CN114844791B

CN114844791B - Cloud service automatic management and distribution method and system based on big data and storage medium

Info

Publication number: CN114844791B
Application number: CN202210789689.5A
Authority: CN
Inventors: 张雄国
Original assignee: Beijing Yueyou Information Technology Co ltd
Current assignee: Beijing Yueyou Information Technology Co ltd
Priority date: 2022-07-06
Filing date: 2022-07-06
Publication date: 2022-09-23
Anticipated expiration: 2042-07-06
Also published as: CN114844791A

Abstract

The method comprises the steps of acquiring enterprise characteristic information and generating enterprise cloud service characteristic data by combining service data information of an enterprise in an enterprise cloud service platform library; performing relation analysis and data mining on the enterprise cloud service characteristic data by using a preset first cloud service model to obtain enterprise cloud service evaluation data; generating an enterprise cloud service characteristic portrait according to the enterprise cloud service characteristic data and the enterprise cloud service evaluation data; evaluating the enterprise cloud service characteristic image according to a preset second cloud service model, distributing the enterprise cloud resource requirements based on the dynamic response factors and outputting a cloud service task queue list; performing enterprise allocation deployment on cloud service resources according to the generated cloud service task queue list; the method comprises the steps of generating an enterprise cloud service characteristic portrait, evaluating and distributing enterprise cloud service resources, outputting a cloud service task queue list to distribute and deploy the enterprise cloud resources, and improving the distribution efficiency and accuracy of the cloud service resources.

Description

Cloud service automatic management and distribution method and system based on big data and storage medium

Technical Field

The application relates to the technical field of big data cloud service, in particular to a cloud service automatic management and distribution method and system based on big data and a storage medium.

Background

With the rapid development of network technologies, the explosive growth of information data makes the demands of enterprises for computing resources increasingly larger and more complex, so that the enterprises are diversified in the demands of cloud service resources, the enterprises want to obtain enough cloud service resources, but the excessive cloud service increases the operation cost burden of the enterprises, and can excessively occupy public cloud service resources, so that the cloud resources cannot be reasonably and fully utilized and distributed.

Under the circumstance, the realization of a reasonable, on-demand and scientific cloud resource allocation mode is particularly important, so that enterprises can obtain the most reasonable cloud resources, and resource vacancy and waste are avoided.

Disclosure of Invention

The application aims to provide a cloud service automatic management and distribution method, a cloud service automatic management and distribution system and a storage medium based on big data, and the distribution efficiency and the precision of cloud service resources can be improved.

The application provides a cloud service automatic management and distribution method based on big data, which comprises the following steps:

acquiring enterprise characteristic information and combining service data information of an enterprise in an enterprise cloud service platform library to generate enterprise cloud service characteristic data;

performing relation analysis and data mining on the enterprise cloud service characteristic data by using a preset first cloud service model to obtain enterprise cloud service evaluation data;

generating an enterprise cloud service characteristic portrait according to the enterprise cloud service characteristic data and the enterprise cloud service evaluation data;

evaluating the enterprise cloud service characteristic image according to a preset second cloud service model, distributing the enterprise cloud resource requirements based on the dynamic response factors and outputting a cloud service task queue list;

and carrying out enterprise allocation and deployment on cloud service resources according to the generated cloud service task queue list.

Optionally, in the cloud service automatic management and allocation method based on big data according to the present application, the acquiring of the enterprise feature information and combining the service data information of the enterprise in the enterprise cloud service platform library to generate enterprise cloud service feature data includes:

acquiring enterprise characteristic information including enterprise attributes, enterprise data total amount, enterprise privacy level, enterprise operation relation and enterprise computing capacity;

generating an enterprise cloud demand data set according to the enterprise data total amount, the enterprise privacy level, the enterprise operation relation and the enterprise calculation capacity of the enterprise;

inputting the enterprise cloud demand data set into an enterprise cloud service platform library to acquire service data information corresponding to the enterprise;

the service data information comprises target cloud capacity data, cloud privacy attribute information, cloud execution scale data and cloud allocation interface data;

and generating enterprise cloud service characteristic data according to the target cloud capacity data, the cloud privacy attribute information, the cloud execution scale data and the cloud allocation interface data.

Optionally, in the cloud service automatic management and distribution method based on big data according to the present application, the obtaining of enterprise cloud service evaluation data by performing relationship analysis and data mining on the enterprise cloud service feature data by using a preset first cloud service model includes:

carrying out cloud service demand resource analysis on the enterprise according to the historical cloud service demand data of the enterprise, and completing enterprise data cloud service relation analysis by combining an enterprise knowledge graph;

mining an enterprise cloud service map and classifying data clouds according to the enterprise cloud service feature data;

and evaluating cloud service resources by combining the enterprise data cloud service relationship, and associating the enterprise mapping relationship cloud with the enterprise cloud service characteristic data to obtain enterprise cloud service evaluation data.

Optionally, in the cloud service automatic management and distribution method based on big data according to the present application, the generating an enterprise cloud service feature representation according to the enterprise cloud service feature data and the enterprise cloud service evaluation data includes:

generating an enterprise cloud service management data set according to the target cloud capacity data, the cloud privacy attribute information, the cloud execution scale data and the enterprise cloud service evaluation data;

the enterprise cloud service management data set comprises cloud resource proportioning data, cloud resource supporting data, cloud mapping relation data and cloud resource framework data;

inputting the enterprise cloud service management data set into a management database of the enterprise cloud service platform library to acquire enterprise cloud service management characteristic information;

and generating an enterprise cloud service characteristic portrait according to the enterprise cloud service management characteristic information.

Optionally, in the cloud service automatic management and allocation method based on big data according to the present application, the evaluating the enterprise cloud service feature image according to a preset second cloud service model, allocating an enterprise cloud resource demand based on a dynamic response factor, and outputting a cloud service task queue list includes:

pre-judging actual requirements of the enterprise cloud service according to a preset second cloud service model of the enterprise cloud service platform library and outputting an evaluation result;

analyzing the data processing type, the data interface level, the data sharing privacy and the key attribute of the enterprise, and outputting cloud service interaction data corresponding to the cloud service characteristic image of the enterprise in a preset time period by combining the evaluation result;

dynamically monitoring cloud service interaction data of the enterprise and dynamically allocating the cloud resource requirements of the enterprise based on dynamic response factors of the enterprise;

and generating and outputting the enterprise cloud service task queue according to the dynamically allocated data.

Optionally, in the cloud service automatic management and allocation method based on big data according to the present application, the allocating and deploying an enterprise to cloud service resources according to the generated cloud service task queue table includes:

arranging according to the dynamically allocated data of the enterprises and combining the enterprise mapping private cloud levels and the demand rating to obtain a cloud service task queue list;

correcting the cloud service task queue table according to historical cloud service demand data of the enterprise in the same period to obtain a corrected cloud service task queue table;

acquiring future prediction cloud service demand data of the enterprise, checking the correction cloud service task queue table and acquiring a target cloud service task queue table;

and allocating and deploying cloud service resources according to the target cloud service task queue list.

In a second aspect, the present application further provides a cloud service automatic management and distribution system based on big data, including: the storage comprises a program of the cloud service automatic management and distribution method based on the big data, and when the program of the cloud service automatic management and distribution method based on the big data is executed by the processor, the following steps are realized:

acquiring enterprise characteristic information and combining the service data information of an enterprise in an enterprise cloud service platform library to generate enterprise cloud service characteristic data;

Optionally, in the cloud service automatic management and distribution system based on big data according to the present application, the acquiring of the enterprise feature information and combining the service data information of the enterprise in the enterprise cloud service platform library to generate enterprise cloud service feature data includes:

generating an enterprise cloud demand data set according to the enterprise data total amount, the enterprise privacy level, the enterprise operation relation and the enterprise computing capacity of the enterprise;

Optionally, in the cloud service automatic management and distribution system based on big data according to the present application, the obtaining of enterprise cloud service evaluation data by performing relationship analysis and data mining on the enterprise cloud service feature data using a preset first cloud service model includes:

carrying out cloud service demand resource analysis on the enterprise according to the historical cloud service demand data of the enterprise, and completing cloud service relation analysis of enterprise data by combining an enterprise knowledge graph;

In a third aspect, the present application further provides a readable storage medium, where the readable storage medium includes a program of a cloud service automatic management and allocation method based on big data, and when the program of the cloud service automatic management and allocation method based on big data is executed by a processor, the method implements the steps of the cloud service automatic management and allocation method based on big data as described in any one of the above.

According to the cloud service automatic management and allocation method, system and readable storage medium based on big data, enterprise cloud service feature data are generated by acquiring enterprise feature information and combining service data information of an enterprise in an enterprise cloud service platform library, enterprise cloud service evaluation data are acquired by performing relation analysis and data mining on the enterprise cloud service feature data through a preset first cloud service model, enterprise cloud service feature images are generated according to the enterprise cloud service feature data and the enterprise cloud service evaluation data, the enterprise cloud service feature images are evaluated according to a preset second cloud service model, enterprise cloud resource requirements are allocated based on dynamic response factors and output cloud service task queue lists, and enterprise allocation deployment is performed on cloud service resources according to the generated cloud service task lists, so that enterprise cloud service feature characteristics are generated based on data processing of the enterprise cloud service platform library, enterprise cloud service resources are evaluated, allocated and output And the cloud service task queue list is used for allocating and deploying the cloud resources of the enterprise, so that the allocation efficiency and the accuracy of the cloud service resources are improved.

Additional features and advantages of the present application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the present application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be 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 that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.

Fig. 1 is a flowchart of a cloud service automatic management and distribution method based on big data according to an embodiment of the present disclosure;

fig. 2 is a flowchart of a method for automatically managing and allocating cloud services based on big data according to an embodiment of the present application;

fig. 3 is a flowchart of a method for automatically managing and allocating cloud services based on big data according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a cloud service automatic management and distribution system based on big data according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined or explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

Fig. 1 is a flowchart illustrating automatic management and distribution of cloud services based on big data according to some embodiments of the present application. The cloud service automatic management and distribution method based on the big data is used for terminal equipment such as mobile phones and computers. The cloud service automatic management and distribution method based on the big data comprises the following steps:

s101, acquiring enterprise characteristic information and combining the enterprise characteristic information with service data information of an enterprise in an enterprise cloud service platform library to generate enterprise cloud service characteristic data;

s102, performing relation analysis and data mining on the enterprise cloud service characteristic data by using a preset first cloud service model to obtain enterprise cloud service evaluation data;

s103, generating an enterprise cloud service characteristic portrait according to the enterprise cloud service characteristic data and the enterprise cloud service evaluation data;

s104, evaluating the enterprise cloud service characteristic image according to a preset second cloud service model, distributing the enterprise cloud resource requirements based on a dynamic response factor and outputting a cloud service task queue list;

and S105, performing enterprise allocation and deployment on cloud service resources according to the generated cloud service task queue list.

The method comprises the steps of firstly obtaining enterprise characteristic information, then combining service data information inquired by an enterprise in an enterprise cloud service platform library to generate enterprise cloud service characteristic data, then combining a first cloud service model preset in the enterprise cloud service platform library to carry out cloud service demand resource analysis and relation analysis on the enterprise, mining and classifying an enterprise cloud service map according to the enterprise cloud service characteristic data to obtain enterprise cloud service evaluation data, generating an enterprise cloud service characteristic portrait by using the enterprise cloud service characteristic data and the enterprise cloud service evaluation data, prejudging the actual demand of the enterprise cloud service by using a preset second cloud service model, outputting the evaluation result, outputting the cloud service characteristic portrait corresponding to the cloud service demand data, dynamically distributing the enterprise cloud resource demand based on dynamic response factors of the enterprise, generating and outputting an enterprise cloud service task queue, enterprise allocation deployment is carried out on cloud service resources according to the task queue list and the combination of enterprise demand rating and forecast demand data, the technology of evaluating and allocating the cloud service resources according to enterprise cloud service demands and the combination of enterprise conditions is achieved, and cloud service management efficiency and cloud resource allocation deployment effects are improved.

Fig. 2 is a flowchart illustrating automatic management and distribution of cloud services based on big data according to some embodiments of the present application. According to the embodiment of the invention, the acquiring of the enterprise characteristic information and the generation of the enterprise cloud service characteristic data by combining the service data information of the enterprise in the enterprise cloud service platform library specifically comprise:

s201, acquiring enterprise characteristic information including enterprise attributes, enterprise data total amount, enterprise privacy level, enterprise operation relation and enterprise computing capacity;

s202, generating an enterprise cloud demand data set according to the enterprise data total amount, the enterprise privacy level, the enterprise operation relation and the enterprise computing capacity of the enterprise;

s203, inputting the enterprise cloud demand data set into an enterprise cloud service platform library to obtain service data information corresponding to the enterprise;

s204, the service data information comprises target cloud capacity data, cloud privacy attribute information, cloud execution scale data and cloud allocation interface data;

and S205, generating enterprise cloud service characteristic data according to the target cloud capacity data, the cloud privacy attribute information, the cloud execution scale data and the cloud allocation interface data.

It should be noted that in order to configure resources adapted to enterprise cloud service requirements, corresponding cloud service requirement data needs to be collected, judged and obtained according to the current situation of enterprise requirements, and service data information suitable for an enterprise is obtained by inputting acquired enterprise characteristic information including enterprise attributes, enterprise data total amount, enterprise privacy level, enterprise operation relation and enterprise computing capacity into an enterprise cloud service platform library to be queried and acquired, wherein the service data information includes target cloud capacity data, cloud privacy attribute information, cloud execution scale data and cloud allocation interface data, so that enterprise cloud service characteristic data meeting the enterprise cloud service requirements are generated, and the enterprise cloud service platform library is a third-party cloud service platform.

Please refer to fig. 3, which is a flowchart illustrating automatic management and distribution of cloud services based on big data according to some embodiments of the present application. According to the embodiment of the invention, the relation analysis and data mining are performed on the enterprise cloud service feature data by using a preset first cloud service model to obtain enterprise cloud service evaluation data, and the method specifically comprises the following steps:

s301, performing cloud service demand resource analysis on the enterprise according to the historical cloud service demand data of the enterprise, and completing cloud service relation analysis of enterprise data by combining an enterprise knowledge graph;

s302, mining an enterprise cloud service map and classifying data clouds according to the enterprise cloud service feature data;

and S303, evaluating cloud service resources by combining the enterprise data cloud service relation, and associating the enterprise mapping relation cloud with the enterprise cloud service characteristic data to obtain enterprise cloud service evaluation data.

It is to be noted that, the enterprise data cloud service relation is analyzed according to a preset first cloud service model, cloud service demand resource analysis is performed on an enterprise according to historical cloud service demand data of the enterprise, the cloud service demand resource analysis comprises a target cloud resource category, cloud computing transient flux, cloud privacy level ratio, the number of enterprise cloud computing nodes, an enterprise private cloud key list and the like, the enterprise data cloud service relation is completed by combining an enterprise knowledge graph, the enterprise knowledge graph comprises a cognition graph layer, a knowledge graph layer and a basic data layer, the enterprise cloud service graph comprises an enterprise cloud service association graph layer, an organization graph layer and a basic data layer, the enterprise cloud service graph is mined and data cloud classified according to the historical cloud service demand data of the enterprise so as to determine cloud resource demand detail of the enterprise and corresponding data cloud classification of an execution node, the cloud service resources of the enterprise are subjected to demand evaluation by combining the cloud service relationship of the enterprise, namely the task demand, the instruction category and the privacy level of the enterprise on the cloud resources, and the mapping relationship cloud of the enterprise and the cloud resources is obtained according to the demand evaluation result of the cloud service resources of the enterprise, namely the mapping relationship cloud of the enterprise and various mixed cloud resources is associated with the target cloud capacity data, the cloud privacy attribute information, the cloud execution scale data and the cloud allocation interface data of the cloud service characteristic data of the enterprise to obtain the cloud service evaluation data of the enterprise, wherein the cloud service evaluation data comprises cloud resource guarantee data, cloud scale magnitude data, cloud communication priority data and cloud data exchange threshold data, and the cloud resource demand of the enterprise, the cloud service support and the cloud resource allocation condition are determined through the associated evaluation of the cloud resource relationship.

According to the embodiment of the invention, the generating of the enterprise cloud service feature portrait according to the enterprise cloud service feature data and the enterprise cloud service evaluation data specifically comprises the following steps:

It should be noted that the enterprise cloud service characteristic image is a digital description of cloud service resource capacity scale, cloud privacy level demand condition, cloud service resource communication and priority demand condition and cloud service resource framework condition of enterprise cloud resource demand, and is an enterprise cloud service demand digital map reflecting enterprise cloud service resource adaptation demand condition, the enterprise cloud service characteristic image comprises module data such as cloud resource capacity data, cloud privacy level mix ratio data, cloud communication interface data and cloud resource framework data, and can reflect enterprise operational force and service force dynamic demand according to the enterprise cloud service characteristic image, so as to conveniently adjust dynamic demand condition of enterprise cloud service resources in real time And inputting the cloud resource supporting data, the cloud mapping relation data and the cloud resource framework data into a management database of the enterprise cloud service platform library to acquire enterprise cloud service management characteristic information and correspondingly generate an enterprise cloud service characteristic portrait.

According to the embodiment of the invention, the evaluating the enterprise cloud service characteristic image according to the preset second cloud service model, allocating the enterprise cloud resource demand based on the dynamic response factor and outputting the cloud service task queue list specifically comprises the following steps:

It should be noted that, because the cloud service resource demand of the enterprise is influenced by the self-constraint condition of the enterprise or the change of the real-time data processing demand of the enterprise, the cloud resource demand of the enterprise needs to be dynamically evaluated and the cloud service resource allocation of the enterprise needs to be adjusted according to the change of the factors influencing the cloud resource demand of the enterprise, firstly, the actual demand of the cloud service of the enterprise is pre-judged through a preset second cloud service model of an enterprise cloud service platform library and an output evaluation result is obtained, the preset second cloud service model is a data processing model of the enterprise cloud service platform library, the demand evaluation result of the enterprise cloud service can be obtained according to the model, the data processing type, the data interface level, the data sharing privacy and the key attribute of the enterprise are analyzed, and the enterprise cloud service interaction data corresponding to the cloud service characteristic image of the enterprise in a preset time period is output by combining the demand evaluation result of the cloud service of the enterprise, the method comprises the steps of obtaining enterprise cloud service interaction data which accords with an enterprise cloud service characteristic image and is adaptive to the current situation of enterprise cloud service requirements according to the requirement evaluation result of the enterprise cloud service and the existing attributes of cloud resources connected with the enterprise, wherein the interaction data is dynamic variable and needs to be dynamically monitored, dynamically monitoring the interaction data, adjusting dynamic distribution of the enterprise cloud resource requirements according to dynamic response factors of the enterprise, the dynamic response factors of the enterprise are dynamic factors obtained by an enterprise cloud service platform library according to the real-time operation state of the enterprise and the data calculation dynamic modulus, reflecting the real-time calculation capacity and the transportation capacity conditions of the enterprise, generating and outputting an enterprise cloud service task queue list according to the adjusted data of dynamic distribution of the enterprise cloud resource requirements, and achieving dynamic distribution and deployment of the enterprise cloud service resource requirements.

According to the embodiment of the invention, the enterprise allocation and deployment of the cloud service resources according to the generated cloud service task queue table specifically comprises the following steps:

correcting the cloud service task queue list according to historical cloud service demand data of the enterprise in the same period to obtain a corrected cloud service task queue list;

It should be noted that, in order to accurately allocate and deploy cloud service resources of an enterprise, a cloud service task queue list is obtained by combining data dynamically allocated by the enterprise and arranging the private cloud level number mapped by the enterprise and the enterprise demand rating, a corrected cloud service task queue list is obtained by correcting the cloud service task queue list according to the cloud service demand data of the same period of the enterprise history, a target cloud service task queue list is obtained by checking the cloud service task queue list according to the future predicted cloud service demand data of the enterprise, and the cloud service resources are allocated and deployed according to the target cloud service task queue list, wherein the future predicted cloud service demand data of the enterprise is predicted by the cloud service demand data of the future provided by the enterprise self-evaluation, and is corrected and checked by the cloud service task queue list, the obtained target cloud service task queue list is more accurate, and the allocation and deployment of cloud service resources are more reasonable and appropriate.

According to the embodiment of the invention, the method further comprises the following steps:

acquiring the data of the resource demand of the segmented cloud service of an enterprise in a historical time period;

obtaining cloud resource application correlation value data according to the actual duty ratio of the cloud service resources of the enterprise in the historical time period;

and correcting the cloud service resource demand of the enterprise in the future in the same time period as the history according to the associated value data to obtain corrected cloud service resource allocation data.

It should be noted that, in order to improve the accurate rate of cloud resource allocation to an enterprise, the data of the cloud service resource demand in segments of the enterprise in a historical time period is obtained, the data of the cloud service resource demand in segments of the enterprise in the historical time period comprises the data of the cloud service resource demand in segments of the enterprise in the historical time period, the actual duty ratio of the cloud service resource in the historical time period is obtained according to the actual occupancy amount of the cloud service resource of the enterprise in the historical time period, the duty ratio is a ratio of the actual cloud service resource occupancy amount to the preset cloud service resource allocation amount, correlation processing is performed on the cloud resource application data according to the duty ratio to obtain associated value data, the cloud service resource demand of the enterprise in the same time period in the future is corrected according to the associated value data, so as to realize the technology of correcting the cloud service resource allocation rate in the same time period in the future according to the actual occupancy amount of the cloud service resource in the historical time period of the enterprise, and the enterprise with overflow of the cloud service resource ratio can be adjusted by using the historical rule, the method has the advantages that the method can be used for adjusting the cloud service resource occupation ratio of the enterprise with insufficient cloud service resource occupation ratio so as to better realize the cloud service resource allocation and avoid the two polarization phenomena of waste and insufficiency of the resource demand.

establishing a cloud service resource occupation threshold and a cloud service resource occupation ratio threshold;

comparing a threshold value according to the actual cloud service resource occupation ratio value in the enterprise historical time period and the cloud service resource occupation threshold value;

if the threshold comparison does not meet the preset threshold comparison requirement, adjusting the resource allocation amount of the enterprise cloud service;

acquiring a comparison result of cloud service resource occupation threshold values of all preset time nodes in the enterprise historical time period, and generating a cloud service resource occupation ratio data set in a centralized manner;

if the ratio of the cloud service resource to the data set is lower than the threshold value of the cloud service resource ratio;

the enterprise is warned and a loss of credit flag is marked in the cloud service task queue list.

It should be noted that, in order to measure the utilization condition of the cloud service resources by the enterprise, a cloud service resource occupation threshold and a cloud service resource occupation threshold are established, a threshold value pair is carried out according to the actual cloud service resource occupation ratio value and the cloud service resource occupation threshold of the enterprise in the historical time period, if the threshold value comparison does not meet the comparison requirement of the preset threshold value, the cloud service resource allocation amount of the enterprise is adjusted, then the comparison result of the cloud service resource occupation threshold of each preset time node in the historical time period of the enterprise is obtained, a cloud service resource occupation ratio data set is generated in a gathering manner, if the occupation ratio of the cloud service resource occupation ratio data set is lower than the cloud service resource occupation ratio threshold, the enterprise is warned, a message loss mark is carried out in a cloud service task queue list, and the actual cloud service resource occupation condition of the enterprise in the historical time period can be identified through the comparison and judgment of the cloud service resource occupation threshold, if the occupancy ratio value is lower than the occupancy threshold value, the enterprise is reflected to be excessively occupied and cloud service resources are wasted, the macroscopic occupancy condition of the enterprise, which occupies the cloud service resources and is overlapped by certain accumulated time nodes in a historical time period, can be identified through comparison and judgment of the cloud service resource occupancy ratio threshold value, the occupancy ratio data set is integrated according to the comparison result of the occupancy threshold values accumulated by the time nodes, namely, the occupancy ratio is obtained through statistics of the overall occupancy ratio, if the occupancy ratio is lower than the occupancy ratio threshold value, the situation that the enterprise frequently occupies too much and wastes the cloud service resources in the historical time period is indicated, the enterprise is warned, and a loss of credit mark is carried out in a cloud service task list, the occupancy threshold value of the cloud service resources and the occupancy threshold value of the cloud service resources are set to be 80-85%, and the occupancy ratio threshold value of the cloud service resources is preferably set to be 85%.

As shown in fig. 4, the present invention also discloses a cloud service automatic management and distribution system based on big data, which includes a memory 41 and a processor 42, where the memory includes a cloud service automatic management and distribution method program based on big data, and when executed by the processor, the cloud service automatic management and distribution method program based on big data implements the following steps:

According to the embodiment of the invention, the acquiring of the enterprise characteristic information and the combining of the service data information of the enterprise in the enterprise cloud service platform library to generate the enterprise cloud service characteristic data specifically comprise:

the service data information comprises target cloud capacity data, cloud privacy attribute information, cloud execution scale data and cloud distribution interface data;

and generating enterprise cloud service characteristic data according to the target cloud capacity data, the cloud privacy attribute information, the cloud execution scale data and the cloud distribution interface data.

According to the embodiment of the invention, the relation analysis and data mining are performed on the enterprise cloud service characteristic data by using a preset first cloud service model to obtain enterprise cloud service evaluation data, and the method specifically comprises the following steps:

mining and data cloud classification are carried out on the enterprise cloud service map according to the enterprise cloud service feature data;

It is to be noted that, the enterprise data cloud service relation is analyzed according to a preset first cloud service model, cloud service demand resource analysis is performed on an enterprise according to historical cloud service demand data of the enterprise, the cloud service demand resource analysis comprises a target cloud resource category, cloud computing transient flux, cloud privacy level ratio, the number of enterprise cloud computing nodes, an enterprise private cloud key list and the like, the enterprise data cloud service relation is completed by combining an enterprise knowledge graph, the enterprise knowledge graph comprises a cognition graph layer, a knowledge graph layer and a basic data layer, the enterprise cloud service graph comprises an enterprise cloud service association graph layer, an organization graph layer and a basic data layer, the enterprise cloud service graph is mined and data cloud classified according to the historical cloud service demand data of the enterprise so as to determine cloud resource demand detail of the enterprise and corresponding data cloud classification of an execution node, the cloud service resources of the enterprise are subjected to demand evaluation in combination with the cloud service relationship of the enterprise, namely task requirements, instruction types and privacy levels of the enterprise on the cloud resources, mapping relationship clouds of the enterprise and the cloud resources, namely mapping relationship clouds of the enterprise and various mixed cloud resources are obtained according to the demand evaluation result of the cloud service resources of the enterprise, and then the mapping relationship clouds of the enterprise and various mixed cloud resources are associated with target cloud capacity data, cloud privacy attribute information, cloud execution scale data and cloud allocation interface data of cloud service characteristic data of the enterprise to obtain enterprise cloud service evaluation data comprising cloud resource guarantee data, cloud scale magnitude data, cloud communication priority data and cloud data exchange threshold data, and the requirements of the enterprise cloud resources, cloud service support and cloud resource allocation conditions are determined through the association evaluation of the cloud resource relationship.

It should be noted that the enterprise cloud service characteristic image is a digital description of cloud service resource capacity scale, cloud privacy level demand condition, cloud service resource communication and priority demand condition and cloud service resource framework condition of enterprise cloud resource demand, and is an enterprise cloud service demand digital image reflecting enterprise adaptive cloud service resource demand condition, the enterprise cloud service characteristic image comprises module data such as cloud resource capacity data, cloud privacy level mixing ratio data, cloud communication interface data and cloud resource framework data, and can reflect dynamic demands of enterprise computing power and service power according to the enterprise cloud service characteristic image, so as to conveniently adjust dynamic demand condition of enterprise cloud service resources in real time And inputting the cloud resource supporting data, the cloud mapping relation data and the cloud resource framework data into a management database of the enterprise cloud service platform library to acquire enterprise cloud service management characteristic information and correspondingly generate an enterprise cloud service characteristic portrait.

and generating and outputting the enterprise cloud service task queue table according to the dynamically allocated data.

According to the embodiment of the invention, the enterprise allocation and deployment are performed on the cloud service resources according to the generated cloud service task queue table, which specifically comprises the following steps:

acquiring future prediction cloud service demand data of the enterprise, checking the correction cloud service task queue table, and acquiring a target cloud service task queue table;

and allocating and deploying cloud service resources according to the target cloud service task queue table.

It should be noted that, in order to accurately allocate and deploy the cloud service resources of the enterprise, the cloud service task queue list is obtained by combining the data dynamically allocated by the enterprise with the private cloud level mapped by the enterprise and ranking the enterprise demand, then the cloud service task queue list is corrected according to the cloud service demand data of the enterprise with the same period in history to obtain a corrected cloud service task queue list, the corrected cloud service task queue list is accurately corrected, the queue list is checked according to the future predicted cloud service demand data of the enterprise to obtain a target cloud service task queue list, the cloud service resources are allocated and deployed according to the target cloud service task queue list, wherein the future predicted cloud service demand data of the enterprise is the prediction of the future cloud service demand data provided by the enterprise self-evaluation, and the cloud service task queue list is corrected and checked, the obtained target cloud service task queue list is more accurate, and the allocation and deployment of cloud service resources are more reasonable and appropriate.

acquiring data of the sectional cloud service resource demand of an enterprise in a historical time period;

It should be noted that, in order to improve the accurate rate of cloud resource allocation to an enterprise, acquire the data of cloud service resource demand in segments of the enterprise in a historical time period, including the data of cloud service resource demand divided by the enterprise in segments in the historical time period, then obtain the actual duty ratio of the cloud service resource according to the actual occupancy of the cloud service resource of the enterprise in the historical time period, the duty ratio is the ratio of the actual cloud service resource occupancy to the preset cloud service resource allocation, perform correlation processing on the cloud resource application data according to the duty ratio to obtain the associated value data, and then correct the cloud service resource demand of the enterprise in the future in the same time period according to the associated value data, so as to realize the technology of correcting the cloud service resource allocation rate in the future in the same time period according to the actual cloud service resource occupancy of the enterprise in the historical time period, and by this correction, the enterprise with overflow of the cloud service resource occupancy ratio can be adjusted according to the historical rule, the method has the advantages that the method can be used for adjusting the cloud service resource occupation ratio of the enterprise with insufficient cloud service resource occupation ratio so as to better realize the cloud service resource allocation and avoid the two polarization phenomena of waste and insufficiency of the resource demand.

It is to be noted that, in order to measure the utilization condition of an enterprise on cloud service resources, a cloud service resource occupation threshold and a cloud service resource occupation ratio threshold are established, a threshold value pair is carried out according to an actual cloud service resource occupation ratio value of the enterprise in a historical time period and the cloud service resource occupation threshold, if the threshold value comparison does not meet the comparison requirement of a preset threshold value, the cloud service resource allocation amount of the enterprise is adjusted, then the comparison result of the cloud service resource occupation threshold of each preset time node in the historical time period of the enterprise is obtained, a cloud service resource occupation ratio data set is generated in a set, if the occupation ratio of the cloud service resource occupation ratio data set is lower than the cloud service resource occupation ratio threshold, the enterprise is warned, a loss mark is carried out in a cloud service task queue list, and the actual cloud service resource occupation condition of the enterprise in the historical time period can be identified through the comparison and judgment of the cloud service resource occupation threshold, if the occupancy ratio value is lower than the occupancy threshold value, the enterprise is reflected to be excessively occupied and wastes cloud service resources, the macroscopic situation that the enterprise occupies the cloud service resources in a certain accumulated time node overlapping mode in a historical time period can be identified through comparison and judgment of the cloud service resource occupancy ratio threshold value, the occupancy ratio data set is integrated according to the comparison result of the occupancy threshold values accumulated by the time nodes, namely the occupancy ratio is obtained through statistics of the overall occupancy ratio, if the occupancy ratio is lower than the occupancy ratio threshold value, the situation that the enterprise frequently occupies the cloud service resources and wastes the cloud service resources in the historical time period is shown, the enterprise is warned, the loss of credit mark is carried out in a cloud service task queue list, the cloud service resource occupancy threshold value and the cloud service resource occupancy threshold value are set to be 80-85%, and the case is preferably set to be 85%.

A third aspect of the present invention provides a readable storage medium, where the readable storage medium includes a program of a cloud service automatic management and allocation method based on big data, and when the program of the cloud service automatic management and allocation method based on big data is executed by a processor, the step of the cloud service automatic management and allocation method based on big data as described in any one of the above is implemented.

The invention discloses a cloud service automatic management and allocation method, a cloud service automatic management and allocation system and a storage medium based on big data, wherein enterprise cloud service characteristic data are generated by acquiring enterprise characteristic information and combining service data information of an enterprise in an enterprise cloud service platform library, relational analysis and data mining are carried out on the enterprise cloud service characteristic data by utilizing a preset first cloud service model to acquire enterprise cloud service evaluation data, an enterprise cloud service characteristic image is generated according to the enterprise cloud service characteristic data and the enterprise cloud service evaluation data, the enterprise cloud service characteristic image is evaluated according to a preset second cloud service model, the enterprise cloud resource demand is allocated based on a dynamic response factor and a cloud service task queue list is output, enterprise allocation and deployment are carried out on cloud service resources according to the generated cloud service task queue list, so that the enterprise cloud service characteristic image is generated based on the data processed by the enterprise cloud service platform library, the enterprise cloud service resource is evaluated and allocated and the cloud service resource is output And the service queue list is used for allocating and deploying the cloud resources of the enterprise, so that the allocation efficiency and the accuracy of the cloud service resources are improved.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit may be implemented in the form of hardware, or in the form of hardware plus a software functional unit.

Those of ordinary skill in the art will understand that: all or part of the steps of implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a readable storage medium, and when executed, executes the steps including the method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.

Alternatively, the integrated unit of the present invention may be stored in a readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.

Claims

1. The cloud service automatic management and distribution method based on big data is characterized by comprising the following steps:

evaluating the enterprise cloud service characteristic image according to a preset second cloud service model, distributing enterprise cloud resource requirements based on dynamic response factors, and outputting a cloud service task queue list;

performing enterprise allocation deployment on cloud service resources according to the generated cloud service task queue list;

the acquiring of the enterprise characteristic information and the combination of the service data information of the enterprise in the enterprise cloud service platform library to generate the enterprise cloud service characteristic data comprises the following steps:

generating enterprise cloud service characteristic data according to the target cloud capacity data, the cloud privacy attribute information, the cloud execution scale data and the cloud allocation interface data;

the method for obtaining enterprise cloud service evaluation data by performing relationship analysis and data mining on the enterprise cloud service characteristic data by using a preset first cloud service model comprises the following steps:

evaluating cloud service resources in combination with the enterprise data cloud service relationship, and associating the enterprise mapping relationship cloud with the enterprise cloud service feature data to obtain enterprise cloud service evaluation data;

the generating of the enterprise cloud service feature portrait according to the enterprise cloud service feature data and the enterprise cloud service evaluation data comprises:

generating an enterprise cloud service characteristic portrait according to the enterprise cloud service management characteristic information;

further comprising:

2. The method for automatically managing and allocating cloud services based on big data according to claim 1, wherein the evaluating the enterprise cloud service feature images according to a preset second cloud service model, allocating enterprise cloud resource requirements based on dynamic response factors, and outputting a cloud service task queue list comprises:

and generating and outputting the cloud service task queue list according to the dynamically allocated data.

3. The method according to claim 2, wherein the enterprise allocation and deployment of cloud service resources according to the generated cloud service task queue table includes:

4. A cloud service automatic management distribution system based on big data is characterized by comprising: the storage comprises a program of the cloud service automatic management and distribution method based on the big data, and when the program of the cloud service automatic management and distribution method based on the big data is executed by the processor, the following steps are realized:

the method for obtaining the enterprise cloud service evaluation data by performing relationship analysis and data mining on the enterprise cloud service characteristic data by using a preset first cloud service model comprises the following steps:

evaluating cloud service resources by combining the enterprise data cloud service relationship, and associating the enterprise mapping relationship cloud with the enterprise cloud service characteristic data to obtain enterprise cloud service evaluation data;

further comprising:

5. The automatic management and distribution system for cloud services based on big data according to claim 4, wherein generating enterprise cloud service feature data according to the acquired enterprise feature information and service data information of an enterprise in an enterprise cloud service platform library comprises:

6. A computer-readable storage medium, wherein the computer-readable storage medium includes a big data based cloud service automatic management allocation method program, and when the big data based cloud service automatic management allocation method program is executed by a processor, the steps of the big data based cloud service automatic management allocation method according to any one of claims 1 to 3 are implemented.