rendered are determined, and then the rendering complexity of each component to be rendered is determined. The rendering complexity of each task to be rendered is determined according to the rendering complexity of each component to be rendered and the rendering complexity of each component to be rendered in each task to be rendered.

[0053] Preferably, in order to more accurately detect the rendering complexity of each component to be rendered and each component to be rendered, when initially constructing the BIM model to be rendered, each component to be rendered and each component to be rendered contained in the model can be partitioned, and different weights are assigned to each partition, and each assigned weight is added to the calculation of the rendering complexity. For example, for the same component to be rendered with the same component information, the rendering complexity outside the building is less than the rendering complexity inside the building. Therefore, initially, each component to be rendered outside the building is assigned a weight that is less than each component to be rendered inside the building. Then, when evaluating the rendering complexity, each initially set weight is added to the calculation to improve the accuracy and comprehensiveness of the rendering complexity detection.

[0054] A preset threshold is set. When the rendering complexity of any task to be rendered is greater than the preset threshold, it is considered that the rendering complexity is high. At this time, the task to be rendered greater than the preset threshold is assigned to the server 102 for rendering. The remaining tasks to be rendered that are not assigned to the server 102 are further assigned in combination with real-time performance.

[0055] In a preferred embodiment, after allocating the tasks to be rendered whose rendering complexity is greater than a preset threshold to the server 102, it also includes: for each of the remaining unallocated tasks to be rendered, determining the task type of the task to be rendered; wherein the task type includes global rendering and local rendering; for each of the remaining unallocated tasks to be rendered, allocating according to the first real-time performance and the second real-time performance, including: for each of the remaining unallocated tasks to be rendered, allocating according to the task type, the first real-time performance and the second real-time performance.

[0056] In a preferred embodiment, for each of the remaining unallocated tasks to be rendered, the tasks to be rendered are allocated according to the task type, the first real-time performance, and the second real-time performance, including: for each of the remaining unallocated tasks to be rendered, when the task type is global rendering and the second real-time performance is greater than the first real-time performance, the task to be rendered is allocated to the server 102; when the task type is local rendering and the first real-time performance is greater than the second real-time performance, the task to be rendered is allocated to the client 103; when the task type is global rendering and the first real-time performance is greater than the second real-time performance, or when the task type is local rendering and the second real-time performance is greater than the first real-time performance When the performance is determined, a performance difference is calculated according to the first real-time performance and the second real-time performance; when the performance difference is less than a preset difference, the tasks to be rendered whose task type is global rendering are allocated to the server 102, and the tasks to be rendered whose task type is local rendering are allocated to the client 103; when the performance threshold is greater than the preset difference, the number of tasks to be rendered on the client 103 and the number of tasks to be rendered on the server 102 are compared; if the number of tasks to be rendered on the client 103 is greater than the number of tasks to be rendered on the server 102, the tasks to be rendered are allocated to the server 102; if the number of tasks to be rendered on the client 103 is less than the number of tasks to be rendered on the server 102, the tasks to be rendered are allocated to the client 103.

[0057] Specifically, for each of the remaining unallocated tasks to be rendered, it is allocated according to the task type to which it belongs, and the task type of each of the remaining unallocated tasks to be rendered is determined; wherein the task type is divided into global rendering and local rendering. For the remaining unallocated tasks to be rendered, they are allocated after a comprehensive analysis based on the task type to which they belong, the first real-time performance of the client 103, and the second real-time performance of the server 102. For each of the remaining unallocated tasks to be rendered, allocation is performed according to the following method.

[0058] When the task type of the task to be rendered is global rendering, and the second real-time performance of the server 102 is greater than the first real-time performance of the client 103, the task to be rendered is allocated to the server 102 for rendering. When the task type of the task to be rendered is local rendering, and the first real-time performance of the client 103 is greater than the second real-time performance of the server 102, the task to be rendered is allocated to the client 103 for rendering. When the task type is global rendering and the first real-time performance is greater than the second real-time performance, the difference between the first real-time performance and the second real-time performance is analyzed, and when the difference is less than a preset difference, it is preferentially allocated to the server 102 for rendering, and when the difference is greater than the preset difference, according to the number of pending rendering tasks in the client 103 and the number of pending rendering tasks in the server 102, the task to be rendered is allocated to the party with the smaller number. When the task type is local rendering and the second real-time performance is greater than the first real-time performance, the difference between the first real-time performance and the second real-time performance is analyzed. When the difference is less than the preset difference, it is preferentially allocated to the client 103 for rendering. When the difference is greater than the preset difference, the task to be rendered is allocated to the party with the smaller number based on the number of pending rendering tasks in the client 103 and the number of pending rendering tasks in the server 102.

[0059] The server 102 and the client 103 each call the mounted engine for rendering according to the assigned rendering tasks, thereby realizing collaborative rendering of the server 102 and the client 103. After the rendering is completed, the server 102 feeds back the rendering result to the client 103, and the client 103 returns the rendering result to the user.

[0060] It should be noted that the system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the system embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0061] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0062] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A dual-engine rendering digital twin system, characterized in that: include: A cloud service platform, a server and a client; wherein the server is equipped with a cloud engine and the client is equipped with a local engine; the cloud engine and the local engine have rendering capabilities; The client is used to respond to a rendering request initiated by a user and transmit the rendering request to a cloud service platform; The cloud service platform is used to determine a number of tasks to be rendered according to the rendering request; when determining the number of tasks to be rendered, obtain the first real-time performance of the client and the second real-time performance of the server; based on the first real-time performance, the second real-time performance and the number of tasks to be rendered, allocate each task to be rendered to the client and the server, so that the client and the server perform data rendering according to the allocated tasks to be rendered.

2. A dual-engine rendered digital twin system as claimed in claim 1, characterized in that: When determining a number of tasks to be rendered, obtaining a first real-time performance of the client and a second real-time performance of the server includes: When determining a number of tasks to be rendered, obtaining a first computing capability, a first memory resource, and a first network bandwidth of the client at the current moment; Determine a first real-time performance according to a first computing capability, a first memory resource, and a first network bandwidth; Obtaining a second computing capability, a second memory resource, and a second network bandwidth of the server at the current moment; The second real-time performance is determined according to the second computing capability, the second memory resource, and the second network bandwidth.

3. A dual-engine rendered digital twin system as claimed in claim 1, characterized in that: Also includes: Modeling module; The modeling module is used to respond to the user's modeling operation and obtain the component information of each component and the device information of each component used to construct the BIM model to be rendered; Establish a BIM model to be rendered based on the component information of each component and the device information of each component; The BIM model to be rendered is uploaded to the cloud service platform.

4. A dual-engine rendered digital twin system as claimed in claim 3, characterized in that: The allocating each to-be-rendered task to the client and the server based on the first real-time performance, the second real-time performance and the plurality of to-be-rendered tasks comprises: Detecting the rendering complexity of each task to be rendered, and determining the rendering complexity of each task to be rendered; Allocate the rendering tasks whose rendering complexity is greater than a preset threshold to the server; Each of the remaining unallocated tasks to be rendered is allocated according to the first real-time performance and the second real-time performance.

5. A dual-engine rendered digital twin system as claimed in claim 4, characterized in that: The detecting the rendering complexity of each task to be rendered to determine the rendering complexity of each task to be rendered includes: For each task to be rendered, obtain the device information of each component to be rendered and the component information of each component to be rendered in the current task to be rendered; Determine the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered according to the device information of each component to be rendered, and determine the rendering complexity of each component to be rendered according to the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered; Determine the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered according to the component information of each component to be rendered, and determine the rendering complexity of each component to be rendered according to the number of faces, material complexity and number of vertices required to render when rendering each component to be rendered; The rendering complexity of the current task to be rendered is determined according to the rendering complexity of each component to be rendered and the rendering complexity of each member to be rendered.

6. A dual-engine rendered digital twin system as claimed in claim 4, characterized in that: After allocating the to-be-rendered tasks with a rendering complexity greater than a preset threshold to the server, the method further includes: For each of the remaining unassigned tasks to be rendered, determine the task type of the task to be rendered; wherein the task type includes global rendering and local rendering; The allocating each of the remaining unallocated tasks to be rendered according to the first real-time performance and the second real-time performance includes: Each of the remaining unallocated tasks to be rendered is allocated according to the task type, the first real-time performance, and the second real-time performance.

7. A dual-engine rendered digital twin system as claimed in claim 6, characterized in that: The allocating each of the remaining unallocated tasks to be rendered according to the task type, the first real-time performance, and the second real-time performance includes: For each of the remaining unassigned tasks to be rendered, when the task type is global rendering and the second real-time performance is greater than the first real-time performance, assigning the task to be rendered to the server; When the task type is local rendering and the first real-time performance is greater than the second real-time performance, allocating the task to be rendered to the client; When the task type is global rendering and the first real-time performance is greater than the second real-time performance, or when the task type is local rendering and the second real-time performance is greater than the first real-time performance, the first real-time performance and the second real-time performance are calculated as a performance difference; When the performance difference is less than a preset difference, the to-be-rendered tasks whose task type is global rendering are allocated to the server, and the to-be-rendered tasks whose task type is local rendering are allocated to the client; When the performance threshold is greater than a preset difference, comparing the number of tasks to be rendered on the client and the number of tasks to be rendered on the server; If the number of tasks to be rendered on the client is greater than the number of tasks to be rendered on the server, the tasks to be rendered will be assigned to the server; If the number of tasks to be rendered on the client is less than the number of tasks to be rendered on the server, the tasks to be rendered are allocated to the client.

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