CN113220400A - Cloud desktop system quality control method based on software definition - Google Patents
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Abstract
The invention discloses a cloud desktop system quality control method based on software definition, which belongs to the field of cloud desktops, and is characterized in that flexible and efficient quality control is realized by constructing an extended service of a cloud desktop based on software definition in the aspect of quality control and processing quality-related data obtained by a quality-related template closely related to a subdivision service mode on the aspect of the extended service around a quality-related model, and a system quality management module is formed by respectively adding corresponding components of the quality control to an application layer, a control layer, an infrastructure layer and a data layer of a cloud desktop system infrastructure; the method specifically comprises the following steps: the quality strategy module and the quality associated data template are newly added on the application layer, the data summarizing module, the prejudgment analysis module and the quality control model training module are newly added on the control layer, the quality sensing module and the task adjusting module are newly added on the infrastructure layer, and the quality associated parameters are newly added on the data layer.
Description
Technical Field
The invention relates to the field of cloud desktop, in particular to a cloud desktop system quality control method based on software definition.
Background
The cloud desktop system is a terminal operating system based on virtualization technology, generally comprises three basic architectures of an access terminal, a cloud server and virtual machine management software, can customize a standardized system template according to an application scene, realizes the localization of the template at a terminal client through a remote desktop transmission protocol, and effectively improves the deployment, operation and maintenance efficiency of the operating system of the terminal.
The existing cloud desktop system is generally deployed in a server cluster mode, and control nodes, computing nodes, storage nodes and network nodes are distributed on different servers, so that the separated cluster architecture can effectively reduce coupling among cloud services and support incremental expansion. Typical technical architectures are: VDI (virtual desktop infrastructure), IDV (intelligent desktop virtualization), RDS (remote desktop service), etc. The VDI is composed of three major parts, namely virtual machine management software, a virtual machine desktop controller and an access terminal, and the functions of load balancing, high availability, distributed storage and the like are realized through a server-based mode; the IDV is a framework of centralized storage and distributed operation, a system image is stored at a server side, and the system can run off-line by downloading a running image desktop through a virtual machine on a local terminal; the RDS is based on a multi-user operating system, a server is configured according to the number of users, and then management software for sharing a cloud desktop is installed on the server with the operating system installed, so that the users can share the same operating system.
In a cloud desktop system in the related art, a software definition mode is adopted to decouple computing, storage, network and application software resources of a traditional terminal, a control method for virtual machine resources is reconstructed, the configuration of a virtual machine is dynamically adjusted, the resource utilization rate is improved, and high-quality on-cloud cooperative office experience with high efficiency, flexibility and low cost is provided for a designer group. As shown in fig. 1, it includes an application layer, a control layer, an infrastructure layer, and a data layer; the control method of the virtual machine is reconstructed, the full life cycle management of the virtual machine is realized, and a user-defined instruction system is provided, wherein the user-defined instruction system comprises five categories of analysis instructions, judgment instructions, scheduling instructions, execution instructions and communication instructions; according to the characteristics of software definition, the cloud desktop function and the virtual machine management process are optimized, so that the cloud desktop function and the virtual machine management process are more personalized.
However, the existing cloud desktop system architecture adopts a customized and standardized system template, one-key deployment delivery is performed, and for multiple links such as storage, network, computation, transmission and the like related to quality control of the cloud desktop system, dynamic adjustment cannot be performed in the system according to different requirements of users, so that high quality and high performance of the system are difficult to guarantee, particularly when the system is abnormal or fails, consistency and integrity of data are certainly influenced to a certain extent, and finally, efficiency and quality of cloud desktop service are greatly influenced.
Disclosure of Invention
The invention provides a cloud desktop system quality control method based on software definition, which adopts a software definition mode to perform real-time monitoring and dynamic adjustment on multiple quality control links of the whole life cycle of a cloud desktop, and provides high-efficiency and high-quality cloud desktop service for users.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: the cloud desktop system quality control method based on software definition realizes flexible and efficient quality control by constructing an extended framework of the software definition cloud desktop in the aspect of quality control and processing quality-related data obtained by a quality-related template closely related to a subdivision service mode on the extended framework around a quality-related model. The method comprises the following specific steps:
1. the cloud desktop architecture based on software definition comprises an expansion architecture, a quality management module and a custom instruction subset in the aspect of quality control. The specific contents are as follows:
the expansion architecture is that corresponding components of quality control are respectively added to an application layer, a control layer, an infrastructure layer and a data layer of an original cloud desktop system infrastructure to form a system quality management module; the quality strategy module and the quality associated data template are newly added at an application layer, the data summarizing module, the prejudgment analysis module and the quality control model training module are newly added at a control layer, the quality sensing module and the task adjusting module are newly added at an infrastructure layer, and the quality associated parameters are newly added at a data layer.
Selecting different subdivision service modes according to user requirements in different fields, and further selecting a proper quality association template, wherein the quality association template consists of multi-link quality association parameters for quality control of a cloud desktop system; through collecting the state, the availability ratio and the utilization ratio of the infrastructure, multi-link quality parameters are sensed, data aggregation and judgment analysis are carried out on the multi-link quality parameters, corresponding quality strategies are formulated, then task adjustment operation is carried out, iteration updating is carried out on the quality parameters, training and upgrading of quality control templates are achieved, and the effects of accurately controlling quality and efficiently executing the system are achieved.
The quality management module specifically comprises a front end, a middle end, a back end and a database;
the front end directly faces to the user requirements, is directly displayed to a user interface through cloud terminal connection, and provides various software and services of the required segmentation applications for the user. The method mainly comprises segmentation application, a cloud terminal and a user interface, wherein the segmentation application is various differentiated applications and services required by segmentation users (such as users in the fields of movie animation, architectural design, industrial design and the like).
The middle end is used for summarizing and managing data, a quality control-oriented deep learning platform is constructed, and a user-defined instruction subset is adopted to judge, confirm and train a quality-associated data model so as to optimize a system quality coefficient and further improve user experience. The method mainly comprises a quality associated data template and a custom instruction set.
The back end is a resource pool formed in a unified deployment cluster mode and comprises a middle server, a scheduling executor and a virtual machine cluster. Further, the middle server can sense and interact data and plays a role in connection between the quality-related data model and the database; the scheduling executor can realize multi-link and cluster scheduling.
The database is responsible for storing and managing the quality associated data collected by the central server and mainly comprises storage parameters, calculation parameters, network parameters and the like.
The user-defined instruction subset can realize dynamic configuration of multiple links facing quality control in the whole cloud desktop life cycle, and the multiple links include five types of analysis instructions, judgment instructions, scheduling instructions, execution instructions and communication instructions.
Further, the instructions to analyze: by executing analysis instructions on the segment application and the quality-associated parameters, the user is ensured to enjoy a high-quality cloud desktop experience.
Further, the subdivision application analysis is to analyze the application and service types required by the user according to the user type and the requirements, and specifically comprises movie animation design, architectural design, industrial design and the like; the quality-related parameter analysis is to analyze each parameter factor and the number of factors of each link influencing the system quality under multiple links (such as a calculation link, a communication link, a storage link and the like) so as to improve the system performance.
The judgment instruction is as follows: and executing a judgment instruction according to the subdivided application categories and the quality associated data of each accessed link, so as to realize the selection and optimization of the quality associated template.
The scheduling instruction: after the judgment instruction is executed on the quality-associated data, a job adjustment strategy is formed, and then an execution scheduler is adopted to execute a scheduling instruction according to the job adjustment strategy so as to control the system quality.
The execution instructions: the method comprises the steps of virtual machine cluster scheduling, job adjustment and optimization upgrading of a quality association template. Further, the virtual machine cluster scheduling refers to flexible scheduling in a cluster according to subdivision application; the operation adjustment refers to the operation adjustment among multiple links according to the quality related data; the optimization and upgrade of the quality correlation template comprises judgment, confirmation and training of the quality correlation template so as to realize the characteristics of flexibility and high quality of the system.
The communication instructions: the communication between the data and the quality associated template ensures accurate quality template through the filtering and deleting of the data, and the purpose of improving the system efficiency is achieved.
2. The quality association model comprises a model structure and a model training module, and the specific contents are as follows:
the multi-link quality correlation model is structurally composed of a plurality of quality influence factors in a cloud desktop system, wherein the quality influence factors comprise a plurality of quality correlation parameters, and a calculation formula of the whole quality correlation model is as follows:
ΔA＝ΔK·ΔB
further, Δ A is a number of quality-affecting factors, including A1..n(ii) a The delta K is a quality correlation model formed by correlation parameters of a plurality of quality influence factors in multiple links, including K1..n(ii) a Delta B is several quality related links in the system, including B1..n。
The model training module provides an upgrading method of a quality association model based on deep learning, different subdivision applications have different quality association coefficients, and the quality coefficients are optimized through continuous iterative updating of quality parameters, so that the quality model is trained, and the quality control effect is achieved. The method comprises the following specific steps:
(1) an initial quality parameter set Δ x;
(2) pre-judging data: different quality correlation coefficients delta y are matched by different subdivision applications, and further the initial quality parameter set is analyzed and prejudged;
(3) model training: the method is completed through a deployed deep learning platform, if delta x is larger than delta y, a model upgrading link is entered, and quality parameters are updated in an iterative mode; and if delta x is less than or equal to delta y, entering an operation adjusting link, and adjusting an operation and initial quality associated parameter set by formulating a quality strategy until the training is successful.
3. The quality association template comprises a template structure, and the specific content is as follows: constructing a quality association template around the quality model, and obtaining the quality association template by deleting and filtering multiple link quality influence factors and association parameters according to different subdivision service modes, wherein the calculation formula is as follows:
Δ a ═ Δ a · Δ L, (where Δ L ═ Δ M · Δ B)
Wherein, the delta A' is a quality correlation template under different subdivision service modes; and the delta L is a simplified quality correlation model, and the delta M is a multi-link quality influence factor in a subdivision service mode.
4. The service mode comprises a mode structure, and the specific content is as follows: the user subdivision service mode is composed of a plurality of influence factors and is obtained by analyzing application scenes, user requirements and user behaviors in different fields, wherein the different fields comprise creative design, architectural design, industrial design and the like; user requirements include price, time, software plug-in type, etc.; user behavior includes office habits, location classification, and the like. The customized segment service model can be applied by different segments, and the calculation formula is as follows:
ΔM＝ΔG·ΔE
wherein, Δ M is a subdivision service mode, including M1..n(ii) a The proportion of Δ G in which each influencing factor is included in G1..n(ii) a Δ E is a number of different factors, including E1..n。
Further, the user selects a corresponding subdivision service mode according to the requirement, the service mode is pre-judged and matched with a corresponding quality association template, and the system judges whether the quality association template needs to be adjusted or not through real-time monitoring and analysis of the accessed quality association data; if the associated data has deviation with the quality template, the task can be dynamically adjusted, the associated parameters are updated, and the quality model is trained, so that the effect of controlling the quality of the system is achieved.
Compared with the prior art, the invention has the beneficial effects that:
according to the quality control method of the cloud desktop system based on the software definition, provided by the invention, a software definition cloud desktop expansion framework facing quality control is constructed, a multi-link quality associated data template is formed by a user subdivision service mode around a quality model, so that the purposes of high-efficiency system quality control and system performance optimization are achieved, and flexible, high-efficiency and high-quality cloud desktop service is provided for users.
Drawings
FIG. 1 is a cloud desktop system expansion architecture based on software definition according to an embodiment of the present invention;
FIG. 2 illustrates a cloud desktop system quality management architecture according to an embodiment of the present invention;
FIG. 3 is a flow chart of a quality control oriented cloud desktop configuration according to an embodiment of the present invention;
FIG. 4 is a multi-link quality correlation data model of an embodiment of the invention;
fig. 5 is a block diagram of a subdivision service model according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
As shown in fig. 1, in the cloud desktop system expansion architecture based on software definition according to this embodiment, corresponding components for quality control are respectively added to an application layer, a control layer, an infrastructure layer, and a data layer of a cloud desktop system infrastructure to form a system quality management module; the method specifically comprises the following steps: the quality strategy module and the quality associated data template are newly added on the application layer, the data summarizing module, the prejudgment analysis module and the quality control model training module are newly added on the control layer, the quality sensing module and the task adjusting module are newly added on the infrastructure layer, and the quality associated parameters are newly added on the data layer.
As shown in fig. 2, the quality management architecture of the cloud desktop system of the embodiment mainly includes a front end, a middle end, a back end and a database;
the front end directly faces to the user requirements, is directly displayed to a user interface through cloud terminal connection, and provides various software and services of the required segmentation applications for the user. The method mainly comprises segmentation application, a cloud terminal and a user interface, wherein the segmentation application is various differentiated applications and services required by segmentation users (such as users in the fields of movie animation, architectural design, industrial design and the like).
The middle end is used for summarizing and managing data, a quality control-oriented deep learning platform is constructed, and a user-defined instruction subset is adopted to judge, confirm and train a quality-associated data model so as to optimize a system quality coefficient and further improve user experience. The method mainly comprises a quality associated data template and a custom instruction set.
The back end is a resource pool formed in a unified deployment cluster mode and comprises a middle server, a scheduling executor and a virtual machine cluster. Further, the middle server can sense and interact data and plays a role in connection between the quality-related data model and the database; the scheduling executor can realize multi-link and cluster scheduling.
The database is responsible for storing and managing the quality associated data collected by the central server and mainly comprises storage parameters, calculation parameters, network parameters and the like.
The user-defined instruction subset can realize dynamic configuration of multiple links facing quality control in the life cycle of the whole cloud desktop, and the multiple links comprise five categories of analysis instructions, judgment instructions, scheduling instructions, execution instructions and communication instructions;
the analysis instructions: by executing analysis instructions on the subdivision application and the quality associated parameters, the user is ensured to enjoy high-quality cloud desktop experience;
the judgment instruction is as follows: executing a judgment instruction according to the subdivided application categories and the quality associated data of each accessed link, and realizing the selection and optimization of the quality associated template;
the scheduling instruction: after the judgment instruction is executed on the quality associated data, a job adjustment strategy is formed, and then an execution scheduler is adopted to execute a scheduling instruction according to the job adjustment strategy so as to control the system quality;
the execution instructions: the method comprises the steps of virtual machine cluster scheduling, job adjustment and optimized upgrading of a quality correlation template;
the communication instructions: the communication between the data and the quality associated template ensures accurate quality template through the filtering and deleting of the data, and the purpose of improving the system efficiency is achieved.
Preferably, the segmentation application analysis is to analyze the types of applications and services required by the user according to the types and requirements of the user, and specifically includes movie animation design, architectural design, industrial design and the like; the quality-related parameter analysis is to analyze each parameter factor and the number of factors of each link influencing the system quality under multiple links so as to improve the system performance; the multi-link comprises a computing link, a communication link, a storage link and the like.
Preferably, the virtual machine cluster scheduling refers to flexible scheduling in a cluster according to subdivision application; the operation adjustment refers to the operation adjustment among multiple links according to the quality related data; the optimization and upgrade of the quality correlation template comprises judgment, confirmation and training of the quality correlation template so as to realize the characteristics of flexibility and high quality of the system.
As shown in fig. 3 to 5, in the quality control method of the cloud desktop system based on software definition according to the embodiment, by constructing an extended service of the cloud desktop based on software definition in the aspect of quality control, and processing quality-related data obtained by a quality-related template closely related to a subdivided service mode around a quality-related model on the extended service, flexible and efficient quality control is achieved.
The quality association model comprises a model structure and a model training module, and specifically comprises the following steps:
the model structure is composed of a plurality of quality influence factors in a quality control multi-link in a cloud desktop system, wherein the quality influence factors are provided with a plurality of quality correlation parameters, and a calculation formula of the whole quality correlation model is as follows:
ΔA＝ΔK·ΔB
further, Δ A is a number of quality-affecting factors, including A1··n(ii) a The delta K is a quality correlation model formed by correlation parameters of a plurality of quality influence factors in multiple links, including K1..n(ii) a Delta B is several quality related links in the system, including B1..n。
The model training module provides an upgrading method of a quality association model based on deep learning, different subdivision applications have different quality association coefficients, and the quality coefficients are optimized through continuous iterative updating of quality parameters, so that the quality model is trained, and the quality control effect is achieved. The method comprises the following specific steps:
(1) an initial quality parameter set Δ x;
(2) pre-judging data: different quality correlation coefficients delta y are matched by different subdivision applications, and further the initial quality parameter set is analyzed and prejudged;
(3) model training: the method is completed through a deployed deep learning platform, if delta x is larger than delta y, a model upgrading link is entered, and quality parameters are updated in an iterative mode; and if delta x is less than or equal to delta y, entering an operation adjusting link, and adjusting an operation and initial quality associated parameter set by formulating a quality strategy until the training is successful.
The quality association template comprises a template structure, and the specific content is as follows: constructing a quality association template around the quality model, and obtaining the quality association template by deleting and filtering multiple link quality influence factors and association parameters according to different subdivision service modes, wherein the calculation formula is as follows:
Δ a ═ Δ a · Δ L, (where Δ L ═ Δ M · Δ B)
Wherein, the delta A' is a quality correlation template under different subdivision service modes; and the delta L is a simplified quality correlation model, and the delta M is a multi-link quality influence factor in a subdivision service mode.
The service mode comprises a mode structure, and the specific content is as follows: the user subdivision service mode is composed of a plurality of influence factors and is obtained by analyzing application scenes, user requirements and user behaviors in different fields, wherein the different fields comprise creative design, architectural design, industrial design and the like; user requirements include price, time, software plug-in type, etc.; user behavior includes office habits, location classification, and the like. The customized segment service model can be applied by different segments, and the calculation formula is as follows:
ΔM＝ΔG·ΔE
wherein, Δ M is a subdivision service mode, including M1..n(ii) a The proportion of Δ G in which each influencing factor is included in G1..n(ii) a Δ E is a number of different factors, including E1..n。
Further, the user selects a corresponding subdivision service mode according to the requirement, the service mode is pre-judged and matched with a corresponding quality association template, and the system judges whether the quality association template needs to be adjusted or not through real-time monitoring and analysis of the accessed quality association data; if the associated data has deviation with the quality template, the task can be dynamically adjusted, the associated parameters are updated, and the quality model is trained, so that the effect of controlling the quality of the system is achieved.
Although the present invention has been described with reference to the preferred embodiments, the embodiments and drawings are not intended to limit the present invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention should be defined by the claims of the present application.
Claims (10)
1. The cloud desktop system quality control method based on software definition is characterized by comprising the following steps: by constructing an expanding service of the cloud desktop based on software definition in the aspect of quality control and processing quality-related data obtained by a quality-related template closely related to a subdivision service mode on the aspect of the expanding service around a quality-related model, flexible and efficient quality control is realized.
2. The quality control method of the cloud desktop system based on the software definition according to claim 1, wherein: the cloud desktop development service based on the software definition in the quality control aspect comprises a development framework, a quality management module and a custom instruction subset.
3. The quality control method of the cloud desktop system based on the software definition according to claim 2, wherein: the expansion framework is as follows: respectively adding corresponding components of quality control in an application layer, a control layer, an infrastructure layer and a data layer of a cloud desktop system infrastructure to form a system quality management module; the method specifically comprises the following steps: the quality strategy module and the quality associated data template are newly added on the application layer, the data summarizing module, the prejudgment analysis module and the quality control model training module are newly added on the control layer, the quality sensing module and the task adjusting module are newly added on the infrastructure layer, and the quality associated parameters are newly added on the data layer.
4. The quality control method of the cloud desktop system based on the software definition according to claim 2 or 3, wherein: the quality management module specifically comprises a front end, a middle end, a back end and a database;
the front end directly faces to the user requirements, is directly displayed to the user interface through cloud terminal connection, and provides various needed segmentation application software and services for the user, wherein the segmentation application software comprises segmentation applications, cloud terminals and the user interface, and the segmentation applications comprise various differentiated applications and services needed by the segmentation user; the segmentation users comprise users in the fields of movie animation, architectural design or industrial design;
the middle end is used for summarizing and managing data, a quality control-oriented deep learning platform is constructed, and a user-defined instruction subset is adopted to judge, confirm and train a quality-associated data model so as to optimize a system quality coefficient and further improve user experience; the middle end comprises a quality associated data template and a user-defined instruction set;
the back end is a resource pool formed in a unified deployment cluster mode and comprises a middle server, a scheduling executor and a virtual machine cluster; the middle server can sense and interact data and plays a role in connection between the quality-related data model and the database; the scheduling actuator can realize the scheduling of multiple links and clusters;
the database is responsible for storing and managing the quality associated data collected by the central server, and comprises storage parameters, calculation parameters and network parameters.
5. The quality control method of the cloud desktop system based on the software definition according to claim 2, wherein: the user-defined instruction subset can realize dynamic configuration of multiple links facing quality control in the life cycle of the whole cloud desktop, and the multiple links comprise five categories of analysis instructions, judgment instructions, scheduling instructions, execution instructions and communication instructions;
the analysis instructions: by executing analysis instructions on the subdivision application and the quality associated parameters, the user is ensured to enjoy high-quality cloud desktop experience;
the judgment instruction is as follows: executing a judgment instruction according to the subdivided application categories and the quality associated data of each accessed link, and realizing the selection and optimization of the quality associated template;
the scheduling instruction: after the judgment instruction is executed on the quality associated data, a job adjustment strategy is formed, and then an execution scheduler is adopted to execute a scheduling instruction according to the job adjustment strategy so as to control the system quality;
the execution instructions: the method comprises the steps of virtual machine cluster scheduling, job adjustment and optimized upgrading of a quality correlation template;
the communication instructions: the communication between the data and the quality associated template ensures accurate quality template through the filtering and deleting of the data, and the purpose of improving the system efficiency is achieved.
6. The quality control method of the cloud desktop system based on the software definition according to claim 5, wherein: the subdivision application analysis is to analyze the application and service types required by the user according to the user type and the requirement, and specifically comprises movie animation design, architectural design or industrial design; the quality-related parameter analysis is to analyze each parameter factor and the number of factors of each link influencing the system quality under multiple links so as to improve the system performance; the multi-link includes a computing link, a communication link or a storage link.
7. The quality control method of the cloud desktop system based on the software definition according to claim 5, wherein: the virtual machine cluster scheduling refers to flexible scheduling in a cluster according to subdivided applications; the operation adjustment refers to the operation adjustment among multiple links according to the quality related data; the optimization and upgrade of the quality correlation template comprises judgment, confirmation and training of the quality correlation template so as to realize the characteristics of flexibility and high quality of the system.
8. The quality control method of the cloud desktop system based on the software definition according to claim 1, wherein: the quality correlation model comprises a model structure and a model training module;
the model structure is composed of a plurality of quality influence factors in a quality control multi-link in a cloud desktop system, the quality influence factors are provided with a plurality of quality correlation parameters, and the calculation formula of the whole quality correlation model is as follows:
ΔA＝ΔK·ΔB
wherein, DeltaA is a plurality of quality influence factors including A1..n(ii) a The delta K is a quality correlation model formed by correlation parameters of a plurality of quality influence factors in multiple links, including K1..n(ii) a Delta B is several quality related links in the system, including B1..n；
The model training module provides an upgrading method of a quality association model based on deep learning, different subdivision applications have different quality association coefficients, the quality parameters are continuously updated in an iterative mode to optimize the quality coefficients, the quality model is trained, and the quality control effect is achieved, and the method specifically comprises the following steps:
step 1: an initial quality parameter set Δ x;
step 2: pre-judging data: different quality correlation coefficients delta y are matched by different subdivision applications, and further the initial quality parameter set is analyzed and prejudged;
and step 3: model training: the method is completed through a deployed deep learning platform, if delta x is larger than delta y, a model upgrading link is entered, and quality parameters are updated in an iterative mode; and if delta x is less than or equal to delta y, entering an operation adjusting link, and adjusting an operation and initial quality associated parameter set by formulating a quality strategy until the training is successful.
9. The quality control method of the cloud desktop system based on the software definition according to claim 1, wherein: the quality correlation template comprises a template structure, and specifically comprises the following steps: constructing a quality association template around the quality model, and obtaining the quality association template by deleting and filtering multiple link quality influence factors and association parameters according to different subdivision service modes, wherein the calculation formula is as follows:
Δ a ═ Δ a · Δ L, (where Δ L ═ Δ M · Δ B)
Wherein, the delta A' is a quality correlation template under different subdivision service modes; and the delta L is a simplified quality correlation model, and the delta M is a multi-link quality influence factor in a subdivision service mode.
10. The quality control method of the cloud desktop system based on the software definition according to claim 1, wherein: the service mode comprises a mode structure, and specifically comprises the following steps: the user subdivision service mode consists of a plurality of influence factors and is obtained by analyzing application scenes, user requirements and user behaviors in different fields, wherein the different fields comprise creative design, architectural design or industrial design; the user demand comprises price, time or software plug-in type; the user behavior comprises office habits or location classifications; the customized segment service model can be applied by different segments, and the calculation formula is as follows:
ΔM＝ΔG·ΔE
wherein, Δ M is quality influence factor of multiple links in subdivision service mode, including M1..n(ii) a The proportion of Δ G in which each influencing factor is included in G1..n(ii) a Δ E is a number of different factors, including E1..n；
The user selects a corresponding subdivision service mode according to the requirement, the service mode is pre-judged and matched with a corresponding quality association template, and the system judges whether the quality association template needs to be adjusted or not by real-time monitoring and analysis of the accessed quality association data; if the associated data has deviation with the quality template, the task can be dynamically adjusted, the associated parameters are updated, and the quality model is trained, so that the effect of controlling the quality of the system is achieved.
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