CN112328286A

CN112328286A - Software development application updating processing method based on cloud computing and software development platform

Info

Publication number: CN112328286A
Application number: CN202011331346.1A
Authority: CN
Inventors: 朱林
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-11-24
Filing date: 2020-11-24
Publication date: 2021-02-05

Abstract

The embodiment of the application provides a software development application update processing method and a software development platform based on cloud computing, application rendering containers from different dimensions are processed in parallel based on a plurality of rendering update elements, and then under the scene of multiple application rendering containers, application rendering mapping strengthening or application rendering mapping optimizing can be carried out on the application rendering containers in different dimensions through application rendering optimizing information, so that the application rendering containers can be optimized in real time, the update matching degree in the updating process of the application rendering containers is improved, and the subsequent application rendering effect is favorably improved.

Description

Software development application updating processing method based on cloud computing and software development platform

Technical Field

The application relates to the technical field of software development based on cloud computing, in particular to a software development application update processing method and a software development platform based on cloud computing.

Background

With the development of internet technology, virtual Desktop (Desktop Virtualization) technology is more and more widely applied. In the virtual desktop technology, an operating system and application software can be configured in a virtual machine of a cloud computing host (such as a cloud computing center), an interactive desktop is virtualized, and a user can perform remote control through a client and the virtual desktop, so that the user can access the virtual desktop as if accessing a desktop of a local operating system.

In the far-end rendering process of the cloud computing center, the application rendering container relates to a large number of rendering optimization tasks, and how to optimize the application rendering container in real time in a multi-application rendering container scene, so that the updating matching degree in the updating process of the application rendering container is improved, the subsequent application rendering effect is improved, and the technical problem to be solved in the field is urgently solved.

Disclosure of Invention

In order to overcome at least the above disadvantages in the prior art, the present application aims to provide a software development application update processing method and a software development platform based on cloud computing, by obtaining rendering update element information corresponding to each rendering update element in the preset statistics of rendering update elements, generating application rendering optimization information corresponding to each rendering updating element according to the rendering updating element information corresponding to each rendering updating element, then adopting the application rendering optimization information corresponding to each rendering updating element, and finally, generating an optimized cloud computing rendering application program for performing application rendering through the rendering chartlet model according to the target application rendering container corresponding to each rendering updating element. By adopting the mode, the application rendering containers from different dimensions are processed in parallel based on the plurality of rendering updating elements, and then under the scene of the multi-application rendering containers, application rendering mapping strengthening or application rendering mapping optimization can be carried out on the application rendering containers in different dimensions through application rendering optimization information, so that the application rendering containers can be optimized in real time, the updating matching degree in the updating process of the application rendering containers is improved, and the subsequent application rendering effect is favorably improved.

In a first aspect, the present application provides a software development application update processing method based on cloud computing, which is applied to a software development platform, where the software development platform is in communication connection with a plurality of distributed software development terminals, and the method includes:

determining each target cloud computing rendering application program in the target rendering optimization program group, acquiring rendering base portrait data corresponding to configuration optimization big data information of each target cloud computing rendering application program in the target rendering optimization program group, and performing rendering updating element positioning based on the rendering base portrait data;

obtaining rendering updating element information corresponding to each rendering updating element in preset statistics rendering updating elements, wherein the rendering updating element information comprises an associated rendering class object, a rendering class object category and rendering preference adjusting information, the associated rendering class object is used for rendering the rendering class object covered by the updating element, the rendering class object category is used for indicating a rendering multi-dimensional vector of the rendering updating element, and the rendering preference adjusting information is used for indicating a weight distribution adjusting condition of a rendering control node in the rendering updating element;

generating application rendering optimization information corresponding to each rendering updating element according to the rendering updating element information corresponding to each rendering updating element, wherein the application rendering optimization information is used for performing application rendering map switching processing or application rendering map strengthening processing on a current application rendering container, and the application rendering optimization information and the rendering updating elements have a one-to-one correspondence relationship;

and processing a current application rendering container corresponding to each rendering updating element by adopting application rendering optimization information corresponding to each rendering updating element to obtain a target application rendering container corresponding to each rendering updating element, and generating an optimized cloud computing rendering application program for performing application rendering through a rendering chartlet model according to the target application rendering container corresponding to each rendering updating element, wherein the application rendering optimization information, the current application rendering container and the target application rendering container have a one-to-one correspondence relationship.

In a possible implementation manner of the first aspect, the obtaining rendering update element information corresponding to each rendering update element in the preset statistics of rendering update elements includes:

detecting each rendering updating element in the preset statistics rendering updating elements to obtain a rendering detection result corresponding to each rendering updating element;

determining rendering preference adjustment information corresponding to each rendering updating element according to the rendering detection result corresponding to each rendering updating element;

determining sampling pixel error information corresponding to each rendering updating element according to the rendering detection result corresponding to each rendering updating element;

acquiring an associated rendering class object corresponding to each rendering updating element and a rendering class object category corresponding to each rendering updating element;

and generating rendering updating element information corresponding to each rendering updating element according to the rendering preference adjustment information corresponding to each rendering updating element, the sampling pixel error information corresponding to each rendering updating element, the associated rendering class object corresponding to each rendering updating element and the rendering class object category corresponding to each rendering updating element.

In a possible implementation manner of the first aspect, the determining, according to the rendering detection result corresponding to each rendering update element, sampling pixel error information corresponding to each rendering update element includes:

and for any one rendering update element in the preset statistics rendering update elements, if the rendering detection result indicates that rendering node information with sampling pixel errors exists in the rendering update elements, determining information of a sampling pixel error process of the rendering node information in the rendering update elements as sampling pixel error information corresponding to each rendering update element.

In a possible implementation manner of the first aspect, the generating, according to the rendering update element information corresponding to each rendering update element, application rendering optimization information corresponding to each rendering update element includes:

acquiring scene hierarchical information corresponding to the rendering class object type from application rendering lens updating information corresponding to the preset associated rendering class object;

performing feature extraction on the scene layering information according to the rendering preference adjustment information to obtain rendering optimization scenario information of which the scene layering information is respectively matched with the rendering preference adjustment information;

determining global application rendering optimization information based on rendering optimization context information of the scene layering information;

determining scene layering error area information in the scene layering information according to the sampling pixel error information, and determining rendering optimization scenario information corresponding to the scene layering error area information;

fusing the global application rendering optimization information and rendering optimization scenario information corresponding to the scene layering error area information to obtain application rendering optimization information corresponding to each rendering updating element, wherein the application rendering optimization information comprises an application rendering map which needs to perform application rendering map switching processing or application rendering map strengthening processing on the current application rendering container;

the rendering preference adjustment information comprises rendering control node distribution, the rendering control node distribution comprises a plurality of rendering control nodes and a rendering switching feature vector connecting the two rendering control nodes, the rendering switching feature vector comprises bounding box switching information and map index information of the rendering switching feature vector, and the rendering control nodes comprise scene layered switching control points and switching objects;

the extracting the features of the scene layering information according to the rendering preference adjustment information to obtain rendering optimization scenario information of which the scene layering information is respectively matched with the rendering preference adjustment information, includes:

determining scene layering switching control points corresponding to the scene layering information in the rendering control node distribution;

determining a calling map index parameter and an optimized map index parameter of the scene hierarchical switching control point in a plurality of rendering control nodes distributed by the rendering control nodes according to the bounding box switching information;

calculating a first rendering optimization scene generated by the calling map index parameter on the scene layering information according to the map index information of the rendering switching eigenvector connecting the scene layering switching control point and the calling map index parameter;

calculating a second rendering optimization scene generated by the calling map index parameter on the scene layering information according to the map index information of the rendering switching eigenvector connecting the scene layering switching control point and the calling map index parameter;

determining rendering optimization scenario information of the scene layering information according to the first rendering optimization scenario and the second rendering optimization scenario;

the processing the current application rendering container corresponding to each rendering updating element by using the application rendering optimization information corresponding to each rendering updating element to obtain the target application rendering container corresponding to each rendering updating element includes:

according to the application rendering optimization information, acquiring a first application rendering map needing to be subjected to application rendering map switching processing on the current application rendering container and a second application rendering map needing to be subjected to application rendering map strengthening processing on the current application rendering container, wherein the application rendering map switching processing or the application rendering map strengthening processing needs to be performed on the current application rendering container;

and switching the first application rendering map according to the corresponding optimization strategy information in the application rendering optimization information, and performing reinforcement processing on the second application rendering map according to the corresponding reinforcement strategy information in the application rendering optimization information.

In a possible implementation manner of the first aspect, the generating an optimized cloud computing rendering application program for performing application rendering through a rendering map model according to the target application rendering container corresponding to each rendering update element includes:

determining an application rendering and drawing filter parameter corresponding to each rendering and updating element according to a target application rendering container corresponding to each rendering and updating element, wherein the application rendering and drawing filter parameter is an application rendering and drawing filter parameter of the target application rendering container on each application rendering texture;

determining rendering control information of a rendering logic device corresponding to each rendering updating element according to the application rendering drawing filter parameter corresponding to each rendering updating element;

and updating and configuring the current target cloud computing rendering application program based on the rendering control information of the rendering logic device corresponding to each rendering updating element to obtain the optimized cloud computing rendering application program corresponding to each rendering updating element.

In a possible implementation manner of the first aspect, the updating and configuring a current target cloud computing rendering application program based on rendering control information of a rendering logic device corresponding to each rendering update element to obtain an optimized cloud computing rendering application program corresponding to each rendering update element includes:

obtaining rendering control associated information of rendering control information of a rendering logic device corresponding to each rendering updating element aiming at each link jumping information in the current target cloud computing rendering application program;

updating and configuring the current target cloud computing rendering application program based on rendering control associated information of each link jump information in the current target cloud computing rendering application program to obtain an optimized cloud computing rendering application program corresponding to each rendering updating element.

In a possible implementation manner of the first aspect, the determining each target cloud computing rendering application program in the target rendering optimization program group, obtaining rendering base portrait data corresponding to configuration optimization big data information of each target cloud computing rendering application program in the target rendering optimization program group, and performing rendering update element positioning based on the rendering base portrait data includes:

acquiring loadable renderer information corresponding to each application rendering node of the distributed software development terminal, and performing renderer window update classification on configuration optimization big data information of the target cloud computing rendering application program based on the loadable renderer information to obtain a corresponding renderer window update classification set;

acquiring a corresponding renderer updating window based on the renderer window updating classification set, and determining rendering resource distribution information of a plurality of renderer updating channels based on the renderer updating window;

respectively inputting the rendering resource distribution information into a plurality of rendering scene kernel functions in a rendering scene calculation model, and performing rendering multi-dimensional vector generation at least once through the rendering scene kernel functions to obtain at least one rendering multi-dimensional vector; wherein at least one rendering multi-dimensional vector generation by the rendering scene kernel is performed based on an associated hierarchical block three-dimensional model associated with rendering multi-dimensional vectors extracted by other rendering scene kernels of the rendering scene kernels;

rendering scene calculation is carried out on a plurality of rendering multidimensional vectors output by the rendering scene kernel functions to obtain rendering scene calculation content, rendering basic portrait data of the renderer updating window under the configuration optimization big data information is obtained based on the rendering scene calculation content, and rendering updating element positioning is carried out based on the rendering basic portrait data of the renderer updating window under the configuration optimization big data information.

In a possible implementation manner of the first aspect, the method further includes:

taking one of the rendering scene kernels as a target rendering scene kernel;

acquiring a first rendering multi-dimensional vector extracted by the target rendering scene kernel function and a second rendering multi-dimensional vector extracted by other rendering scene kernel functions except the target rendering scene kernel function in the rendering scene kernel functions;

when the renderer updating channel of the second rendering multi-dimensional vector does not match the renderer updating channel of the first rendering multi-dimensional vector, performing optimized setting on the second rendering multi-dimensional vector, wherein the renderer updating channel of the second rendering multi-dimensional vector after optimized setting is the same as the renderer updating channel of the first rendering multi-dimensional vector;

performing rendering multi-dimensional vector generation on the optimally set content matching information of the second rendering multi-dimensional vector and the first rendering multi-dimensional vector through the target rendering scene kernel function;

wherein the second rendering multi-dimensional vector has at least two statistics; the method further comprises the following steps:

when a second rendering multi-dimensional vector of which the renderer updating channel is not matched with the renderer updating channel of the first rendering multi-dimensional vector and a second rendering multi-dimensional vector of which the renderer updating channel is matched with the renderer updating channel of the first rendering multi-dimensional vector exist at the same time, optimizing and setting the second rendering multi-dimensional vector of which the renderer updating channel is not matched with the renderer updating channel of the first rendering multi-dimensional vector, performing reverse optimization and setting on the second rendering multi-dimensional vector of which the renderer updating channel is matched with the renderer updating channel of the first rendering multi-dimensional vector, wherein the renderer updating channel of the second rendering multi-dimensional vector after optimization and the renderer updating channel of the second rendering multi-dimensional vector after reverse optimization are the same as the renderer updating channel of the first rendering multi-dimensional vector;

performing rendering multi-dimensional vector generation on the second rendering multi-dimensional vector after optimized setting, the second rendering multi-dimensional vector after reverse optimized setting and content matching information of the first rendering multi-dimensional vector through the target rendering scene kernel function;

wherein the method further comprises:

when the renderer updating channel of the second rendering multi-dimensional vector is matched with the renderer updating channel of the first rendering multi-dimensional vector, performing reverse optimization setting on the second rendering multi-dimensional vector, wherein the renderer updating channel of the second rendering multi-dimensional vector after the reverse optimization setting is the same as the renderer updating channel of the first rendering multi-dimensional vector;

and performing rendering multi-dimensional vector generation on the content matching information of the second rendering multi-dimensional vector and the first rendering multi-dimensional vector after reverse optimization setting through the target rendering scene kernel function.

In a possible implementation manner of the first aspect, the rendering resource distribution information at least includes first rendering resource distribution information, second rendering resource distribution information, and third rendering resource distribution information, and the rendering scene calculation model includes a first rendering scene kernel function, a second rendering scene kernel function, and a third rendering scene kernel function, where the first rendering resource distribution information, the second rendering resource distribution information, and the third rendering resource distribution information respectively correspond to rendering resource distribution information of a material resource, a map resource, and an animation resource, and the first rendering scene kernel function, the second rendering scene kernel function, and the third rendering scene kernel function respectively correspond to rendering scene kernel functions of a material resource, a map resource, and an animation resource;

the step of inputting the rendering resource distribution information into a plurality of rendering scene kernel functions in a rendering scene calculation model respectively, and generating at least one rendering multi-dimensional vector by performing at least one rendering multi-dimensional vector generation through the rendering scene kernel functions includes:

inputting the first rendering resource distribution information into a first rendering scene kernel function to generate a rendering multi-dimensional vector of a first rendering scene calculation layer, and obtaining the rendering multi-dimensional vector extracted by the first rendering scene kernel function in the first rendering scene calculation layer;

rendering scene calculation is carried out on the rendering multi-dimensional vector extracted by the first rendering scene kernel function on the first rendering scene calculation layer and the second rendering resource distribution information, and a corresponding rendering scene calculation element of the second rendering scene kernel function on the second rendering scene calculation layer is obtained;

acquiring a rendering multi-dimensional vector extracted by the first rendering scene kernel function on a first rendering scene calculation layer, and using the rendering multi-dimensional vector as a corresponding rendering scene calculation element of the first rendering scene kernel function on a second rendering scene calculation layer;

performing first rendering multi-dimensional vector generation of a second rendering scene calculation layer on rendering scene calculation elements corresponding to the first rendering scene kernel in the second rendering scene calculation layer through the first rendering scene kernel to obtain a rendering multi-dimensional vector extracted by the first rendering scene kernel at the second rendering scene calculation layer for the first time;

performing first rendering multi-dimensional vector generation of a second rendering scene calculation layer on rendering scene calculation elements corresponding to the second rendering scene kernel in the second rendering scene calculation layer through the second rendering scene kernel to obtain a rendering multi-dimensional vector extracted by the second rendering scene kernel for the first time in the second rendering scene calculation layer;

transmitting the rendering multi-dimensional vector firstly extracted by the first rendering scene kernel function at a second rendering scene calculation layer to the second rendering scene kernel function, and transmitting the rendering multi-dimensional vector firstly extracted by the second rendering scene kernel function at the second rendering scene calculation layer to the first rendering scene kernel function;

rendering scene calculation is carried out on the rendering multi-dimensional vector which is firstly extracted by the first rendering scene kernel function at a second rendering scene calculation layer and the rendering multi-dimensional vector transmitted by the second rendering scene kernel function through the first rendering scene kernel function, and rendering multi-dimensional vector generation is carried out on content matching information;

rendering scene calculation is carried out on the rendering multi-dimensional vector which is firstly extracted by the second rendering scene kernel function at a second rendering scene calculation layer and the rendering multi-dimensional vector transmitted by the first rendering scene kernel function through the second rendering scene kernel function, and rendering multi-dimensional vector generation is carried out on content matching information;

rendering scene calculation is carried out on the rendering multi-dimensional vector extracted by the first rendering scene kernel function on the second rendering scene calculation layer, the rendering multi-dimensional vector extracted by the second rendering scene kernel function on the second rendering scene calculation layer and the third rendering resource distribution information, and a rendering scene calculation element corresponding to the third rendering scene kernel function on the third rendering scene calculation layer is obtained;

and generating a rendering multi-dimensional vector of a third rendering scene calculation layer by the first rendering scene kernel function based on the rendering multi-dimensional vector extracted by the first rendering scene kernel function at the second rendering scene calculation layer, generating a rendering multi-dimensional vector of the third rendering scene calculation layer by the second rendering scene kernel function based on the rendering multi-dimensional vector extracted by the second rendering scene kernel function at the second rendering scene calculation layer, and generating a multi-dimensional vector of the third rendering scene calculation layer by the third rendering scene kernel function based on corresponding rendering scene calculation elements of the third rendering scene kernel function at the third rendering scene calculation layer.

In a second aspect, an embodiment of the present application further provides a cloud computing-based software development application update processing apparatus, which is applied to a software development platform, where the software development platform is in communication connection with a plurality of distributed software development terminals, and the apparatus includes:

the first obtaining module is used for determining each target cloud computing rendering application program in the target rendering optimization program group, obtaining rendering base portrait data corresponding to configuration optimization big data information of each target cloud computing rendering application program in the target rendering optimization program group, and performing rendering updating element positioning based on the rendering base portrait data;

a second obtaining module, configured to obtain rendering update element information corresponding to each rendering update element in preset statistics of rendering update elements, where the rendering update element information includes an associated rendering class object, a rendering class object category, and rendering preference adjustment information, where the associated rendering class object is used to render the rendering class object covered by the update element, the rendering class object category is used to indicate a rendering multidimensional vector of the rendering update element, and the rendering preference adjustment information is used to indicate a weight distribution adjustment condition of a rendering control node in the rendering update element;

the first generation module is used for generating application rendering optimization information corresponding to each rendering updating element according to the rendering updating element information corresponding to each rendering updating element, wherein the application rendering optimization information is used for performing application rendering mapping switching processing or application rendering mapping enhancement processing on a current application rendering container, and the application rendering optimization information and the rendering updating elements have a one-to-one correspondence relationship;

and a second generation module, configured to process, by using the application rendering optimization information corresponding to each rendering update element, a current application rendering container corresponding to each rendering update element to obtain a target application rendering container corresponding to each rendering update element, and generate an optimized cloud computing rendering application program for performing application rendering through a rendering chartlet model according to the target application rendering container corresponding to each rendering update element, where the application rendering optimization information, the current application rendering container, and the target application rendering container have a one-to-one correspondence relationship.

In a third aspect, an embodiment of the present application further provides a cloud computing-based software development application update processing system, where the cloud computing-based software development application update processing system includes a software development platform and a plurality of distributed software development terminals communicatively connected to the software development platform;

the software development platform is used for:

In a fourth aspect, an embodiment of the present application further provides a software development platform, where the software development platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one distributed software development terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the cloud computing-based software development application update processing method in the first aspect or any one of the possible implementation manners in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer is caused to execute the cloud computing-based software development application update processing method in the first aspect or any one of the possible implementation manners of the first aspect.

Based on any one of the above aspects, the application rendering optimization information corresponding to each rendering update element in the preset statistics rendering update elements is generated by obtaining the rendering update element information corresponding to each rendering update element, then the current application rendering container corresponding to each rendering update element is processed by using the application rendering optimization information corresponding to each rendering update element, so as to obtain the target application rendering container corresponding to each rendering update element, and finally the optimized cloud computing rendering application program for application rendering through the rendering map model is generated according to the target application rendering container corresponding to each rendering update element. By adopting the mode, the application rendering containers from different dimensions are processed in parallel based on the plurality of rendering updating elements, and then under the scene of the multi-application rendering containers, application rendering mapping strengthening or application rendering mapping optimization can be carried out on the application rendering containers in different dimensions through application rendering optimization information, so that the application rendering containers can be optimized in real time, the updating matching degree in the updating process of the application rendering containers is improved, and the subsequent application rendering effect is favorably improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is an application scenario diagram of a cloud computing-based software development application update processing system according to an embodiment of the present application;

fig. 2 is a schematic flowchart of a cloud computing-based software development application update processing method according to an embodiment of the present application;

fig. 3 is a functional module schematic diagram of a cloud computing-based software development application update processing apparatus according to an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a software development platform for implementing the cloud computing-based software development application update processing method according to the embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

Fig. 1 is an interaction diagram of a cloud computing-based software development application update processing system 10 according to an embodiment of the present application. The cloud computing-based software development application update processing system 10 may include a software development platform 100 and a distributed software development terminal 200 communicatively connected to the software development platform 100. The cloud computing-based software development application update processing system 10 shown in fig. 1 is merely one possible example, and in other possible embodiments, the cloud computing-based software development application update processing system 10 may also include only some of the components shown in fig. 1 or may also include other components.

Based on the inventive concept of the technical solution provided by the present application, the software development platform 100 provided by the present application can be applied to scenes such as smart medical care, smart city management, smart industrial internet, general service monitoring management, and the like, in which a big data technology or a cloud computing technology can be applied, and for example, the software development platform can also be applied to scenes such as but not limited to new energy automobile system management, smart cloud office, cloud platform data processing, cloud game data processing, cloud live broadcast processing, cloud automobile management platform, block chain financial data service platform, and the like, but is not limited thereto.

In this embodiment, the software development platform 100 and the distributed software development terminal 200 in the cloud computing-based software development application update processing system 10 may cooperatively execute the cloud computing-based software development application update processing method described in the following method embodiment, and the detailed description of the following method embodiment may be referred to for the execution step portions of the specific software development platform 100 and the distributed software development terminal 200.

In order to solve the technical problem in the foregoing background art, fig. 2 is a schematic flowchart of a cloud computing-based software development application update processing method provided in an embodiment of the present application, and the cloud computing-based software development application update processing method provided in this embodiment may be executed by the software development platform 100 shown in fig. 1, and the cloud computing-based software development application update processing method is described in detail below.

Step S110, determining each target cloud computing rendering application program in the target rendering optimization program group, acquiring rendering basic portrait data corresponding to configuration optimization big data information of each target cloud computing rendering application program in the target rendering optimization program group, and performing rendering updating element positioning based on the rendering basic portrait data.

Step S120, obtaining rendering updating element information corresponding to each rendering updating element in the preset statistics rendering updating elements.

In this embodiment, the rendering update element information may specifically include an associated rendering class object, a rendering class object category, and rendering preference adjustment information. For example, the associated rendering class object may be used to render a rendering class object covered by the update element (e.g., a rendering class object for rendering any one element in a map in a certain game scene, a rendering class object for enabling an animation in a certain page loading process, etc.), the rendering class object is used to indicate a rendering multidimensional vector for rendering the update element, the rendering multidimensional vector may refer to vector information of a rendering data stream corresponding to the rendering class object in the rendering process, such as vector information in two dimensions (X, Y direction), or may refer to vector information in three dimensions (X, Y, Z direction), the rendering preference adjustment information is used to indicate a weight distribution adjustment condition of a rendering control node within the rendering update element, such as a weight distribution adjustment of a rendering control node in a certain page loading process, or a weight distribution adjustment of a rendering control node of any one element in a map in a certain game scene . The rendering control node may be a control node used for controlling a rendering object and a rendering parameter (such as a rendering action, a rendering speed, a rendering range, and the like) in a rendering process, and may be implemented by a common animation control function, which is not described in detail herein.

Step S130, generating application rendering optimization information corresponding to each rendering update element according to the rendering update element information corresponding to each rendering update element.

In this embodiment, the application rendering optimization information is used to perform application rendering map switching processing or application rendering map enhancement processing on the current application rendering container, and the application rendering optimization information and the rendering update elements have a one-to-one correspondence relationship. The application rendering map switching processing on the current application rendering container may mean that the current application rendering container has application rendering maps which need to be adaptively optimized, so as to be adapted to rendering update element information corresponding to the current rendering update element. The application rendering map enhancement processing on the current application rendering container may mean that the application rendering map of the current application rendering container needs adaptive enhancement, and the calling strength of the application rendering map may be enhanced.

And step S140, processing the current application rendering container corresponding to each rendering updating element by adopting the application rendering optimization information corresponding to each rendering updating element to obtain a target application rendering container corresponding to each rendering updating element, and generating an optimized cloud computing rendering application program for performing application rendering through the rendering chartlet model according to the target application rendering container corresponding to each rendering updating element.

The application rendering optimization information, the current application rendering container and the target application rendering container have a one-to-one correspondence relationship. Therefore, the experience of subsequent cloud computing rendering can be improved by generating the final optimized cloud computing rendering application program.

Based on the above steps, the embodiment can generate application rendering optimization information corresponding to each rendering update element according to the rendering update element information corresponding to each rendering update element, and then process the current application rendering container corresponding to each rendering update element to obtain the target application rendering container corresponding to each rendering update element, thereby generating an optimized cloud computing rendering application program for performing application rendering through the rendering map model. By adopting the mode, the application rendering containers from different dimensions are processed in parallel based on the plurality of rendering updating elements, and then under the scene of the multi-application rendering containers, application rendering mapping strengthening or application rendering mapping optimization can be carried out on the application rendering containers in different dimensions through application rendering optimization information, so that the application rendering containers can be optimized in real time, and the subsequent application rendering effect can be improved.

In a possible implementation manner, for step S120, in the process of obtaining rendering update element information corresponding to each rendering update element in the preset statistics of rendering update elements, the following exemplary sub-steps may be implemented, and are described in detail below.

And a substep S121, detecting each rendering updating element in the preset statistics rendering updating elements to obtain a rendering detection result corresponding to each rendering updating element.

And a substep S122, determining rendering preference adjustment information corresponding to each rendering updating element according to the rendering detection result corresponding to each rendering updating element.

And a substep S123 of determining sampling pixel error information corresponding to each rendering update element according to the rendering detection result corresponding to each rendering update element.

For example, for any one of the preset statistics rendering update elements, if the rendering detection result indicates that rendering node information with a sampling pixel error exists in the rendering update element, the information of the sampling pixel error process of the rendering node information in the rendering update element is determined as the sampling pixel error information corresponding to each rendering update element.

And a substep S124, obtaining the associated rendering class object corresponding to each rendering update element and the rendering class object class corresponding to each rendering update element.

And a substep S125, generating rendering update element information corresponding to each rendering update element according to the rendering preference adjustment information corresponding to each rendering update element, the sampling pixel error information corresponding to each rendering update element, the associated rendering class object corresponding to each rendering update element, and the rendering class object category corresponding to each rendering update element.

Further, on this basis, as a possible implementation manner, in the process of generating the application rendering optimization information corresponding to each rendering update element according to the rendering update element information corresponding to each rendering update element, regarding step S130, the following exemplary sub-steps may be implemented, and are described in detail as follows.

And a substep S131 of obtaining scene hierarchy information corresponding to the rendering class object type from the application rendering lens updating information corresponding to the preset associated rendering class object.

And a substep S132 of extracting the characteristics of the scene layering information according to the rendering preference adjustment information to obtain rendering optimization scenario information in which the scene layering information is respectively matched with the rendering preference adjustment information.

And a substep S133 of determining global application rendering optimization information based on the rendering optimization scenario information of the scene layering information.

And a substep S134, determining scene layering error region information in the scene layering information according to the sampling pixel error information, and determining rendering optimization scenario information corresponding to the scene layering error region information.

And a substep S135, fusing the rendering optimization information of the global application and the rendering optimization scenario information corresponding to the scene layering error region information to obtain the application rendering optimization information corresponding to each rendering updating element.

In this embodiment, the application rendering optimization information includes an application rendering map that needs to perform application rendering map switching processing or application rendering map enhancement processing on the current application rendering container.

The rendering preference adjustment information comprises rendering control node distribution, the rendering control node distribution comprises a plurality of rendering control nodes and rendering switching eigenvectors connected between the two rendering control nodes, the rendering switching eigenvectors comprise bounding box switching information and map index information of the rendering switching eigenvectors, and the rendering control nodes comprise scene layered switching control points and switching objects.

On this basis, for the sub-step S132, it can be realized by the following exemplary embodiments.

(1) And determining scene layering switching control points corresponding to the scene layering information in the rendering control node distribution.

(2) And determining calling map index parameters and optimizing map index parameters of scene layered switching control points in a plurality of rendering control nodes distributed in the rendering control nodes according to the bounding box switching information.

For example, a scene hierarchy switching control point may refer to a temporal switching control point or a spatial switching control point in a switching control process of different map hierarchies in a rendered scene.

(3) And calculating a first rendering optimization scene generated by the calling map index parameter on the scene layering information according to the map index information of the rendering switching feature vector connecting the scene layering switching control point and the calling map index parameter.

(4) And calculating a second rendering optimization scene generated by the calling map index parameter on the scene layering information according to the map index information of the rendering switching feature vector connecting the scene layering switching control point and the calling map index parameter.

(5) And determining rendering optimization scenario information of the scene layering information according to the first rendering optimization scenario and the second rendering optimization scenario.

Thus, for step S140, in the process of obtaining the target application rendering container corresponding to each rendering update element by processing the current application rendering container corresponding to each rendering update element by using the application rendering optimization information corresponding to each rendering update element, the following exemplary sub-steps may be implemented, and are described in detail as follows.

And a substep S141, obtaining a first application rendering map which needs to perform application rendering map switching processing on the current application rendering container and a second application rendering map which needs to perform application rendering map strengthening processing on the current application rendering container according to the application rendering optimization information including the application rendering map which needs to perform application rendering map switching processing or application rendering map strengthening processing on the current application rendering container.

And a substep S142, switching the first application rendering map according to the corresponding optimization strategy information in the application rendering optimization information, and performing reinforcement processing on the second application rendering map according to the corresponding reinforcement strategy information in the application rendering optimization information.

In a possible implementation manner, still referring to step S140, in the process of generating an optimized cloud computing rendering application program for application rendering through the rendering map model according to the target application rendering container corresponding to each rendering update element, the process may be implemented through the following exemplary sub-steps, which are described in detail below.

And a substep S143, determining the application rendering filter parameters corresponding to each rendering updating element according to the target application rendering container corresponding to each rendering updating element.

It is worth to be noted that the application rendering filter parameter is an application rendering filter parameter of the target application rendering container on each application rendering texture.

And a substep S144, determining rendering control information of the rendering logic device corresponding to each rendering updating element according to the application rendering drawing filter parameter corresponding to each rendering updating element.

And in the substep S145, updating and configuring the current target cloud computing rendering application program based on the rendering control information of the rendering logic device corresponding to each rendering updating element to obtain the optimized cloud computing rendering application program corresponding to each rendering updating element.

For example, the rendering control associated information of the rendering logic device corresponding to each rendering update element for each link jump information in the current target cloud computing rendering application program may be obtained, and the current target cloud computing rendering application program is updated and configured based on the rendering control associated information of each link jump information in the current target cloud computing rendering application program, so as to obtain the optimized cloud computing rendering application program corresponding to each rendering update element.

In a possible implementation manner, based on the above description, for step S110, in a process of determining each target cloud computing rendering application in the target rendering optimization program group, obtaining rendering base portrait data corresponding to configuration optimization big data information of each target cloud computing rendering application in the target rendering optimization program group, and performing rendering update element positioning based on the rendering base portrait data, the following exemplary sub-steps may be implemented, and are described in detail as follows.

And a substep S111, acquiring loadable renderer information corresponding to each application rendering node of the distributed software development terminal, and performing renderer window update classification on configuration optimization big data information of the target cloud computing rendering application program based on the loadable renderer information to acquire a corresponding renderer window update classification set.

In this embodiment, the loadable renderer information may be used to represent a renderer activated in a rendering process, and the configuration optimization big data information may refer to a data collection configuration parameter related to a data collection request initiated by a developer based on the aforementioned invocation evaluation parameter of the target cloud computing rendering application. The renderer window update classification set may refer to a renderer window which is obtained from a pre-configured index database according to configuration optimization big data information of the target cloud computing rendering application and is matched with each loadable renderer, and specifically may include a time window, a space window and the like in a rendering process.

And a substep S112, acquiring a corresponding renderer updating window based on the renderer window updating classification set, and determining rendering resource distribution information of a plurality of renderer updating channels based on the renderer updating window.

In this embodiment, the renderer update window may include an update window condition of each loadable renderer on each rendering test, the renderer update channel may refer to a renderer update channel node set formed by each rendering test, and the rendering resource distribution information may refer to a feature extraction condition of rendering resources of each rendering test in scheduling the renderer update process.

And the substep S113 is to input the rendering resource distribution information into a plurality of rendering scene kernel functions in the rendering scene calculation model respectively, and perform rendering multi-dimensional vector generation at least once through the rendering scene kernel functions to obtain at least one rendering multi-dimensional vector.

In this embodiment, at least one rendering multidimensional vector generation by the rendering scene kernel is performed based on the associated hierarchical block three-dimensional model, and the associated hierarchical block three-dimensional model is associated with rendering multidimensional vectors extracted by other rendering scene kernels of the rendering scene kernels.

And a substep S114, performing rendering scene calculation on a plurality of rendering multidimensional vectors output by the rendering scene kernel functions to obtain rendering scene calculation contents, obtaining rendering basic portrait data of the renderer updating window under the configuration optimization big data information based on the rendering scene calculation contents, and performing rendering updating element positioning based on the rendering basic portrait data of the renderer updating window under the configuration optimization big data information.

In this embodiment, the rendering base portrait data of the renderer update window under the configuration optimization big data information may be used to represent a rendering portrait mapping set of the renderer update window in a subsequent rendering update element positioning process, that is, a rendering portrait set of the mapping positioning data node in a rendering update element positioning process, so as to search for rendering update elements according to matching logic of the rendering portrait sets, thereby performing rendering update element positioning.

Based on the above steps, in this embodiment, rendering resource distribution information of a plurality of renderer update channels is determined based on a renderer update window, the rendering resource distribution information is respectively input to a plurality of rendering scene kernel functions in a rendering scene calculation model, each rendering scene kernel function performs at least one rendering multi-dimensional vector generation to obtain at least one rendering multi-dimensional vector, and the at least one rendering multi-dimensional vector generation is performed based on an associated hierarchical block three-dimensional model, the associated hierarchical block three-dimensional model is associated with rendering multi-dimensional vectors extracted from other rendering scene kernel functions of the plurality of rendering scene kernel functions, so that at least one exchange and fusion can be performed between rendering multi-dimensional vectors extracted from different rendering scene kernel functions, rendering multi-dimensional vectors at different levels can be further performed with rendering scene calculation, and the representation capability of positioning rendering update elements can be improved by enriching the levels of the rendering multi-dimensional vectors, thus the positioning pertinence is better.

In a possible implementation manner, on the basis of the above scheme, in this embodiment, one of the rendering scene kernels of the multiple rendering scene kernels may be further used as a target rendering scene kernel, and then a first rendering multidimensional vector extracted by the target rendering scene kernel is obtained, and second rendering multidimensional vectors extracted by other rendering scene kernels except the target rendering scene kernel in the multiple rendering scene kernels are obtained.

In this way, when the renderer update channel of the second rendering multi-dimensional vector does not match the renderer update channel of the first rendering multi-dimensional vector, the second rendering multi-dimensional vector is optimally set, and the renderer update channel of the second rendering multi-dimensional vector after the optimal setting is the same as the renderer update channel of the first rendering multi-dimensional vector. Therefore, rendering multi-dimensional vector generation can be performed on the optimally set content matching information of the second rendering multi-dimensional vector and the first rendering multi-dimensional vector through the target rendering scene kernel function.

Wherein the second rendering multi-dimensional vector has at least two statistics.

On the basis, when a second rendering multi-dimensional vector of the renderer updating channel, of which the renderer updating channel is not matched with the first rendering multi-dimensional vector, and a second rendering multi-dimensional vector of the renderer updating channel, of which the renderer updating channel is matched with the first rendering multi-dimensional vector exist at the same time, the second rendering multi-dimensional vector of the renderer updating channel, of which the renderer updating channel is not matched with the first rendering multi-dimensional vector, is optimally set, the second rendering multi-dimensional vector of which the renderer updating channel is matched with the first rendering multi-dimensional vector is inversely optimally set, and the renderer updating channel of the second rendering multi-dimensional vector after optimal setting and the renderer updating channel of the second rendering multi-dimensional vector after inverse optimal setting are all the same as the renderer updating channel of the first rendering multi-dimensional vector.

In this way, the rendering multi-dimensional vector generation can be performed on the second rendering multi-dimensional vector after the optimization setting, the second rendering multi-dimensional vector after the reverse optimization setting and the content matching information of the first rendering multi-dimensional vector through the target rendering scene kernel function.

For another example, on the basis, when the renderer update channel of the second rendering multi-dimensional vector matches the renderer update channel of the first rendering multi-dimensional vector, the second rendering multi-dimensional vector is subjected to reverse optimization setting, and the renderer update channel of the second rendering multi-dimensional vector after the reverse optimization setting is the same as the renderer update channel of the first rendering multi-dimensional vector. Therefore, rendering multi-dimensional vector generation can be performed on the second rendering multi-dimensional vector and the content matching information of the first rendering multi-dimensional vector after reverse optimization setting through the target rendering scene kernel function.

For example, in a possible implementation manner, regarding step S113, in the process of inputting the rendering resource distribution information into a plurality of rendering scene kernels in the rendering scene calculation model respectively, and performing at least one rendering multidimensional vector generation through the rendering scene kernels to obtain at least one rendering multidimensional vector, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1131, respectively inputting the rendering resource distribution information into a plurality of rendering scene kernel functions in the rendering scene calculation model.

And a substep S1132, for one rendering scene kernel function, performing rendering multidimensional vector generation on corresponding rendering resource distribution information through the rendering scene kernel function, acquiring a second rendering multidimensional vector extracted through other rendering scene kernels of the rendering scene kernels and performing partition service matching on the first rendering multidimensional vector after the first rendering multidimensional vector is generated through the rendering multidimensional vector, and then continuing to perform rendering multidimensional vector generation based on a partition service matching result so as to alternately perform rendering multidimensional vector generation and partition service matching.

Still further, in step S112, in the process of determining rendering resource distribution information of multiple renderer update channels based on the renderer update window, index matching of the renderer update channels may be performed on the renderer update window to obtain rendering resource distribution information of multiple different renderer update channels.

For example, the positioning information nodes of the renderer update channels in the index matching renderer update window may be aligned to each renderer update channel, and all the positioning information nodes may be spliced according to the association relationship between the positioning information nodes, so as to obtain rendering resource distribution information of a plurality of different renderer update channels.

Thus, in another parallel possible implementation manner, for step S113, in the process of respectively inputting rendering resource distribution information into a plurality of rendering scene kernel functions in the rendering scene calculation model and performing at least one rendering multidimensional vector generation through the rendering scene kernel functions to obtain at least one rendering multidimensional vector, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1133, respectively inputting the rendering resource distribution information into a plurality of rendering scene kernel functions in the rendering scene calculation model.

The rendering resource distribution information is respectively in one-to-one correspondence with one of the rendering scene kernel functions.

And a substep S1134 of performing at least one rendering multidimensional vector generation on the corresponding rendering resource distribution information through the rendering scene kernel function. And the renderer updating channel for rendering the multi-dimensional vector is consistent with the renderer updating channel for rendering the rendering resource distribution information corresponding to the scene kernel function.

In a parallel possible implementation manner, the rendering resource distribution information at least includes first rendering resource distribution information, second rendering resource distribution information, and third rendering resource distribution information, and the rendering scene calculation model includes a first rendering scene kernel function, a second rendering scene kernel function, and a third rendering scene kernel function, where the first rendering resource distribution information, the second rendering resource distribution information, and the third rendering resource distribution information respectively correspond to rendering resource distribution information of a material resource, a map resource, and an animation resource, and the first rendering scene kernel function, the second rendering scene kernel function, and the third rendering scene kernel function respectively correspond to rendering scene kernel functions of a material resource, a map resource, and an animation resource.

On this basis, still referring to step S113, in the process of respectively inputting rendering resource distribution information into a plurality of rendering scene kernel functions in the rendering scene calculation model and performing at least one rendering multidimensional vector generation through the rendering scene kernel functions to obtain at least one rendering multidimensional vector, the following exemplary sub-steps may be implemented, which are described in detail as follows.

And in the substep S1135, inputting the first rendering resource distribution information into the first rendering scene kernel function to perform rendering multi-dimensional vector generation of the first rendering scene calculation layer, so as to obtain a rendering multi-dimensional vector extracted by the first rendering scene kernel function in the first rendering scene calculation layer.

And in the substep S1136, performing rendering scene calculation on the rendering multidimensional vector and the second rendering resource distribution information extracted by the first rendering scene kernel function in the first rendering scene calculation layer to obtain a corresponding rendering scene calculation element of the second rendering scene kernel function in the second rendering scene calculation layer.

In the sub-step S1137, a rendering multidimensional vector extracted by the first rendering scene kernel at the first rendering scene calculation layer is obtained and used as a rendering scene calculation element corresponding to the first rendering scene kernel at the second rendering scene calculation layer.

And in the substep S1138, performing first rendering multidimensional vector generation on the rendering scene calculation element corresponding to the first rendering scene kernel at the second rendering scene calculation layer by using the first rendering scene kernel, so as to obtain a rendering multidimensional vector first extracted by the first rendering scene kernel at the second rendering scene calculation layer.

In the sub-step S11391, through the second rendered scene kernel function, the rendering scene calculation element corresponding to the second rendered scene kernel function in the second rendered scene calculation layer is subjected to the first rendering multidimensional vector generation of the second rendered scene calculation layer, so as to obtain the rendering multidimensional vector first extracted by the second rendered scene kernel function in the second rendered scene calculation layer.

In the sub-step S11392, the rendering multi-dimensional vector first extracted by the first rendering scene kernel at the second rendering scene calculation layer is transferred to the second rendering scene kernel, and the rendering multi-dimensional vector first extracted by the second rendering scene kernel at the second rendering scene calculation layer is transferred to the first rendering scene kernel.

And a substep S11393, performing rendering scene calculation on the rendering multidimensional vector first extracted by the first rendering scene kernel at the second rendering scene calculation layer and the rendering multidimensional vector transferred by the second rendering scene kernel through the first rendering scene kernel, and performing rendering multidimensional vector generation on the content matching information.

And a substep S11394, performing rendering scene calculation on the rendering multidimensional vector first extracted by the second rendering scene kernel at the second rendering scene calculation layer and the rendering multidimensional vector transferred by the first rendering scene kernel through the second rendering scene kernel, and performing rendering multidimensional vector generation on the content matching information.

And in the substep S11395, performing rendering scene calculation on the rendering multidimensional vector extracted by the first rendering scene kernel function in the second rendering scene calculation layer, the rendering multidimensional vector extracted by the second rendering scene kernel function in the second rendering scene calculation layer, and the third rendering resource distribution information to obtain a rendering scene calculation element corresponding to the third rendering scene kernel function in the third rendering scene calculation layer.

And a substep S11396, performing rendering multidimensional vector generation of the third rendering scene calculation layer on the rendering multidimensional vector extracted by the first rendering scene kernel on the second rendering scene calculation layer based on the first rendering scene kernel, performing rendering multidimensional vector generation of the third rendering scene calculation layer on the rendering multidimensional vector extracted by the second rendering scene kernel on the second rendering scene calculation layer based on the second rendering scene kernel, and performing rendering multidimensional vector generation of the third rendering scene calculation layer on a corresponding rendering scene calculation element of the third rendering scene kernel on the third rendering scene calculation layer through the third rendering scene kernel.

Further, in a possible implementation manner, for step S114, in the process of performing rendering scene calculation on a plurality of rendering multidimensional vectors output by a plurality of rendering scene kernels to obtain rendering scene calculation content, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S1143 of obtaining a plurality of rendering multidimensional vectors output by the rendering scene kernel functions and determining a target rendering multidimensional vector of a global renderer updating channel in the plurality of rendering multidimensional vectors.

And a substep S1144 of performing rendering scene calculation on other rendering multidimensional vectors except the target rendering multidimensional vector in the plurality of rendering multidimensional vectors, wherein the renderer updating channels of the other rendering multidimensional vectors after the rendering scene calculation are the same as the renderer updating channel of the target rendering multidimensional vector.

And a substep S1145 of listing other rendering multidimensional vectors and target rendering multidimensional vectors after the rendering scene calculation to obtain the rendering scene calculation content.

For example, in a further possible implementation, still with respect to step S110, in determining each target cloud computing rendering application in the target rendering optimizer group, the following exemplary sub-steps may be implemented, which are described in detail below.

Step S101, a target evaluation rendering object and a target comparison rendering object of the target cloud computing rendering application program are obtained.

In this embodiment, the target evaluation rendering class object may include a test rendering class object in a test rendering class object set of the target cloud computing rendering application program. The target comparison rendering class object may include a comparison rendering class object that the target cloud computing rendering application calls from the comparison rendering class object set according to the target evaluation rendering class object. The target cloud computing rendering application may refer to an application for executing a rendering service of the virtual desktop service in the cloud, and may perform resource configuration based on the actual rendering service, such as mapping resources, texture resources, and the like, but is not limited thereto. In some possible embodiments, the process of the comparison rendering class object, which is called by the target cloud computing rendering application from the comparison rendering class object set according to the target evaluation rendering class object, may refer to a process of the target cloud computing rendering application matching the comparison rendering class object according to the rendering requirement or rendering condition of the target evaluation rendering class object.

In this embodiment, a rendering related parameter is pre-calibrated between any one of the test rendering class objects in the test rendering class object set and any one of the comparison rendering class objects in the comparison rendering class object set, and the rendering related parameter may be used to identify a degree of correlation between the test rendering class object and the comparison rendering class object. For example, the degree of correlation may refer to the degree of similarity of rendering logic under the rendering service between the test rendering class object and the comparison rendering class object, such as the degree of similarity of rendering speed, rendering resources, rendering time, and the like. For example, the degree of correlation may refer to a degree of similarity of rendering logic under the rendering service between the test rendering class object and the comparison rendering class object, such as a degree of similarity of rendering speed, rendering resources, rendering time, and the like, and the definition of the specific degree of similarity may determine a coincidence rate of some parameters (e.g., a coincidence rate of rendering resources), or a proximity degree (e.g., a difference of rendering speed, a difference of rendering time, and the like), which is not limited in detail herein.

Step S102, determining calling test parameters of the target cloud computing rendering application program based on the calling rendering related parameters between the target comparison rendering object and the target evaluation rendering object, wherein the calling test parameters represent the matching degree of the calling first similarity comparison rendering object.

In this embodiment, the first similarity comparison rendering class object refers to a comparison rendering class object whose degree of correlation with the target evaluation rendering class object satisfies the condition of degree of correlation. The correlation degree condition may refer to whether the correlation degree reaches a correlation threshold value.

Step S103, determining a rendering optimization program group to which the target cloud computing rendering application program belongs based on the calling test parameters of the target cloud computing rendering application program.

In this embodiment, by determining the rendering optimization program group to which the target cloud computing rendering application program belongs, it is possible to facilitate subsequent testers to perform targeted data collection based on the rendering optimization program groups in different evaluation test intervals, for example, to collect a large amount of configuration optimization big data information, so as to facilitate subsequent software development optimization on the target cloud computing rendering application program.

And step S104, carrying out software development optimization on the target cloud computing rendering application programs grouped by each rendering optimization program based on the collected configuration optimization big data information.

In this embodiment, the collected configuration optimization big data information may be obtained by performing data screening after the data collection template configured by the tester is used, and the specific obtaining manner may refer to related technologies, which are not the technical problems that are intended to be solved in the embodiments of the present application, and are not described herein again.

Based on the above steps, in this embodiment, on one hand, based on the target evaluation rendering object invoked according to the target comparison rendering object, an invocation test parameter of the target cloud computing rendering application program is determined, the dimension of the first similar test rendering object similar to the target comparison rendering object is invoked from the target cloud computing rendering application program to test the target cloud computing rendering application program, and under the condition that the rendering effects of the plurality of target cloud computing rendering application programs tested through the related technology are similar, the plurality of target cloud computing rendering application programs can be further compared through the invocation test parameter of the target cloud computing rendering application program, so that the precision of the rendering effect test of the target cloud computing rendering application program is improved; on the other hand, based on the preset calibrated rendering related parameters between the called target evaluation rendering object and the target comparison rendering object, the calling test parameters of the target cloud computing rendering application program are determined, the relation between each called target evaluation rendering object and each target comparison rendering object does not need to be manually marked, the time consumption of testing is reduced, and the testing efficiency of the target cloud computing rendering application program is improved; and the test result is not limited by the knowledge and experience of the tester, the quality fluctuation of the test result is reduced, and the matching degree of the test target cloud computing rendering application program is improved.

In one possible implementation manner, for step S101, the test rendering class object set and the comparison rendering class object set may be obtained by:

(1) and acquiring a preset rendering class object set for testing the target cloud computing rendering application program, and clustering the preset rendering class objects with the correlation degree reaching a target correlation degree threshold into a rendering class object list.

(2) And calibrating the rendering related parameters between every two preset rendering objects in each rendering object list as the target rendering related parameters corresponding to each rendering object list.

(3) And taking part of the preset rendering class objects in each cluster as test rendering class objects in the test rendering class object set to obtain a test rendering class object set.

(4) And taking other preset rendering objects except the test rendering object in each cluster as comparison rendering objects to obtain a comparison rendering object set.

Next, with respect to step S102, the following several alternative examples will be given for explanation.

In one possible implementation manner, the target evaluation rendering class object may include a plurality of objects for step S102, and based on this, step S102 may be implemented by the following sub-steps, which are described in detail below.

And a substep S1021, determining an Nth target comparison rendering class object called by the target cloud computing rendering application program according to each target evaluation rendering class object, wherein N is a positive integer.

In the sub-step S1022, based on the rendering correlation parameter between each target evaluation rendering class object and the corresponding retrieved nth target comparison rendering class object, a statistic of the nth target comparison rendering class object, of the nth target comparison rendering class objects retrieved according to each target evaluation rendering class object, whose correlation degree with the corresponding target evaluation rendering class object reaches the first correlation degree threshold is determined.

And a substep S1023 of comparing the statistic of the determined Nth target comparison rendering class object with the proportion weight of the total statistic of the target evaluation rendering class object, and determining the calling test parameter of the target cloud computing rendering application program.

In another possible implementation manner, for step S102, when one target evaluation rendering class object is included, the first calling matching degree of the target evaluation rendering class object may be determined as a calling test parameter of the target cloud computing rendering application.

For another example, when the target evaluation rendering class object includes a plurality of target evaluation rendering class objects, the invocation test parameter of the target cloud computing rendering application program may be determined based on the first invocation matching degree of the plurality of target evaluation rendering class objects.

The first calling matching degree of a target evaluation rendering class object can be obtained in the following way:

(1) and determining a first statistic of the target comparison rendering class objects, of the target comparison rendering class objects called according to one target evaluation rendering class object, of which the correlation degree with the target evaluation rendering class object reaches a second correlation degree threshold value, based on the rendering correlation parameters between each target comparison rendering class object called according to the target evaluation rendering class object and the target evaluation rendering class object.

(2) And determining the first statistic and the proportion weight of the total statistic of a target comparison rendering class object called according to the target evaluation rendering class object as the first calling matching degree of the target evaluation rendering class object.

In another possible implementation manner, for step S102, when one target evaluation rendering class object is included, the second calling matching degree of the target evaluation rendering class object may be determined as a calling test parameter of the target cloud computing rendering application.

For another example, when the target evaluation rendering class object includes a plurality of target evaluation rendering class objects, the invocation test parameter of the target cloud computing rendering application program may be determined based on the second invocation matching degree of the plurality of target evaluation rendering class objects.

Wherein, the second retrieval matching degree of the target evaluation rendering class object can be obtained by the following method:

(1) and determining second statistic of the target comparison rendering class objects, of the target comparison rendering class objects called according to one target evaluation rendering class object, of which the correlation degree with the target evaluation rendering class object reaches a third correlation degree threshold value, based on the rendering correlation parameters between each comparison rendering class object in the comparison rendering class object set and one target evaluation rendering class object.

(2) And determining a third statistic of the comparison rendering class objects, wherein the correlation degree of each comparison rendering class object in the comparison rendering class object set with one target evaluation rendering class object reaches a third correlation degree threshold value.

(3) And determining the proportion weight of the second statistic and the third statistic as a second calling matching degree of the target evaluation rendering class object.

In another possible implementation manner, for step S102, when one target evaluation rendering class object is included, the calling sequencing matching degree of the target evaluation rendering class object may be determined as a calling test parameter of the target cloud computing rendering application.

For another example, when the target evaluation rendering class object includes a plurality of target evaluation rendering class objects, the calling test parameters of the target cloud computing rendering application program may be determined based on the calling order matching degree of the plurality of target evaluation rendering class objects.

The calling, sorting and matching degree of a target evaluation rendering class object can be obtained in the following way:

(1) and determining a second similarity comparison rendering class object from the target comparison rendering class objects retrieved according to the target evaluation rendering class object based on the rendering correlation parameter between each target comparison rendering class object retrieved according to the target evaluation rendering class object and the target evaluation rendering class object, wherein the second similarity comparison rendering class object comprises the target comparison rendering class object of which the correlation degree with the target evaluation rendering class object reaches a fourth correlation degree threshold value.

(2) A first retrieval ordering of each second similarity comparison rendering class object in the second similarity comparison rendering class objects is determined. And determining a second calling sequence of each second similarity comparison rendering class object in the target comparison rendering class objects called according to one target evaluation rendering class object.

(3) And determining the sum of the first calling order and the second calling order of each second similar comparison rendering class object as the calling order matching degree of a target evaluation rendering class object.

In another possible implementation manner, for step S102, when one target evaluation rendering class object is included, the calling sequencing deviation value of the target evaluation rendering class object may be determined as a calling test parameter of the target cloud computing rendering application.

For another example, when the target evaluation rendering class object includes a plurality of target evaluation rendering class objects, the calling test parameters of the target cloud computing rendering application program may be determined based on the calling sequencing deviation values of the plurality of target evaluation rendering class objects.

The calling and sorting deviation value of one target evaluation rendering class object can be obtained in the following mode:

(1) and determining each target comparison rendering class object called according to one target evaluation rendering class object, and obtaining a third calling sequence in each called target comparison rendering class object.

(2) And determining a first calling deviation reference value based on a rendering correlation parameter between each target comparison rendering class object called according to one target evaluation rendering class object and a third calling sequence of each target comparison rendering class object called according to one target evaluation rendering class object.

For example, the target comparison rendering class objects are sorted according to rendering related parameters between the target comparison rendering class objects and the target evaluation rendering class objects, which are called by one target evaluation rendering class object, and the sorting result is compared with the third calling sorting of the target comparison rendering class objects to obtain a first calling deviation reference value.

(3) And determining a fourth calling sequence corresponding to each target comparison rendering class object called according to one target evaluation rendering class object.

And the fourth calling ordering is determined based on the size of the rendering related parameter between each target comparison rendering class object called according to one target evaluation rendering class object and one target evaluation rendering class object.

(4) And determining a second calling deviation reference value based on the rendering related parameters between each target comparison rendering class object called according to one target evaluation rendering class object and the fourth calling sequence of each target comparison rendering class object called according to one target evaluation rendering class object.

Similarly, the target comparison rendering objects may be sorted according to the rendering related parameter between each target comparison rendering object and one target evaluation rendering object, which is called by one target evaluation rendering object, and the sorting result is compared with the fourth calling sorting of each target comparison rendering object to obtain a second calling deviation reference value.

(5) And determining the proportion weight of the first calling deviation reference value and the second calling deviation reference value as the calling sequencing deviation value of a target evaluation rendering class object.

In one possible implementation manner, for step S103, the following exemplary sub-steps may be implemented in the process of determining the rendering optimization program group to which the target cloud computing rendering application belongs based on the invocation test parameter of the target cloud computing rendering application, which is described in detail below.

And step S1031, obtaining a target parameter interval corresponding to the calling test parameter of the target cloud computing rendering application program.

In this embodiment, the range of the calling test parameters corresponding to different parameter intervals may be preconfigured, so that the target parameter interval corresponding to the calling test parameter of the target cloud computing rendering application program may be obtained according to the correspondence.

And a substep S1032 of determining a rendering optimization program group pre-associated with the target parameter interval as a rendering optimization program group to which the target cloud computing rendering application program belongs.

Therefore, subsequent testers can conveniently perform targeted data collection based on the rendering optimization program groups in different evaluation test intervals, for example, a large amount of configuration optimization big data information is collected, so that software development optimization is performed on the target cloud computing rendering application program in the subsequent process.

Fig. 3 is a schematic functional module diagram of a cloud computing-based software development application update processing apparatus 300 according to an embodiment of the present disclosure, and in this embodiment, functional modules of the cloud computing-based software development application update processing apparatus 300 may be divided according to a method embodiment executed by the software development platform 100, that is, the following functional modules corresponding to the cloud computing-based software development application update processing apparatus 300 may be used to execute each method embodiment executed by the software development platform 100. The cloud computing-based software development application update processing apparatus 300 may include a first obtaining module 310, a second obtaining module 320, a first generating module 330, and a second generating module 340, where functions of the functional modules of the cloud computing-based software development application update processing apparatus 300 are described in detail below.

The first obtaining module 310 is configured to determine each target cloud computing rendering application in the target rendering optimization program group, obtain rendering base portrait data corresponding to configuration optimization big data information of each target cloud computing rendering application in the target rendering optimization program group, and perform rendering update element positioning based on the rendering base portrait data. The first obtaining module 310 may be configured to perform the step S110, and for a detailed implementation of the first obtaining module 310, reference may be made to the detailed description of the step S110.

A second obtaining module 320, configured to obtain rendering update element information corresponding to each rendering update element in a preset number of rendering update elements, where the rendering update element information includes an associated rendering class object, a rendering class object category, and rendering preference adjustment information, the associated rendering class object is used to render the rendering class object covered by the update element, the rendering class object category is used to indicate a rendering multi-dimensional vector of the rendering update element, and the rendering preference adjustment information is used to indicate a weight distribution adjustment condition of a rendering control node in the rendering update element. The second obtaining module 320 may be configured to perform the step S120, and for a detailed implementation of the second obtaining module 320, reference may be made to the detailed description of the step S120.

The first generating module 330 is configured to generate application rendering optimization information corresponding to each rendering update element according to rendering update element information corresponding to each rendering update element, where the application rendering optimization information is used to perform application rendering map switching processing or application rendering map enhancement processing on a current application rendering container, and the application rendering optimization information and the rendering update elements have a one-to-one correspondence relationship. The first generating module 330 may be configured to execute the step S130, and the detailed implementation of the first generating module 330 may refer to the detailed description of the step S130.

A second generating module 340, configured to process, by using the application rendering optimization information corresponding to each rendering update element, a current application rendering container corresponding to each rendering update element to obtain a target application rendering container corresponding to each rendering update element, and generate an optimized cloud computing rendering application program for performing application rendering through a rendering chartlet model according to the target application rendering container corresponding to each rendering update element, where the application rendering optimization information, the current application rendering container, and the target application rendering container have a one-to-one correspondence relationship. The second generating module 340 may be configured to execute the step S140, and for a detailed implementation of the second generating module 340, reference may be made to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the first obtaining module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the first obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

Fig. 4 is a schematic diagram illustrating a hardware structure of a software development platform 100 for implementing the cloud computing-based software development application update processing method, according to an embodiment of the present disclosure, and as shown in fig. 4, the software development platform 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the first obtaining module 310, the second obtaining module 320, the first generating module 330, and the second generating module 340 included in the cloud-computing-based software development application update processing apparatus 300 shown in fig. 3), so that the processor 110 may execute the cloud-computing-based software development application update processing method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiver action of the transceiver 140, so as to perform data transceiving with the distributed software development terminal 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the software development platform 100, which implement principles and technical effects similar to each other, and details of this embodiment are not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, an embodiment of the present application further provides a readable storage medium, where a computer executing instruction is stored in the readable storage medium, and when a processor executes the computer executing instruction, the method for processing the update of the software development application based on cloud computing is implemented.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which the elements and sequences are processed, the use of alphanumeric characters, or the use of other designations in this specification is not intended to limit the order of the processes and methods in this specification, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A software development application update processing method based on cloud computing is applied to a software development platform, the software development platform is in communication connection with a plurality of distributed software development terminals, and the method comprises the following steps:

acquiring rendering update element information corresponding to each rendering update element in the rendering update elements in a preset service range, wherein the rendering update element information comprises an associated rendering class object, a rendering class object category and rendering preference adjustment information, the associated rendering class object is used for rendering the rendering class object covered by the update element, the rendering class object category is used for indicating a rendering multi-dimensional vector of the rendering update element, and the rendering preference adjustment information is used for indicating a weight distribution adjustment condition of a rendering control node in the rendering update element;

2. The cloud-computing-based software development application update processing method according to claim 1, wherein the obtaining rendering update element information corresponding to each rendering update element in the rendering update elements in the preset service range includes:

detecting each rendering updating element in the rendering updating elements in the preset service range to obtain a rendering detection result corresponding to each rendering updating element;

3. The cloud-computing-based software development application update processing method according to claim 1, wherein the determining, according to the rendering detection result corresponding to each rendering update element, sampling pixel error information corresponding to each rendering update element includes:

and aiming at any one rendering update element in the rendering update elements in the preset service range, if the rendering detection result is that rendering node information with sampling pixel errors exists in the rendering update elements, determining the information of the sampling pixel error process of the rendering node information in the rendering update elements as the sampling pixel error information corresponding to each rendering update element.

4. The cloud-computing-based software development application update processing method according to claim 2, wherein the generating application rendering optimization information corresponding to each rendering update element according to the rendering update element information corresponding to each rendering update element includes:

5. The cloud computing-based software development application update processing method according to any one of claims 1 to 4, wherein the step of generating an optimized cloud computing rendering application program for application rendering through a rendering map model according to the target application rendering container corresponding to each rendering update element includes:

6. The cloud computing-based software development application update processing method according to claim 5, wherein the updating and configuring the current target cloud computing rendering application program based on the rendering control information of the rendering logic device corresponding to each rendering update element to obtain the optimized cloud computing rendering application program corresponding to each rendering update element includes:

7. The cloud computing-based software development application update processing method according to any one of claims 1 to 6, wherein the step of determining each target cloud computing rendering application in the target rendering optimization program group, obtaining rendering base portrait data corresponding to configuration optimization big data information of each target cloud computing rendering application in the target rendering optimization program group, and performing rendering update element positioning based on the rendering base portrait data includes:

acquiring loadable renderer information corresponding to each application rendering node of the target cloud computing rendering application program, and performing renderer window update classification on configuration optimization big data information of the target cloud computing rendering application program based on the loadable renderer information to obtain a corresponding renderer window update classification set;

8. The cloud computing-based software development application update processing method of claim 7, further comprising:

taking one of the rendering scene kernels as a target rendering scene kernel;

wherein the number of the second rendering multi-dimensional vectors is at least two; the method further comprises the following steps:

wherein the method further comprises:

9. The cloud-computing-based software development application update processing method according to claim 7, wherein the rendering resource distribution information at least includes first, second, and third rendering resource distribution information, and the rendering scene computation model includes first, second, and third rendering scene kernels, wherein the first, second, and third rendering resource distribution information respectively correspond to rendering resource distribution information of material resources, map resources, and animation resources, and the first, second, and third rendering scene kernels respectively correspond to rendering scene kernels of material resources, map resources, and animation resources;

10. A software development platform, characterized in that the software development platform comprises a processor, a machine-readable storage medium, and a network interface, the machine-readable storage medium, the network interface and the processor are connected through a bus system, the network interface is used for being connected with at least one distributed software development terminal in a communication manner, the machine-readable storage medium is used for storing programs, instructions or codes, and the processor is used for executing the programs, instructions or codes in the machine-readable storage medium to execute the software development application update processing method based on cloud computing according to any one of claims 1 to 9.